Archive for the 'Systems Thinking' Category

Is the term ‘absolute truth’ meaningless?

Published July 19, 2020 Design discourse , Evaluation of plans , Systems Thinking 2 Comments

Thorbjørn Mann, July 2020

Some thoughts about ‘absolute truths’, systems thinking and humanity’s challenges. An exploration of knowledge needed for a discourse that I suggest is critically significant for systems thinking related to questions about what to do to about humanity’s big challenges.  I apologize for the roundabout  but needed explanation.

‘What better be done’: absolute truth? 

There are recurring posts in Systems Thinking groups, that insist on decisions being made by focusing on the ‘right’ things’, or what better (best) be done, implying that what is ‘better be done’ is a matter of ‘absolute, objective truth’. Thus, any suggestions about the issue at hand are being derailed — dismissed —  by calling them mere subjective opinions and by repeating the stern admonition to following the absolute truth of ‘doing what better be done’, as if all other suggestions were not already efforts to do so. 

Questions about what those truths may be  are sidestepped or answered by the claim that they are so absolute, objective and self-evidently true that they don’t need explanation or supporting evidence. Heretical questions about this are countered with the question such as  “are you questioning that there are absolute truths”? Apart from the issue whether this may be a tactic by proponent  of an answer (the one declared to be an absolute truth) to get the proponents’ answer accepted,  is it an effort to sidestep the question of what should be done altogether stalling it in the motherhood issue of absolute truth? At any rate, raising questions. 

Does this call for a closer examination about the notion of ‘absolute truths’, and how one can get to know them? What is an ‘absolute truth’ (as compared to about a not so absolute one?) 

Needed distinctions

There may be some distinctions that need reminder (being old distinctions) and clarification,  beginning with the following:  

‘IS’- States of affairs in ‘reality’  versus statements about those 

There exist situations, states of affairs ‘s’ constituting what we call ‘reality’. Existing, they ‘are’. Whether we know them or not; (mostly. we don’t.) And if we know and recognize such a state, we call it ‘true’.  But isn’t that less a ‘property’ of a state ‘s’,  than a label attached to the statement, about ‘s’? About ‘s’,  is it not sufficient to simply say ‘it is’?  So what do we mean by the expression ‘absolute truth’? As a a statement about ‘s’ , it would  seem to imply that there are states of affairs that ‘are’ ‘absolutely true’ and others that aren’t? So would it not  be necessary to offer an explanation of this difference? If there isn’t one, does  the ‘absolute’ part become meaningless and unnecessary?  

So the practical use of ‘true’ or ‘false’ really refers to statements, claims about reality, not reality itself. When we are describing a specific situation ‘s’  or even claiming that it exists, we are making a claim, a statement.  When such a statement matches the actual state of affairs with regard to s, we feel entitled to say that the statement is ‘true’. Again: ‘truth’ is not a property of states of affairs but a judgment statement about ‘content’ statements or claims. 

About the claims of a statement ‘matching’ the actual state of affairs. Do we really know ‘reality’, and how would we know? Discussions and attempted demonstrations  about this tend to use simple concepts — for example: “How many triangles are depicted in this diagram?”. The simple ‘answers’ are both ‘obviously true’ (even though people are occasionally disagreeing even about those) —  but  upon examination based on different understood definitions of the concepts involved. The definitions are not always stated explicitly, which is a problem: it leads to the troublesome situation where one of disagreeing parties can honestly refer to answers based on ‘their’ definition’ as ‘true’ and to other answers  as  ‘false’ (and consequently questioning the sanity or goodwill intentions of anybody claiming otherwise). So are all those answers ‘absolutely true’ but only each given the appropriate related definitions and understanding? 

The understanding of ‘triangle’ in the diagram example may be  that of “three points not on the same straight line in a plane, connected by visible straight lines.”  There may be a fixed ‘true’ number of such triangles in the diagram. But if the definition of ‘triangle’ is just “three points not on the same straight line'”,  and it is left open whether the diagram itself intends to show a plane or a space, the answers become quite different and even uncountable (‘infinitely many, given the infinitely many points on a plane or in a space depicted by the diagram, that exist in triangular position relative to each other).

The term ‘depicted’ also requires explanation: does it only refer to triangles ‘identified’ by lines connecting three selected points, lines drawn by a color different from the color of the ‘plane (or space) of the diagram? If drawn by the same color, are they n o t  ‘depicted’? Do the edges and corners of the diagram picture ‘count’ as ‘depicting’ the lines and apex of a triangle, or not?  So even in this simple ‘noncontroversial’ example,  there are many very plausible answers, and the decision to call one or some of them ‘absolute truth’ begins to look somewhat arbitrary. 

Probability

The label ‘true’ or ‘false’ apply to existing or past states of affairs. Do they also apply to claims about the future (that is, to forecasts, predictions),   The predicted states of affairs  are, by definition, not ‘true’ yet. The best we can do is to say that such a statement is more or less ‘probable’: a matter of degrees we express by a number  from 0 (totally unsure) to 1(virtually certain) or by a ‘percentage’ number between zero and 100. 

Actually, we usually are not totally certain about the truth even of our claims about actual ‘current ‘ or ‘always’- states of affairs. We find that we often make such claims only to find out later that we were wrong, or only approximately right about a given situation. Even more so, about more complex claims such as whether a causes b  and whiter it will do so in the future. But it is fair to say that when we make such claims, we aim and hope to be as close to the actual situation or effect as possible. Can we just say that we should acknowledge the degree of certainty — or ‘plausibility’ — of our statements? Or acknowledge that a speaker may be totally certain about their claim, but listeners are entitled to have and express less certainty — e.g by assigning a different certainty, probability or — I suggest –‘plausibility’  to the claim? Leaving a crumb of plausibility for the ‘black swan’?

‘OUGHT’ claims and their assessment:  ‘Plausibility’ rather that ‘truth’ 

For some other kinds of claims, the labels ‘true’ or ‘false’ are plainly not appropriate, not even ‘probable’. Those are the ‘ought’-claims we use when discussing problem situations (understood as  as discrepancies between what somebody considers to be the case or probable, and what that person feels ‘ought’ to be the case). The state of affairs we ‘ought’  to seek ( or the means we feel we ought to apply to achieve the desired state) are– equally by definition — not ‘true’ yet.  So should we use a different term?  I have suggested that the label ‘plausible’ may serve, for all these claims, expressed as a number n (for example ‘1’) between -n (totally implausible, virtually improbable or the opposite being true) and +n (virtually certain)  with the midpoint zero denoting ”don’t know’, ‘can’t tell’.  Reminder: these labels express just our states of knowledge or opinion, not the states of affairs to which they refer: we make decisions on the basis of our limited knowledge and opinions, not on reality itself (which we know only approximately or may be unsure about). 

How can we gain plausibility of claims? 

The question then is:  How do we get to know whether any of these claims are ‘true’  or probable, or plausible, and to what degree? Matching? Or: — since we can rarely attain complete certainty (knowing that there can be ‘black swans’ to shatter that certainty) — how can we increase our degree of plausibility we feel we can attach to a given claim.? What are the means by which we gain plausibility about claims? Possibilities are: 

1)  For ‘fact’-claims: 

1a) Personal observation, experiments, measurements, demonstration, ‘tests’. 

1b) Inference from other fact-claims and observations, using ‘logically valid’ reasoning schemes;  

1c) From ‘authorities’: other persons we trust to have properly done (1a) or (1b), and can or have explained this;

1d) Declaring them ”self-evident’  and thus not needing further explanation. 

2)  For ‘ought- claims:

2a) The items equivalent to (1a) obviously don’t apply:  So: Personal preference, desire, need, accepted common goals or ‘laws’

2b) Inference? The problem here is that inferences with ‘ought  or what I call ‘planning arguments’ — claims are inherently not (deductively) ‘valid’ from a formal logic point of view and because the label ‘true’ does not apply. However: for some of the factual premises in these arguments, reasons (1) will apply and are appropriate.

2c) From authorities:  Either because they have done 2a or 2b, or because they have social status to ‘order’, command ought-claims?

2d) ‘Self-evidence’?  For example: ‘moral norms’? Laws? 

Is ‘self-evident’ equal to ‘absolute’?

We could add claims about ‘meaning’, definition etc. as a third category. For all, is the claims of ‘absolute truth” equivalent to ‘self-evident?  It is the only one for which explanation justification, evidence is not offered, even claimed to be impossible, unneeded. What this means is:  if there are differences of opinion about a claim, can the proponent of such a claim expect to persuade others to come to accept it as theirs?  What if both parties should honestly claim / believe that theirs is the absolute truth? Claiming ‘absolute truth’ or ‘right’ or ‘self-evidence’ is not  a good persuasion argument, but if repeated sufficiently often (brainwashing) surprisingly, effective, history tells us.  If justification (e.g. by demonstration) is attempted, it turns into one of the other kinds.

So, for all these claims and their ‘justification’ support, different people can have different opinions (different plausibility degrees). This is all too frequently observed, and  the source of all disagreements, quarrels, fights, wars. The latter item (war) suggest that there is a missing means for acquiring knowledge: the application of coercion. force, violence, or in the extreme, the annihilation of  persons of different opinions. The omission is based on the feeling that  it is somehow ‘immoral’ (no matter how frequently it is actually applied in human societies, from the upbringing of children to ‘law enforcement’ and warfare).  

The need to shift attention to ‘decision criteria’ and modes acknowledging irreconcilable differences of opinion

There is, for all the goodwill admonished by religious, philosophical and political leaders, the problem that even with ample efforts of explanation and offering exhortation, reasons, arguments, definitions, situations may occur where agreement on the claims involved cannot be achieved — yet the emergencies, problems, challenges demand that ‘something must be done’. 

What this means, in my opinion, is that the noble quest for ‘truth’, probability, even plausibility as the better guide for community, social decisions — ‘solution’ criteria — making decisions based on the basis of the merit (value, plausibility) of contributions to the discourse about what we ought to do  (that we ideally would all agree on!) must be shifted to a different question: what criteria can we use to guide our decisions in the face of significant differences in our opinions about the information supplied in the discourse? The criteria for evaluation of quality, plausibility of proposed solutions  should be part of but are not the same as the criteria for good decisions.  It is interesting to note that the most common decision mode – voting — in effect dismisses all the merit concerns of the ‘losing’ minority. Arguably, it should be considered a crude crutch to the claim of ‘democratic’ ideals: equality, justice, fairness to all;  But also, that the very crisis cry ‘”Something must be done” is often used as an exhortation tool to somehow generate ‘unity’ of opinions. 

Issues for Systems Thinking

I suggest that this is an important set of issues  for systems thinking. Systems Thinking has been claimed to offer ‘the best currently available foundation for tackling humanity’s challenges. But has it focused its work predominantly on the ‘IS’ questions of the planning and policy-making discourse, rather than on the ‘ought’ issues? On better understanding of the (existing) systems in we will have to interfere? On better prediction of different plan proposals’ future performance (simulation)? Sure, those tasks are immensely important and the work on these questions admirable. But are they the whole task? 

As far as I can see, the other (‘ought’) part of planning and policy-making work — both the development of a) better evaluation, (development of measures of the merit of planning discourse contributions leading to ‘solution merit’  criteria) and b) the development of better criteria for planning decisions, in the face of acknowledged disagreement about the merit of information contributed to the discourse are at best still in the embryonic state. Systems thinking appears to many (perhaps unfairly so)  as suggesting that decisions should be based on the assessment of ‘facts’ data alone, ignoring the proper assessment of ‘ought’ claims and how they must be combined with the ‘facts- claims to support better decisions.   

The development of a better planning discourse platform

Of course, the ‘discourse’ itself about these issues is currently in a state that does not appear to lead to results for either of the above criteria: the design of the discourse for crafting meaningful decisions about humanity’s challenges is itself an urgent challenge. If I had not convinced myself, in the course of thinking about these issues, that ‘absolute truth’ is a somewhat inappropriate  or even meaningless term, I would declare this a main ‘absolutely truth and important’ task we face.  

–o– 

EVALUATION IN THE PLANNING DISCOURSE — SYSTEMS THINKING, MODELING AND EVALUATION IN PLANNING

Published February 1, 2020 Design discourse , Discourse contribution rewards , Evaluation aspects , Evaluation of plans , Public policy discourse , Systems Modeling , Systems Thinking Leave a Comment

An effort to clarify the role of deliberative evaluation in the planning and policy-making process. Thorbjørn Mann , February 2020. (DRAFT)

SYSTEMS THINKING / MODELING AND EVALUATION IN PLANNING

 

Evaluation and Systems in Planning  — Overview

The contribution of systems perspective and tools to planning.

In just about any discourse about improving approaches to planning and policy-making, there will be claims containing reference to ‘systems’: ‘systems thinking’, ‘systems modeling and simulation’, the need to understand ‘the whole system’, the counterintuitive behavior of systems. Systems thinking as a whole mental framework is described as ‘humanity’s currently best tool for dealing with its problems and challenges. There are by now so many variations, sub-disciplines, approaches and techniques, even definitions of systems and systems approaches on the academic as well as the consulting market, that even a cursory description of this field would become a book-length project.

The focus here is the much narrower issue of the relationship between this ‘systems perspective’ and various evaluation tasks in the planning discourse. This sketch will necessarily be quite general, not doing adequate justice to many specific ‘brands’ of systems theory and practice. However, looking at the subject from the planning / evaluation perspective will identify some significant issues that call for more discussion.

Evaluation judgments at many stages of systems projects and planning

A survey of many ‘systems’ contributions reveals that ‘evaluation’ judgments are made at many stages of projects claiming to take a systems view – like the finding that evaluation takes place at the various stages of planning projects whether explicitly guided by systems views or not. Those judgments are often not even acknowledged as ‘evaluation’, and done by very different patterns of evaluation (as described in the sections exploring the variety of evaluation judgment types and procedures.)

The similar aims of systems thinking and evaluation in planning

Systems practitioners feel that their work contributes well (or ‘better’ than other approaches) to the general aims of planning: such as
– to understand the ‘problem’ that initiates planning efforts;
– to understand the ‘system’ affected by the problem, as well as
– the larger ‘context’ or ‘environment’ system of the project;
– to understand the relationships between the components and agents, especially the ‘loops’ of such relationships that generates the often counterintuitive and complex systems behavior;
– to understand and predict the effects (costs, benefits, risks) and performance of proposed interventions in those systems (‘solution’) over time; both ‘desired’ outcomes and potentially ‘undesirable’ or even unexpected side-and after-effects;
– to help planners develop ‘good’ plan proposals,
– and to reach recommendations and/or decisions about plan proposals that are based on due consideration of all concerns for parties affected by the problem and proposed solutions, and of the merit of ‘all’ the information, contributions, insights and understanding brought into the process.
– To the extent that those decisions and their rationale must be communicated to the community for acceptance, these investigations and judgment processes should be represented in transparent, accountable form.

