Simulating Macro-Level Effects from Micro-Level Observations

We consider the fruits of integrating agent-based modeling (ABM) with lab-based experimental research with human subjects. While both ABM and lab experiments have similar aims — to identify the rules, tendencies, and heuristics by which individual agents make decisions and respond to external stimuli — they work toward their common goal in notably different ways. Behavioral-lab research typically exposes human subjects to experimental manipulations, or treatments, to make causal inferences by observing variation in response to the treatment. ABM researchers ascribe individual simulated "agents" with decision rules describing their behavior and subsequently attempt to replicate "macro" level empirical patterns. Integrating ABM and lab experiments presents advantages for both sets of researchers. ABM researchers will benefit from exposure to a larger set of empirically-validated mechanisms that can add nuance and refinement to their models of human behavior and system dynamics. Lab-oriented researchers will gain from ABM a method for assessing the validity and magnitude of their findings, adjudicating between competing mechanisms, developing new theory to test in the lab, and exploring macro-level, long-run implications of subtle, micro-level observations that can be difficult to observe in the field. We offer an example of this mixed-method approach related to status, social networks, and job search and issue guidance for future research attempting such integration