  Fuzzy Cognitive Maps (FCMs) are computational models that represent how
factors (nodes) change over discrete interactions based on causal impacts
(weighted directed edges) from other factors. This approach has traditionally
been used as an aggregate, similarly to System Dynamics, to depict the
functioning of a system. There has been a growing interest in taking this
aggregate approach at the individual-level, for example by equipping each agent
of an Agent-Based Model with its own FCM to express its behavior. Although
frameworks and studies have already taken this approach, an ongoing limitation
has been the difficulty of creating as many FCMs as there are individuals.
Indeed, current studies have been able to create agents whose traits are
different, but whose decision-making modules are often identical, thus limiting
the behavioral heterogeneity of the simulated population. In this paper, we
address this limitation by using Genetic Algorithms to create one FCM for each
agent, thus providing the means to automatically create a virtual population
with heterogeneous behaviors. Our algorithm builds on prior work from Stach and
colleagues by introducing additional constraints into the process and applying
it over longitudinal, individual-level data. A case study from a real-world
intervention on nutrition confirms that our approach can generate heterogeneous
agents that closely follow the trajectories of their real-world human
counterparts. Future works include technical improvements such as lowering the
computational time of the approach, or case studies in computational
intelligence that use our virtual populations to test new behavior change
interventions.
