Plural Rationality and Interactive Decision Processes

Most political theorists share the basic assumption that the pursuit of self-interest lies at the heart of political behaviour. In consequence, theoretical approaches in political analysis, diverse though they may be, can all be assembled under one rubric — the politics of interest. In this perspective, the political realm is seen as an arena into which individual or group interests enter in some fashion, to be dealt with by certain processes and to be transformed into outcomes, policies or outputs.¹ This notion of political processes treats political society, not as a single entity—a community—but as fragmented into groups that are distinguished by their respective interests. On this view, groups and their interests constitute the essence of politics, providing the conceptual terms in which political behaviour is to be explained.

Plural rationality and interest as cultural factors in national planning are almost untackled subjects of scientific enquiry. This has been a challenge for the present paper, although only the first steps could be made in it towards a comprehensive study. The course of consideration will be as follows: Strongly linking human beings and communities to the basic nature of planning, it was possible to find frames which have helped to analyze and synthetize some relevant features of plural rationality and interest in national planning work. Using these frames, important insights were derived which might facilitate further and more detailed examinations.

Most mathematical models in management science and symbol-manipulating programs in artificial intelligence attempt to describe the relevant problematic world in terms of facts, decisions or actions taken in the present and often also in the future, and relationships specifying how facts and decisions combine to generate new facts. Alternative decisions or policies are compared and one is chosen according to some specified rule. This description is general enough to include not only the more traditional approaches but also decision analysis (where the present is frequently taken as an undecomposed single fact and possible futures are decomposed into sequences of choices and events with associated subjective probabilities) and expert systems (where the rules by which facts combine to ultimately produce a decision usually take the form of “if...then...” inferences). Decision support systems generally dispense with the rules for choosing a decision, leaving that up to the human user, but still depend on facts, and relationships for modeling the future so as to answer various “what if” questions. When, in any sense, the problematic world is decomposed into facts, rules, and relationships in the course of addressing a problem, we shall say that the decision is based on calculative rationality.

Life goes on. Every once in a while we seem to be able to isolate particular events, isolable not only in principle but in practice too. For example, there are situations in which we say that we are making a decision or that a decision has been made. Such decision events are nexuses, and they may also be turning points. I want to describe two kinds of isolable events—little and big decisions—and then describe a variety of situations—economy, law, rites of passage, heroic action, judgment, and entrepreneurship—which make use of them. My motivation is to enlarge our notion of decisionmaking: to include both religious conversion and consumer choice, both transcendent struggle and deliberate planning.

Naturally, there is only one real world but people are not directly living in this single real world. J. Ortega y Gasset wrote that “man must ever be grounded on some beliefs, and that the structure of his life will depend primordially on the beliefs on which he is grounded”. Beliefs, “always constitute a system insofar as they are effective beliefs”. (ORTEGA Y GASSET, J. 1963: 283–284). According to this we can say that belief systems are those media by which people are able to live in the real world.

In an essay on risk perception, Kenneth Arrow writes: “The concept of rationality has been basic to most economic analysis.”¹ Arrow has a specific conception of rationality in mind, a major implication of which is the expected utility hypothesis. This hypothesis says that a rational individual assesses alternative choices in terms of expected utility — the aggregated, probability-weighted utilities of each alternative’s possible consequences — and then chooses the alternative that maximizes this amount.

People, both smart and dumb, often do not behave the way normative theorists say they should behave. But what is even more frustrating, some (otherwise) smart people, who know how they should behave according to some impeccably beautiful normative theory, like the maximization of subjective expected utility (SEU) — you can clearly see my biases — nevertheless, do not always follow those guidelines.

In many multi-actor decision situations the parties involved do not share the same perception of rationality, the same basic values or the same information. This could be because the actors come from different cultural backgrounds (Thompson, 1984). Yet even quite culturally diverse actors can achieve agreement if they recognize their diversity, are willing to learn and exchange information, and agree on the legitimacy of some negotiation procedure or on some principles of fairness for use in mediation. These concepts seem difficult to formalize; yet, as shown later, formalization or abstraction is an important part of the cognition process, and thus necessary for a deeper understanding of the problem.

For an improvement of human relations within and between nations a better understanding of interactive human decision making is needed. In economic research concerned with decision making there are two contrasting approaches: the classical theory of the absolute rational decision maker, who maximizes his utility function, and the behavior oriented theory of the limited rational decision maker, who searches for a satisfying alternative.

The distinction between a descriptive or predictive theory on the one hand and a prescriptive or normative one on the other is sufficiently clear. Still there is a region of overlap between them. Generally speaking, a descriptive theory deals with what is and a prescriptive one with what ought to be. But “ought” can be understood in two senses: in terms of a value system and in terms of an idealized situation. Questions of value do not enter the realm of physical science. Nevertheless we can speak of normative theories of physical phenomena in the sense of our expectations of what we should observe under idealized conditions, such as in perfect vacuum, thermodynamic equilibrium, etc. In fact, since physical theories consist for the most part of mathematical models of physical phenomena, they are, strictly speaking, normative theories, dealing with how things “ought” to behave under idealized conditions, rather than with how they actually behave. What makes physical theory also descriptive (and predictive) is the circumstance that the mathematical models are often very good approximations to reality, so that “what is” turns out to be quite close to “what ought to be.”

