Transcript Simulation of Bounded Rationality: Towards Economic

Analyses of Bounded Rationality:
Towards Economic Decision-Making
Farley S.M. Nobre
Email: [email protected] or [email protected]
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
Analysis and Design of Organisational Systems:
Towards a Unified Theory
Part I
1. Problem Choice and
Analysis
Part V
Part II
2. Solution Design:
Definitions, Variables,
and Propositions
5. Evidences and
Conclusions: Theory as
Proposed vs. Findings
Parts III and IV
4. Data Analysis
3. Data Gathering
Fig.1. Thesis Structure and Its Process of Theorizing
Contents: Part I – Problem Analysis
i.
Classical Theories on Rationality
ii.
Bounded Rationality Theory
iii.
The Genesis of Bounded Rationality Theories
iv.
Economic Decision-Making and Approximate Reasoning
v.
Organisations and Conflicts
vi.
Conclusions
Contents: Part II – Solution Design
i.
Proposal: CTP - explores bounded rationality theories
ii.
Cognitive Psychology Models
iii.
A Model of Information-Processing Systems
iv.
Knowledge Representation and Organisation
v.
Computing Perceptions for Decision-Making
vi.
Conclusions
Motivations
(i)
(ii)
(iii)
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].
Part I:
Problem Analysis
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].
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:
a)
b)
c)
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.
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.
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.
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.
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).
Part I: (iv) Economic Decision-Making and
Approximate Reasoning
[Zadeh, 1994] - Source: New York Times
Solutions for Travelling Sallesman Problem
Numbe of Cities
Accuracy of Solution
Computing Time
100,000
1.00%
2 days
100,000
0.75%
7 months
1,000,000
3.50%
3.5 hours
Eg.
- Parking a car
- Travelling sallesman problem
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 and incompleteness 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.
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).
Part II:
Solution Design
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.
Part II: (ii) Cognitive Psychology Models
Perceptual Symbol Systems
Analogue Modal
Symbols
Extraction
Reference
Perceptual States
(si=1,...,M)
Perceptual Symbols
(pi=1,...,M)
Figure 1: Perceptual Symbol Systems
[Barsalou, L.W., 1999] Perceptual Symbol Systems. Behavioral and Brain Science, 22.
Part II: (ii) Cognitive Psychology Models
Amodal Symbol Systems (Information-Processing Systems)
Transduction
Perceptual States
(si=1,...,M)
Reference
Arbitrary Amodal
Symbols
(CAR = yi=1,...,N)
(wheels = y1,...,4)
(doors = y5,6)
(colour = y7)
…
New Representational
Structure
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.
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.
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.
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]
Part II: (iv) Knowledge Representation
Membership Functions of Fuzzy Sets [Zadeh, 1965]
R1: If inflation is low THEN cost is cheap
R2: If Inflation is high THEN cost is high
(s)
(s)
1
1
0.
5
0.
5
0
5
inflation = {low, high}
10
high
low
high
low
Inflation (%)
0
100
200 Cost (US$)
cost = {cheap, expensive}
Part II: (v) Computing Perceptions for Decision-Making
•
IF-THEN rules:
•
R1: IF incentives are high AND production is efficient THEN
organisational satisfaction is moderate.
•
R2: IF incentives are low AND production is efficient THEN
organisational satisfaction is moderate.
•
R3: IF incentives are high AND production is poor THEN
organisational satisfaction is moderate.
•
R4: IF incentives are low AND production is poor THEN
organisational satisfaction is bad.
Deriving conclusions from fuzzy rules of inference
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].
References
1.
[Barsalou, L.W., 1999] Perceptual Symbol Systems. Behavioral and Brain Science, 22.
2.
[March, J.G. 1994] A Primer on Decision Making: How Decisions Happen. The Free Press.
3.
[March, J.G. and Simon, H.A. 1958] Organizations. 1st Ed. John Wiley & Sons, Inc.
4.
[March, J.G. and Simon, H.A. 1993] Organizations. 2nd Ed. John Wiley & Sons, Inc.
5.
[Newell, A. and Simon, H.A. 1972] Human Problem Solving. Prentice-Hall.
6.
[Simon, H.A. 1997a] Models of Bounded Rationality: Empirically Grounded Economic Reason. Vol.3.
The MIT Press. (1st Ed. publisged in 1947).
7.
[Simon, H.A. 1997b] Administrative Behavior: A Study of Decision-Making Processes in
Administrative Organizations. The FREE Press.
8.
[Zadeh, L.A. 1962] From Circuit Theory to System Theory. Proceedings of the IRE, 50: 856-865.
9.
[Zadeh, L.A. 1965] Fuzzy Sets. Information and Control, 8: 338-353.
10.
[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.
11.
[Zadeh, L.A. 1994] Soft Computing and Fuzzy Logic. IEEE Software, November: 48-56.
12.
[Zadeh, L.A. 2001] A New Direction in AI: Toward a Computational Theory of Perceptions. AI
Magazine. Spring: 73-84.