The discussion about whether and to what extent Artificial Intelligence technology can meaningfully support the planning process with contributions similar or equivalent to human thinking is largely dominated by controversies about what constitutes thinking. An exploration of the reasoning patterns in the various phases of human planning discourse could produce examples for that discussion, leaving the determination of that definition label ‘thinking’ open for the time being.
One specific example (only one of several different and equally significant aspects of planning):
People propose plans for action, e.g. to solve problems, and then engage in discussion of the ‘pros and cons’ of those plans: arguments. A typical planning argument can be represented as follows:
“Plan A should be adopted for implementation, because
i) Plan A will produce consequences B, given certain conditions C, and
ii) Consequences B ought to be pursued (are desirable); and
iii) Conditions C are present (or will be, at implementation).
Question 1: could such an argument be produced by automated technological means?
This question is usually followed up by question 2: Would or could the ‘machine’ doing this be able (or should it be allowed) to also make decisions to accept or reject the plan?
Can meaningful answer to these questions be found? (Currently or definitively?)
Beginning with question 1: Formulating such an argument in their minds, humans draw on their memory — or on explanations and information provided during the discourse itself — for items of knowledge that could become premises of arguments:
‘Factual-instrumental’ knowledge of the form “FI (A –> X)”, for example (“A will cause X’, given conditions C;
‘Deontic’ Knowledge: of the form “D(X)” or “X ought to be’ (is desirable)”, and
Factual Knowledge of the form “F ( C)” or “Conditions C are given”.
‘Argumentation-pattern knowledge’: Recognition that any of the three knowledge items above can be inserted into an argument pattern of the form
D(A) <– ((A–> X)|C)) & D(X) & F( C)).
(There are of course many variations of such argument patterns, depending on assertion or negation of the premises, and different kinds of relations between A and X.)
It does not seem to be very difficult to develop a Knowledge Base (collection) of such knowledge items and a search-and-match program that would assemble ‘arguments’ of this pattern.
Any difficulties arguably would be more related to the task of recognizing and suitably extracting such items (‘translating’ it into the form recognizable to the program) from the human recorded and documented sources of knowledge, than to the mechanics of the search-and-match process itself. Interpretation of meaning: is an item expressed in different words equivalent to other terms that are appropriate to the other potential premises in an argument?
Another slight quibble relates to the question whether and to what extent the consequence qualifies as one that ‘ought to be’ (or not) — but this can be dealt with by reformulating the argument as follows:
“If (FI(A –> X|C) & D(X) & F( C)) then D(A)”.
(It should be accompanied by the warning that this formulation that ‘looks’ like a valid logic argument pattern is in fact not really applicable to arguments containing deontic premises, and that a plan’s plausibility does not rest on one single argument but on the weight of all its pros and cons.)
But assuming that these difficulties can be adequately dealt with, the answer to question 1) seems obvious: yes, the machine would be able to construct such arguments. Whether that already qualifies as ‘thinking’ or ‘reasoning’ can be left open; the significant realization is equally obvious: that such contributions could be potentially helpful contributions to the discourse. For example, by contributing arguments human participants had not thought of, they could be helping to meet the aim of ensuring — as much as possible — that the plan will not have ‘unexpected’ undesirable side-and-after-effects. (One important part of H. Rittel’s very definition of design and planning.)
The same cannot as easily be said about question 2.
The answer to that question hinges on whether the human ‘thinking’ activities needed to make a decision to accept or reject the proposed plan can be matched by ‘the machine’. The reason is, of course, that not only the plausibility of each argument will have to be ‘evaluated’, judged, (by assessing the plausibility of each premise) but also that the arguments must be weighed against one another. (A method for doing that has been described e.g in ‘The Fog Island Argument” and several papers.)
So a ‘search and match’ process as the first part of such a judgment process would have to look for those judgments in the data base, and the difficulty here has to do with where such judgments would come from.
The prevailing answers for factual-instrumental premises as well as for fact-premises — premises i) and iii) — are drawing on ‘documented’ and commonly accepted truth, probability, or validity. Differences of opinion about claims drawn from ‘scientific’ and technical work, if any, are decided by a version of ‘majority voting’ — ‘prevailing knowledge’, accepted by the community of scientists or domain experts, ‘settled’ controversies, derived from sufficiently ‘big data’ (“95% of climate scientists…”) can serve as the basis of such judgments. It is often overlooked that the premises of planning arguments, however securely based on ‘past’ measurements, observations etc, are inherently predictions. So any certainty about their past truth must at least be qualified with a somewhat lesser degree of confidence that they will be equally reliably true in future: will the conditions under which the A –> X relationships are assumed to hold, be equally likely to hold in the future? Including the conditions that may be — intentionally or inadvertently — changed as a result of future human activities pursuing different aims than those of the plan?