Judgment in early versus late stages of the process

Looking at these aims, it seems that ‘systems-guided’ projects tend to focus on the ‘early’ information (data) -gathering and ‘understanding’ aspects of planning – more than on the decision-making activities. These ‘early’ activities do involve judgment of many kinds, aiming at understanding ‘reality’ based on the gathering and analysis of facts and data. The validity of these judgments is drawn from standards of what may loosely be called ‘scientific method’ – proper observation, measurement, statistical analysis. There is no doubt that systems modeling, looking at the components of the ‘whole’ system, and the relationships between them, and the development of simulation techniques have greatly improved the degree of understanding both of the problems and the context that generates them, as well as the prediction of proposed effects (performance) of interventions: of ‘solutions’. Less attention seems to be given to the evaluation processes leading up to decisions in the later stages. Some justifications, guiding attitudes, can be distinguished to explain this:

Solution quality versus procedure based legitimatization on of decisions

One attitude, building on the ‘scientific method’ tools applied in the data-gathering and model-building phases, aims at finding ‘optimal’ (ideally, or at least ‘satisficing’) solutions described by performance measures from the models. Sophisticated computer-assisted models and simulations are used to do this; the performance measures (that must be quantifiable, to be calculated) derived from ‘client’ goal statements or from surveys of affected populations, interpreted by the model-building consultants: experts. One the one hand, their expert status is then used to assert validity of results. But on the other hand, increasingly criticized for the lack of transparency to the lay populations affected by problems and plans: questioning the experts’ legitimacy to make judgments ‘on behalf of’ affected parties. If there are differences of opinions, conflicts about model assumptions, these are ‘settled’ – must be settled – by the model builders in order for the programs to yield consistent results.

This practice (that Rittel and other critics called ‘first generation systems approach’) was seen as a superior alternative to traditional ways of generating planning decisions: the discussions in assemblies of people or their representatives, characterized by raising questions and debating the ‘pros and cons’ of proposed solutions – but then making decisions by majority voting or accepting the decisions of designated or self-designated leaders. Both of these decision modes obviously are not meeting all of the postulated expectations in the list above: voting implies dominance of interests of the ‘majority’ and potential disregard on the concerns of the minority; leader’s decisions could lack transparency (much like expert advice) leading to public distrust of the leader’s claim of having given due consideration to ‘all’ concerns affecting people.

There were then some efforts to develop procedures (e.g. formal evaluation procedures) or tools such as the widely used but also widely criticized ‘Benefit-Cost’ analysis tried to extend the ‘calculation based’ development of valid performance measures into the stage of criteria based on the assessment of solution quality to guide decisions. These were not equally widely adopted, for various reasons such as the complicated and burdensome procedures, again requiring experts to facilitate the process but arguably making public participation more difficult. A different path is the tendency to make basic ‘quality’ considerations ‘mandatory’ as regulations and laws, or ‘best practice’ standard. Apart from tending to set ‘minimum’ quality levels as requirement e.g. for building permits, this represents a movement to combine or entirely replace quality-based planning decision-making with decisions that draw their legitimacy from having been generated and following procedures.

This trend is visible both in approaches that specify procedures to generate solutions by using ‘valid’ solution components or features postulated by a theory (or laws): having followed those steps then validates the solution generated removes the necessity to carry out any complicated evaluation procedure. An example of this is Alexander’s ‘Pattern Language’ – though the ‘systems’ aspect is not as prevalent in that approach. Interestingly, that same stratagem is visible in movements that focus on processes aimed at mindsets of groups participating in special events, ‘increasing awareness’ of the nature and complexity of the ‘whole system’ but then rely on solutions ‘emerging’ from the resulting greater awareness and understanding that aim at consensus acceptance in the group for the results generated, that then do not need further examination by more systematic, quantity-focused deliberation procedures. The invoked ‘whole system’ consideration, together with a claimed scientific understanding of the true reality of the situation calling for planning intervention is a part of inducing that acceptance and legitimacy. A telltale feature of these approaches is that debate, argument, and the reasoning scrutiny of supporting evidence involving opposing opinions tends to be avoided or ‘screened out’ in the procedures generating collective ‘swarm’ consensus.

The controversy surrounding the role of ‘subjective’, feeling-based, intuitive judgments versus ‘objective’ measurable, scientific facts (not just opinions) as the proper basis for planning decisions also affects the role of systems thinking contributions to the planning process.

None of the ‘systems’ issues related to evaluation in the planning process can be considered ‘settled’ and needing no further discussion. The very basic ‘systems’ diagrams and models of planning may need to be revised and expanded to address the role and significance of evaluation, as well as argumentation, the assessment of the merit of arguments and other contributions to the discourse, and the development of better decision modes for collective planning decision-making.

–o–

The Agenda of Many Important but Connected Issues

Published January 18, 2020 'Next System' , control of power , Governance , Public policy discourse , Systems Thinking Leave a Comment

Are the agenda platforms of governance candidates consisting of single ‘highest priority’ issues realistic? Aren’t all the issues so tightly connected that none can be resolved without the others?
Attempting to understand, I see this chain:

1 Humanity is confronted by many unprecedented challenges to its survival.

2 There is little if any agreement about how these problems should be addressed.

3 There is a growing sense that current systems of governance are inadequate to address and convincingly resolve these problems: Calls are raised for ‘systemic change’ and ‘a new system’.

4 While there are many well-intentioned theories, initiatives, experiments already underway, to develop new ways of doing things in many domains,

5 There is little if any agreement about what such a ‘new system’ should look like, and very different ideas are promoted in ways that seem more polarizing than unified. We — humanity — do not yet know what works and what does not work: some major ‘systems’ that were tried over recent centuries have turned into dramatic failures.

6 There is much promotion of the many ‘new’ and old ideas, but not enough communication and sharing of experiences among the initiatives for discussion, evaluation and cooperative adoption. Meanwhile, the crises intensify.

So, before attempting another grand system based on inadequate understanding and acceptance, whose failure we cannot afford, it seems that a number of steps are needed:

7 Encouraging the many diverse (usually small scale, local) initiatives and experiments;

8 Supporting these efforts (financially and with information and other resources) regardless of their differences, on condition of their acceptance of some agreements:
a) to avoid getting in each other’s way;
b) to share information about their experiences: successes and failures, for systematic discussion and evaluation, into a common resource repository;
c) to cooperate in a common discourse aiming at necessary (even if just intermediate) decisions — the common ‘rules of the road’ to avoid conflict and facilitate mutual aid in emergencies and system failures.

9 To facilitate the aims in point 8, it will be necessary to develop
a) a common ‘global’ discourse platform accessible to all parties affected by an issue or problem
b) with a system of meaningful incentives for participation to access all information and concerns that must be given ‘due consideration’ in decisions’
c) with adequate translation support not only between different natural languages but also for disciplinary ‘jargon’ into conversational language;
d) new tools for assessment of the merit of information,
e) and new decision-making criteria and procedures based on the merit of contributions (since traditional voting will be inapplicable to issues affecting many parties in different ways across traditional boundaries that define voting rights).

10 It will also be necessary to develop
a) new means for ensuring that common agreements reached will actually be adhered to. Especially at the global level, these tools cannot be based on coercive ‘enforcement’ (which would require an entity endowed with greater power and force that any potential violator — a force which then would become vulnerable to the temptation of abuse of power that arguably is itself one of the global challenges). Instead, development should aim at
b) preventive sanctions triggered by the very attempt at violation, and
c) other innovative means of control of power.

I submit that all of these considerations will have to be pursued simultaneously: without them, any attempt to successfully resolve or mitigate the crises and problems (point 1) will be unsuccessful. The agenda of governance agencies and candidates for public office should include the entire set of interlinked aspects, not just isolated ‘priority’ items. Of course I realize that the practice of election campaign posters, 30-second ads or Twitter posts effectively prevents the communication of comprehensive platforms of this nature. What can we realistically hope for?

‘CONNECTING THE DOTS’ OF SOME GOVERNANCE PROBLEMS

Published May 9, 2018 control of power , Design discourse , Discourse contribution rewards , Evaluation of plans , Governance , Public policy discourse , Systems Thinking , Uncategorized Leave a Comment
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There is much discussion about flaws of ‘democratic’ governance systems, supposedly leading to increasingly threatening crises. Calls for ‘fixing’ these challenges tend to focus on single problems, urging single ‘solutions’. Even recommendations for application of ‘systems thinking’ tools seem to be fixated on the phase of ‘problem understanding’ of the process; while promotions of AI (artificial / augmented intelligence) sound like solutions are likely to be found by improved collection and analysis of data, of information in existing ‘knowledge bases’. Little effort seems devoted to actually ‘connecting the dots’ – linking the different aspects and problems, making key improvements that serve multiple purposes. The following attempt is an example of such an effort to develop comprehensive ‘connecting the dots’ remedies – one that itself arguably would help realize the ambitious dream of democracy, proposed for discussion. A selection (not a comprehensive account) of some often invoked problems, briefly:

“Voter apathy” The problem of diminishing participation in current citizen participation in political discourse and decisions / elections, leading to unequal representation of all citizens’ interests;

“Getting all needed information”
The problem of eliciting and assembling all pertinent ‘documented’ information (‘data’) but also critical ‘distributed’ information especially for ‘wicked problems’, – but:

“Avoiding information overload”
The phenomenon of ‘too much information’, much of which may be repetitive, overly rhetorical, judgmental, misleading (untruthful) or irrelevant;

“Obstacles to citizens’ ability to voice concerns”
The constraints to citizens’ awareness of problems, plans, overview of discourse, ability to voice concerns;

“Understanding the problem”
Social problems are increasingly complex, interconnected, ill-structured, explained in different, often contradicting ways, without ‘true’ (‘correct) or ‘false’ answers, and thus hard to understand, leading to solution proposals which may result in unexpected consequences that can even make the situation worse;

“Developing better solutions”
The problem of effectively utilizing all available tools to the development of better (innovative) solutions;

“Meaningful discussion”
The problem of conducting meaningful (less ‘partisan’ and vitriolic, more cooperative, constructive) discussion of proposed plans and their pros and cons;

“Better evaluation of proposed plans”
The task of meaningful evaluation of proposed plans;

“Developing decisions based on the merit of discourse contributions”
Current decision methods do not guarantee ‘due consideration’ of all citizens’ concerns but tend to ignore and override as much as the contributions and concerns of half of the population (voting minority);

“The lack of meaningful measures of merit of discourse contributions”
Lack of convincing measures of the merit of discourse contributions: ideas, information, strength of evidence, weight of arguments and judgments;

“Appointing qualified people to positions of power”
Finding qualified people for positions of power to make decisions that cannot be determined by lengthy public discourse — especially those charged with ensuring

“Adherence to decisions / laws / agreements”
The problem of ‘sanctions’ ensuring adherence to decisions reached or issued by governance agencies: ‘enforcement’ – (requiring government ‘force’ greater than potential violators leading to ‘force’ escalation;

“Control of power”
To prevent people in positions of power from falling victim to temptations of abusing their power, better controls of power must be developed.

Some connections and responses:

Problems and remedies network

Details of possible remedies / responses to problems, using information technology, aiming at having specific provisions (‘contribution credits’) work together with new methodological tools (argument and quality evaluation) to serve multiple purposes:

“Voter apathy”

Participation and contribution incentives: for example, offering ‘credit points’ for contributions to the planning discourse, saved in participants’ ‘contribution credit account’ as mere ‘contribution’ or participation markers, (to be evaluated for merit later.)

“Getting all needed information”
A public projects ‘bulletin board’ announcing proposed projects / plans, inviting interested and affected parties to contribute comments, information, not only from knowledge bases of ‘documented’ information (supported by technology) but also ‘distributed, not yet documented information from parties affected by the problem and proposed plans.

“Avoiding information overload”
Points given only for ‘first’ entries of the same content and relevance to the topic
(This also contributes to speedy contributions and assembling information)

“Obstacles to citizens’ ability to voice concerns”
The public planning discourse platform accepts entries in all media, with entries displayed on public easily accessible and regularly (ideally real-time) updated media, non-partisan

“Understanding the problem”
The platform encourages representation of the project’s problem, intent and ‘explanation’ from different perspectives. Systems models contribute visual representation of relationships between the various aspects, causes and consequences, agents, intents and variables, supported by translation not only between different languages but also from discipline ‘jargon’ to natural conversational language.

“Developing better solutions”
Techniques of creative problem analysis and solution development, (carried out by ‘special techniques’ teams reporting results to the pain platform) as well as information about precedents and scientific and technology knowledge support the development of solutions for discussion

“Meaningful discussion”
While all entries are stored for reference in the ‘Verbatim’ repository, the discussion process will be structured according to topics and issues, with contributions condensed to ‘essential content’, separating information claims from judgmental characterization (evaluation to be added separately, below) and rhetoric, for overview display (‘IBIS’ format, issue maps) and facilitating systematic assessment.

“Better evaluation of proposed plans”
Systematic evaluation procedures facilitate assessment of plan plausibility (argument evaluation) and quality (formal evaluation to mutually explain participants’ basis of judgment) or combined plausibility-weighted quality assessment.

“Meaningful measures of merit”
The evaluation procedures produce ‘judgment based’ measures of plan proposal merit that guide individual and collective decision judgments. The assessment results also are used to add merit judgments (veracity, significance, plausibility, quality of proposal) to individuals’ first ‘contribution credit’ points, added to their ‘public credit accounts’.

“Decision based on merit”
For large public (at the extreme, global) planning projects, new decision modes and criteria are developed to replace traditional tools (e.g. majority voting)

“Qualified people to positions of power”
Not all public governance decisions need to or can wait for the result of lengthy discourse, thus, people will have to be appointed (elected) to positions of power to make such decisions. The ‘public contribution credits’ of candidates are used as additional qualification indicators for such positions.

“Control of power”

Better controls of power can be developed using the results of procedures proposed above: Having decision makers ‘pay’ for the privilege of making power decisions using their contribution credits as the currency for ‘investments’ in their decision: Good decision will ‘earn’ future credits based on public assessment of outcomes; poor decisions will reduce the credit accounts of officials, forcing their resignation if depleted. ‘Supporters’ of officials can transfer credits from their own accounts to the official’s account to support the official’s ability to make important decisions requiring credits exceeding their own account. They can also withdraw such contributions if the official’s performance has disappointed the supporter.
This provision may help reduce the detrimental influence of money in governance, and corresponding corruption.

“Adherence to decisions / laws / agreements”
One of the duties of public governance is ‘enforcement’ of laws and decisions. The very word indicates the narrow view of tools for this: force, coercion. Since government force must necessarily exceed that of any would-be violator to be effective, this contributes both to the temptation of corruption, — to abuse their power because there is no greater power to prevent it, and to the escalation of enforcement means (weaponry) by enforces and violators alike. For the problem of global conflicts, treaties, and agreements, this becomes a danger of use of weapons of mass destruction if not defused. The possibility of using provisions of ‘credit accounts’ to develop ‘sanctions’ that do not have to be ‘enforced’ but triggered automatically by the very attempt of violation, might help this important task.

 

On the use of criterion functions to explain our basis of judgment. A Tavern Talk

Published September 1, 2017 Design discourse , Evaluation of plans , Public policy discourse , Systems Thinking , Uncategorized 1 Comment

Summary:
In the discourse about collective plans, the process of evaluation – making judgments about whether a plan is ‘good enough’ or which of several plans is the better, — differences of opinions occur, which can be resolved by mutual explanation of the basis of evaluation judgments. The discussion here is focused on the role of ‘criterion functions’ in this process. These functions – showing how ones subjective ‘goodness’ judgments depend on ‘objectively’ measurable criteria – make it possible to explain one’s basis of judgment in much more specific detail than is usually done even in the most cooperative group processes. Some key insights of the discussion are the following:

While such detailed explanation is possible and conceptually not overly challenging, including this process in actual decision-making procedures would add cumbersome provisions to a planning discourse already calling for more structure than many participants are used to and feel comfortable with. The issues of ‘aggregation’ – of individuals’ partial judgments into overall judgments, and especially of individual judgments into ‘group’ evaluation measures are potential sources of controversy.
It becomes clear that any overall ‘measures’ or judgments that can meaningfully guide decisions cannot be derived in ‘top-down’ fashion from general ‘meta values’ and ‘common good’ concepts but must be constructed ‘bottom-up’ from individual participants’ concerns and understanding of specific situations and context.

