Fuzzy Cognitive Maps

​Dr. Philippe J. Giabbanelli received his B.S. from Université Côte d'Azur (France) and his M.Sc. and Ph.D. from Simon Fraser University (Canada). He worked as a researcher at the University of Cambridge (UK) and as a tenure-track faculty at several nationally ranked American universities, where he developed a variety of courses on predictive modeling and artificial intelligence. He taught fuzzy cognitive maps (FCMs) from the perspective of AI, as an object of study for network science, or as a tool in modeling and simulation. His research focuses on developing and applying AI to support population health interventions. He has published about 130 articles (mostly with his students), covering multiple aspects of FCM research from the elicitation and aggregation of causal maps to their structural validation or their combination with other techniques such as agent-based modeling. Dr. Gonzalo Nápoles received his B.S. and M.Sc. from the Central University of Las Villas (Cuba) and his Ph.D. from Hasselt University (Belgium) and Maastricht University (the Netherlands). Currently, he is a tenured assistant professor at the Department of Cognitive Science and Artificial Intelligence, Tilburg University (the Netherlands). He has taught fuzzy cognitive maps (FCMs) in several courses, including the First Summer School on Fuzzy Cognitive Mapping held in Volos (Greece). His research focuses on developing learning algorithms for FCM models, understanding their mathematical properties, and exploiting their potentialities in pattern classification and time series forecasting settings. He was a recipient of the Cuban Academy of Science Award for his contributions to the FCM field. More recently, his research efforts have shifted toward developing fair machine learning algorithms that can intrinsically be explained (to a large extent) and methods to mitigate implicit and explicit bias.

• 1. Fuzzy Cognitive Maps: Best Practices and Modern Methods.- 2. Creating an FCM with participants in an interview or workshop setting.- 3. Principles of simulations with FCMs.- 4. Hybrid Simulations.- 5. Analysis of Fuzzy Cognitive Maps.- 6. Extensions of Fuzzy Cognitive Maps.- 7. Creating FCM models from quantitative data with evolutionary algorithms.- 8. Advanced learning algorithm to create FCM models from quantitative data.- 9. Introduction to Fuzzy Cognitive Map-based classification.- 10. Addressing accuracy issues of Fuzzy Cognitive Map-based classifiers.- Index.