The question becomes even more controversial for the deontic (ought-) premises of the planning arguments. Where do the judgments come from by which their plausibility and importance can be determined? Humans can be asked to express their opinions — and prevalent social conventions consider the freedom to not only express such judgments but to have them given ‘due consideration’ in public decision-making (however roundabout and murky the actual mechanisms for realizing this may be) as a human right.
Equally commonly accepted is the principle that machines do not ‘have’ such rights. Thus, any judgment about deontic premises that might be used by a program for evaluating planning arguments would have to be based on information about human judgments that can be found in the data base the program is using. There are areas where this is possible and even plausible. Not only is it prudent to assign a decidedly negative plausibility to deontic claims whose realization contradicts natural laws established by science (and considered still valid…like ‘any being heavier than air can’t fly…’). But there also are human agreements — regulations and laws, and predominant moral codes — that summarily prohibit or mandate certain plans or parts of plans; supported by subsequent arguments to the effect that we all ought not break the law, regardless of our own opinions. This will effectively ‘settle’ some arguments.
And there are various approaches in design and planning that seem to aim at finding — or establishing — enough such mandates or prohibitions that, taken together, would make it possible to ‘mechanically’ determine at least whether a plan is ‘admissible’ or not — e.g. for buildings, whether its developer should get a building permit.
This pattern is supported in theory by modal logic branches that seek to resolve deontic claims on the basis of ‘true/false’ judgments (that must have been made somewhere by some authority) of ‘obligatory’, ‘prohibited’, ‘permissible’ etc. It can be seen to be extended by at last two different ‘movements’ that must be seen as sidestepping the judgment question.
One is the call for society as a whole to adopt (collectively agree upon) moral, ethical codes whose function is equivalent to ‘laws’ — from which the deontic judgment about plans could be derived by mechanically applying the appropriate reasoning steps — invoking ‘Common Good’ mandates supposedly accepted unanimously by everybody. The question whether and how this relates to the principle of granting the ‘right’ of freely holding and happily pursuing one’s own deontic opinions is usually not examined in this context.
Another example is the ‘movement’ of Alexander’s ‘Pattern Language’. Contrary to claims that it is a radically ‘new’ theory, it stands in a long and venerable tradition of many trades and disciplines to establish codes and collections of ‘best practice’ rules of ‘patterns’ — learned by apprentices in years of observing the masters, or compiled in large volumes of proper patterns. The basic idea is that of postulating ‘elements’ (patterns) of the realm of plans, and relationships between these, by means of which plans can be generated. The ‘validity’ or ‘quality’ of the generated plan is then guaranteed by the claim that each of the patterns (rules) are ‘valid’ (‘true’, or having that elusive ‘quality without a name’). This is supported by showing examples of environments judged (by intuition, i.e. needing no further justification) to be exhibiting ‘quality’, by applications of the patterns. The remaining ‘solution space’ left open by e.g. the different combinations of patterns, then serves as the basis for claims that the theory offers ‘participation’ by prospective users. However, it hardly needs pointing out that individual ‘different’ judgments — e.g. based on the appropriateness of a given pattern or relationship — are effectively eliminated by such approaches. (This assessment should not be seen as a wholesale criticism of the approach, whose unquestionable merit is to introduce quality considerations into the discourse about built environment that ‘common practice’ has neglected.)
The relevance of discussing these approaches for the two questions above now becomes clear: If a ‘machine’ (which could of course just be a human, untiringly pedantic bureaucrat assiduously checking plans for adherence to rules or patterns) were able to draw upon a sufficiently comprehensive data base of factual-instrumental knowledge and ‘patterns or rules’, it could conceivably be able to generate solutions. And if the deontic judgments have been inherently attached to those rules, it could claim that no further evaluation (i.e. inconvenient intrusion of differing individual judgments would be necessary.
The development of ‘AI’ tools of automated support for planning discourse — will have to make a choice. It could follow this vision of ‘common good’ and valid truth of solution elements, universally accepted by all members of society. Or it could accept the challenge of a view that it either should refrain from intruding on the task of making judgments, or going to the trouble of obtaining those judgments from human participants in the process, before using them in the task of deriving decisions. Depending on which course is followed, I suspect the agenda and tasks of current and further research and development and programming will be very different. This is, in my opinion, a controversial issue of prime significance.