Familiar claims of planning decision makers ‘to act on behalf of others (clients, users), with adequate knowledge of those others’ concerns and basis of judgment, but not having gone to the trouble of doing this, are unfounded and should be viewed with reserve.
Examination of criterion functions make it obvious that the quest for and claim of having found ‘optimal’ solutions is unrealistic: no plan will achieve the ‘best possible’ scores on all evaluation aspects even for individuals, and different people will have very different but legitimate criterion functions.

Considering plans or policies whose effects will occur – and change – over time, adds another level of complexity. System simulation models can track the performance of variables (criteria) over time, but do not show associated ‘goodness’ judgments (obviously, since this would either be just one person’s assessment, of some aggregated measure of judgments that have not been included in the system modeler’s data.

The examination of how evaluation judgments for different plans could be tracked over time (as a function of the simulated variable tracks) suggests a different decision guide: the (time-discounted) degree of improvement of a plan over the current or predicted problem situation under the ‘do nothing’ option.

ooo

The discussion takes place in the hypothetical ‘Fog Island Tavern’.

– Hey Bog-Hubert – what kind of critters are you guys talking about? I couldn’t quite get it coming in, but it sounds like serious wildlife?

– Good morning Renfroe. Well, I guess you could call them critters, but not the kind you mean. Actually, we were talking about criterion functions. About how we can explain what we mean when we’re making judgments about, say, proposed plans. Calling them good, so-so, or bad, or anything in-between.

– Huh. Plans, as in what to do about saving the beaches where each storm washes away more of the sand?

– Yes, or about what to do about the climate change that makes the storms worse and the oceans rise.

– What, you guys can’t even come up with a plan for beach preservation on this little island, and now you want to talk about the oceans and global climate?

– Well, Renfroe, it looks like the problems of coming to some agreements about what to do are the same everywhere, just at a different scale.

– So where do your critters come in on that one?

– I guess we need to back track a little on that. It has to do with evaluation, say of different proposed plans, to decide which one is best, or whether any of them should be implemented. You could start by looking at them and then make an offhand judgment, say ‘good’; ‘not good’. If there are several proposals, you may want to use a more detailed scale, for example one with seven points ranging from ‘couldn’t be worse = – 3, to ‘couldn’t be better’: +3, with a midpoint of zero meaning ‘don’t know’ or ‘so-so’. In a group you’ll have to agree on some common scale. Now somebody asks you why you rate proposal X so high, and solution Y so low, since they came up with very different ratings from yours. So you may want to talk about the reasons, the basis of your judgments, maybe you each know something the other doesn’t know or see, that should be considered in making the decision. What do you do? How do you explain what makes a solution good or bad, in your view?

– Well, you could look at the various costs and benefits, and how well the plan will work for what it is meant to do?

– And how good it looks, if its’ a building or some thing we are looking at or living in?

– Yes, Sophie.

– And don’t we also have to worry about those ‘unexpected side-and-after-effects’ of plans, that people always talk about but keep forgetting?

– Good point. Next we list all those considerations or ‘aspects’ and make sure that we all mean the same thing when we name them. But we can only consider what people bring up in the discourse, so it’s important to make sure that is organized so as to let everybody put in their views. Now you can give each plan a ‘goodness’ judgment score – on the same scale — for each of those aspects. Let’s call those ‘partial’ judgments – all together they make up your ‘overall’, whole judgment for each plan. You’ll then have to explain how exactly all of those scores make up the overall judgment.

– And looking at the different aspects, you may want to reconsider the first overall, offhand judgment you made?

– Good point, Vodçek. Deliberating already. Learning. Excellent. But to get to the criterion functions: You realize how an explanation of a judgment the goodness score always consists of showing how the judgment relates to something else. That something else can be another judgment – look at how Sophie suggested that her overall ‘goodness’ judgment of, say, a building, should depend in part on the beauty of that building – which of course would be another judgment, and people may have different opinions about that. But the relationship would be that of some ‘degree of beauty’ about which she would have to make another ‘goodness’ judgment to explain how it contributes to the overall building goodness judgment.

– That’s not much on an explanation though, is it?

– Right. So you could ask her what, in her mind, makes a building beautiful. What would be you answer, Sophie?

– Well, if I got the sense that he just wants to annoy me with all these questions – because I can see how each answer will just lead to another one, and where will that end – I could just say that if he can’t see it he just doesn’t understand beauty and tell him to get lost. But if I think he really wants to learn what I see as beautiful, I might suggest that it has to do with, say, its proportions.

– Ah. Now we are getting closer to the criterion issue. Because proportions are really measurement relations: in a rectangle, the length of the short side to the length of the longer one. The relationship is now something we can ‘objectively’ measure. Quantitative. And many people are really adamant about making our decisions based on objective ‘facts’ and measures. So would it satisfy those folks to show them a graph that has the ‘objective’ measure on one axis – the horizontal one, say, and our judgment scale on the vertical one: If you are convinced that the most beautiful proportion is the ‘golden ratio’ – 1:1.618… the graph would touch the + 3 line at that 1.618… point, and go down from there in both directions. Like this, for example: Down to 1:1 (the proportion of the square) on one side, and down to -3 for infinitely long rectangles: 1:∞

Fig 1. A criterion function for proportion judgments

– Hey, didn’t we look at that proportion thing a while back? But then we looked at both sides on the 1:1 point – if the right side is for ‘vertical’ rectangles, the left one continues past the 1:1 point for horizontal ones.

– And if I remember correctly, didn’t somebody point out that there are many people who feel that the square, or the one based on the diagonal of the square, the √2 relationship, is the most beautiful proportion?

– You’re right. But that was in another book, a fat one, if I recall? So let’s not get too distracted by the details here. But perhaps we should just remember that there are many different forms of such functions: the one where ‘zero’ on the variable represents -3 on the judgment scale, which goes on to approach +3 at infinity, its opposite that starts with +3 at zero on the criterion scale and approaches -3 at infinity, the opposite of the golden ratio curve that goes from -3 at some value of our criterion and rises towards +3 on both sides.

Fig. 2. Different criterion function types

– Can’t think of an example for that one though. Except perhaps weird ones like people’s appreciation for apartment levels going up towards the higher floors and +3 at the penthouse – but with a sudden drop to -3 at floor 13?

– Goes to show that people’s preferences can take strange contortions… But those are personal judgments. I guess they are entitled to hold those, privately. Now, what about collective decisions?

– Yes, Vodçek. That is the point where we left off when Renfroe came in. We can explain how our subjective goodness judgment relates to some objective measurement. And this is useful: I can ask somebody to make a decision on my behalf if I give him my criterion function to make a selection – even though I know that his criterion function likely has a different shape and the high +3 judgment score is in a different place. But the issue we started from was the idea that there should be measurements – expressing values – that everybody should agree on, so that collective decisions could be guided by those values and measurements. ‘Meta-values’?

– Yes, that notion seems to make perfect sense to many people – for example the feel that trying to stop climate change is so important for the survival of human civilization – that it is such a meta-value that everybody should agree on so that we can start taking more effective action. And that this is even an ethical, a moral duty. So they can’t understand why there are some people who don’t agree.

– Right. So we were trying to untangle the possible reasons why they don’t.

– Short of declaring them all just blindly indoctrinated by political or religious views or outright fraudulent misrepresentations, eh?

– Yes, let’s not go there. I guess there may have been some confusion because at first there was no distinction between the ‘meta-values – that were quite general and abstract – and the corresponding measures one would need to actually guide decisions: You can’t really argue against a concept like the Common Good as a guiding value – but when it comes to pin down just what that common good is, and how to get an ‘objective’ measure for it, history shows that there’s plenty of disagreement both about the ‘common’ part – my family, tribe, town, country, my religion, humanity, all life on the planet? And even more vicious: about the part what the ‘good’ might be.

– I agree; and the attempt to justify those different views have led to some pretty desperate contortions of moral guidelines for decisions to meet those good things: I remember the old ‘dulce et decorum est pro patria mori’ (‘sweet and decorous it is to die for one’s country’), or promising all kinds of heavenly rewards in the hereafter to those willing to fight and kill and die for religious ideas.

– Well, if your tribe or country or your faith is your ‘common’, and somebody is attacking it, what’s wrong with that?

– I guess it becomes questionable when these definitions lead to more of the good is ending up with some people – the fellows who are spouting these glorious quotes and values and then get to make the decisions ‘on behalf’ of everybody else, while many of the everybody else are doing the dying and sacrificing. If it becomes apparent that these value leaders and doers have actually been acting more on behalf of their own good, the trust in those values is eroded. No matter how they are justified: by philosophical theories, by divine revelations (mostly revealed only to some special prophets), by political theories, or scientific theories. Or systems thinking. Or holistic thinking and awareness.

– Even scientific and systemic investigations?

That’s the tragedy, right. You may argue that science and systems tools are our best available tools for guiding action, based on objective measurements and facts, and have all the evidence and replicable observations to support them: if they then are used by leaders, — governments or ‘movements’ — to postulate ‘meta values’ to guide actions ‘on behalf of’ others, they will run into the same trust issues as all the other attempts to do the same, — unless…

– Unless what?

Well: unless they can also offer guarantees that those leaders are not just acting on their own behalf. Even if Congress were full of scientists and systems thinkers: if they make laws to deprive people of health insurance while themselves enjoying the best insurance and health care, no matter what justification they offer for their actions, they would run into trust problems. So that would be one condition.

– One condition, Vodçek? Are there others?

– In theory, yes. Its’ actually part of the job of the legislature: isn’t it in the constitution, even, that they should make laws on behalf of their constituents. And the representative notion is, of course, that representatives should be part of the communities they represent and communicate with then so that they know what the community wants? And then go and vote for laws that realize those preferences.

– Which they should do even if they don’t agree with them? Even if they know better? Doesn’t the constitution also state that they should vote according to their own conscience?

– Right: there seems to be some contradiction there. And it’s leading to that constant tug-of-war between the principle of electing representatives whose judgment we trust — to vote in view of the common good – even if we don’t agree, — because we don’t have all the information? Or because we are suffering from doctrinaire blindness or stupidity, or ethical or moral depravity? And to the alternative of passing laws by referendum, regardless of what the representatives are saying.

– Well, there’s the practical issue: we can’t possibly make all laws by referendum, can we? And if we are too ignorant to give our representatives proper guidance about how to vote so they have to vote by their own better knowledge and information and conscience, should we be allowed to vote on laws?

– But we are allowed to vote for the representatives? Isn’t there an expectation that the representatives should provide enough information to their constituents to know and understand the best available basis of judgment for the needed actions and also be confident that the representatives’ basis of judgment is sound enough to let them vote according to their own judgment conscience if and when necessary?

– Right. Theory, perhaps; current practice of governance looks a bit different. But that’s where our question about the criterion functions come in, again: They are part of the process of not only making up our minds about what actions to take or to support, but also of explaining our basis of judgment to each other. In the extreme, so that we can trust somebody else to make judgments and take action on our behalf – because we have conveyed our mutual basis of judgment well enough, as well as made sure it does give adequate consideration to all available information and concerns.

– So what we are trying to clear up for ourselves is this question: Are there general or universal ‘meta-values’ that can give us adequate criteria to guide effective action — not only let leaders invoke those values without an way of checking whether they actually are served by the proposed actions? And how would those criteria / measures come to be identified and agreed upon?
– Even more specifically, whether those measures should be determined by objective measurement, and then used by some people who are able or entitled to initiate ‘effective action’ to not only do so, — on behalf of everybody else – but to claim that this is ethically and morally defensible and necessary.

– And to declare everybody who disagrees to be ethically misguided – really: to be ‘bad’ or ‘evil’ people.

– Do they come right out and say so?

– I think both sides are, in so many words, unfortunately. So the question of whether there are ‘meta-values’ with related performance measures that everybody should adopt and thus support the resulting actions, is an important one. The sticking part being, to support collective actions taken on our behalf, and perhaps compelling us to take certain everyday actions ourselves, in pursuit of those meta-values. Didn’t we say, a little while ago, that somebody can take action on my behalf if I have given him my criterion functions? – But now it looks like the suggestion is the other way around: that there are criterion functions people tell us we should adopt, to comply with the ethical demands of the Meta Values. So we are trying to understand what that really means.

– Okay: so, let us assume that we have persuaded the participants in the climate change debate to explain their basis of judgment to each other, as much as possible by use of criterion functions. Diagrams that explain how or subjective ‘goodness’ judgments g about proposed plans relate to some ‘objective’, measurable properties of climate change aspects. Take the example of the ‘evil’ CO2 that we are adding to the atmosphere. Assuming that there is some agreement about how much that matters – a different scientific question that also needs sorting out in many people’s minds. And assuming there’s some meaningful agreement about how and where those levels of CO2 will be measured.

– What do you mean – are agreements about CO2 needed? Isn’t that just a simple scientific fact?

– No, Sophie: It makes a difference whether you measure CO2 content in the atmosphere, and at what height, or in the oceans. All those things must be sorted out. Then: is the ‘amount’ or ‘percentage’ of CO2 a good choice of criterion for explaining our judgments about the quality of a plan to improve things?

+ Sure: Haven’t scientists found out, in may serious studies, that the amount of CO2, at the time when we began so see substantial human-based climate change, was some value ‘c*’? Whether you wish to use the actual measure of the amount of CO2 in the atmosphere or its percentage, does not really matter, at least for the sake of this question. Now, say a plan or policy P1 has been proposed to try to get things back in line, whatever that means. So a person tries to explain how her judgment score on some scale +U, to –U, say +3 to -3 depends on how close a proposed plan comes to that ‘goal’ of c*. Would you say that to get a ‘goodness score of +3 (meaning ‘couldn’t possibly get any better’, on a ‘goodness scale of +3 ‘couldn’t possibly get any better, to -3 meaning ‘couldn’t possibly get any worse’ with a midpoint of zero ‘’so-so’, do don’t know’), the plan would have to achieve a return to that value ‘c*’? So you’d draw up a criterion function with the top of the curve touching the +3 line at c*. But another person B thinks that a slightly higher level of CO2 would be OK given some measures in the plan to mitigate it faster. It looks like this:

Figure 3: Criterion functions of two persons, for a plan to return the CO2 value to a desirable CO2 level
It shows their different judgment basis regarding what that level c* should be, showing the CO2-measure on the x-axis and the judgment score on the y-axis. Any lower or higher than that would get a lower score.

 

– Okay; I see. And you are saying that in principle, every participant in the evaluation would be entitled to his or her own curve? Even one that doesn’t have the highest score at ‘c*’?

–  Now why would you – or anybody – put the highest score anywhere else?

– Well, aren’t you guys saying that having spewed so much CO2 into the atmosphere since it was at that benign level ‘c*’ has done a lot of damage already? So to repair that damage – to get the glaciers to grow back, for example, or to cool down the oceans, – it would be necessary to reduce the CO2 to a lower level than ‘c*’ – at least for a while? If you could calculate that level, that’s where a person B might put the +3 score. And give that score to another plan P2 that would achieve that level. Does that make sense?

Figure 4 – Criterion functions about assessment of CO2 levels relative to the ‘current state’
and the goal of ‘returning’ to a previous better state by temporarily lowering the target state below c*

– Wait a minute: wouldn’t it be better to look at the effectiveness of a plan to reduce the amount of CO2 – at least, as you say, for a while, until the effects of increased CO2 level have been ‘repaired’?

