Simulating Macro-Level Effects from Micro-Level Observations
Authored by William Rand, Edward Bishop Smith
Date Published: 2018
DOI: 10.1287/mnsc.2017.2877
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Abstract
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. Integration of 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.
Tags
Decision Making
Social networks
Agent-based modeling
Dynamics
Segregation
Theory
activation
networks
Social norms
Rational choice
Design of Experiments
Experiments
Psychology
Income inequality
Applications
Research
methods
Field experiment
Weak
ties