(From a letter to a friend who has been working, writing and publishing on the problems of ‘design’.) Thorbjoern Mann, May 2015
I have been busy trying to communicate with the systems folks on LinkedIn about the role of argumentation in systems modeling — there seems to be an obstinate blind spot (or hole?) in their oh so holistic minds about that. I have yet to see a systems diagram in which the various issues (contentious questions, for example regarding assumptions of the model variables and parameters, about which people might disagree) are not somehow assumed to be ‘settled’. No more discussion. Curious, it is making me feel a little like someone trying to fill those open minds (they insist) with the precious grains of my speculations only to see them run out of the bottoms of those minds (there are holes top and bottom, and the bottom ones are larger?) like ocean sand.
So every once in a while I resort to wise books like the Designology volume you graciously sent me, for reassurance that the design perspective is one to be valued, respected, and further explored. I especially am fascinated by the heroic efforts in that book — and elsewhere — to identify and locate the proper role of design in the academic landscape of disciplines and departments. And the more I think about it, I sense how much of a monster this thing must look like from the point of view of, say, a ministry of education confronted with demands for proper designation of funds and personnel and labels (department names) let alone assignment of leadership roles to this ‘design’ phenomenon.
For it seems to be a little like that curious object some people have used to test prospective designers’ visual imagination: the thing that has a square profile if seen from one side, a triangle from another, and a circle from a third direction. Design indeed looks like a handful of different disciplines, depending on the angle from which it is seen. The literature is replete with complaints about the difficulty of agreeing on a common definition of design.
For example: Let’s say we start, arbitrarily, from some proposed explanation that design has something to do with problem solving. Looking at a problem as a discrepancy between a state of affairs as it IS (or will be, if nothing is done), and as it OUGHT to be, raising the need or desire to find out HOW it may be transformed from the former to the latter. A closer attention to the IS part may get us to look not only at the facts of the current situation and their adequate determination and description, but also at the causes that made things get this way, trying to understand the forces and laws at work in that process. This may have to do with physical aspects of reality, suggesting an approach like the scientific method of natural sciences to validate and understand it: Does this not look like Science? But not only science in the sense of the ‘hard’ natural sciences, because physical conditions and artifacts involved in problems have effects on people, their minds (psychological, physiological) and relations: Social science. The designer must have some adequate understanding of both ‘kinds’ of science in order to deal with the challenge of doing something meaningful about it.
Looking at the other end, though, the OUGHT aspect, a first impression that it also has a social sciences flavor — user needs, for example — soon gives way to a sense that there may be more esoteric aspects at work: vision, dreams, desires, imagination, aesthetics: aspects for which either science label clearly is not appropriate. In fact, the label OUGHT evokes connections to quite different disciplines: those that explore the good, morality, ethics, norms. So should design actually be situated in the philosophy department?
This is not a very common idea. Rather, it is the imagination aspect, or more specifically, the need to use visual images to communicate about the proposed results of this activity, that has led many to see the essence of design in the tools we have to help our own and the audience’s understanding and ability to ‘see’ proposed solutions: Drawing, model-building, perspective, rendering, with their closeness to painting and sculpture: Obviously: it’s (a kind of) an Art? Even given more recent tools of computer programs for virtual visual walk-through presentations. This is a historically a more widely embraced notion.
However, there are more, less ‘artistic’ tools designers need to persuasively present solution ideas to clients and the public. Proofs of validity, affordability, safety: diagrams, calculations. More like the tools engineers are using?
Wait: persuasion? Yes, designers will have to spend some effort trying to convince others of the advantages of the solution — mainly the ones who are expected to pay for its implementation. This is partly the stuff of ‘storytelling’ many design teachers admonish their students to cultivate — what will it be like to live in this great proposed solution? But also, when things are heating up, of argumentation: exploring, discussing the pros and cons of the proposals.
Arguments? Doesn’t that have to do with logic, rhetoric? But the disciplines in charge of argumentation haven’t paid much attention to the kinds of arguments we are using all the time in the design and planning discourse, so they do not have much room for the concerns of design in their curricula — but it’s argumentation, all right. Even the structure of these ‘planning arguments’ clearly indicate the multifaceted nature of the concerns involved:
“We ought to adopt proposal X
1) implementing X will result in consequence Y provided conditions C are given;
2) we ought to pursue consequence Y,
3) conditions C are indeed present?”
