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Updated: Jul 14, 2025
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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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Toward Understanding of the Social Hysteresis: Insights From Agent-Based Modeling
Katarzyna Sznajd-Weron1, Arkadiusz Jȩdrzejewski2, Barbara Kamińska1
1Department of Management Systems and Organization Development, Wrocław University of Science and Technology.
Summary
Agent-based models (ABMs) offer new insights into hysteresis, a phenomenon seen in social systems. These models reveal how collective memory and external factors drive hysteresis in social behaviors.
Area of Science:
- Social Sciences
- Computational Social Science
- Physics
Background:
- Hysteresis explains social phenomena like political polarization and vaccination compliance.
- Understanding collective memory is key to social dynamics.
Purpose of the Study:
- To demonstrate the utility of agent-based models (ABMs) for studying hysteresis in social systems.
- To provide insights into the mechanisms driving hysteresis through computational modeling.
Main Methods:
- Illustrative example from physics to explain hysteresis and collective memory.
- Presentation of hysteresis in psychological and social contexts.
- Application of two agent-based models (Ising and q-voter models) for binary decisions.
Main Results:
- Hysteresis in ABMs arises from external factors (e.g., policies, media) and internal noise.
- Demonstration of hysteresis in simple binary decision models.
- Clarification of relationships between hysteresis, order-disorder transitions, and bifurcation.
Conclusions:
- Agent-based models provide valuable tools for understanding complex social phenomena like hysteresis.
- ABMs offer a framework to analyze the interplay of individual behavior and system-level memory.
- The study highlights the advantages of ABMs in social science research.
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