– Good question, at least for showing that even the choice of criterion is a controversial issue. You could say that it might be even better to look at how close to the ideal CO2 level a plan can get with its reduction effectiveness, and show how that relates to what would happen if nothing is done? Which is always an alternative ‘plan’?

– Good. But the question is really about whether the criterion of ‘c*’ or ‘c•’ is an appropriate one to base you judgment upon: Both plans are claiming to reach those values – claims that should be assessed for their plausibility, don’t forget – but at different points in time. So which one is ‘better’?

– I guess we should say: the plan that gets there ‘sooner’ – all other aspects being equal, for the sake of the argument.

– Yes, but doesn’t that mean you really have a different criterion? Specifically: the criterion of ‘time for the plan to get to c* — or c•? Which really is a different criterion, so which one are you going to use?

– Does it matter whether we use different levels of desirable c’s as long as we all come up with a goodness judgment g, and we each have explained what that judgment depends upon?

– If the outcome is that your judgment will support P1 while –‘s score will prefer P2 as the better plan – all else being equal, as you say: the problem isn’t really solved yet, is it?

      Figure 5 – Criterion function of plan effectiveness of reduction of CO2 in relation to the ideal ‘stable’ c* level
and to the plan alternative of ‘doing nothing’.

 

– Hey guys, aren’t you missing something essential here? Well, you did sort of stumble on it, – with your remark ‘at least for a while’.

– Huh? Explain, Vodçek. What’s missing?

– Time, of course. In your criterion functions so far, you seem to make an assumption that your plans P1 and P2 will somehow ‘achieve’ some level of CO2 one they got implemented, and that things would then stay that way? But come on, that’s not how things work, is it? Even a plan for this kind of issue would have to involve activities, policies, processes, all continuing over time. It can’t be a sudden magic spell to change things overnight. And then it would take time for the actual CO2 level to change back to whatever level you’d have in mind — if the plan works. But how long is that going to take? And is it going to stay at that level? Shouldn’t that be part of your systemic ‘due consideration’?

– You are right. So let me see – looks like we have to draw up a kind of three-dimensional criterion function, is that what you are implying?

– Right. If you are serious about this idea of ‘explaining how your subjective judgment relates to objective criteria’. Not everybody does…

– Okay, I see your point. So we draw the diagram like this: Time on the horizontal right-left axis, CO2 – level on the horizontal up-down axis y, and your judgment on the vertical axis z.

– And before you ask, I think Dexter would say simulation models to predict the track of the CO2 levels for each plan over time can and have been developed, given reliable data…

– But now, what’s your score for those plans? If the CO2 levels and judgments change all the time? By the way, wouldn’t you have to also include a simulated track for the ‘current’ state of affairs and its future track if nothing is done?

+ Right. So we have the time CO2 simulation with three tracks – like this?

Figure 6: Criterion function of criterion changes and corresponding judgments over time

–  Okay, what about instead looking at the difference between the desired targets and what the plans are actually achieving, over time? It would require looking at the total measure of those differences over time, and I agree that it would also require an agreement about the time frame we are willing to look at. People keep talking about the world we are going to leave for our grandchildren, from a more systemic and holistic perspective – but don’t offer any specifics about how we might distinguish one plan from another, or even from just doing nothing?

– I like your point about looking at the difference, the degree of improvement we can expect from a plan. What would that look like in your diagram?

– Well, the measure would have to be one like the area of the judgment ‘surface’ between two alternatives? Because we agree that in order to reach some kind of meaningful overall measure, it will have to be made up of judgment’ measures, right? But I’m sure that at least for individual judgment systems, ways can be found to calculate that, just as there are equations that describe the simple line functions in the previous examples. But for the time assessment, it will have to include some feature of ‘discounting’ the judgments over time – like calculating the ‘present’ value of a series of income or cost payments over time. Work to do, eh? Because you can see in the diagram that if a plan ‘works’, the improvement judgment area will get wider and wider over time – but after since the probabilities / plausibility judgments will also get more and more uncertain for long future time periods, they will become less and less important factors in the final overall judgment.

– I see what you mean. Not because we don’t care about future generations, mind you – but simply because we just can’t be certain about our predictions about the future.

– Right. Meanwhile, the discourse may become more productive if we threw more of these kinds of diagrams into the fray? Like this one about the judgment surfaces – I agree it needs discussion?

– So you may have to look at the actual results that those changes in CO2 will bring about, over time? Like: levels of ocean rise expected until the remedy sets in – will it actually recede if the plans are any good? I haven’t heard anybody even mention that yet… And provisions for what to do about sea level change until it stops rising?

Figure 7 – Criterion functions showing judgment surfaces of comparative improvement over time

 

– Coming to think of that – have there been any plans put out there with that kind of details?

– I admit, I haven’t been following all the reports on that controversy – but I am not aware of any.

– Hey, here’s a test for promoters of a plan: ask them whether they are willing to put up down payments for new ocean front real estate that will emerge if their plan works as intended?

– Renfroe… Ah well, maybe you’ve got something there. Get Al to put down money for property on the beach in front of Mar-a-Lago?

– You guys are giving me a headache. How are we ever going to get moving on the problem if you keep analyzing things to death and wasting time with stupid pranks?

– Well, who was claiming and boasting about taking a more systemic and holistic look at these problems, and of making decisions based on objective facts? It may be useful to turn that big talk down a notch, and focus on the painful nitty-gritty aspects of how to make these decisions. So that people can agree on something specific instead of calling each other names, and resisting plans because of distrust – deserved or not – about what the promoters for plan P1 or P2 are really after? Like power? The next election? More profit for one industry rather than another? That lack of trust may be the real reason why people keep resisting plans to do something about problems. But can’t talk about them because those suspected motives or their lack of them aren’t being part of the discourse?

– Ah: the discourse. Yes. How to talk about all that without starting to call each other names or throwing rocks at each other and storefronts… What does that take? Where’s Abbé Boulah?
oooo

About meta-values to guide more effective action about global crises.

Published August 23, 2017 'Next System' , Design discourse , Public policy discourse , Systems Thinking , Uncategorized 3 Comments
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The increasing urgency and global scope of crises like climate change has incited discussions for how humanity can take more more effective action about these challenges. One suggestion was for the common adoption of ‘systemic meta-values’ that would guide decisions (LinkedIn ‘Systems Thinking World’:  ‘Can systemic values  help overcome the doctrinal blindness…) The suggestion triggered some discussion about the advisability of the idea, that highlighted its importance. The LI format with its length limitations of posts prevented a more constructive and ‘systemic’ examination. So it seems it might be meaningful to develop a more comprehensive look at the range of arguments, on a platform that permits longer entries.

For the sake of argument, let’s assume that some meaningful meta-value or values can be identified. To assess whether and how well they / it would guide decisions about ‘more effective actions’ (About climate change or any other issue), questions arise, such as:
A) How would it come about?
B) How would it come to be ‘accepted’?
C) How will we know what ‘more effective actions’ are available that could or should be taken?
D) How would it be established whether a meta-value applies to a specific issue?
E) How would such actions be implemented? More specifically”
F) What, if any, changes in governance (local and global), economic, production and distribution, education, decision-making etc. would be needed?

About A): Possibilities:
a) Meta-Value formulations would be identified and stated (by whom?) as ‘axioms’ — self-evident concepts in their general ‘meta-‘ form and needing no other justification or explanation?
b) Such a concept would have to be ‘constructed’ and ‘aggregated’ from more detailed, individual value judgments about what is good, ethical, etc.?
c) Other?

About B): Possibilities:
a) The concept would (if Aa) have to be presented to everybody; if self-evident, it would be inevitably accepted by everyone?
b) ‘Brain-washing’, indoctrination: incessant instruction, propaganda?
c) Presented as derived from widely accepted and recognized authorities: philosophical, religious: divine commandments; ‘infallible’ papal announcements), other?
d) Imposed by power, with various justification stories a-c; “on behalf of ….” subjects;
e) Involving everybody (at least potentially) in its construction / negotiation, so as to make it everybody’s ‘own’ creation?
f) Other?

About C):
a) Building on ‘tried and true’ traditional knowledge in established ‘Knowledge Bases’
b) Using existing and new scientific knowledge to develop new tools and action possibilities, ‘tested’ by ‘calculation, simulation, (controlled experiments at small scale?
c) Other? E.g. Pure ‘exploration’?

About item D):
a) If general validity is accepted, no distinctions and adaptation to specific situation is needed?
b) Must be established on a case-by-case, situation and context base for every issue;
c) Other?

About item E) and F):
a) If the meta-value is indeed ‘self-evident’, would it not be ‘automatically’ applied by everybody in all institutions (which may adapt accordingly without prior re-organization)?
b) Some of all of the traditional social institutions may have to be re-designed in order to make more effective action possible;
c) New institutions will have to be established to replace existing ones?
d) New institutions developed ‘parallel’ to the existing ones, gradually taking over decisions-making and implementation of needed actions that could not be achieved by the ‘old’ systems?
===
If I were asked to suggest what should be done, based ‘on the best of my current knowledge and understanding’ which I can only offer as a contribution to the discussion, here is my take on those items: I would start out on the following positions: Ab; Be; Cb; Db; E/Fd.

I do not believe we currently have compelling, definitive answers to any of the above questions, that are likely to be globally accepted soon enough for any ‘global’ ‘unified’ solution. I also believe that we are at a stage where people are interested in kinds of ‘pursuits of happiness’ that consist of ‘making a difference’ in their lives — and consider this a part of their ‘human right. This desire will inevitably determine their acceptance and application of values to their personal lives, and issues of global common urgency and concern cannot easily be exempted from this aspect. I also believe that — even for such global issues as climate change — there will be so many different local context conditions that overall unified meta-principles would have to be adapted in many different ways. To the point where the top-down adaptation process would not look much less complicated than a bottom-up ‘construction’ process.
I agree that there will have to be some global decisions, agreements — I like to use the model’ of the rules of the road: we will have to agree on which side of the road we’ll drive on for all of us to reach our different destinations. But we do not yet have a platform on which proposals and concerns, arguments can be shared, and decisions reached that are based on the merit of the contributions to the discussion. This is the most important collective task we face, in my opinion.
I have sketched my suggestions about the steps to be taken in some essays, books and papers. Briefly, they consist of the following ideas:

1 We should encourage and support all the different experiments, initiatives, ‘alternative’ developments that are proposed and already going on. The emphasis is on difference, not unified action: They may be based on different, even contradictory principles. We need to know more about what works and what doesn’t before embarking on ‘unified’ global systems. The meta-value I might suggest, if any, for this aspect, is: Support collective decisions to make as many different initiatives possible but compatible. The condition for that support and tolerance is twofold:
a) the mutual agreement not to ‘get in each others’ way’ (hurting, destroying, limiting, unnecessarily constraining); and
b) agreement to openly share their experience — successes and failures — for increasing our global knowledge about what works and what doesn’t.

2 These experiences as well as proposals for ‘global’ agreements should be brought into a ‘global’ platform I have tentatively called ‘Planning Discourse Support System’. which should be developed with urgency. It must have a number of features that are not currently part of the international ‘platforms’ such as the UN, EU, ASEAN or similar governance models, or the various ‘social media’ networks. Mainly: wide public access and participation, better means of providing overview of the core of information being assembled, systematic means of assessing the merit of contributions, and decision tools that are transparently based on that merit, instead of traditional ‘voting’ modes. (I have developed tools, e.g. for the systematic assessment of planning arguments, and ideas for the design of such platforms, for discussion. Most of those are available on Academia.edu or on my WordPress blog AbbéBoulah.com.).

The platform should be established ‘parallel’ to existing institutions — see item E/Fd above. It should focus on as few necessary agreements as possible. The agenda of ‘projects’ should include the development of means for ensuring that ‘global agreements are actually adhered to — means or ‘sanctions’ that would reduce the need for ‘enforcement’ by, as the word implies, force, coercion’ and the related issue of control of power. One item, overall or part of the task for each of the projects, would be the construction and acceptance of measures of performance and the underlying criteria, values or meta-values as applicable to that project.

A hypothetical ‘perfect’ artificial argumentative systems planner — D R A F T

Published July 31, 2017 'Next System' , Argument patterns , Design discourse , Governance , Public policy discourse , Rittel , Systems Modeling , Systems Thinking 1 Comment

A tavern discussion looking at the idea of an artificial planning discourse participant from the perspectives of the argumentative model and the systems thinking perspectives, expanding both (or mutually patching up their shortcomings), and inadvertently stumbling upon potential improvements upon the concept of democracy.

Customers and patron of a fogged-in island tavern with nothing better to do,
awaiting news on progress on the development of a better planning discourse
begin an idly speculative exploration of the idea of an artificial planner:
would such a creature be a better planning discourse participant?

– Hey Bog-Hubert: Early up and testing Vodçeks latest incarnation of café cataluñia forte forte? The Fog Island Tavern mental alarm clock for the difficult-to-wakeup?

– Good morning, professor. Well, have you tried it? Or do you want to walk around in a fogged-in-morning daze for just a while longer?

– Whou-ahmm, sorry. Depends.

– Depends? On what?

– Whether this morning needs my full un-dazed attention yet.

– Makes sense. Okay. Let me ask you a question. I hear you’ve been up in town. Did you run into Abbé Boulah, by any chance? He’s been up there for a while, sorely neglecting his Fog Island Tavern duties here, ostensibly to help his buddy at the university with the work on his proposals for a better planning discourse system. Hey, Sophie: care to join us?

– Okay, good morning to you too. What’s this about a planning system?

– I’m not sure if it’s a ‘system’. I was asking the professor if he has heard whether Abbé Boulah and his buddy have made any progress on that. It’s more like a discourse platform than a ‘system’ – if by ‘system’ you mean something like an artificial planning machine – a robot planner.

– Oh, I’m relieved to hear that.

– Why, Sophie?

– Why? Having a machine make our plans for our future? That would be soo out of touch. Really. Just when we are just beginning to understand that WE have to take charge, to redesign the current ‘MeE’ system, from a new Awareness of the Whole, of our common place on the planet, in the universe, our very survival as a species? That WE have to get out from under that authoritarian, ME-centered linear machine systems thinking, to emerge into a sustainable, regenerative NEW SYSTEM?

– Wow. Sounds like we are in more trouble than I thought. So who’s doing that, how will we get to that New System?

– Hold on, my friends. Lets not get into that New System issue again – haven’t we settled that some time ago here – that we simply don’t know yet what it should be like, and should try to learn more about what works and what doesn’t, before starting another ambitious grand experiment with another flawed theory?

– Okay, Vodçek, good point. But coming to think about it – to get there, — I mean to a better system with a better theory — wouldn’t that require some smart planning? You can’t just rely on everybody coming to that great awareness Sophie is taking about, for everything just to fall into place? So wouldn’t it be interesting to just speculate a bit about what your, I mean Abbé Boulah’s buddy’s planning machine, would have to do to make decent plans?

– You mean the machine he doesn’t, or, according to Sophie, emphatically shouldn’t even think about developing?

– That’s the one.

– Glad we have that cleared up… Well, since we haven’t heard anything new about the latest scandals up in town yet, it might be an interesting way to pass the time.

– Hmm.

– I hear no real objections, just an indecisive Hmm. And no, I don’t have any news from Abbé Boulah either – didn’t see him. He tends to stay out of public view. So it’s agreed. Where do we start?