This is the first in a projected series of papers on solutions to games in matrix and extensive form. The predominant solution concept in the literature is that of the noncooperative equilibrium put forward by Nash (1951).

This paper addresses some aspects of group decision making by coalitions. In Section 2 we analyse and illustrate efficiency concepts with regard to group decision making. Section 3 — which is the main part of the paper — is devoted to the design of an interactive decision support system for group decision making by coalitions. This interactive decision support system is not based on the assumption that individual or collective preference systems can be represented by preference functions.

Fishery resource management is an intrinsically dynamic problem where current harvest decisions affect the possibilities of harvesting in the future. Moreover, in the case of two or more harvesting agents, the harvesters face a complicated dynamic game problem the solution of which depends on the behavioural strategies adopted (cooperative or non-cooperative management, myopic or foresighted optimization etc). Decision making in such an environment means negotiations and bargaining on the management strategies as well as on the planning horizon considered. These questions will be discussed in Section 2. In Section 3 we will illustrate problems which are related to the practical negotiation policies in international fishery management.

A macromodel should have the following features: We snould not try to control a macrosystem against its eigendynamics.

Interactive programming methods are composed of three main factors, i.e., (1) preference information elicited from the decision maker, (2) scalarization functions and (3) numerical methods for auxiliary scalar optimization. In developing interactive programming methods, it seems very important to make effective use of various devices in these three factors. Above all, aspiration levels are promising as the preference information elicited from the decision maker, since they are easy and intuitive to answer. Moreover, the weighted Tchebyshev norm can be effectively used as a scalarization function for obtaining a Pareto solution. In this paper, we discuss each components in interactive multiobjective programming methods.

A number of recent developments (for example, the rapid deterioration of European and North American forests, or the increasing conflict between the trade unions, industrial managers and the government over issues such as unemployment, the national debt, wages, etc.) have highlighted the severe shortcomings of the conventional modeling techniques (e.g., econometric models or technoeconomic approaches) used as a basis for decision making.

The purpose of this paper is to describe recent developments in DIDASS methodology and implementation. The DIDASS system, which is based on the paradigm of satisficing decision making and the theory of multiple criteria optimization, has been the subject of numerous papers and reports. The principles of the method and selected applications were presented by Grauer et al. (1984).

Decision analysis in the area of multiobjective resource allocation calls for decision support based on specially devised software. Such is the case presented here. The necessary condition for development of such software is a thorough identification of decision environment so that the following is assured:

In many practical applications, the final choice from among discrete alternatives is based on several criteria, which can be quantitative or qualitative. By the term quantitative we mean that the decision-maker is able to present his preferences over alternatives on some cardinal (interval or ratio) scale. If we call a criterion qualitative, we mean that the decision-maker can only express ordinal preferences by stating which of a pair of alternatives he prefers most. Very often the criteria are clustered in such a way that they have a natural hierarchical structure. In any case, we can assume that this kind of hierarchy can be constructed.

This paper presents some work done by the authors as part of a project aimed at the construction of a model of Polish agriculture [1]. The project has been undertaken by an interdisciplinary team composed of researchers from the Systems Research Institute, the Institute of Agricultural Economy, the Institute for Rural and Agricultural Development, in close cooperation with the State Planning Commission and the Ministry of Agriculture. The project represents a joint case study performed with the cooperation of the Food and Agriculture Program and the System and Decision Sciences Program of the International Institute for Applied Systems Analysis. Due to limitations on the size of the paper, only a brief outline is presented here. Details of the research may be found in [1],[2],[3].

A means of determining the optimal temperature for cultivation of a cucumber crop in a greenhouse is presented. The optimal temperature is derived from a comparison with a standard temperature regime and is selected on the basis of two criteria: (1) expected income from an early crop and (2) heating costs.

The nonlinear problem is solved using the reference point approach as implemented in the DIDASS/N software package.

DISCRET has been developed to deal with multicriteria optimization and decision making problems with a finite number of discrete alternatives. The following problem structure is assumed:

This paper presents selected numerical results obtained during an experimental session at the Conference on Plural Rationality and Interactive Decision Processes. The description of the problem is given earlier in this volume.

The aim of the experimental session reported here is twofold. First, to give an empirical evidence of applicability of the Decision Analysis Aid based on the Skeleton Method. Second, to enable discussion on the proposed approach and its confrontation with various experience represented by participants of the conference.

The basic idea of our interactive method was proposed by Wierzbicki (1982). The method was implemented and several modifications tested by Fortuna and Kruś (1983). A number of experiments based on this method have been carried out for a regional planning problem. This paper presents the results of one such experiment. A general outline of the method is also given.

Intensive research, especially over the last 10 to 15 years, into approaches to multiple criteria decision making has led to the development of a large number of formal methods. There is, however, generally little agreement — and often fierce dissent — among the proponents of different techniques as to the relative merits of these procedures.