Why Bother with What Others Tell You? An Experimental Data-Driven Agent-based Model

Riccardo Boero†, Giangiacomo Bravo‡, Marco Castellani° and Flaminio Squazzoni°

• Corresponding author: Riccardo Boero, riccardo.boero@unito.it

† Department of Economic and Financial Sciences, University of Torino, Italy ‡ Department of Social Sciences, University of Torino, Italy ° Department of Social Studies, University of Brescia, Italy

TITLE: Why Bother with What Others Tell You? an Experimental Data-Driven Agent-Based Model*

ABSTRACT: In real life, agents do not decide what to do simply by relying on their computational capabilities as they often communicate with qualified people and are influenced by information received by third parties. This happens, for instance, when private estate owners look for competent builders to restore property or when entrepreneurs try to sort out trustworthy partners to sub-contract critical production. This is a well-known situation in markets characterized by uncertainty, information asymmetries and ambiguity. In such situations, the quality of goods exchanged is difficult to evaluate and guarantee, moral hazard among the parties involved in a transaction can take place and market failures can easily ensue (e.g., Akerlof 1970; Williamson 1979). Along these lines, this paper investigates how reputation and social information can affect, at the micro level, the performance of economic agents in uncertain environments and, at the macro level, the exploration capability of the system as a whole. In particular, we focus on agents' capability of detecting reliable information sources while dealing with risky investment and exploration processes. We therefore compare social systems where agents are atomized entities that rely solely on their individual experience and systems where agents can rely on reputation mechanisms. The method suggested in this paper is a combination of lab experiments and agent-based models. This combination has mutual advantages. The lab can offer sound, clean and informative data on agents' behavior which are pivotal for evidence-based models. On the other hand, agent-based modeling complements the lab since it allows to explore complex interaction structures and long time evolution which is impossible to investigate in the lab. Agent-based models can both allow for macro-level implication analyses of experimental evidence and provide new interpretations for the lab. In our view, there is a mutual cross-fertilization that is good for social simulation (e.g., Boero and Squazzoni 2005; Janssen and Ostrom 2006). Therefore, our first step was to design a lab experiment with robust data on human behavior in a controlled decision setting (Boero and Squazzoni 2005). To capture the widest possible range of individual decisions in uncertain environments, we created an ad hoc experiment similar to an external observation of controlled human decisions. Unlike game theory experiments, where experiments are designed to closely follow a standard theory which predicts how agents behave, our experiment was explicitly designed to trace the highest heterogeneity and variety of participant behavior. As a consequence, it did not try to understand the impact of treatment of a particular variable, nor to analyze the systemic consequence of the treatment itself, but rather to identify bottom-up human behavior embedded in an uncertain environment. The second step was to analyze behavioral patterns in the laboratory. The third step was to design an agent-based model to validate our experimental patterns, by re-running the experiment and replicating the behavioral patterns. The assumption was that, if we were able to replicate the experimental results exactly, this would mean that the abstraction imposed on the experimental data had no relevant information loss. The following step was to design an experimental evidence-based model to introduce interaction among agents, pass from a micro to macro level, and explore the impact of certain relevant simulation parameters on the macro outcomes.

Back to Swarmfest2010_program