– Well: how about at the beginning? What triggers a planning project? How does it start?

Initializing triggers for planning?

– Good idea, Sophie. Somebody having a problem – meaning something in the way things are, that are perceived as unsatisfactory, hurtful, ugly, whatever: not the way they ought to be?

– Or: somebody just has a bright idea for doing something new and interesting?

– Or there’s a routine habit or institutional obligation to make preparations for the future – to lay in provisions for a trip, or heating material for the winter?

– Right: there are many different things that could trigger a call for ‘doing something about it’ – a plan. So what would the machine do about that?

– You are assuming that somebody – a human being – is telling the machine to do something? Or are you saying that it could come up with a planning project on its own?

– It would have to be programmed to recognize a discrepancy between what IS and what OUGHT to be, about a problem or need, wouldn’t it? And some human would have had to tell him that. Because it’s never the machine (or the human planner working on behalf of people) hurting if there’s a problem; its only people who have problems.

– So it’s a Him already?

– Easy, Sophie. Okay: A She? You decide. Give her, him, it a name. So we can get on with it.

– Okay. I’d call it the APT – Abominable Planning Thing. And it’s an IT, a neuter.

– APT it is. Nicely ambiguous… For a moment I thought you meant Argumentative Planning Tool. Or Template.

– Let’s assume, for now, that somebody told it about a problem or a bright idea. So what would that APT do?

Ground rules, Principles?
Due consideration of all available information;
Whole system understanding guiding decisions
towards better (or at least not worse) outcomes
for all affected parties

– Wait: Shouldn’t we first find out some ground rules about how it’s going to work? For example, it wouldn’t do to just come up with some random idea and say ‘this is it’?

– Good point. You have any such ground rules in mind, professor?

– Sure. I think one principle is that it should try to gather and ‘duly consider’ ALL pertinent information that is available about the problem situation. Ideally. Don’t you agree, Sophie? Get the WHOLE picture? Wasn’t that part of the agenda you mentioned?

– Sounds good, professor. But is it enough to just ‘have’ all the information? Didn’t someone give a good description of the difference between ‘data’ (just givens, messages, numbers etc) and ‘information’ – the process of data changing someone’s stat of knowledge, insight, understanding?

– No, you are right. There must be adequate UNDERSTANDING – of what it means and how it all is related.

– I see a hot discussion coming up about what that really means: ‘understanding’… But go on.

– Well, next: wouldn’t we expect that there needs to be a process of developing or drawing a SOLUTION or a proposed PLAN – or several – from that understanding? Not just from the stupid data?

– Det er da svœrt så fordringfull du er idag, Sophie: Now you are getting astoundingly demanding here. Solutions based on understanding?

– Oh, quit your Norwegian bickering. I’ll do even more demanding: Mustn’t there be a way to CONNECT all that understanding, all the concerns, data, facts, arguments, with any proposed DECISION, especially the final one that leads to action, implementation. If we ever get to that?

– Are you considering that all the affected folks will expect that the decision should end up making things BETTER for them? Or at least not WORSE than before? Would that be one of your ground rules?

– Don’t get greedy here, Vodçek. The good old conservative way is to ask some poor slobs to make some heroic Sacrifices for the Common Good. “mourir pour des idées, d’accord, mais de mort lente…”  as George Brassens complains. But you are right: ideally, that would be a good way to put the purpose of the effort.

– All right, we have some first principles or expectations. We’ll probably add some more of those along the way, but I’d say it’s enough for a start. So what would our APT gizmo do to get things moving?

Obtaining information
Sources?

– I’d say it would start to inquire and assemble information about the problem’s IS state, first. Where is the problem, who’s hurting and how, etc. What caused it? Are there any ideas for how to fix it? What would be the OUGHT part — of the problem as well as a bright idea as the starting point?

– Sounds good, Bog-Hubert. Get the data. I guess there will be cases where the process actually starts with somebody having a bright idea for a solution. But that’s a piece of data too, put it in the pile. Where would it get all that information?

– Many sources, I guess. First: from whoever is hurting or affected in any way.

– By the problem, Vodçek? Or the solutions?

– Uh, I guess both. But what if there aren’t any solutions proposed yet?

– It means that the APT will have to check and re-check that whenever someone proposes a solution — throughout the whole process, doesn’t it? It’s not enough to run a single first survey of citizen preferences, like they usually do to piously meet the mandate for ‘citizen participation’. Information gathering, research, re-research, analysis will accompany the whole process.

– Okay. It’s a machine, it won’t get tired of repeated tasks.

– Ever heard of devices overheating, eh? But to go on, there will be experts on the particular kind of problem. There’ll be documented research, case studies from similar events, the textbooks, newspapers, letters to the editor, petitions, the internet. The APT would have to go through everything. And I guess there might have to be some actual ‘observation’, data gathering, measurements.

Distinctions, meaning
Understanding

– So now it has a bunch of stuff in its memory. Doesn’t it have to sort it somehow, so it can begin to do some real work on it?

– You don’t think gathering that information is work, Sophie?

– Sure, but just a bunch of megabytes of stuff… what would it do with it? Don’t tell me it can magically pull the solution from that pile of data!

– Right. Some seem to think they can… But you’ll have to admit that having all the information is part of the answer to our first expectation: to consider ALL available information. The WHOLE thing, remember? The venerable Systems Thinking idea?

– Okay. If you say so. So what to you mean by ‘consider’ – or ‘due consideration’? Just staring at the pile of data until understanding blossoms in your minds and the solution jumps out at you like the bikini-clad girl out of the convention cake? Or Aphrodite rising out of the data ocean?

– You are right. You need to make some distinctions, sort out things. What you have now, at best, are a bunch of concepts, vague, undefined ideas. The kind of ‘tags’ you use to google stuff.

– Yeah. Your argumentation buddy would say you’d have to ask for explanations of those tags – making sure it’s clear what they mean, right?

– Yes. Now he’d also make the distinction that some of the data are actual claims about the situation. Of different types: ‘fact’-claims about the current situation; ‘ought’ claims about what people feel the solution should be. Claims of ‘instrumental’ knowledge about what caused things to become what they are, and thus what will happen when we do this or that: connecting some action on a concept x with another concept ‘y’ – an effect. Useful when we are looking for x’s to achieve desired ‘y’s that we want – the ‘ought’ ideas – or avoid the proverbial ‘unexpected / undesirable’ side-and after-effect surprises of our grand plans: ‘How’ to do things.

– You’re getting there. But some of the information will also consist of several claims arranged into arguments. Like: “Yes, we should do ‘x’ (as part of the plan) because it will lead to ‘y’, and ‘y’ ought to be…” And counterarguments: “No, we shouldn’t do ‘x’ because x will cause ‘z’ which ought not to be.”

– Right. You’ve been listening to Abbé Boulah’s buddy’s argumentative stories, I can tell. Or even reading Rittel? Yes, there will be differences of opinion – not only about what ought to be, but about what we should do to get what we want, about what causes what, even about what Is the case. Is there an old sinkhole on the proposed construction site? And if so, where? That kind of issue. And different opinions about those, too. So the data pile will contain a lot of contradictory claims of all kinds. Which means, for one thing, that we, –even Spock’s relative APT — can’t draw any deductively valid conclusions from contradictory items in the data. ‘Ex contradictio sequitur quodlibet’, remember – from a contradiction you can conclude anything whatever. So APT can’t be a reliable ‘artificial intelligence’ or ‘expert system’ that gives you answers you can trust to be correct. We discussed that too once, didn’t we – there was an old conference paper from the 1990s about it. Remember?

– But don’t we argue about contradictory opinions all the time – and draw conclusions about them too?
– Sure. Living recklessly, eh? All the time, and especially in planning and policy-making. But it means that we can’t expect to draw ‘valid’ conclusions that are ‘true or false’, from our planning arguments. Just more or less plausible. Or ‘probable’ – for claims that are appropriately labeled that way.

Systems Thinking perspective
Versus Argumentative Model of Planning?

– Wait. What about the ‘Systems Thinking’ perspective — systems modeling and simulation? Isn’t that a better way to meet the expectation of ‘due consideration’ of the ‘whole system’? So should the APT develop a systems model from the information it collected?

– Glad you brought that up, Vodçek. Yes, it’s claimed to be the best available foundation for dealing with our challenges. So what would that mean for our APT? Is it going to have a split robopersonality between Systems and the Argumentative Model?

– Let’s look at both and see? There are several levels we can distinguish there. The main tenets of the systems approach have to do with the relationships between the different parts of a system – a system is a set of parts or entities, components, that are related in different ways – some say that ‘everything is connected / related to everything else’ – but a systems modeler will focus on the most significant relationships, and try to identify the ‘loops’ in that network of relationships. Those are the ones that will cause the system to behave in ways that can’t be predicted from the relationships between any of the individual pairs of entities in the network. Complexity; nonlinearity. Emergence.

– Wow. You’re throwing a lot of fancy words around there!

– Sorry, Renfroe; good morning, I didn’t see you come in. Doing okay?

– Yeah, thanks. Didn’t get hit by a nonlinearity, so far. This a dangerous place now, for that kind of thing?

– Not if you don’t put too much brandy in that café cataluñia Vodçek is brewing here.

– Hey, lets’ get back to your systems model. Can you explain it in less nonlinear terms?

– Sure, Sophie. Basically, you take all the significant concepts you’ve found, put them into a diagram, a map, and draw the relationships between them. For example, cause-effect relationships; meaning increasing ‘x’ will cause an increase in ‘y’. Many people think that fixing a system can best be done by identifying the causes that brought the state of affairs about that we now see as a problem. This will add a number or new variables to the diagram, to the ‘understanding’ of the problem.

– They also look for the presence of ‘loops’ in the diagram, don’t they? – Where cause-effect chains come back to previous variables.

– Right, Vodçek. This is an improvement over a simple listing of all the pro and con arguments, for example – they also talk about relationships x – y, but only one at a time, so you don’t easily see the whole network, and the loops, in the network. So if you are after ‘understanding the system’, seeing the network of relationships will be helpful. To get a sense of its complexity and nonlinearity.

– I think I understand: you understand a system when you recognize that it’s so loopy and complex and nonlinear that its behavior can’t be predicted so it can’t be understood?

– Renfroe… Professor, can you straighten him out?

– Sounds to me like he’s got it right on, Sophie. Going on: Of course, to be really helpful, the systems modeler will tell you that you should find a way to measure each concept, that is, find a variable – a property of the system that can be measures with precise units.

– What’s the purpose of that, other than making it look more scientific?

– Well, Renfroe, remember the starting point, the problem situation. Oh, wait, you weren’t here yet. Okay; say there’s a problem. We described it as a discrepancy between what somebody feels Is the case and what Ought to be. Somebody complains about it being too hot in here. Now just saying: ‘it’s too hot; it ought to be cooler’, is a starting point, but in order to become useful, you need to be able to say just what you mean by ‘cooler’. See, you are stating the Is/Ought problem in terms of the same variable ‘temperature’. So too even see the difference between Is and Ought, you have to point to the levels of each. 85 degrees F? Too hot. Better: cool it to 72. Different degrees or numbers on the temperature scale.

– Get it. So now we have numbers, math in the system. Great. Just what we need. This early in the morning, too.

– I was afraid of that too. It’s bound to get worse…nonlinear. So in the argumentative approach – the arguments don’t show that? Is that good or bad?

– Good question. Of course you can get to that level, if you bug them enough. Just keep asking more specific questions.

– Aren’t there issues where degrees of variables are not important, or where variables have only two values: Present or not present? Remember that the argumentative model came out of architectural and environmental design, where the main concerns were whether or not to provide some feature: ‘should the entrance to the building be from the east, yes or no?’ or ‘Should the building structure be of steel or concrete?’ Those ‘conceptual’ planning decisions could often be handled without getting into degrees of variables. The decision to go with steel could be reached just with the argument that steel would be faster and cheaper than concrete, even before knowing just by how much. The arguments and the decision were then mainly yes or no decisions.

– Good points, Vodçek. Fine-tuning, or what they call ‘parametric’ planning comes later, and could of course cause much bickering, but doesn’t usually change the nature of the main design that much. Just its quality and cost…

Time
Simulation of systems behavior

– Right. And they also didn’t have to worry too much about the development of systems over time. A building, once finished, will usually stay that way for a good while. But for policies that would guide societal developments or economies, the variables people were concerned about will change considerably over time, so more prediction is called for, trying to beat complexity.

– I knew it, I knew it: time’s the culprit, the snake in the woodpile. I never could keep track of time…

– Renfroe… You just forget winding up your old alarm clock. Now, where were we? Okay: In order to use the model to make predictions about what will happen, you have to allocate each relationship step to some small time unit: x to y during the first time unit; y to z in the second, and so on. This will allow you to track the behavior of the variables of the system over time, give some initial setting, and make predictions about the likely effects of your plans. The APT computer can quickly calculate predictions for a variety of planning options.

– I’ve seen some such simulation predictions, yes. Amazing. But I’ve always wondered how they can make such precise forecasts – those fine crisp lines over several decades: how do they do that, when for example our meteorologists can only make forecasts of hurricane tracks of a few days only, tracks that get wider like a fat trumpet in just a few days? Are those guys pulling a fast one?

– Good point. The answer is that each simulation only shows the calculated result of one specific set of initial conditions and settings of relationships equations. If you make many forecasts with different numbers, and put them all on the same graph, you’d get the same kind of trumpet track. Or even a wild spaghetti plate of tracks.

– I am beginning to see why those ‘free market’ economists had such an advantage over people who wanted to gain some control of the economy. They just said: the market is unpredictable. It’s pointless to make big government plans and laws and regulations. Just get rid of all the regulations, let the free market play it out. It will control and adapt and balance itself by supply and demand and competition and creativity.

– Yeah, and if something goes wrong, blame it on the remaining regulations of big bad government. Diabolically smart and devious.

– But they do appreciate government research grants, don’t they? Wait. They get them from the companies that just want to get rid of some more regulations. Or from think tanks financed by those companies.

– Hey, this is irresponsibly interesting but way off our topic, wouldn’t you say?

– Right, Vodçek. Are you worried about some government regulation – say, about the fireworks involved in your café catastrofia? But okay. Back to the issue.

– So, to at least try to be less irresponsible, our APT thing would have systems models and be able to run simulations. A simulation, if I understand what you were saying, would show how the different variables in the system would change over time, for some assumed initial setting of those variables. That initial setting would be different from the ‘current’ situation, though, wouldn’t it? So where does the proposed solution in the systems model come from? Where are the arguments? Does the model diagram show what we want to achieve? Or just the ‘current state’?

Representation of plan proposals
and arguments in the systems model?
Leverage points

– Good questions, all. They touch on some critical problems with the systems perspective. Let’s take one at a time. You are right: the usual systems model does not show a picture of a proposed solution. To do that, I think we’ll have to expand a little upon our description of a plan: Would you agree that a plan involves some actions by some actors, using some resources acting upon specific variables in the system? Usually not just one variable but several. So a plan would be described by those variables, and the additional concepts of actions, actor, resources etc. Besides the usual sources of plans, — somebody’s ‘brilliant idea’, some result of a team brainstorming session, or just an adaptation of a precedent, a ‘tried and true’ known solution with a little new twist, —  the systems modeler may have played around with his model and identified some ‘leverage points’ in the system – variables where modest and easy-to-do changes can bring about significant improvement elsewhere in the system: those are suggested starting points for solution ideas.

– So you are saying that the systems tinkerer should get with it and add all the additional solution description to the diagram?

