Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data

• URL: http://arxiv.org/abs/2202.07065v1
• Date: Mon, 14 Feb 2022 22:11:58 GMT
• Title: Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data
• Authors: Maciej K Wozniak, Samvel Mkhitaryan, Philippe j. Giabbanelli
• Abstract summary: Fuzzy Cognitive Maps (FCMs) are computational models that represent how factors (nodes) change over discrete interactions. In this paper, we use Genetic Algorithms to create one FCM for each agent, thus providing the means to automatically create a virtual population with heterogeneous behaviors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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