225
Chapter XVII
Agent-Based Modeling with
Boundedly Rational Agents
Eva Ebenhöh
University of Osnabrück, Germany
Claudia Pahl-Wostl
University of Osnabrück, Germany
ABSTRACT
This chapter introduces an agent-based modeling framework for reproducing micro behavior
in economic experiments. It gives an overview of the theoretical concept which forms the
foundation of the framework as well as short descriptions of two exemplary models based on
experimental data. The heterogeneous agents are endowed with a number of attributes like
cooperativeness and employ more or less complex heuristics during their decision-making
processes. The attributes help to distinguish between agents, and the heuristics distinguish
between behavioral classes. Through this design, agents can be modeled to behave like real
humans and their decision making is observable and traceable, features that are important
when agent-based models are to be used in collaborative planning or participatory modelbuilding processes.
INTRODUCTION
Modeling human behavior is challenging. Modelers of agent-based models face a choice and
a trade-off: how simple and traceable or realistic and psychologically plausible should agent
behavior be modeled. Theory does not provide
much guidance in this respect since numerous
and sometimes contradictory theories on human behavior exist. We chose to model agents
as boundedly rational. We base their behavior
on observation, both from experimental economics and field studies.
The agents in our models are characterized
by a set of attributes, which have been derived
from experimental data and theoretical ap-
Copyright © 2007, Idea Group Inc., distributing in print or electronic forms without written permission of IGI is prohibited.
Agent-Based Modeling with Boundedly Rational Agents
proaches. They have expectations about their
environment and the other agents. In this way,
agent diversity is introduced. They exhibit
boundedly rational behavior through aspiration
levels and the use of simple heuristics.
This chapter introduces our modeling approach and the tool developed for our purposes
which provides an environment for creating
agent-based models with boundedly rational
decision making. The focus lies on developing
models based on economic experiments, but the
tool is expandable to include field studies as
well. Experimental economics provides us with
a rich database of human behavior in simple,
well-defined settings. The experimental results
can be compared and reproduced. This allows
extracting behavioral regularities from the data
and using these to define micro behavior in the
model. This is important because different micro behavior can lead to similar aggregate
results. This chapter includes two exemplary
models of economic experiments.
The software tool provides the agents with
attributes and a set of heuristics that can be
expanded to include new heuristics that fit
better to problems which have not yet been
modeled. The modeler can set parameters, like
which kind of agents use which kind of heuristics. He or she can include new heuristics and
learning processes, as well as new decision
environments. With this tool it is possible to
compare different micro behavior as well as
reactions on different model framings. We
expect this modeling approach to make it possible for stakeholders to identify with agent
behavior and thus facilitate group model building and collaborative planning.
The remainder of this chapter is organized
as follows. The section “Bounded Rationality”
is a brief introduction to this theory of human
behavior and its advantages for our purposes.
In the section “Attributes and Heuristics,” our
modeling approach is described. In the section
226
“Exemplary Models,” two different models are
described that reproduce data from economic
experiments. These are the voluntary contribution mechanism with and without punishment,
and appropriation games with and without communication. Some concluding remarks are made
about the relevance of the modeling approach
presented in this chapter. The appendix provides some technical details for modelers who
want to use the tool.
BOUNDED RATIONALITY
The decision environments considered here are
explicitly those in which the classical economic
model of decision making fails to make correct
predictions of actual human behavior. With
classical model we refer to subjectively expected utility maximization of perfectly rational
actors. The situations we are interested in
include social dilemmas in which individual
rationality differs from group rationality, and
gift exchange situations in which gift giving
seems to follow norms of reciprocity or fairness. At least in these situations the classical
economic model of perfect rationality has to be
enriched or complemented by theories that
explain instances of cooperation and reciprocity.
Bounded rationality was introduced as an
alternative to perfect rationality as the principle
of human decision making (Selten, 1990). Instead of grounding a behavioral theory in optimization processes with a number of rather
unrealistic assumptions on the capabilities and
preferences of humans, bounded rationality is
psychologically plausible (Gigerenzer & Todd,
1999; Gigerenzer & Selten, 2001).
The main idea is that learned heuristics are
the basis of human behavior instead of permanent optimization. Heuristics are simple behavioral patterns that are triggered by the decision
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