This ubiquitous argument pattern (of course there are many variations due to different assertion / negation of terms, and different relations between X and Y) contains at least two or three different kinds of premises: the factual-instrumental premise 1, the deontic premise 2, and the factual premise claim 3. If questioned, each of these will have to be supported with very different kinds of reasons: the kind of evidence we could loosely call scientific method for premises 1 and 3, but based on conceptual agreements about the meaning of the things we are talking about. Reasons which employ arguments found in the familiar catalogues of reliable logical and statistical inference, observation, data-gathering, measurement. A closer scrutiny of the catch-all premise 3 might reveal that the conditions C include all the variables, values, and relationship parameters of a systems model. The ‘Systems Thinking’ community (referring to a variety of different emerging ‘brands’ of systems studies) would this argue that holistic understanding and modeling of the systems into which designers are intervening is a necessity, and this is the concern of premises 1 and especially 3.
But for premise 2, the supporting arguments will be of the same kind of ‘planning argument’ type. From the point of view of formal logic, these arguments are not ‘valid’ in the sense of deductive syllogisms whose conclusions must be accepted as true if all the premises are true. They are merely ‘inconclusive’ at best, no matter how recklessly we use and accept this kind of reasoning in everyday planning discourse. That very recklessness being a strong argument in favor of designers studying such reasoning more carefully than is currently the case… What to call this perspective?
Coming back to the impression that design is more like engineering. There is good evidence for this: the question of HOW to transform the unpleasant IS condition to the desirable OUGHT requires the application of scientific knowledge — science, again — to the task of putting together tools, processes, resources to generate solutions and to evaluate them, test them to see if they will meet the requirements and withstand damaging forces. And in the production of modern architecture, there are many different kinds of engineers involved — engineering had to divide itself into many different sub-disciplines, each drawing on their own branch of science. The available and needed knowledge has become too rich and complex for any single professional to master them all. This means that effecive coordination of all these activities in the design process requires at least an adequate understanding of the different engineering branches and their vocabulary, concerns, criteria, to make sense of it all. Ideally. So perhaps it was appropriate for many architecture schools to be located in Institutes of Technology rather that in art schools such as the Beaux Arts?
The successful practitioners of this kind of art, though, (the ones who consistently win commissions for significant work) find themselves facing a quite different challenge: that of running a business. And some of the well-known sources of jokes about architects refer to their frequent troubles of this kind. For example: meeting deadlines: time management, and even more seriously, staying within the budget. A case for including more management, business and economics material in the education of designers?
What, besides an understanding of engineering, business, and economics, — we might as well throw in the various disciplines exploring the aspect of sustainability and ecological impact of their buildings — does this mean for the poor architects? The ones who got through architecture school even in spite of the required structures courses that gave their artistic minds so much trouble? It becomes a very different activity: to guide and orchestrate — the word is very apt for the assembly of different disciplines and professions — the activities of all these people in the design process. Not only there, but of course also in the subsequent implementation process, with different professionals. The architect there has to become a project manager — if he hasn’t given up that role to yet another, different profession. But a good design has to take the implementation process into account as an important determining factor: if it can’t be built, if it takes too long, if there are too many possibilities of accidents or failures along the way, his prospects for successful creation of solutions are slim.
Creating, designing, then, involves all these considerations and skills. And while this little sketch considered only the architect of buildings (the word ‘architect’ has been taken over by many other ‘designing’ roles such as software developers and even turned into a verb; old Vitruvius must be rotating in his grave) it should be easy to see how this multiple perspective feature applies to many other areas of modern life. Yes: for the academic department designer, ‘design’ is a monster, and the proper role and placement of design education is a very wicked problem.
It raises a number of important questions for how research (the science of design) and education for all the professions that will have to deal with design ought to be organized, funded and guided. The current confused attitude and treatment — best characterized as the infamous ‘benevolent negligence’ quip by Senator Moynihan about race relations — perhaps has the advantage that many different people in many different realms are forced to creatively deal with it. But it can’t, by any measure, be called a convincing, efficient design. This very point, in my opinion, is calling for increased attention and discussion. Perhaps a conference? A research project (if research is the proper word, after all these questions…)? A ‘design’ competition? A large online public planning discourse?
