Rational Choice Theory: A Forum for
Exchange of Ideas between the Hard and
Social Sciences in Predictive Behavioral
Modeling
Sun-Ki Chai
Dept. of Sociology
University of Hawai‘i

The Social Sciences and Predictive
Behavioral Modeling
• Unprecedented interest among “hard” scientists in study of human behavior
• Remarkably little use of existing social science theory or method, even those that adopt formal, positivist approaches
• Why?
– Technical inadequacy?
– Lack of familiarity?
– Methodological incompatibility?

Agent-Based Computational Modeling and
Rational Choice
• ABCM is dominant approach to predictive behavioral modeling among computer scientists and engineers.
• Rational Choice is by far the dominant theoretical approach to predictive behavioral model in the social sciences.
• Until recently, the literatures developed without much dialogue or cross-citation.
• Social scientists have recently taken greater interest in
ABCM.
• Still seen as alternative approaches rather than complements

Why is Rational Choice Theory Important to
Hard Scientists?
• subject to greater formal development and elaboration than any other approach
• applied to a much wider range of empirical behavioral phenomena
• spatiality, biology, and yes, culture, can be incorporated and indeed enhance rational choice models
• contrary to conventional wisdom, compatible with either analytic or computational solutions
• is both more general (in assumptions) and broader (in types of application) than conventional agent-based modeling

Positive, Formal Rational Choice as One Type of
Agent-Based Modeling?
• both approaches take, often incompatible, multiple forms, however . . .
• like computational agent-based modeling, an individuallevel approach that seeks to predict system-level outcomes through complex processes of aggregation
(emergence as contested term)
• common roots in axiomatic models of behavior (Von
Neumann/Morgenstern)
• game theory a formalization common to both approaches, though tends to be used differently

Qualitative Rational Choice as a Bridge to the
Social Sciences and Humanities
• Rational choice is used qualitatively as well as quantitatively
– comparative historical rational choice
– institutional rational choice
– “folk psychology”
• Is used positive, normative, and interpretively
– prescriptive models of justice and ethics
– rational interpretation of personal narratives

Basic Assumptions of Rational Choice
• Though based on common set of axioms, there is no one
“single” rational choice model. “Thin” version includes:
– logically consistent beliefs that do not violate laws of probability
– “well-behaved” utility – strict order, completeness, asymmetry, and transitivity
– actors choosing in order to maximize utility given beliefs
• “Thick” version adds:
– self-regarding, materialistic (money, power, health), isomorphic utility
– information-based (observation and valid inference) beliefs, common knowledge of rationality

Introducing Culture into “Thin” Rational
Choice
Conventional Rationality-based approach: single model generalizable to multiple, even novel contexts theories can be cumulated into larger whole tends to produce falsifiable predictions (though often anomalous)
Conventional Culture-based approach: sensitive to social differences and personal development deeper and more nuanced depiction of social process avoids predictive anomalies (because it avoids prediction)

Main Steps and Hurdles to Integration
• specifying dimensions of culture in general fashion
• retaining simplicity and analytical tractability
• formalizing in way that is compatible with choicetheoretic models of action across full-range of environments
• modeling cultural change algorithmically
• combining generality and predictive determinacy
Conventional cultural typologies, e.g. (modern vs. traditional,
Hofstede and “comparative capitalisms”) tend to focus on first two points but do not provide general implication for behavior.

• Development of a general, predictive model of cultural change
• Integration with choice-theoretical model of action
• Software implementation into decisionsupport and simulation environments

• Representation of culture through gridgroup framework
• Modeling of cultural change through coherence model
• Implementation in simulation and decisionsupport systems

Grid-Group Framework for Representation of Culture
ABSTRACT DIMENSIONS
Grid = extent to which social rules prescribe and restrict action
Group = extent to which identity is directed towards others
Widely used in Cult./Soc. Anthropology and Political Science: Douglas
1970, 1978; Douglas and Wildavsky 1982; Wildavsky et al. 1990.
Adapted for choice-theoretic models in Chai and Wildavsky 1993;
Chai and Swedlow 1998.

Attributes of Grid-Group Framework
• More abstract than competing frameworks for representing cultural differences
• Operationalization methods straightforward and well-tested
• Works well as frontend to “thin” rational choice models of decision-making
• Fits with abstract dimensions of social organization found in social theories, e.g. regulation and integration
• Decomposes into four major cultural types
• individualist – low grid / low group
• fatalist – high grid / low group
• hierarchical – high grid / high group
• egalitarian – low grid / low group

Grid-Group Transformations within Defined
Group Boundaries
Groupness-transformed payoff: y i
= (
 j<>i g i x j
) + x i
Gridness-transformed payoff: u i
= y i
(ord(a i
= o i
) + (1 –h i
) ord(a i
<> o i
)) where g i i, a i and h i are group and grid coefficients for individual is her action, x j is untransformed payoff, and o i her specified operation under standard procedures .

Concepts and Assumptions of
Coherence Model
Expected Regret: subjective probability-weighted difference between maximal utility possible in a particular state of the environment and the utility provided by a chosen set of actions
Coherence: expected regret of zero
PREFERENCE AND BELIEF ASSUMPTIONS OF MODEL
• Meta-optimization
• Environment constrains Beliefs
• No “Yogic Utility”
Parametric form, but not parametric values, determined by exposure to social communication
Forms considered in order of message prevalence of communications describing such forms, but parameter weightings can be accepted or rejected.
c.f. Chai 2001.

Intuitions behind Model actors are engaged in a collective process of constructing their own identities this process is aimed at creating an individual and collective sense of self that is both positive and consistent preferences and beliefs are not mere precursors to action, but there is a mutually causative relationship between these entities

Coherence (preference-based): adjustment of g, h to minimize d
Expected Regret (single-period, individual form):
d =
 s
(u(s,a*(s)) – u(s,a))) p(s) ds where
a*(s)=argmax a

A
u(s,a) a=argmax a

A, s

S
 s
u(s,a) p(s) ds
s states of the environment, a actions, u utility function, and p subjective probabilities

Some non-intuitive implications of coherence model. . .
Means will become ends (functional autonomy of motives) iff there exists there exists perception of some state of environment where alternative actions superior
Sour grapes / forbidden fruit effect caused by actions that are perceived to preserve / alter the status quo more than alternatives
Wishful / unwishful thinking strongest when an individual adopts actions that are subject to more / less variation in comparison to alternatives
Effects depend on and magnify in proportion to subjective probability and extent to which chosen action will be suboptimal

Some implications linking structure to culture
Mutual altruism will be generated in groups engaging in repeated collective action, particularly where public goods are generated more reliably than private goods
Materialistic culture will be generated by clearly defined structures of mobility in which the relative returns to vocational choices is not circumstance-dependent
Explicit ideologies will be adopted by groups whose members face incoherence with regards to a similar set of action choices.

“Cultural” implications built into conventional economic models
Risk seeking or aversion: x = f(w); f’’(w) > 0 f’’(w) < 0 implies risk aversion; f”(w) > 0 implies risk seeking
“Subjective” material payoffs are not a linear function of the quantity of goods
Time discounting: U = ∑
T
δ t u
Cumulative utility is a function of period-specific utilities multiplied by a
“discount factor” representing devaluation of deferred utility