Cited By
View all- Mkhitaryan SGiabbanelli P(2021)How modeling methods for fuzzy cognitive mapping can benefit from psychology researchProceedings of the Winter Simulation Conference10.5555/3522802.3522971(1-12)Online publication date: 13-Dec-2021
Fuzzy cognitive maps in agent based models: a practicial implementation example
Article No.: 36, Pages 1 - 11
Abstract
Agent Based Social Simulation (ABSS) has been used to create simulations with agents that contain cognitive models to achieve more realistic and explainable results by mimicking human cognition. However when using this technique the decision model used by agents is often a black box. Fuzzy Cognitive Maps (FCMs) have been used to provide clear and transparent representations of cognition and could be used as models for agents in simulations to achieve results with better transparency. Utilizing FCMs to represent agents' cognitions in social and organizational modeling is still an emerging field with limited work done to show how FCM and ABM can be combined for greater model interpretability. This paper provides 2 major contributions: (1) the use of FCM for agent's design in social simulation as compared to previous research using different cognitive models, and (2) a system architecture consisting of 8 components that combine FCM and ABM for simulation.
References
[1]
Antonie J. Jetter, Kasper Kok. 2014. "Fuzzy Cognitive Maps for futures studies---A methodological assessment of concepts and methods." Futures 61, 45--57.
[2]
Axelrod, Robert. 1997. "The dissemination of culture: A model with local convergence and global polarization." Journal of conflict resolution 41.2: 203--226.
[3]
Balaban, M. A. 2014. "Toward a theory of multi-method modelling." Proceedings of the Winter Simulation Conference. Savanah, GA.
[4]
Beinhocker, Eric D. 2006. The Origin of Wealth. McKinsey and Company.
[5]
Birgit Müller, Friedrich Bohn, Gunnar Dreßler, Jürgen Groeneveld, Christian Klassert, Romina Martin, Maja Schlüter, Jule Schulze, Hanna Weise, Nina Schwarz. 2013. "Describing human decisions in agent-based models e ODD + D, an extension of the ODD protocol." Environmental Modelling & Software 48: 37--48.
[6]
Brian G. Giles, C. Scott Findlay, George Haas, Brenda LaFrance, Wesley Laughing, Sakakohe Pembleton. 2007. "Integrating conventional science and aboriginal perspectives on diabetes using fuzzy cognitive maps." Social Science & Medicine 3: 562--576.
[7]
Carley, Kathleen M. 2009. "Computational modeling for reasoning about the social behavior of humans." Computational and Mathematical Organization Theory 1: 47--59.
[8]
Chad Vicknair, Michael Macias, Zhendong Zhao, Xiaofei Nan, Yixin Chen, Dawn Wilkins. 2010. "A comparison of a graph database and a relational database: a data provenance perspective." Proceedings of the 48th annual Southeast regional conference. Oxford, Mississippi, USA. 1--6.
[9]
Christopher W. H. Davis, Philippe J. Giabbanelli, Antonie J. Jetter. 2019. "The Intersection of Agent Based Models and Fuzzy Cognitive Maps: A Review of an Emerging Hybrid Modeling Practice." 2019 Winter Simulation Conference (WSC). National Harbor, MD: IEEE. 1292--1303.
[10]
David M. Eddy, William Hollingworth, J. Jaime Caro, Joel Tsevat, Kathryn M. McDonald, John B. Wong. 2012. "Model Transparency and Validation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7." Medical Decision Making 5: 733--743.
[11]
Davide Secchi, Nicole L. Gullekson. 2016. "Individual and organizational conditions for the emergence and evolution of bandwagons." Computational and Mathematical Organization Theory 1: 88--133.
[12]
Davidsson, Paul. 2002. "Agent based social simulation: A computer science view." Journal of artificial societies and social simulation 1: 1.
[13]
Denise M. Case, Chrysostomos D. Stylios. 2016. "Fuzzy Cognitive Map to model project management problems." 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS). El Paso, USA. 1--6.
[14]
Elmar Kiesling, Markus Günther, Christian Stummer, Lea M. Wakolbinger. 2012. "Agent-based simulation of innovation diffusion: a review." Central European Journal of Operations Research 20 (2): 183--230.
[15]
Elpiniki I. Papageorgiou, Jose L. Salmeron. 2013. "A review of fuzzy cognitive maps research during the last decade." IEEE Transactions on Fuzzy Systems 21 (1): 66--79.
[16]
Elpiniki I. Papageorgiou, Miklós F. Hatwágner, Adrienn Buruzs, László T. Kóczy. 2017. "A concept reduction approach for fuzzy cognitive map models in decision making and management." Neurocomputing 232: 16--33.
[17]
Emilio Sulis, Antonio Di Leva. 2017. "An agent-based model of a business process: The use case of a hospital emergency department." International Conference On Business Process Management 124--132.
[18]
Epstein, Joshua M. 1999. "Agent-based computational models and generative social science." Complexity 4 (5): 41--60.
[19]
Eric A. Lavin, Philippe J. Giabbanelli, Andrew T. Stefanik, Steven A. Gray, Robert Arlinghaus. 2018. "Should we simulate mental models to assess whether they agree?" Proceedings of the Annual Simulation Symposium. Baltimore, Maryland: ACM. 6.
[20]
Fioretti, Guido. 2013. "Agent-based simulation models in organization science." Organizational Research Methods 16 (2): 227--242.
[21]
Gerardo Felix, Gonzalo Nápoles, Rafael Falcon, Wojciech Froelich, Koen Vanhoof. 2017. "A review on methods and software for fuzzy cognitive maps." Artificial intelligence review (Artificial intelligence review) 52 (3): 1707--1737.
