Analyses of Bounded Rationality: Towards Economic Decision-Making

1. Analyses of Bounded Rationality: Towards Economic Decision-Making Farley S.M. Nobre Email: fsn019@bham.ac.uk or farley.nobre@chek.com Home Page: http://web.bham.ac.uk/fsn019/fsnobre.html Ph.D. Student The University of Birmingham, England Guest Researcher The Humboldt-University of Berlin, Germany Seminar of Behavioural Economics July 11th 2003

2. Part I 1. Problem Choice and Analysis Part V Part II 5. Evidences and Conclusions: Theory as Proposed vs. Findings 2. Solution Design: Definitions, Variables, and Propositions Parts III and IV 4. Data Analysis 3. Data Gathering Analysis and Design of Organisational Systems: Towards a Unified Theory Fig.1. Thesis Structure and Its Process of Theorizing

3. Contents: Part I – Problem Analysis • Classical Theories on Rationality • Bounded Rationality Theory • The Genesis of Bounded Rationality Theories • Economic Decision-Making and Approximate Reasoning • Organisations and Conflicts • Conclusions

4. Contents: Part II – Solution Design • Proposal: CTP - explores bounded rationality theories • Cognitive Psychology Models • A Model of Information-Processing Systems • Knowledge Representation and Organisation • Computing Perceptions for Decision-Making • Conclusions

5. Motivations • Organisations subsume economic decision-making and problem solving processes that involve trade-offs among alternatives characterised by uncertainties and incompleteness of information. Such processes lead organisational members to both intra-individual and group conflicts. • The former conflict arises in an individual mind and it can emerge from the influence of others. The latter type arises from differences between the choices made by distinct individuals in the organisation. In this case, individual participants are not in conflict but the organisation as a whole is. • The intra-individual and group conflicts that arise in organisations as exposed in (ii) are determined by cognitive limits of humans, and thus these conflicts cannot be solved by incentive and reward systems - i.e. inducements. Such cognitive limitations are synonymous of bounded rationality [March and Simon, 1993].

6. Part I:Problem Analysis

7. Part I: (i) Classical Theories on Rationality • Rationality is synonymous of: • optimal choice; • optimal procdures and outcomes (intelligence); • statistical decision analysis. • Rationality is defined as: • A particular class of procedures for making choices [March, 1994].

8. Part I: (i) Classical Theories on Rationality [Simon, 1997a] • The Theory of Subjective Utility (SEU): • It underlies neo-classical economics; • It postulates that choices are made: • among a given, fixed set of alternatives; • with (subjectively) known probability distributions of outcomes for each; • And in such a way to maximize the expected value of a given utility function.

9. Part I: (ii) Bounded Rationality Theory • Bounded Rationality [Simon, 1947; and March and Simon, 1958]: • It is also concerned with rational choice; • But it takes into account the cognitive limitations of the decision maker; • It is concerned with human decision-making processes; • It is investigated on the basis of empirical knowledge of the capabilities of the human mind, and thus on the basis of psychology research. • Humans have limitations of both: • Knowledge and computational capcity: • For discovering alternatives; • Computing their consequences under certainty or uncertainty; • And making comparisons among them.

10. Part I: (ii) Bounded Rationality Theory • Theories of Bounded Rationality [Simon, 1997a]: • Can be generated by relaxing one or more of the assumptions of the SEU theory. • New assumptions subsume that: • Alternatives are not simply given, and thus they have to be generated by some processes; • probability distributions of outcomes are unknown, and thus they have to be estimated by some procedures; • Satisfactory is used rather than optimal or maximal standards; • Probability distributions are unknown and they cannot be estimated due to the sources uncertainty - like vagueness, instead of ambiguity.

11. Part I: (iii) The Genesis of Bounded Rationality • Bounded rationality emerged with the advent in cognitive psychology research (Bruner and Piaget), and thus cognitive science and artificial intelligence along the 1950’s [Newell and and Simon, 1972]. • Cognitive psychology deals with high mental processes, rather than with stimuli and responses of behaviourism. • Cognitive psychology aims the scientific research on models of human mind and its processes like perception, attention, categorisation, concept formation, knowledge representation, memory, language, probelm solving, decision making - among others.

12. Part I: (iv) Economic Decision-Making and Approximate Reasoning • Bounded Rationality is: • Synonymous of Economic Decision-Making. • Since it concerns the use of cognitive processes to the achievement of low solution cost, robutness, and tractability to the reality. • Agents have cognitive limitations, but they are also constrained by time and space. • Humans [Zadeh, 1965 and 1973]: • Have a remarkable ability for reasoning in complex environmnets, under uncertainties, where information is ill-defined, incomplete, or lacking in reliability. • Human reasoning is approximate rathen than exact (driving a car in a havy traffic, sharing stocks, and so on).

13. Part I: (iv) Economic Decision-Making and Approximate Reasoning [Zadeh, 1994] - Source: New York Times Eg. - Parking a car - Travelling sallesman problem

14. Part I: (v) Organisations and Conflicts Organisations of Today: (i) The members of organisations are decision makers and problem solvers [March and Simon, 1993]. (ii)Processes of decision-making and problem solving involve trade-offs among alternatives characterised by uncertainties andincompleteness of information, and hence they lead organisational members to both intra-individual and group conflicts. (iii)The intra-individual and group conflicts that arise in organisations as exposed in (ii) are determined by cognitive limits of humans, and thus these conflicts cannot be solved by incentive and reward systems. Such cognitive limitations are synonymous of bounded rationality. (iv) The members of organisations have motives that differ from organisational goals. (Use of incentive and reward systems for alignment and equilibria). (v)Organisations shape participants’ behaviour through social structure, technology, and goals, and participants shape organisations through their behaviour, motives, and cognitive skills. (vi) The environment shapes organisations (i.e. their social structure, technology, goals, participants, and behaviour), through its sources of complexity and uncertainty, but also through information, services, goods, and so technology. (vii) Organisations also shape the environment through the same means.