The various crises facing humanity will require significant changes in current practice, habits, behaviors. Such changes cannot be imposed by governments or other authorities without running the risk of creating resentment, resistance and possible violent confrontation, adding to the dangers. The decisions to be taken must arise from a participatory discourse that is accessible to all parties potentially affected by a plan or decisions, in which all contributions, questions, suggestions and arguments are heard, and in which the merit of such contributions will have a visible impact on the decisions taken. Current governance practice does not provide this. The missing elements are first, a platform or framework for such a discourse, and second, a way of measuring the merit of contributions, the merit of arguments. Without such a measure, decisions can all too easily ignore or even go against the result of discussion; the perception that this is the case even in current ‘democratic’ regimes explains the voter ‘apathy’ — the declining participation in elections: the sense that one’s vote does not really make a difference in the decisions made by the people elected.
There are various commendable efforts and programs on the market that aim at improving planning and policy-making, political discourse. A common concern is ‘argument mapping’, ‘debate mapping’ — the effort to provide a convenient overview of the discussion through graphic representations of the relationships between the discussion elements: issues, claims, proposals, arguments. The tools currently on the market do not yet meet the requirements for a systematic and transparent evaluation. To encourage the further development of these tools, it may be helpful to summarize these requirements: the following is a first attempt to do so.
The arguments we use in such planning discussions have not received the attention of logic, even informal logic, or rhetoric, that one would expect given their ubiquity: humanity quarrels about ‘what we ought to do’ as much if not more that about the ‘facts’ of the world. The arguments used in such discussions are of a type I have called ‘design arguments’ or ‘planning arguments’. Even in informal logic textbooks, where they are discussed, for example, as ‘proposal arguments’ , their structure is not analyzed sufficiently well to permit a systematic evaluation. An approach for such evaluation of planning arguments has been presented e.g. in the article ‘The structure and evaluation of planning arguments’ in Informal Logic December 2010. Elaborating on that discussion is the following brief exploration of how planning arguments should be represented, and presented in argument maps, for example, so as to facilitate evaluation.
Recapping: The typical planning argument can be described as follows:
The proposed plan or decision — denoted here as ‘x’
is supported (or attacked) by the argument:
‘X ought to be adopted (implemented) (the ‘conclusion’)
x is related to effect y (the ‘factual-instrumental premise)
y ought to be pursued. (the deontic premise)
A more elaborate version might include some qualifications , say, of conditions c under which the relationship between x and y holds, and an assertion that those conditions are indeed (or are not ) present, now condensed in a form that uses the symbol ‘F’ for a factual premises, ‘and ‘D; for the deontic (ought) premise:
F(x REL y | c)
and (D( y )
F ( c )
The relation REL is a common label for any of the usual links between x and y: a ‘categorical’ link or claim (e.g.: ‘x IS y’); a causal claim (‘x CAUSES y’) or a ‘resemblance claim’ (‘x is LIKE y’); according to each case at hand, there may be variations or other connections invoked.
In textbooks discussion of ‘proposal arguments’, this structure is usually not presented completely. Thus, an argument maybe rendered as ‘x should be adopted because it causes y’; or ‘x ought to be because its effect y is desirable’. In both cases, only one premise is explicitly stated. The practice of omitting premises that ‘can be taken for granted’, (resulting in an ‘enthymeme’ — an incomplete argument) is common, as already Aristotle made clear. But such an argument can be opposed on very different grounds: An opponent of ‘x’ may not be convinced that x will indeed result in y. Another opponent may agree that x does cause y but does not consider y desirable. A third participant may feel that yes, y might be a good thing, and even agree that x may be helpful in getting y, but only if certain conditions are present, and since they are not, hold that implementing x is not warranted. Yet another observer may simply feel that x is not the best way to get y: a different plan should be considered. These objections are aimed at different premises, some of which are not explicitly stated.
This means that if the argument is to be evaluated in any meaningful way, the elements at which these opinions are directed must all be stated explicitly, visibly. This is the first of several ‘rules’ needed to ensure meaningful evaluation:
The Premise Completeness Rule:
All premises of a planning argument
— the factual-instrumental premise, including qualifying conditions as applicable;
— the deontic premise;
— the factual premise regarding qualifying conditions
must be stated explicitly.
