Combining fuzzy cognitive maps with agent-based modeling: Frameworks and pitfalls of a powerful hybrid modeling approach to understand human-environment interactions. (September 2017)

• Record Type: Journal Article
• Authors: Giabbanelli, Philippe J.; Gray, Steven A.; Aminpour, Payam
• Is Part Of: Environmental modelling & software. Volume 95(2017)
• Page Start: 320
• Page End: 325
• Publication Date: 2017-09
• Subjects: Agent based model -- Fuzzy cognitive maps -- Hybrid modeling; ABM Agent Based Model -- FCM Fuzzy Cognitive Map; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.06.040
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Agent-based modeling (ABM) is an established technique to capture human-environment interactions in socio-ecological systems. As a micro-model, it explicitly represents each agent, such that heterogeneous decision-making processes (e.g. based on the beliefs and experiences of stakeholders) can anticipate the socio-environmental consequences of aggregated individual behaviors. In contrast to ABM, Fuzzy Cognitive Mapping takes a macro-level view of the world that represents causal connections between concepts rather than individual entities. Researchers have expressed interest in reconciling the two, i.e. taking a hybrid approach and drawing of the strengths of each to more accurately model socio-ecological interactions. The intuition is to take FCMs, which can be quickly developed using participatory modeling tools and use them to create a virtual population of agents with sophisticated decision-making processes. In this paper, we detail two ways in which this combination can be done, and highlight the key questions that modelers need to be mindful of. Highlights: We contrast two ways of combining fuzzy cognitive mapping and agent-based modeling. We highlight key practical aspects to examine when implementing these combinations. We summarize the core functionalities that current software has or needs to develop.