Decision Making Process

Concepts and Methods

Denis Bouyssou, Didier Dubois, Henri Prade, Marc Pirlot, Denis Bouyssou, Didier Dubois, Henri Prade, Marc Pirlot

• Publisher: Wiley-ISTE
• Year: 2013
• ISBN:
  9781118619520
• Edition: 1
• Topic: Business
• Subtopic: Decision Making
• Index: Business

This book provides an overview of the main methods and results in the formal study of the human decision-making process, as defined in a relatively wide sense. A key aim of the approach contained here is to try to break down barriers between various disciplines encompassed by this field, including psychology, economics and computer science. All these approaches have contributed to progress in this very important and much-studied topic in the past, but none have proved sufficient so far to define a complete understanding of the highly complex processes and outcomes. This book provides the reader with state-of-the-art coverage of the field, essentially forming a roadmap to the field of decision analysis.

The first part of the book is devoted to basic concepts and techniques for representing and solving decision problems, ranging from operational research to artificial intelligence. Later chapters provide an extensive overview of the decision-making process under conditions of risk and uncertainty. Finally, there are chapters covering various approaches to multi-criteria decision-making. Each chapter is written by experts in the topic concerned, and contains an extensive bibliography for further reading and reference.

Chapter 1

From Decision Theory to Decision-aiding Methodology ¹

1.1. Introduction

Quite often I get asked what my job is.When I reply that I work in decision aiding, people remain perplexed and quite often ask ‘aiding what decisions?’.

Indeed, decision making is an activity that every person does every day. We all make decisions constantly, from the simplest ‘should I take my umbrella?’ [POO 92] to the more complex ‘how should the international disarmament treaty be applied?’ [JPV 98].We also make decisions at all levels e.g. individual: ‘should I get a divorce?’ [WAT 83], organizational: ‘how do we schedule the crew shifts?’ [CAP 98] and interorganizational: ‘which trace for the highway?’ [OST 93].

Indeed, decision making is an activity that every person does every day. We all make decisions constantly, from the simplest ‘should I take my umbrella?’ [POO 92] to the more complex ‘how should the international disarmament treaty be applied?” [JPV 98].We also make decisions at all levels e.g. individual: ‘should I get a divorce?’ [WAT 83], organizational: ‘how do we schedule the crew shifts?’ [CAP 98] and interorganizational: ‘which trace for the highway?’ [OST 93]. During such decision processes we often ask for help, advice or support from friends, experts or consulting companies. Several questions arise. Is it conceivable that a decision-aiding methodology could exist independently from any specific domain, one which could be used in all such situations? Can an expert in decision aiding exist who is not an expert in any particular domain? What would the difference be between such an expert and a psychotherapist, a physician, a lawyer, an expert in logistics or your best friend?

What characterizes decision aiding, both from a scientific and a professional point of view, is the fact that it is both formal and abstract. By ‘formal’ the use of formal languages is meant, ones which reduce the ambiguity of human communication. By ‘abstract’ the use of languages that are independent from a specific domain of discourse is meant. The basic idea is that the use of such languages implies the adoption of a model of ‘rationality’: a key concept in decision aiding. Does it make sense to use such a language always and in any context? Obviously not. Being abstract and formal presents several disadvantages:

– it is much less effective with respect to human communication;

– it has a cost (not necessarily monetary);

– reducing ambiguity might not be desirable; and

– it imposes a limiting framework on peoples intuition and creativity.

Nevertheless, there are also several advantages which, in some circumstances, can be interesting [BOUY 00]:

– It allows the participants in a decision process to talk the same language, a fact that improves transparency of the process and possibly increases participation (for an example see [BAN 01]).

– It allows the identification of the underlying structure of a decision problem (if there is any) and therefore allows the re-use of procedures and models (see any textbook on Operational Research, e.g. [WIL 90]).

– It is not affected by the biases of human reasoning that are due to education or tradition [RIV 94].

– It may help to avoid the common errors that are due to an informal use of formal methods. A typical case is the use of averages as a universal grading procedure [BOUY 00].

In general terms, a formal and abstract language allows us to better analyze, understand, explain and justify a problem or a solution. It should be noted that organizations, companies, institutions, entreprises and ourselves ask for and use formal methods of decision aiding. Students are promoted using the average of their grades. Traffic restrictions are applied based on a pollution index. Credit demands are rejected because of the client’s credit rating. Production is scheduled, highways are designed and networks are administrated using formal methods of decision support. In reality, decision aiding is present in many aspects of our everyday life. People do not necessarily use this term, but there is always a formal and abstract language which is used in all the above examples. Therefore, when the expression ‘decision aiding’ is used, the use of a formal and abstract language in order to handle problem situations faced by individuals and/or organizations is meant.

In this chapter we first examine a brief history of the evolution of this domain from a scientific and a professional point of view (section 1.2). Such a historical reconstruction pretends neither to be complete nor rigorously organized. Several readers might feel disappointed that some very important scientific achievements are not recognized. Indeed, this is an essay which reflects my very personal point of view and is biased by at least three factors.

– Scientific: I am not an expert in all areas of decision theory and operations research and I tend to emphasize in my presentation what I know better.

– Professional: the real world experiences of decision aiding that I had the opportunity to conduct do not cover all different aspects of practicing decision aiding, so that I have a partial vision of this complex reality.

– Geographical: being a (western) European I have not been exposed to the bulk of the contributions produced in decision theory and operations research just behind the corner [e.g. KEN 83, KWA 62] and this is a severe limitation.