It is necessary to clarify that some claims of arguments — that are often part of argument pattern representations in popular textbooks — should NOT be included in the display of a single planning argument because they are really arguments about ‘successor issues’: issues arising from challenges to main argument premises. Even the widely accepted representation of arguments by Toulmin (The Uses of Argument, 1958) makes this mistake: his argument diagram
D (Datum) ————————–> Q (qualification) —–> C (conclusion)
though not a planning argument, is an example of selective inclusion of premises that really are parts of successor issue arguments. Here, the Warrant is the premise making the connection between D and C; the backing B is the arguer’s preventive move in anticipation of a challenge to that premise. But any premise can usually be challenged on several kinds of grounds, not only one. So either the backing should properly include all those grounds (which of course would make the argument unwieldy and complicated), or the inclusion of one such ground to bolster the warrant is a selective complication of the main argument with one partial argument for the successor issue: Is the warrant W true? (or plausible?– the preferred term for argument evaluation). For that matter, isn’t it possible to also challenge the Data (D)? So could the argument not contain another claim supporting the veracity or validity of the data claim? The upshot of this is that for a useful representation of the arguments in a map, or a tool for evaluation, the argument itself should be reduced to its basic structure. For the planning argument, a resulting ‘map’ would look like this:
Issue / argument map, generic
The Overall Argument Completeness Rule
The generic map above shows only three arguments, which may be all that have actually been entered in a discussion. In argumentation textbooks, the emphasis is usually on the analysis of individual arguments — just as in formal logic, or even scientific method, the truth or falsity of a claim is taken to be adequately established by means of one single valid argument with true premises. It is curious that the familiarity of the ‘careful weighing of pros and cons’ often heard in official speeches is not reflected in the academic analysis of the arguments that constitute such pros and cons, specifically in the examination of the question of how such weighing might actually be done. The practice of argumentation in the political arena looks even less reassuring: political advertising tends to focus only on a few ‘key’ issues and arguments, and the relentless repetition of those points in TV and radio spots.
A modest amount of reflection should show that for some thorough deliberative effort of evaluation of the merit of pro and con arguments to reach a meaningful decision, all pro and con arguments should be included in the evaluation. That is, all potential effects of a proposed plan should be looked at and evaluated. The rationale for greater citizen participation in public planning and policy-making is in part the fact that the information of all such effects is distributed in the citizenry — the people who are affected have that knowledge, so they must be called upon to bring it into the discussion. Reliance on experts (who are usually not or very differently affected by government plans) cannot guarantee that all such pertinent knowledge is brought to bear on the decision. The only area where a thorough examination of all aspects is attempted is the practice of ‘benefit / cost analysis’ applied to big government or business planning. But this technique is invariably carried out by experts, public participation is mostly prohibited by the specialized terminology and technique.
The implication of this issue is that the discourse about public plans must be carefully orchestrated to ensure that all ‘pros and cons’ are actually raised and identified so that they can be included in the evaluation. On the one hand, people must be encouraged to contribute that information; on the other hand, the ‘overview’ representation of the set of aspects should not be obscured by repetition and rhetorical embroidery. Both requirements are difficult to satisfy.Some participants may not wish to reveal advantages a plan would bestow upon them — that other might consider unfair; or identify disadvantages to other parties (that these are not aware of) if this would require remedies reducing their own benefits. This has led me to suspect that the discourse must be considered systemically incomplete (and therefore, evaluation results should not be used directly as decision criteria). Nevertheless, the aim must be for all pros and cons to be brought out to be considered.
For the map representation of a discussion, this raises the question whether maps should ‘suggest’ issues that might be important to examine — even if they haven’t been raised by actual human participants but by some enhanced search engine, for example. Maps might show ‘potential issues’ in shades of grey as compared to highlighted issues that have actually been raised. The systematic generation of issues, even the construction of potential arguments by artificial intelligence programs based on information stored in data banks are both within reach of technological feasibility, and should be discussed carefully. This is a topic for a different investigation, however.
Besides other criticisms of the methodology — for example, the difficulty of assigning monetary costs or benefits to ‘intangible’ aspects — a key problem inherent in cost-benefit is that the effects of a plan must be declared as costs or benefits (by the experts) as perceived by some entity (e.g. the government funding the analysis) — an entity that is just one party, one side in the controversy. This is the subject of the next point:
The Pro / Con Identification Rule
In cost-benefit studies as well as in most if not all argument mapping programs, aspects and arguments are identified as ‘pro’ or ‘con’ (‘costs’ and ‘benefits’) — a practice that on the surface seems crucial for anyone trying to carefully review all the pros and cons in order to reach a deliberated decision. And in discussions, arguments are certainly entered by participants as supporting or opposing a proposed plan. So it seems eminently plausible that the maps should reflect this.