– Yes. And that would raise some new questions. What are those resources needed for the solution? Where would they come from, are they available? What will they cost? And more: wouldn’t just getting all that together cause some new effects, consequences, that weren’t in the original data collection, and that some other people than those who originally voiced their concerns about the problem would now be worried about? So your data collection component will have to go back to do some more collecting. Each new solution idea will need its own new set of information.

– There goes your orderly systematic procedure all right. That may go on for quite some time, eh?

– Right. Back and forth, if you want to be thorough. ‘Parallel processing’. And it will generate more arguments that will have to be considered, with questions about how plausible the relationship links are, how plausible the concerns about the effects – the desirable / undesirable outcomes. More work. So it will often be shouted down with the usual cries of ‘analysis paralysis’.

Intelligent analysis of data:
Generating ‘new’ arguments?

– Coming to think of it: if our APT has stored all the different claims it has found – in the literature, the textbooks, previous cases, and in the ongoing discussions, would it be able to construct ‘new’ arguments from those? Arguments the actual participants haven’t thought about?

– Interesting idea, Bog-Hubert. – It’s not even too difficult. I actually heard our friend Dexter explain that recently. It would take the common argument patterns – like the ones we looked at – and put claim after claim into them, to see how they fit: all the if-then connections to a proposal claim would generate more arguments for and against the proposal. Start looking at an ‘x’ claim of the proposal. Then search for (‘google’)  ‘x→ ?’:  any ‘y’s in the data that have been cited as ‘caused by x’. If a ‘y’ you found was expressed somewhere else as ‘desirable or undesirable’ – as a deontic claim, — it makes an instant ‘new’ potential argument. Of course, whether it would work as a ‘pro’ or a ‘con’ argument in some participant’s mind would depend on how that participant feels about the various premises.

– What are you saying, professor? This doesn’t make sense. A ‘pro’ argument is a ‘pro’ argument, and ‘con’ argument is a ‘con’ argument. Now you’re saying it depends on the listener?

– Precisely. I know some people don’t like this. But consider an example. People are discussing a plan P; somebody A makes what he thinks is a ‘pro’ argument: “Let’s do P because P will produce Q; and Q is desirable, isn’t it?” Okay, for A it is a pro argument, no question. Positive plausibility, he assumes, for P→Q as well as for Q; so it would get positive plausibility pl for P. Now for curmudgeon B, who would also like to achieve Q but is adamant that P→Q won’t work, (getting a negative pl) that set of premises would produce a negative pl for P, wouldn’t it? Similarly, for his neighbor C, who would hate for Q to become true, but thinks that P→Q will do just that, that same set of premises also is a ‘con’ argument.

– So what you’re saying is that all the programs out there, that show ‘dialogue maps’ identifying all arguments as pro or con, as they were intended by their authors, are patently ignoring the real nature and effects of arguments?

– I know some people have been shocked – shocked — by these heretical opinions – they have been written up. But I haven’t seen any serious rebuttals; those companies, if they have heard of them have chosen to ignore them. Haven’t changed their evil ways though…

– So our devious APT could be programmed to produce new arguments. More arguments. Just what we need. The arguments can be added to the argument list, but I was going to ask you before: how would the deontic claims, the ‘oughts’, be shown in the model?

– You’d have to add another bubble to each variable bubble, right? Now, we have the variable itself, the value of each variable in the current IS condition, the value of the variable if it’s part of a plan intervention, and the desired value – hey: at what time?

– You had to put the finger on the sore spot, Vodçek. Bad boy. Not only does this make the diagram a lot less clean, simple, and legible. Harder to understand. And showing what somebody means by saying what the solution ought to achieve, when all the variables are changing over time, now becomes a real challenge. Can you realistically expect that a desired variable should stay ‘stable’ at one desired value all the time, after the solution is implemented? Or would people settle for something like: remaining within a range of acceptable values? Or, if a disturbance has occurred, return to a desired value after some reasonably short specified time?

– I see the problem here. Couldn’t the diagram at least show the central desired value, and then let people judge whether a given solution comes close enough to be acceptable?

– Remember that we might be talking about a large number of variables that represent measures of how well all the different concerns have been met by a proposed solution. But if you don’t mind complex diagrams, you could add anything to the systems model. Or you can use several diagrams. Understanding can require some work, not just sudden ‘aha!’ enlightenment.

Certainty about arguments and predictions
Truth, probability, plausibility and relative importance of claims

– And we haven’t even talked about the question of how sure we can be that a solution will actually achieve a desired result.

– I remember our argumentative friends at least claimed to have a way to calculate the plausibility of a plan proposal based on the plausibility of each argument and the weight of relative importance of each deontic, each ought concern. Would that help?

– Wait, Bog-hubert: how does that work, again? Can you give us the short explanation? I know you guys talked about that before, but…

– Okay, Sophie: The idea is this: a person would express how plausible she thinks each of the premises of an argument are. On some plausibility scale of, say +1 which means ‘totally plausible’, to -1 which means ‘totally implausible; with a midpoint zero meaning ‘don’t know, can’t tell’. These plausibility values together will then give you an ‘argument plausibility’ – on the same scale, either by multiplying them or taking the lowest score as the overall result. The weakest link in the chain, remember. Then: multiplying that plausibility with the weight of relative importance of the ought- premise in the argument, which is a value between zero and +1 such that all the weights of all the ‘oughts’ in all the arguments about the proposal will add up to +1. That will give you the ‘argument weight’ of each argument; and all the argument weights together will give you the proposal plausibility – again, on the same scale of +1 to -1, so you’d know what the score means. A value higher than zero means it’s somewhat plausible; a value lower than zero and close to -1 means it’ so implausible that it should not be implemented. But we aren’t saying that this plausibility could be used as the final decision measure.

– Yeah, I remember now. So that would have to be added to the systems model as well?

– Yes, of course – but I have never seen one that does that yet.

‘Goodness’ of solutions
not just plausibility?

– But is that all? I mean: ‘plausibility’ is fine. If there are several proposals to compare: is plausibility the appropriate measure? It doesn’t really tell me how good the plan outcome will be? Even comparing a proposed solution to the current situation: wouldn’t the current situation come up with a higher plausibility — simply because it’s already there?

– You’ve got a point there. Hmm. Let me think. You have just pointed out that both these illustrious approaches – the argumentative model, at last as we have discussed it so far, as well as the systems perspective, for all its glory, have both grievously sidestepped the question of what makes a solution, a systems intervention ‘good’ or bad’. The argument assessment work, because it was just focused on the plausibility of arguments; as the first necessary step that had not been looked at yet. And the systems modeling focusing on the intricacies of the model relations and simulation, leaving the decision and its preparatory evaluation, if any, to the ‘client.’ Fair enough; they are both meritorious efforts, but it leaves both approaches rather incomplete. Not really justifying the claims of being THE ultimate tools to crack the wicked problems of the world. It makes you wonder: why didn’t anybody call the various authors on this?

– But haven’t there long been methods, procedures for people to evaluate to the presumed ‘goodness’ of plans? Why wouldn’t they have been added to either approach?

– They have, just as separate, detached and not really integrated extra techniques. Added, cumbersome complications, because they represent additional effort and preparation, even for small groups. And never even envisaged for large public discussions.

– So would you say there are ways to add the ‘goodness’ evaluation into the mix? We’ve already brought systems and arguments closer together? You say there are already tools for doing that?

– Yes, there are. For example, as part of a ‘formal’ evaluation procedure, you can ask people to explain the basis of their ‘goodness’ judgment about a proposed solution by specifying a ‘criterion function’ that shows how that judgment depends on the values of a system variable. The graph of it looks like this: On one axis it would have positive (‘like’, ‘good’, desirable’) judgment values on the positive side, and ‘dislike’, ‘bad’, ‘undesirable ‘ values on the negative one, with a midpoint of ‘neither good nor bad’ or ‘can’t decide’. And the specific system variable on the other axis, for example that temperature scale from our example a while ago. So by drawing a line in the graph that touches the ‘best possible’ judgment score at the person’s most comfortable temperature, and curves down towards ‘so-so, and down to ‘very bad’ and ultimately ‘intolerable’, couldn’t get worse’, a person could ‘explain’ the ‘objective’, measurable basis of her subjective goodness.

– But that’s just one judgment out of many others she’d have to make about all the other system variables that have been declared ‘deontic’ targets? How would you get to an overall judgment about the whole plan proposal?

– There are ways to ‘aggregate’ all those partial judgments into an overall deliberated judgment. All worked out in the old papers describing the procedure. I can show you that if you want. But that’s not the real problem here – you don’t see it?

– Huh?

The problem of  ‘aggregation’

of many different personal, subjective judgments
into group or collective decision guides

– Well, tell me this, professor: would our APTamajig have the APTitude to make all those judgments?

– Sorry, Bog-Hubert: No. Those judgments would be judgments of real persons. The APT machine would have to get those judgments from all the people involved.

– That’s just too complicated. Forget it.

– Well, commissioner, — you’ve been too quiet here all this time – remember: the expectation was to make the decision based on ‘due consideration’ of all concerns. Of everybody affected?

– Yes, of course. Everybody has the right to have his or her concerns considered.

– So wouldn’t ‘knowing and understanding the whole system’ include knowing how everybody affected feels about those concerns? Wasn’t that, in a sense, part of your oath of office, to serve all members of the public to the best of your knowledge and abilities? So now we have a way to express that, you don’t want to know about that because it’s ‘too complicated?

– Cut the poor commissioner some slack: the systems displays would get extremely crowded trying to show all that. And adding all that detail will not really convey much insight.

– It would, professor, if the way that it’s being sidestepped wasn’t actually a little more tricky, almost deceptive. Commissioner, you guys have some systems experts on your staff, don’t you? So where do they get those pristine performance track printouts of their simulation models?

– Ah. Huh. Well, that question never came up.

– But you are very concerned about public opinion, aren’t you? The polls, your user preference surveys?

– Oh, yeah: that’s a different department – the PR staff. Yes, they get the Big Data about public opinions. Doing a terrific job at it too, and we do pay close attention to that.

– But – judging just from the few incidents in which I have been contacted by folks with such surveys – those are just asking general questions, like ‘How important is it to attract new businesses to the city?’ Nobody has ever asked me to do anything like those criterion functions the professor was talking about. So if you’re not getting that: what’s the basis for your staff recommendations about which new plan you should vote for?

– Best current practice: we have those general criteria, like growth rate, local or regional product, the usual economic indicators.

– Well, isn’t that the big problem with those systems models? They have to assume some performance measure to make a recommendation. And that is usually one very general aggregate measure – like the quarterly profit for companies. Or your Gross National Product, for countries. The one all the critics now are attacking, for good reasons, I’d say, — but then they just suggest another big aggregate measure that nobody really can be against – like Gross National Happiness or similar well-intentioned measures. Sustainability. Systemicity. Whatever that means.

– Well, what’s wrong with those? Are you fixin’ to join the climate change denier crowd?

– No, Renfroe. The problem with those measures is that they assume that all issues have been settled, all arguments resolved. But the reality is that people still do have differences of opinions, there will still be costs as well as benefits for all plans, and those are all too often not fairly distributed. The big single measure, whatever it is, only hides the disagreements and the concerns of those who have to bear more of the costs. Getting shafted in the name of overall social benefits.

Alternative criteria to guide decisions?

– So what do you think should be done about that? And what about our poor APT? It sounds like most of the really important stuff is about judgments it isn’t allowed or able to make? Would even a professional planner named APT – ‘Jonathan Beaujardin APT, Ph.D M.WQ, IDC’ — with the same smarts as the machine, not be allowed to make such judgments?

– As a person, an affected and concerned citizen, he’d have the same right as everybody else to express his opinions, and bring them into the process. As a planner, no. Not claiming to judge ‘on behalf’ of citizens – unless they have explicitly directed him to do that, and told him how… But now the good Commissioner says he wouldn’t even need to understand his own basis of judgment,  much less make it count in the decision?

– Gee. That really explains a lot.

– Putting it differently: Any machine – or any human planner, for that matter, however much they try to be ‘perfect’ – trying to make those judgments ‘on behalf’ of other people, is not only imperfect but wrong, unless it has somehow obtained knowledge about those feelings about good or bad of others, and has found an acceptable way of reconciling the differences into some overall common ‘goodness’ measure. Some people will argue that there isn’t any such thing: judgments about ‘good or ‘bad’ are individual, subjective judgments; they will differ, there’s no method by which those individual judgments can be aggregated into a ‘group’ judgment that wouldn’t end up taking sides, one way or the other.

– You are a miserable spoilsport, Bog-Hubert. Worse than Abbé Boulah! He probably would say that coming to know good and bad, or rather thinking that you can make meaningful judgments about good or bad IS the original SIN.

– I thought he’s been excommunicated, Vodçek? So does he have any business saying anything like that? Don’t put words in his mouth when he’s not here to spit them back at you. Still, even if Bog-Hubert is right: if that APT is a machine that can process all kinds of information faster and more accurate than humans, isn’t there anything it can do to actually help the planning process?

– Yes, Sophie, I can see a number of things that can be done, and might help.

– Let’s hear it.

– Okay. We were assuming that APT is a kind of half-breed argumentative-systems creature, except we have seen that it can’t make up either new claims nor plausibility nor goodness judgments on its own. It must get them from humans; only then can it use them for things like making new arguments. If it does that, — it may take some bribery to get everybody to make and give those judgments, mind you – it can of course store them, analyze them, and come up with all kinds of statistics about them.
One kind of information I’d find useful would be to find out exactly where people disagree, and how much, and for what reasons. I mean, people argue against a policy for different reasons – one because he doesn’t believe that the policy will be effective in achieving the desired goal – the deontic premise that he agrees with – and the other because she disagrees with the goal.

– I see: Some people disagree with the US health plan they call ‘Obamacare’ because they genuinely think it has some flaws that need correcting, and perhaps with good reasons. But others can’t even name any such flaws and just rail against it, calling it a disaster or a trainwreck etc. because, when you strip away all the reasons they can’t substantiate, simply because it’s Obama’s.

– Are you saying Obama should have called it Romneycare, since it was alleged to be very similar to what Romney did in Massachusetts when he was governor there? Might have gotten some GOP support?

– Let’s not get into that quarrgument here, guys. Not healthy. Stay with the topic. So  our APT would be able to identify those differences, and other discourse features that might help decide what to do next – get more information, do some more discussion, another analysis, whatever. But so far, its systems alter ego hasn’t been able to show any of that in the systems model diagram, to make that part of holistic information visible to the other participants in the discourse.

– Wouldn’t that require that it become fully conscious of its own calculations, first?

– Interesting question, Sophie. Conscious. Hmm. Yes: my old car wouldn’t show me a lot of things on the dashboard that were potential problems – whether a tire was slowly going flat or the left rear turn indicator was out – so you could say it wasn’t aware enough, — even ‘conscious?’ — of those things to let me know. The Commissioner’s new car does some of that, I think. Of course my old one could be very much aware but just ornery enough to leave me in the dark about them; we’ll never know, eh?

– Who was complaining about running off the topic road here just a while ago?

– You’re right, Vodçek: sorry. The issue is whether and how the system could produce a useful display of those findings. I don’t think it’s a fundamental problem, just work to do. My guess is that all that would need several different maps or diagrams.

Discourse –based criteria guiding collective decisions?