15. Part I: (vi) Conclusions • Bounded rationality theories complement classical theories on rationality, but they also extend them to the analysis of human decision-making behaviour as it happens in real-world (everyday) situations. • New approaches of decision analysis has to be considered in order to coupe with uncertainties that do not lie with statistical and analytical tools as applied to rational choices under certainty and risk (probabilities).

16. Part II:Solution Design

17. Part II: (i) Proposal CTP [Zadeh, 2001]CTP - Computational Theory of Perceptions • Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measuments, and so any computation of numbers: • Parking a car; • Playing golf; • Cooking a meal; • And summarizing a story. • Instead, humans use information which are formed from perceptions – like information of time, distance, colour, lenght, spped, possibility, likelihood, truth, and so on.

18. Part II: (ii) Cognitive Psychology ModelsPerceptual Symbol Systems Figure 1: Perceptual Symbol Systems [Barsalou, L.W., 1999] Perceptual Symbol Systems. Behavioral and Brain Science, 22.

19. Part II: (ii) Cognitive Psychology ModelsAmodal Symbol Systems (Information-Processing Systems) Figure 2: Amodal Symbol Systems [Barsalou, L.W., 1999] Perceptual Symbol Systems. Behavioral and Brain Science, 22. [Newell, A. and Simon, H.A. 1972] Human Problem Solving. Prentice-Hall.

20. Part II: (iii) A Model of Information-Processing Systems Environment Memory Receptors Processor Effectors Figure 3: A Model of Information-Processing Systems [Newell, A. and Simon, H.A. 1972] Human Problem Solving. Prentice-Hall.

21. Part II: (iii) CTP - receptor • CTP concerns a collection of description of perceptions expressed in a natural language. • Examples: • It is unlikely that there will be a significant increase in the price of oil in the near feature. • Diana is young. • Traffic is heavy. • Inflation is low and stocks are a little cheaper. • Most Swedes are tall. • Usually Robert returns from work at abot 6 pm.

22. Part II: (iii) CTP - receptor • Natural language involves linguistic variables: • Inflation = [very high, high, not very high, moderate, low,...] • Cost = [expensive, cheap] • Age = [very young, young, middle age, old, very old] • Status = [rich, not so poor, poor]

23. (s) (s) high low high low 1 1 0.5 0.5 0 200 100 Cost (US$) 0 10 5 Inflation (%) inflation = {low, high} cost = {cheap, expensive} Part II: (iv) Knowledge RepresentationMembership Functions of Fuzzy Sets [Zadeh, 1965] R1: If inflation is low THEN cost is cheap R2: If Inflation is highTHEN cost is high

24. Part II: (v) Computing Perceptions for Decision-Making • IF-THEN rules: • R1: IF incentives are highAND production is efficient THEN organisational satisfaction is moderate. • R2: IF incentives are lowAND production is efficient THEN organisational satisfaction is moderate. • R3: IF incentives are highAND production is poor THEN organisational satisfaction is moderate. • R4: IF incentives are lowAND production is poor THEN organisational satisfaction is bad. Deriving conclusions from fuzzy rules of inference

25. Part II: (vi) Conclusions • Fuzzy sets and fuzzy logic are new approaches that explore uncertainties in decision-making processes by using natural language based information; • They support CTP and they emerged as a new approach to deal with complex problems as those found in social sciences; • They were proposed to fulfil the gap between analyses of non-living (machines) and living systems (behavioural) [Zadeh, 1962].

26. References • [Barsalou, L.W., 1999] Perceptual Symbol Systems. Behavioral and Brain Science, 22. • [March, J.G. 1994]A Primer on Decision Making: How Decisions Happen. The Free Press. • [March, J.G. and Simon, H.A. 1958]Organizations. 1st Ed. John Wiley & Sons, Inc. • [March, J.G. and Simon, H.A. 1993]Organizations. 2nd Ed. John Wiley & Sons, Inc. • [Newell, A. and Simon, H.A. 1972]Human Problem Solving. Prentice-Hall. • [Simon, H.A. 1997a]Models of Bounded Rationality: Empirically Grounded Economic Reason. Vol.3. The MIT Press.(1st Ed. publisged in 1947). • [Simon, H.A. 1997b]Administrative Behavior: A Study of Decision-Making Processes in Administrative Organizations. The FREE Press. • [Zadeh, L.A. 1962] From Circuit Theory to System Theory. Proceedings of the IRE, 50: 856-865. • [Zadeh, L.A. 1965] Fuzzy Sets. Information and Control, 8: 338-353. • [Zadeh, L.A. 1973] Outline of a New Approach to the Analysis of Complex Systems and Decision Process. IEEE Transactions on Systems, Man, and Cybernetics, 3 (1): 28-44. • [Zadeh, L.A. 1994] Soft Computing and Fuzzy Logic. IEEE Software, November: 48-56. • [Zadeh, L.A. 2001] A New Direction in AI: Toward a Computational Theory of Perceptions. AI Magazine. Spring: 73-84.