[22]
Glykas, Michael. 2010. Fuzzy cognitive maps: Advances in theory, methodologies, tools and applications. Springer Science & Business Media.
[23]
Goldspink, Chris. 2002. "Methodological implications of complex systems approaches to sociality: Simulation as a foundation for knowledge." Journal of Artificial Societies and Social Simulation 5 (1): 1--19.
[24]
Heng-Li Yang, Ted CT Wu. 2008. "Knowledge sharing in an organization." Technological Forecasting and Social Change 75 (1): 1128--1156.
[25]
Hidetomo Ichihashi, Katsuhiro Honda, Fumiaki Matsuura. 2006. "ROC analysis of FCM classifier with Cauchy weight." SCIS & ISIS SCIS & ISIS 2006. Japan Society for Fuzzy Theory and Intelligent Informatics 1912--1917.
[26]
Hummon, Norman P. 2000. "Utility and dynamic social networks." Social Networks 22 (3): 221--249.
[27]
J. Doyne Farmer, Duncan Foley. 2009. "The economy needs agent-based modelling." Nature 460 (7256): 685.
[28]
Jetter, Antonie J. 2006. "Fuzzy cognitive maps for engineering and technology management: What works in practice?" Technology Management for the Global Future 2: 498,512, 8--13.
[29]
Kathleen M. Carley, David M. Svoboda. 1996. "Modeling organizational adaptation as a simulated annealing process." Sociological methods & research 25 (1): 138--168.
[30]
Kathleen M. Carley, Michael J. Prietula, Zhiang Lin. 1998. "Design versus cognition: The interaction of agent cognition and organizational design on organizational performance." Journal of Artificial Societies and Social Simulation 1 (3): 1--19.
[31]
Kent D Miller, Brian T. Pentland, Seungho Choi. 2012. "Dynamics of performing and remembering organizational routines." Journal of Management Studies 49 (8): 1536--1558.
[32]
Kent D. Miller, Shu-Jou Lin. 2010. "Different truths in different worlds." Organization Science 21 (1): 97--114.
[33]
Kosko, Bart. 1986. "Fuzzy cognitive maps." International journal of man-machine studies (International journal of man-machine studies) 24(1), 65--75.
[34]
Lavin, Eric A., and Philippe J. Giabbanelli. 2017. "Analyzing and simplifying model uncertainty in fuzzy cognitive maps." Winter Simulation Conference (WSC). IEEE.
[35]
Muhammad Amer, Antonie Jetter, Tugrul Daim. 2011. "Development of fuzzy cognitive map (FCM)-based scenarios for wind energy." International Journal of Energy Sector Management 564--584.
[36]
Nicolaj Siggelkow, Jan W. Rivkin. 2005. "Speed and search: Designing organizations for turbulence and complexity." Organization Science 16 (2): 101--122.
[37]
Nigel Gilbert, Klaus Troitzsch. 2005. Simulation for the social scientist. UK: McGraw-Hill Education.
[38]
Noshad Rahimi, Antonie J. Jetter, Charles M. Weber, Katherine Wild. 2018. "Soft Data analytics with fuzzy cognitive maps: modeling health technology adoption by elderly women." Advanced Data Analytics in Health 59--74.
[39]
Papageorgiou, Elpiniki I. 2013. Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms. Springer Science & Business Media.
[40]
Papageorgiou, Elpiniki I. 2011. "Learning algorithms for fuzzy cognitive maps---a review study." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42 (2): 150--163.
[41]
Philippe J. Giabbanelli, Steven A. Gray, Payam Aminpour. 2017. "Combining fuzzy cognitive maps with agent-based modeling: Frameworks and pitfalls of a powerful hybrid modeling approach to understand human-environment interactions." Environmental modelling & software 95: 320--325.
[42]
Sara Mehryar, Richard Sliuzas, Nina Schwarz, Ali Sharifi. 2019. "From individual fuzzy cognitive maps to agent based models: modelling multi-factorial and multi-stakeholder decision making for water scarcity." Journal of environmental management 250: 109482.
[43]
Sun, Ron. 2006. Cognition and multi-agent interaction: From cognitive modeling to social simulation. Cambridge: Cambridge University Press.
[44]
Sun, Ron. 2007. "Cognitive Social Simulation Incorporating Cognitive Architectures." IEEE Intelligent Systems 22 (5): 33--39.
[45]
Wojciech Stach, Lukasz Kurgan, Witold Pedrycz, Marek Reformat. 2005. "Genetic learning of fuzzy cognitive maps." Fuzzy sets and systems 153 (3): 371--401.
[46]
Ye, Peijun, Shuai Wang, and Fei-Yue Wang. 2017. "A general cognitive architecture for agent-based modeling in artificial societies." IEEE Transactions on Computational Social Systems 5 (1): 176--185.
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Published In
May 2020
791 pages
ISBN:9781713812883
- Conference Chair:
- Alberto A. Del Barrio,
- General Chairs:
- Fernando J. Barros,
- Xiaolin Hu,
- Program Chair:
- Hamdi Kavak
Publisher
Society for Computer Simulation International
San Diego, CA, United States
Publication History
Published: 19 May 2020
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View all- Mkhitaryan SGiabbanelli P(2021)How modeling methods for fuzzy cognitive mapping can benefit from psychology researchProceedings of the Winter Simulation Conference10.5555/3522802.3522971(1-12)Online publication date: 13-Dec-2021
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