– So let’s assume that not only all those judgments could be gathered, stored, analyzed and the results displayed in a useful manner. All those individual judgments, the many plausibility and judgment scores and the resulting overall plan plausibility and ‘goodness’ judgments. What’s still open is this: how should those determine or at least guide the overall group’s decision? In a way that makes it visible that all aspects, all concerns were ‘duly considered’, and ending up in a result that does not make some participants feel that their concerns were neglected or ignored, and that the result is – if not ‘the very best we could come up with’ then at least somewhat better than the current situation and not worse for anybody?

– Your list of aspects there already throws out a number of familiar decision-making procedures, my friend. Leaving the decision to authority, which is what the systems folks have cowardly done, working for some corporate client, (who also determines the overall ‘common good’ priorities for a project, that will be understood to rank higher than any individual concerns) – that’s out. Not even pretending to be transparent or connected to the concerns expressed in the elaborate process. Even traditional voting, that has been accepted as the most ‘democratic’ method, for all its flaws. Out. And don’t even mention ‘consensus’ or the facile ‘no objection?‘ version. What could our APT possibly produce that can replace those tools? Do we have any candidate tools?

– If you already concede that ‘optimal’ solutions are unrealistic and we have to make do with ‘not worse – would it make sense to examine possible adaptations to one of the familiar techniques?

– It may come to that if we don’t find anything better – but I’d say let’s look at the possibilities for alternatives in the ideas we just discussed, first? I don’t feel like going through the pros and cons about our current tools. It’s been done.

– Okay, professor: Could our APT develop a performance measure made up of the final scores of the measures we have developed? Say, the overall goodness score modified by the overall plausibility score a plan proposal achieved?

– Sounds promising.

– Hold your horses, folks. It sounds good for individual judgment scores – may even tell a person whether she ought to vote yes or no on a plan – but how would you concoct a group measure from all that – especially in the kind of public asynchronous discourse we have in mind? Where we don’t even know what segment of the whole population is represented by the participants in the discourse and its cumbersome exercises, and how they relate to the whole public populations for the issue at hand?
– Hmm. You got some more of that café catawhatnot, Vodçek?

– Sure – question got you flummoxed?

– Well, looks like we’ll have to think for a while. Think it might help?

– What an extraordinary concept!

– Light your Fundador already, Vodçek, and quit being obnoxious!

– Okay, you guys. Lets examine the options. The idea you mentioned, Bog-Hubert, was to combine the goodness score and the plausibility score for a plan. We could do that for any number of competing plan alternatives, too.

– It was actually an idea I got from Abbé Boulah some time ago. At the time I just didn’t get its significance.

– Abbé Boulah? Let’s drink to his health. So we have the individual scores: the problem is to get some kind of group score from them. The mean – the average – of those scores is one; we discussed the problems with the mean many times here, didn’t we? It obscures the way the scores are distributed on the scale: you get the same result from a bunch of scores tightly grouped around that average as you’d get from two groups of extreme scores at opposite ends of the scale. Can’t see the differences of opinion.

– That can be somewhat improved upon if you calculate the variance – it measures the extent of disagreement among the scores. So if you get two alternatives with the same mean, the one with the lower variance will be the less controversial one. The range is a crude version of the same idea – just take the difference between the highest and the lowest score; the better solution is the one with a smaller range.

– What if there’s only one proposal?

– Well, hmm; I guess you’d have to look at the scores and decide if it’s good enough.

– Let’s go back to what we tried to do – the criteria for the whole effort: wasn’t there something about making sure that nobody ends up in worse shape in the end?

– Brilliant, Sophie – I see what you are suggesting. Look at the lowest scores in the result and check whether they are lower or higher than, than …

– Than what, Bog-Hubert?

– Let me think, let me think. If we had a score for the assessment of the initial condition for everybody (or for the outcome that would occur if the problem isn’t taken care of) then an acceptable solution would simply have to show a higher score than that initial assessment, for everybody. Right? The higher the difference, even something like the average, the better.

– Unusual idea. But if we don’t have the initial score?

– I guess we’d have to set some target threshold for any lowest score – no lower than zero (not good, not bad) or at least a + 0.5 on a +2/-2 goodness scale, for the worst-off participant score? That would be one way to take care of the worst-off affected folks. The better-off people couldn’t complain, because they are doing better, according to their own judgment. And we’d have made sure that the worst-off outcomes aren’t all that bad.

– You’re talking as if ‘we’ or that APT thing is already set up and doing all that. The old Norwegian farmer’s rule says: Don’t sell the hide before the bear is shot! It isn’t that easy though, is it? Wouldn’t we need a whole new department, office, or institution to run those processes for all the plans in a society?

– You have a point there, Vodçek. A new branch of government? Well now that you open that Pandora’s box: yes, there’s something missing in the balance.

– What in three twisters name are you talking about, Bog-Hubert?

– Well, Sophie. We’ve been talking about the pros and cons of plans. In government, I mean the legislative branch that makes the laws, that’s what the parties do, right? Now look at the judicial branch. There, too, they are arguing – prosecutor versus defense attorney – like the parties in the House and Senate. But then there’s a judge and the jury: they are looking at the pros and cons of both sides, and they make the decision. Where is that  jury or judge ‘institution’ in the legislature? Both ‘chambers’ are made up of parties, who too often look like they are concerned about gaining or keeping their power, their majority, their seats, more than the quality of their laws. Where’s the jury? The judge? And to top that off: even the Executive is decided by the party, in a roundabout process that looks perfectly designed to blow the thinking cap off every citizen. A spectacle! Plenty of circenses but not enough panem. Worse than old Rome…

– Calm down, Bog-Hubert. Aren’t they going to the judiciary to resolve quarrels about their laws, though?

– Yes, good point. But you realize that the courts can only make decisions based on whether a law complies with the Constitution or prior laws – issues of fact, of legality. Not about the quality, the goodness of the law. What’s missing is just what Vodçek said: another entity that looks at the quality and goodness of the proposed plans and policies, and makes the decisions.

– What would the basis of judgment of such an entity be?

– Well, didn’t we just draw up some possibilities? The concerns are those that have been discussed, by all parties. The criteria that are drawn from all the contributions of the discourse.  The party ‘in power’ would only use the criteria of its own arguments, wouldn’t it? Just like they do now… Of course the idea will have to be discussed, thought through, refined. But I say that’s the key missing element in the so-called ‘democratic’ system.

– Abbé Boulah would be proud of you, Bog-Hubert. Perhaps a little concerned, too? Though I’m still not sure how it all would work, for example considering that the humans in the entity or ‘goodness panel’ are also citizens, and thus likely ‘party’. But that applies to the judge and jury system in the judicial as well. Work to do.

– And whatever decision they come up with, that worst-off guy could still complain that it isn’t fair, though?

– Better that 49% of the population peeved and feeling taken advantage of? Commissioner: what do you say?

– Hmmm. That one guy might be easier to buy off than the 49%, yes. But I’m not sure I’d get enough financing for my re-election campaign with these ideas. The money doesn’t come from the worst-off folks, you know…

– Houston, we have a problem …

—

Systems Models and Argumentation in the Planning Discourse

Published October 11, 2015 Argument patterns , Design discourse , Public policy discourse , Systems Modeling , Systems Thinking , Uncategorized Leave a Comment

The following study will try to explore the possibility of combining the contribution of ‘Systems Thinking’ 1 — systems modeling and simulation — with that of the ‘Argumentative Model of Planning’ 2 expanded with the proposals for systematic and transparent evaluation of ‘planning arguments’.
Both approaches have significant shortcomings in accommodating their mutual features and concerns. Briefly: While systems models do not accommodate and show any argumentation (of ‘pros and cons’) involved in planning and appear to assume that any differences of opinion have been ‘settled’, individual arguments used in planning discussions do not adequately convey the complexity of the ‘whole system’ that systems diagrams try to convey. Thus, planning teams relying on only one of these approaches to problem-solving and planning (or any other single approach exhibiting similar deficiencies) risk making significant mistakes and missing important aspects of the situation.
This mutual discrepancy raises the suggestion to resolve it either by developing a different model altogether, or combining the two in some meaningful way. The exercise will try to show how some of the mutual shortcomings could be alleviated — by procedural means of successively feeding information drawn from one approach to the other, and vice versa. It does not attempt to conceive a substantially different approach.

Starting from a very basic situation: Somebody complains about some current ‘Is’-state of the world (IS) he does not like: ‘Somebody do something about IS!’

The call for Action (A plan is desired) raises a first set of questions besides the main one: Should the plan be adopted for implementation: D?:
(Questions / issues will be italicized. The prefixes distinguish different question types: D for ‘deontic or ‘ought-questions; E for Explanatory questions, I for Instrumental of actual-instrumental questions, F for factual questions; the same notation can be applied to individual claims):

E( IS –>OS)?              What kind of action should that be?
which can’t really be answered before other questions are clarified, e.g.:
E(IS)?                Description of the IS-state?
E(OS)?              What is the ‘ought-state (OS) that the person feels ought to be? Description?
(At this point , no concrete proposal has been made — just some action called for.)
D(OS)?              Should OS become the case?
(This question calls for ‘pros and cons’ about the proposed state OS), and
I(IS –> OS)?    How can IS be changed to OS?

Traditional approaches at this stage recommend doing some ‘research’. This might include both the careful gathering of data about the IS situation, as well as searching for tools, ‘precedents’ of the situation, and possible solutions used successfully in the past.

At this point, a ‘Systems Thinking’ (ST) analyst may suggest that, in order to truly understand the situation, it should be looked at as a system, and a ‘model’ representing that system be developed. This would begin by identifying the ‘elements’ or key variables V of the system, and the relationships R between them. Since so far, very little is known about the situation, the diagram of the model would be trivially simple:

(IS) –> REL –> (OS)

or, more specifically, representing the IS and OS states as sets of values of variables:

{VIS} –> REL(IS/OS) –> {VOS}

(The {…} brackets indicate that there may be a set of variables describing the state).

So far, the model simply shows the IS-state and the OS-state, as described by a variable V (or a set of variables), and the values for these variables, and some relationship REL between IS and OS.

Another ST consultant suggests that the situation — the discrepancy between the situation as it IS and as it ought to be (OS), as perceived by a person [P1] may be called a ‘problem’ IS/OS, and to look for a way to resolve it by identifying its ‘root cause’ RC :

E(RC of IS)?       What is the root cause of IS?
and
F(RC of IS)?       Is RC indeed the root cause of IS?

Yet another consultant might point out that any causal chain is really potentially infinitely long (any cause has yet another cause…), and that it may be more useful to look for ‘necessary conditions’ NC for the problem to exist, and perhaps for ‘contributing factors’ CF that aggravate the problem once occurring (but don’t ’cause’ it):

E(NC of IS/OS)?     What are the necessary conditions for the problem to exist?
F(NC of IS/OS)?     Is the suggested condition actually a NC of the problem?
and
E(CF of IS/OS)       What factors contribute to aggravate the problem once it occurs?
F(CF of IS/OS)?

These suggestions are based on the reasoning that if a NC can be identified and successfully removed, the problem ceases to exist, and/or if a CF can be removed, the problem could at least be alleviated.

Either form of analysis is expected to produce ideas for potential means or Actions to form the basis of a plan to resolve the problem and can be put up for debate. As soon as such a specific plan of action is described, it raises the question:

E(PLAN A)?        Description of the plan?
and
D(PLAN A)?        Should the plan be adopted / implemented?

The ST model-builder will have to include these items in the systems diagram, with each factor impacting specific variables or system elements V.

RC       –> REL(RC-IS)      –> {V(IS)}
{NC}   –> REL(NC-IS)      –> { V(IS) }     –> REL    –> {V(OS)}
{CF}    –> RELCF-IS)        –> {V(IS)}

Elements in ‘{…}’ brackets denote sets of items of that type. It is of course possible that one such factor influences several or all system elements at the same time, rather than just one. Of course, Plan A may include aspects of NC, CF, or RC. If these consist of several variables with their own specific relationships, they will have to be shown in the model diagram as such.

An Argumentative Model (AM) consultant will insist that a discussion be arranged, in which questions may be raised about the description of any of these new system elements and whether and how effectively they will actually perform in the proposed relationship.

Having invoked causality, questions will be raised about what further effects, ‘consequences’ CQ the OS-state will have, once achieved; what these will be like, and whether they should be considered desirable, undesirable (the proverbial ‘unexpected consequences’ or side-effects, or merely neutral effects. To be as thorough as the mantra of Systems Thinking demands, to consider ‘the whole system’, that same question should be raised about the initial actions of PLAN A: It may have side-effects not considered in the desired problem-solution OS: should they be included in the examination of the desired ‘Ought-state? So:

For {OS} –> {CQof OS}:

E(CQ ofOS)?        (what is/are the consequences? Description?)
D(CQofOS)?         (is the consequence desirable/ undesirable?)

For —> CQ of A:

E(CQ of A)?
and
D(CQ of A)?

For the case that any of the consequence aspects are considered undesirable, additional measures might be suggested, to avoid or mitigate these effects, which then must be included in the modified PLAN A’, and the entire package be reconsidered / re-examined for consistency and desirability.

The systems diagram would now have to be amended with all these additions. The great advantage of systems modeling is that many constellations of variable values can be considered as potential ‘initial settings’ of a system simulation run, (plan alternatives) and the development of each variable can be tracked (simulated) over time. In any system with even moderate complexity and number of loops — variables in a chain of relationships having causal relationships of other variables ‘earlier’ in the chain — the outcomes will become ‘nonlinear’ and quite difficult and ‘counter-intuitive’ to predict. Both the possibility of inspection of the diagram showing ‘the whole system’ and the exploration of different alternatives contribute immensely to the task of ‘understanding the system’ as a prerequisite to taking action.

While systems diagrams do not usually show either ‘root’ causes, ‘necessary conditions’, or ‘contributing factors’ of each of the elements in the model, these will now have to be included, as well as the actions and needed resources of PLANS setting the initial conditions to simulate outcomes. A simplified diagram of the emerging model, with possible loops, is the following:

(Outside uncontrolled factors (context)

    /                   /                       |                     |                |           \               \        \

PLAN->REL -> (RC, NC, CF) -> REL -> (IS) -> REL -> (OS) -> REL ->(CQ)

\              \                \                      |            |             |            /           /             /

forward and backward loops

 

A critical observer might call attention to a common assumption in simulation models — a remaining ‘linearity’ feature that may not be realistic: In the network of variables and relationships, the impact of a change in one variable V1 on the connected ‘next’ variable V2 is assumed to occur stepwise during one time unit i of the simulation, and the change in the following variable V3 in the following time unit i+1, and so on. Delays in these effect may be accounted for. But what if the information about that change in time unit i is distributed throughout the system much faster — even ‘almost instantaneously’, compared to the actual and possibly delayed substantial effects (e.g. ‘flows’) the diagram shows with its explicit links? Which might have the effect that actors, decision-makers concerned about variables elsewhere in the system for reasons unrelated to the problem at hand, might take ‘preventive’ steps that could change the expected simulated transformation? Of course, such actors and decision-makers are not shown…

Systems diagrams ordinarily do not acknowledge that — to the extent there are several parties involved in the project, and affected in different ways by either the initial problem situation or by proposed solutions and their outcomes — those different parties will have significantly different opinions about the issues arising in connection with all the system components, if the argumentation consultant manages to organize discussion. The system diagram only represents one participant’s view or perspective of the situation. It appears to assume that what ‘counts’ in making any decisions about the problem are only the factual, causal, functional relationships in the system, as determined by one (set of) model-builder. Thus, those responsible for making decisions about implementing the plan must rely on a different set of provisions and perspectives to convert the gained insights and ‘understanding’ of the system and its working into sound decisions.

