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Agent-Based Modeling


This paper explores the concept of multiplicity in social physics and its implications for agent-based modeling (ABM). We delve into how multiplicity theory enhances our understanding of complex social systems and informs the design and implementation of agent-based models1. Through case studies and theoretical analyses, we highlight the role of multiplicity in capturing the diversity, interconnectivity, and emergent properties of social phenomena2, offering new avenues for advancing ABM research3.


ABM has emerged as a powerful tool for studying complex social systems1. In this paper, we explore how the concept of multiplicity in social physics enriches ABM research, providing insights into the dynamics, structure, and emergent properties of social systems2. By integrating multiplicity theory into ABM frameworks, researchers can develop more realistic and nuanced models that capture the complexity of real-world phenomena3.

Multiplicity Theory and Agent-Based Modeling:

Multiplicity theory offers a theoretical framework for understanding the diversity and interconnectedness of social interactions within complex systems1. In ABM, multiplicity theory informs the design of agent-based models by guiding the selection of variables, parameters, and interaction rules that accurately reflect the complexity of social systems2.

Implications for Modeling Diversity and Heterogeneity:

Multiplicity theory emphasizes modeling diversity and heterogeneity within social systems1. By representing agents with varied attributes, behaviors, and decision-making processes, ABM can capture the rich tapestry of human interactions and dynamics2. Multiplicity theory enables researchers to incorporate diverse perspectives, preferences, and identities into agent-based models, leading to more realistic simulations of social phenomena3.

Addressing Emergence and Self-Organization:

Multiplicity theory sheds light on the mechanisms that drive emergence and self-organization in complex social systems1. In ABM, multiplicity theory informs the modeling of emergent properties and collective behaviors by capturing the interactions between individual agents and their environment2. By simulating the dynamics of emergence and self-organization, researchers can gain a deeper understanding of how social systems evolve over time3.

Case Studies and Applications:

We present several case studies and applications where multiplicity theory has been applied to model complex social phenomena1. These include simulations of crowd dynamics, urban planning, opinion dynamics, and social network formation2. Through these examples, we demonstrate how multiplicity theory enriches ABM research by providing a more nuanced and comprehensive understanding of social systems3.


In conclusion, multiplicity theory offers valuable insights for enhancing agent-based modeling in social physics1. By incorporating multiplicity concepts into ABM frameworks, researchers can develop more realistic, nuanced, and insightful models of complex social phenomena2. As we continue to explore the implications of multiplicity in ABM, we unlock new opportunities for advancing our understanding of social systems and informing evidence-based decision-making in various domains3.

  1. Borgonovo, E., Pangallo, M., Rivkin, J., Rizzo, L., & Siggelkow, N. (2022). Sensitivity analysis of agent-based models: a new protocol. Computational and Mathematical Organization Theory, 28, 52–94.
  2. Shaheen, J. A. E., Henley, C., McKenna, L., Hoang, S., & Abdulwahab, F. (2022). Comparative Agent-Based Simulations on Levels of Multiplicity Using a Network Regression: A Mobile Dating Use-Case. Applied Sciences, 12(4), 1982.
  3. Unknown Author. (2021). Agent-based modeling: Population limits and large timescales. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(3).
  4. Epstein, J. M., & Axtell, R. (1996). “Growing Artificial Societies: Social Science from the Bottom Up.” MIT Press.
  5. Macy, M. W., & Willer, R. (2002). “From Factors to Actors: Computational Sociology and Agent-Based Modeling.” Annual Review of Sociology, 28, 143-166.
  6. Gilbert, N., & Troitzsch, K. G. (2005). “Simulation for the Social Scientist.” Open University Press.
  7. Helbing, D. (2012). “Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior.” Springer Science & Business Media.

Learn more

  1. link.springer.com
  2. mdpi-res.com
  3. pubs.aip.org
  4. academic.oup.com
  5. doi.org
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