Abstract
Fuzzy Cognitive Maps (FCMs) are computational models that represent how factors (nodes) change over discrete steps 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.
This project is supported by the Department of Computer Science & Software Engineering at Miami University.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amirkhani, A., Papageorgiou, E.I., Mohseni, A., Mosavi, M.R.: A review of fuzzy cognitive maps in medicine: taxonomy, methods, and applications. Comput. Methods Programs Biomed. 142, 129–145 (2017)
Bakhtavar, E., Valipour, M., Yousefi, S., Sadiq, R., Hewage, K.: Fuzzy cognitive maps in systems risk analysis: a comprehensive review. Complex Intell. Syst. 7(2), 621–637 (2020). https://doi.org/10.1007/s40747-020-00228-2
Davis, C.W., Giabbanelli, P.J., Jetter, A.J.: The intersection of agent based models and fuzzy cognitive maps: a review of an emerging hybrid modeling practice. In: 2019 Winter Simulation Conference (WSC), pp. 1292–1303. IEEE (2019)
Epstein, L.H., Myers, M.D., Raynor, H.A., Saelens, B.E.: Treatment of pediatric obesity. Pediatrics 101(Supplement_2), 554–570 (1998)
Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K., Bello, R.: A review on methods and software for fuzzy cognitive maps. Artif. Intell. Rev. 52(3), 1707–1737 (2017). https://doi.org/10.1007/s10462-017-9575-1
Firmansyah, H.S., Supangkat, S.H., Arman, A.A., Giabbanelli, P.J.: Identifying the components and interrelationships of smart cities in Indonesia: supporting policymaking via fuzzy cognitive systems. IEEE Access 7, 46136–46151 (2019)
Giabbanelli, P., Fattoruso, M., Norman, M.L.: CoFluences: simulating the spread of social influences via a hybrid agent-based/fuzzy cognitive maps architecture. In: Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pp. 71–82 (2019)
Giabbanelli, P.J.: Solving challenges at the interface of simulation and big data using machine learning. In: 2019 Winter Simulation Conference (WSC), pp. 572–583. IEEE (2019)
Giabbanelli, P.J., Crutzen, R.: Creating groups with similar expected behavioural response in randomized controlled trials: a fuzzy cognitive map approach. BMC Med. Res. Methodol. 14(1), 1–19 (2014)
Giabbanelli, P.J., Jackson, P.J., Finegood, D.T.: Modelling the joint effect of social determinants and peers on obesity among canadian adults. In: Theories and Simulations of Complex Social Systems, pp. 145–160. Springer (2014). https://doi.org/10.1007/978-3-642-39149-1_10
Giabbanelli, P.J., Torsney-Weir, T., Mago, V.K.: A fuzzy cognitive map of the psychosocial determinants of obesity. Appl. Soft Comput. 12(12), 3711–3724 (2012)
Giles, B.G., Findlay, C.S., Haas, G., LaFrance, B., Laughing, W., Pembleton, S.: Integrating conventional science and aboriginal perspectives on diabetes using fuzzy cognitive maps. Soc. Sci. Med. 64(3), 562–576 (2007)
Gray, S., Hilsberg, J., McFall, A., Arlinghaus, R.: The structure and function of angler mental models about fish population ecology: the influence of specialization and target species. J. Outdoor Recreat. Tour. 12, 1–13 (2015)
Groumpos, P.P.: Intelligence and fuzzy cognitive maps: scientific issues, challenges and opportunities. Stud. Inform. Control 27(3), 247–264 (2018)
Lavin, E.A., Giabbanelli, P.J., Stefanik, A.T., Gray, S.A., Arlinghaus, R.: Should we simulate mental models to assess whether they agree? In: Proceedings of the Annual Simulation Symposium, pp. 1–12 (2018)
Mkhitaryan, S., Giabbanelli, P.J., de Vries, N.K., Crutzen, R.: Dealing with complexity: how to use a hybrid approach to incorporate complexity in health behavior interventions. Intell.-Based Med. 3, 100008 (2020)
Mkhitaryan, S., Giabbanelli, P.J., Wozniak, M.K., Napoles, G., de Vries, N.K., Crutzen, R.: FCMpy: a python module for constructing and analyzing fuzzy cognitive maps (2021)
Mourhir, A.: Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management. Environ. Modell. Softw. 135, 104891 (2021)
Papageorgiou, E.I.: Learning algorithms for fuzzy cognitive maps-a review study. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(2), 150–163 (2011)
Pearson, E.S., D ”’AGOSTINO, R.B., Bowman, K.O.: Tests for departure from normality: comparison of powers. Biometrika 64(2), 231–246 (1977)
Pedrycz, W.: Why triangular membership functions? Fuzzy Sets Syst. 64(1), 21–30 (1994)
Poczeta, K., Yastrebov, A., Papageorgiou, E.I.: Learning fuzzy cognitive maps using structure optimization genetic algorithm. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 547–554. IEEE (2015)
Resnicow, K., Vaughan, R.: A chaotic view of behavior change: a quantum leap for health promotion. Int. J. Behav. Nutr. Phys. Act. 3(1), 1–7 (2006)
Springvloet, L., et al.: Short-and medium-term efficacy of a web-based computer-tailored nutrition education intervention for adults including cognitive and environmental feedback: randomized controlled trial. J. Med. Internet Res. 17(1), e3837 (2015)
Stach, W., Kurgan, L., Pedrycz, W.: A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets Syst. 161(19), 2515–2532 (2010)
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)
Stach, W., Pedrycz, W., Kurgan, L.A.: Learning of fuzzy cognitive maps using density estimate. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(3), 900–912 (2012)
Wang, C., Liu, J., Wu, K., Ying, C.: Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm. Appl. Soft Comput. 108, 107441 (2021)
Acknowledgements
We thank Dr Jens Mueller for his assistance with the RedHawk high-performance computing cluster at Miami University. We also benefited from the feedback of Drs Rik Crutzen, Nanne K. de Vries, and Anke Oenema.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wozniak, M.K., Mkhitaryan, S., Giabbanelli, P.J. (2022). Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-08757-8_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08756-1
Online ISBN: 978-3-031-08757-8
eBook Packages: Computer ScienceComputer Science (R0)