Several types of theories and corresponding consultants are offering suggestions for how to do this. Given the particular way their expertise is currently brought into planning processes, they usually reflect just the main concerns of the clients they are working for. In business, the decision criterion is, obviously, the company’s competitive advantage resulting in reliable earnings: profit, over time. Thus for each ‘alternative’ plan considered (different initial settings in the system), and the actions and resources needed to achieve the desired OS, the ‘measure of performance’ associated with the resulting OS will be profit — earnings minus costs. For government consultants (striving to ‘run government like a business?’) the profit criterion may have to be labeled somewhat differently — say: ‘benefit’ and ‘cost’ of government projects, and their relationship such as B-C or the more popular B/C, the benefit-cost ratio. For overall government performance, the ‘Gross National Product’ GNP is the equivalent measure. The shortcomings and problems associated with such approaches led to calls for using ‘quality of life‘ or ‘happiness‘ or Human Development Indices instead, and criteria for sustainability and ecological aspects All or most such approaches still suffer from the shortcoming of constructing overall measures of performance: shortcomings because they inevitably represent only o n e view of the problems or projects — differences of opinion or significant conflicts are made invisible.

In the political arena, any business and economic considerations are overlaid if not completely overridden by the political decision criteria — voting percentages. Most clearly expressed in referenda on specific issues, alternatives are spelled out, more or less clearly, so as to require a ‘yes’ or ‘no’ vote, and the decision criterion is the percentage of those votes. Estimates of such percentages are increasingly produced by opinion surveys sampling just a small but ‘representative’ number of the entire population, and these aim to have a similar effect on decision-makers.

Both Systems Thinkers and advocates of the extended Argumentative Model are disheartened about the fact that in these business and governance habits, all the insight produced by their respective analysis efforts seem to have little if no visible connection with the simple ‘yes/no’, opinion poll or referendum votes. Rightfully so, and their concern should properly be with constructing better mechanisms for making that connection. From the Argumentative Model side, such an effort has been made with the proposed evaluation approach for planning arguments, though with clear warnings against using the resulting ‘measures’ of plan plausibility as convenient substitutes for decision criteria. The reasons for this have to do with the systemic incompleteness of the planning discourse: there is no guarantee that all the concerns that influence a person’s decision about a plan that should be given ‘due consideration — and therefore should be included in the evaluation — actually can and will be made explicit in the discussion.

To some extent, this is based on different attitudes discourse participants will bring to the process. The straightforward assumption of mutual trust and cooperativeness aiming at mutually beneficial outcomes — ‘win-win’ solutions — obviously does not apply to all such situations. Though there are many well-intentioned groups and initiatives that try to instill and grow such attitudes, especially when it comes to global decisions about issues affecting all humanity such as climate, pollution, disarmament, global trade and finance. The predominant business assumption is that of competition, seeing all parties as pursuing their own advantages at the expense of others, resulting in zero-sum outcomes: win-lose solutions. There are a number of different situations that can be distinguished as to whether the parties share or have different attitudes in the same discourse with the ‘extreme’ positions being complete sharing the same attitude, having attitudes on the opposite ends of the scale; or something in-between which might be called indifference to the other side’s concerns — as long as they don’t intrude on their own concerns, in which case the attitudes likely shift to the win-lose position at least for that specific aspect.

The effect of these issues can be seen by looking at the way a single argument about some feature of a proposed plan might be evaluated by different participants, and how the resulting assessments would change decisions. Consider, for the sake of simplicity, the argument in favor of a Plan A by participant P1:

D(PLAN A)!         Position (‘Conclusion’) : Plan A ought to be adopted)
because
F((VA –>REL(VA–>VO) –> VO) | VC      Premise 1: Variable V of  plan  A  will result in (e.g. cause) Variable VO , given condition C;
and
D(VO)                   Premise 2: Variable VO ought to be aimed for;
and
F(VC)                     Premise 3: Variable VC is the case.

Participant P1 may be quite confident (but still open to some doubt) about these premises, and of being able to supply adequate evidence and support arguments for them in turn. She might express this by assigning the following plausibility values to them, on the plausibility scale of -1 to + 1, for example:
Premise 1: +0.9
Premise 2: +0.8
Premise 3: +0.9
One simple argument plausibility function (multiplying the plausibility judgments) would result in argument plausibility of   +0.658;     a  not completely ‘certain’ but still comfortable result supporting the plan. Another participant P2 may agree with premises 1 and 2, assigning the same plausibility values to those as P1, but having considerable doubt as to whether the condition VC is indeed present to guarantee the effect of premise 1, expressed by the low plausibility score of +0.1 which would yield an argument plausibility of +0.07; a result that can be described as too close to ‘don’t know if VA is such a good idea’. If somebody else — participant P3 — disagrees with the desirability of VO, and therefore assigns a negative plausibility of, say, -0.5 to premise 2 while agreeing with P1 about the other premises, his result would be – 0.405, using the same crude aggregation formula. (These are of course up for discussion.)  The issue of weight assignment has been left aside here, assuming only the one argument, so there is only one argument being considered and the weight of its deontic premise is 1, for the sake of simplicity. The difference in these assessments raises not only the question of how to obtain a meaningful common plausibility value for the group, as a guide for its decision. It might also cause P1 to worry whether P3 would consider taking ‘corrective’ (in P1’s view ‘subversive’?) actions to mitigate the effect of VA should the plan be adopted e.g. by majority rule, or by following the result of some group plan plausibility function such as taking the average of the individual argument plausibility judgments as a decision criterion. (This is not recommended by the theory). And finally: should these assessments, with their underlying assumptions of cooperative, competitive, or neutral, disinterested attitudes, and the potential actions of individual players in the system to unilaterally manipulate the outcome, be included in the model and its diagram or map?

While a detailed investigation of the role of these attitudes on cooperative planning decision-making seems much needed, this brief overview already makes it clear that there are many situations in which participants have good reasons not to contribute complete and truthful information. In fact, the prevailing assumption is that secrecy, misrepresentation, misleading and deceptive information and corresponding efforts to obtain such information from other participants — spying — are part of the common ‘business as usual’.

So how should systems models and diagrams deal with these aspects? The ‘holistic’ claim of showing all elements so as to offer a complete picture and understanding of a system arguably would require this: ‘as completely as possible’. But how? Admitting that a complete understanding of many situations actually is not possible? What a participant does not contribute to the discourse, the model diagram can’t show. Should it (cynically?) announce that such ‘may’ be the case — and that therefore participants should not base their decisions only on the information it shows? To truly ‘good faith’ cooperative participants, sowing distrust this way may be perceived as somewhat offensive, and itself actually interfere with the process.

The work on systems modeling faces another significant unfinished task here. Perhaps a another look at the way we are making decisions as a result of planning discussions can help somewhat.

The discussion itself assumes that it is possible and useful towards better decisions — presumably, better than decisions made without the information it produces. It does not, inherently, condone the practice of sticking to a preconceived decision no matter what is being brought up (nor the arrogant attitude behind it: ‘my mind is made up, no matter what you say…’) The question has two parts. One is related to the criteria we use to convert the value of information to decisions. The other concerns the process itself: the kinds of steps taken, and their sequence.

It is necessary to quickly go over the criteria issue first — some were already discussed above. The criteria for business decision-makers discussed above, that can be assumed to be used by the single decision-maker at the helm of a business enterprise (which of course is a simplified picture): profit, ROI, and its variants arising from planning horizon, sustainability and PR considerations, are single measures of performance attached to the alternative solutions considered: the rule for this decision ‘under certainty’ is: select the solution having the ‘best’ (highest, maximized) value. (‘Value’ here is understood simply as the number of the criterion.) That picture is complicated for decision situations under risk, where outcomes have different levels of probability, or complete uncertainty, where outcomes are not governed by predictable laws, nor even probability, but by other participants’ possible attempts to anticipate the designer’s plans, and will actively seek to oppose them. This is the domain of decision and game theory, whose analyses may produce guidelines and strategies for decisions — but again, different decisions or strategies for different participants in the planning. The factors determining these strategies are arguably significant parts of the environment or context that designers must take into account — and systems models should represent — to produce a viable understanding of the problem situation. The point to note is that the systems models permit simulation of these criteria — profit,  life cycle economic cost or performance, ecological damage or sustainability — because they are single measures, presumably collectively agreed upon (which is at least debatable). But once the use of plausibility judgments as measures of performance is considered as a possibility, — even as aggregated group measures — the ability of systems models and diagrams to accommodate them becomes very questionable, to say the least. It would require the input of many individual (subjective) judgments, which are generated as the discussion proceeds, and some of which will not be made explicit even if there are methods available for doing this.

This shift of criteria for decision-making raises the concerns about the second question, the process: the kinds of steps taken, by what participants, according to what rules, and their sequence. If this second aspect does not seem to need or require much attention — the standard systems diagrams again do not show it — consider the significance given to it by such elaborate rule systems as parliamentary procedure, ‘rules of order’ volumes, even for entities where the criterion for decisions is the simple voting percentage. Any change of criteria will necessarily have procedural implications.

By now, the systems diagram for even the simple three-variable system we started out with has become so complex that it is difficult to see how it might be represented in a diagram. Adding the challenges of accounting for the additional aspects discussed above — the discourse with controversial issues, the conditions and subsequent causal and other relationships of plan implementation requirements and further side-effects, and the attitudes and judgments of individual parties involved in and affected by the problem and proposed plans, are complicating the modeling and diagram display tasks to an extent where they are likely to lose their ability to support the process of understanding and arriving at responsible decisions; I do not presume to have any convincing solutions for these problems and can only point to them as urgent work to be done.

ST-AM 4

Evolving ‘map’ of  ‘system’ elements and relationships, and related issues

Meanwhile, from a point of view of acknowledging these difficulties but trying, for now, to ‘do the best we can with what we have’, it seems that systems models and diagrams should continue to serve as tools to understand the situation and to predict the performance of proposed plans — if some of the aspects discussed can be incorporated into the models. The construction of the model must draw upon the discourse that elicits the pertinent information (through the ‘pros and cons’ about proposal). The model-building work therefore must accompany the discourse — it cannot precede or follow the discussion as a separate step. Standard ‘expert’ knowledge based analysis — conventional ‘best practice’ and research based regulations, for example, will be as much a part of this as the ‘new’, ‘distributed’ information that is to be expected in any unprecedented ‘wicked’ planning problem, that can only be brought out in the discourse with affected parties.

The evaluation preparing for decision — whether following a customary formal evaluation process or a process of argument evaluation — will have to be a separate phase. Its content will now draw upon and reflect the content of the model. The analysis of its results — identifying the specific areas of disagreement leading to different overall judgments, for example — may lead to returning to previous design and model (re-)construction stages: to modify proposals for more general acceptability, or better overall performance, and then return to the evaluation stage supporting a final decision. Procedures for this process have been sketched in outline but remain to be examined and refined in detail, and described concisely so that they can be agreed upon and adopted by the group of participants in any planning case before starting the work, as they must, so that quarrels about procedure will not disrupt the process later.

Looking at the above map again, another point must be made. It is that once again, the criticism of systems diagrams seems to have been ignored, that the diagram still only expresses one person’s view of the problem. The system elements called ‘variables’, for example, are represented as elements of  ‘reality’, and the issues and questions about those expected to give ‘real’ (that is, real for all participants) answers and arguments. Taking the objection seriously, would we not have to acknowledge that ‘reality’ is known to us only imperfectly, if at all, and that each of us has a different mental ‘map’ of it? Thus, each item in the systems map should perhaps be shown as multiple elements referring to the same thing labeled as something we think we know and agree about: but as one bubble of the item for each participant in the discourse? And these bubbles will possibly, even likely, not being congruent but only overlapping, at best, and at worst covering totally different content meaning — the content that is then expected to be explained and explored in follow-up questions? Systems Thinking has acknowledged this issue in principle — that ‘the map (the systems model and diagram) is NOT the landscape‘ (the reality). But this insight should itself be represented in a more ‘realistic’ diagram — realistic in the sense that it acknowledges that all the detail information contributed to the discourse and the diagram will be assembled in different ways by each individual into different, only partially overlapping ‘maps’. An objection might be that the system model should ‘realistically’ focus on those parts of reality that we can work with (control? or at least predict?) — with some degree of ‘objectivity’ — the overlap we strive for with ‘scientific’ method of replicable experiments, observations, measurements, logic, statistical conformation? And that the concepts different participants carrying around in the minds to make up their different maps are just ‘subjective’ phenomena that should ‘count’ in our discussions about collective plans only to the extent they correspond (‘overlap’) to the objective measurable elements of our observable system?   The answer is that such subjective elements as individual perspectives about the nature of the discourse as cooperative or competitive etc. are phenomena that do affect the reality of our interactions. Mental concepts are ‘real’ forces in the world — so should they not be acknowledged as ‘real’ elements with ‘real’ relationships in the relationship network of the system diagram?

We could perhaps state the purpose of the discourse as that of bringing those mental maps into sufficiently close overlap for a final decision to become sufficiently congruent in meaning and acceptability for all participants: the resulting ‘maps’ along the way having a sufficient degree of overlap. What is ‘sufficient’ for this, though?   And does that apply to all aspects of the system? Are not all our plans in part also meant to help us to pursue our own, that is: our different versions of happiness? We all want to ‘make a difference’ in our lives — some more than others, of course — and each in our own way.  The close, complete overlap of our mental maps is a goal and obsession of societies we call ‘totalitarian’. If that is not what we wish to achieve, should the principle of plan outcomes leaving and offering (more? better?) opportunities for differences in the way we live and work in the ‘ought-state’ of problem solutions,  be an integral element of our system models and diagrams? Which would be represented as a description of the outcome consisting of ‘possibility’ circles that have ‘sufficient’ overlap, sure, but also a sufficient degree of non-overlap ‘difference’ opportunity outside of the overlapping area. Our models and diagrams and system maps don’t even consider that. So is Systems Thinking, proudly claimed as being ‘the best foundation for tackling societal problems’ by the Systems Thinking forum, truly able to carry the edifice of future society yet? For its part, the Argumentative Model claims to accommodate questions of all kinds of perspectives, including questions such as these, — but the mapping and decision-making tools for arriving at meaningful answers and agreements are still very much unanswered questions. The maps, for all their crowded data, have large undiscovered areas.

The emerging picture of what a responsible planning discourse and decision-making process for the social challenges we call ‘wicked problems’, would look like, with currently available tools, is not a simple, reassuring and appealing one. But the questions that have been raised for this important work-in-progress, in my opinion, should not be ignored or dismissed because they are difficult. There are understandable temptations to remain with traditional, familiar habits — the ones that arguably often are responsible for the problems? — or revert to even simpler shortcuts such as placing our trust in the ability and judgments of ‘leaders’ to understand and resolve tasks we cannot even model and diagram properly. For humanity to give in to those temptations (again?) would seem to qualify as a very wicked problem indeed.

—
Notes:

1 The understanding of ‘systems thinking’ (ST) here is based on the predominant use of the term in the ‘Systems Thinking World’ Network on LinkedIn.

2 The Argumentative Model (AM) of Planning was proposed by H. Rittel, e.g. in the paper ‘APIS: A Concept for an Argumentative Planning Information System’, Working paper 324, Institute of Urban and Regional Development, University of California, 1980. It sees the planning activity as a process in which participants raise issues – questions to which there may be different positions and opinions, and support their positions with evidence, answers and arguments. From the ST point of view, AM might just be considered a small, somewhat heretic sect within ST…

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