However, this practice hides the fact that effects of plans may not be beneficial for all people affected; indeed, one person’s ‘benefit’ (and thus ‘pro’ argument) may be another person’s ‘cost’ -(and thus a ‘con’ argument). In addition, once beginning the evaluation process, people will assign different weights and expressions of agreement / disagreement to different premises. these can have the effect of turning an an argument intended as a ‘pro’ argument and even initially accepted as such by the evaluator into a ‘con’ argument for that person: I may look at an argument meant to support plan x by pointing out that it will cause effect y given conditions c, and find that while I indeed believe that x will produce y, upon reflection y does not seem such a good idea. Or that I believe both that x will cause y under conditions c, and y is a worthy goal, but that conditions c are not present, which makes the effort to implement x a futile one. But seeing the argument identified in a map as a ‘pro’ argument may make it look like an established point, and that I have made a mistake: the map is ‘taking sides’ in the evaluation, as it were: the side of the agency funding the analysis, or simply the side of the participant entering that particular argument.
For that reason, it is better to refrain from accepting the intended ‘pro’ and ‘con’ label of arguments in the map. Whether an argument is a pro or con reason for a specific person is a result of that person’s assessment, not the proponent’s intention. Therefore, both in the list or collection of arguments, in evaluation forms and in argument maps, the labeling of arguments as supporting or opposing should be avoided. (This is a main reason for my rejection of most ‘debate-mapping’ and ‘argument mapping’ programs and techniques on the market today.)
The Rule of Rejecting some Arguments
(e.g. characterization, ad hominem, authority arguments, ‘meta-arguments’)
The previous ‘completeness’ rule may be misunderstood as advocating the admission of all kinds of arguments into maps and in the evaluation process. There are some important exceptions: for instance, arguments or premises that merely characterize a plan or claim, but don’t offer a reason for such characterization. The remark “This is a crazy idea” is indeed a forceful opposition statement against a proposal. But it is not really an argument — and therefore should not be entered into either formal evaluation forms nor argument maps. The same is true for positive (‘like’ or “wow, what a beautiful, creative proposal) expressions of support. They have the same status as ad hominem arguments (‘the author of the plan is a crook’) or arguments from authority (the principle goes back to Aristotle!’) — they suggest that the number of supporters, or the character of proponents, the fame of a philosopher who endorsed a concept, are adequate reasons to accept a claim. Once stated fully as such, the fallacy usually becomes obvious. Now sure, we agree that denigrating the messenger because of his flawed character is not by itself a good indication of the quality of the message — but is the citing of authorities not a common practice, even a condition for respectability in scientific work? How can it be wrong or inadmissible?
To the extent such expressions do have a legitimate place in the discourse and evaluation process, they are recommendations of how we should evaluate the plausibility of individual claims of an argument, they are not arguments about the plan x themselves. We accept an argument from a scientific authority because we assume that such a famous scientist would have very good reasons, evidence, data, valid calculations, measurements to back up his claim. Even so, such arguments often deteriorate into silly discussions not about that evidence for a claim, but about the reliability of the authority’s judgment, hurling stories about many other silly, untrue things that person also believed against the authority’s unchallenged record — all having nothing to do with the merit of the claim itself. So the venerable academic practice of citing sources belongs in the body of arguments and evidence of successor issues, not in the main argument about a plan nor in the maps showing the relationships between the issues and claims:
The first-level arguments about a plan should not contain
– arguments of characterization;
– ad hominem arguments (positive or negative);
– arguments from authority;
The same reservations hold for ‘meta’-arguments that make claims about the set of arguments in the discussion, or even in principle: “There is no reason to support this proposal”; “All the arguments of the opponent are fallacious”; “We haven’t heard any quantitative evidence questioning the validity of the proposal…” and the like. This is not to say that such observations do not have a place in discussions. They can serve an important purpose — such as to remind participants to provide substantial evidence, data, and support for their arguments. But these meta-arguments talk about the state of the discourse, not about the proposed plan — and therefore should likewise be omitted from representations of the discussion, argument maps, or evaluation tools of that plan itself. Perhaps there should be a separate ‘commentator’ rubric for such observations about the state and quality of the discussion itself.