In section 1.3, I will present and discuss different decision-aiding approaches that have been introduced during the 60 years of existence of this discipline: normative, descriptive, prescriptive and constructive approaches. I will try to explain the differences among these approaches by examining the origin of their particular ‘models of rationality’. In section 1.4, I will place myself within a constructive decisionaiding approach and I will discuss how a decision-aiding process is structured. In order to do that I will examine the ‘artifacts’ produced by such a process: the representation of a problem situation, the definition of a problem formulation, the construction of an evaluation model and the formulation of a final recommendation. Such a presentation will allow me to differentiate decision aiding from other areas of scientific investigation such as automatic decision making.

The ultimate message I wish to deliver with this chapter is that decision aiding is a human activity that can be (and actually has been) the subject of scientific investigation. Different decision theories have been developed with specific characteristics. At the same time, different decision-aiding practices have been developed either as a result of testing theoretical conjectures or as a result of aiding real decision makers (individuals, organization or collective entities) in their work.

There is no one-to-one correspondence between theories and practices. Nevertheless, I consider that all such theories and practices define a whole which I will call ‘decision-aiding methodology’. The reader should note that in the text I use the term methodology in a very precise way: reasoning about methods. I claim that we have several methods, but we should establish a common methodology for decision-aiding purposes. Such reflections are discussed in the conclusions section. At the end of the chapter a long but definitely partial list of references is provided (an exhaustive presentation of the literature being impossible).

1.2. History

1.2.1. Genesis and youth

We can fix the origin of decision aiding as sometime just before the second world war, in the studies conducted by the British army on their new radar system installation and their efforts to break the German secret communication code (1936–37). The reader can get a flavor of this period in [BOW 04, KIR 02]. It is the first time the term ‘operational research’ (‘operations research’ in the USA) appears.

The problem of how decisions are or ought to be taken by individuals, organizations and institutions was previously discussed by Aristotle [ARI 90] and, more recently, during the 18th century (see [BERN 38] on probability, [EUL 36] on combinatorial problems and [BOR 81, CON 85] on voting and social choice procedures) and also at the beginning of the 20th century ([PAR 06] on economic problems under multiple dimensions, [FAY 49, TAYL 11] on the scientific management of enterprises, [DEF 36, DEF 37, KOL 33, RAM 31] on probability theory and [TUR 37] on decidability).

In all these contributions, the concept of decision is central. It should be mentioned that, in order to argue for their thesis that probability only exists in terms of subjective belief, both Ramsey and de Finetti have used what is known today as comparison of lotteries and the associated preferences of a decision maker. “If the option of a for certain is indifferent with that of β if p is true and γ if p is false, we can define the subject’s degree of belief in p as the ratio of the difference between α and γ to that between β and γ. This amounts roughly to defining the degree of belief in p by the odds at which the subject could bet on p, the bet being conducted in terms of differences of values as defined” [RAM 31, p. 179–180].

In any case, it was the undeniable success of operations research in supporting military and intelligence activities of the allies that grounded the idea that decision making (and decision aiding) can be studied using a scientific approach and that general models of decision support were possible. Towards the end of the 1940s, several fundamental contributions appeared in linear programming [DAN 48, KAN 39], decision and game theory [NAS 50, NAS 51, VON 44] and in algorithmics and the definition of machines able to solve any problem [TUR 50].

It was during that period that the first scientific societies of operations research (in the UK in 1948, in the USA in 1950) and the first scientific journals appeared [BLA 50]. The first real-world applications of this new discipline (in non-military applications) appeared [DAN 51] as well as the first companies specializing in decision aiding (but this term was not used at that time). The best-known example is the Rand corporation.Within Rand, operations research was developed into a science to be applied to the multiple problems of the new post-war industrialization.

Such first contributions and experiences were characterized by the search for formal structures underlying precise decision problems and the use of mathematics and logic as a modeling language. For an interesting presentation of the origins of these contributions as have been perceived by their authors themselves, see [LEN 91]. The first steps in this direction strengthened the idea that complex decision problems can be modeled through the use of a simple rationality model (maximize a utility function of the decision maker’s decision variables, a function which is expected to faithfully represent the decision maker’s preferen...

Table of contents

• Cover
• Title Page
• Copyright
• Preface
• Chapter 1: From Decision Theory to Decision-aiding Methodology
• Chapter 2: Binary Relations and Preference Modeling
• Chapter 3: Formal Representations of Uncertainty
• Chapter 4: Human Decision: Recognition plus Reasoning
• Chapter 5: Multiple Objective Linear Programming
• Chapter 6: Constraint Satisfaction Problems
• Chapter 7: Logical Representation of Preferences
• Chapter 8: Decision under Risk: The Classical Expected Utility Model
• Chapter 9: Decision under Uncertainty: The Classical Models
• Chapter 10: Cardinal Extensions of the EU Model Based on the Choquet Integral
• Chapter 11: A Survey of Qualitative Decision Rules under Uncertainty
• Chapter 12: A Cognitive Approach to Human Decision Making
• Chapter 13: Bayesian Networks
• Chapter 14: Planning under Uncertainty with Markov Decision Processes
• Chapter 15: Multiattribute Utility Theory
• Chapter 16: Conjoint Measurement Models for Preference Relations
• Chapter 17: Aggregation Functions for Decision Making
• Chapter 18: Subjective Evaluation
• Chapter 19: Social Choice Theory and Multicriteria Decision Aiding
• Chapter 20: Metric and Latticial Medians
• List of Authors
• Index