The Rule of Rewarding Participation
The last observation above raises another important issue: that of the degree and sincerity of participation in the discussion. Just like the phenomenon of ‘voter apathy’ held responsible for low voter turnout in elections, the experience with efforts to engage participants in online discussion to ratchet up their contributions from just exchanging comments to the more demanding task of collaborative writing more comprehensive summaries or reports on the results of their discourse has been disappointing. Even the extra effort to switch to a different platform without the normal length limits of online discussion posts, and permitting the inclusion of visual material (maps, pictures) has been ‘too much’ for discussion participants normally quite eager to exchange arguments and share material researched on the web.
It is misplaced to accuse such people of ‘apathy’ or merely being motivated by the excitement of the online discussion (the nature of this motivation may not be very well understood yet). The reason for voter apathy and this reluctance of discussion participants might be more properly seen in the lack of meaningful rewards for such engagement. Voters who perceive — with or without justification — that their votes do not have a significant impact on government decisions, will be less eager to vote; discussion participants who don’t see what difference a summary of their contributions would make in the larger scheme of things will not be eager to go beyond the venting of their frustrations and exchange of opinions. Most online discussions ‘die down’ after some time without having reached any meaningful resolution of the subject debated.
Online social networks have tried to respond to this phenomenon with features such as the count of ‘friends’ or ‘network connections’ — or simple evaluation devices in the form of ‘like’ and ‘dislike’ (thumbs up or down) buttons. These efforts turn into quite meaningless competitive numbers efforts, which suggests nothing more that how meaningless they are (how many ‘friends’ do we have on Facebook that we wouldn’t even know if we met them in the street?) — but are encouraged by the networks because they help the advertising part of their enterprise.
It turns out that the suggested tool of argument evaluation for the discourse framework might offer a better approach to the problem of rewarding participants for their contribution. Going beyond the mere count of posts in a discussion, the evaluation of argument plausibility and argument weight (the argument’s plausibility modified by the weight of relative importance of its deontic premise) of planning arguments, as evaluated by the entire group of participants in the evaluation exercise, can be directly used as a measure for the value of a participant’s contributions. (The details of scoring are developed in more detail in a paper on a proposed argumentative planning and argument evaluation game; draft available on request.)
This feature leads to the possibility of building up a reputation record of different types of contributors: for example, a participant’s contribution to the development (through modification) of the plan eventually adopted or recommended; the ‘creative’ contributor supplying innovative solution ideas; the solid ‘researcher’ finding information pertinent to the discussion on the net, the ‘influential’ participant whose arguments lead other participants to change their minds; the ‘thorough / in-depth deliberating participant’ who is delving more deeply into the evidence and support for argument premises in successor issues; the person with the most reliable offhand judgment whose initial assessment turns out to be closest to the final deliberated result by the entire group, and so on).
The possibility of building up such cooperative contribution records — that might be included in a person’s resume for job applications or profile for public office positions — could provide the needed reward mechanism for constructive participation in discussions about significant public issues.
The Rule of Improving Proposed Plans rather than forcing a decision
One aspect of the purpose of public discourse deserves some special consideration. There are various reasons for the widespread perception of argumentation as an adversarial, divisive activity. For example: the spectacle of many ‘debates’ of candidates for public office, where the aim of each debater is to make the opponent look less fit for the job by refuting the opponents arguments, or goading the opponent into making foolish assertions (that can then be used in ‘attack ads’). Even more so, the decision mechanism applied both in elections and decisions in ‘decision-making bodies’ in government and private enterprise: majority voting. It will provide a decision, which may be convenient or even critical in some cases — but at the expense of ignoring the arguments, the concerns of a significant minority of participants. The practice of enforcing ‘party discipline’ in voting in parliamentary bodies is entirely obviating discussion — if the majority party has the votes, no debate is necessary. The victory celebrations of the winners of such votes overshadow the fact that the quality of the plans or policies voted upon has totally disappeared from the process.
The introduction of merit of discourse measures into such discussions could help reverse this problem: the contribution rewards to individual participants could — and should — be structured to favor the development of ‘better’ proposals. By this is meant, here, plans modified step by step from the initial proposal by amendments or changes, in response to concerns expressed by participants, and with the aim of achieving a greater degree of approval from a larger group of participants, and at least acceptance as ‘not making things worse than before’ by the adversely affected minorities. The goal of ‘complete consensus’ is an ideal that may be too difficult to achieve in many cases, and tempt lone dissenting holdouts to adopt a position of de facto ‘dictating’ no action. But a discourse participation reward structured to encourage the improvement of plan proposals rather than mere majority vote decisions may help improve not only the discourse about public issues but the resulting decisions as well.