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Threshold model with anticonformity under random sequential updating
Bartłomiej Nowak1, Michel Grabisch2, Katarzyna Sznajd-Weron1
1Department of Theoretical Physics, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.
Physical Review. E
|June 16, 2022
Summary
This study examines binary decision-making with anticonformity, revealing that social hysteresis and critical mass effects in innovation diffusion emerge only with specific heterogeneous threshold distributions.
Area of Science:
- Social dynamics and network science
- Computational social science
- Agent-based modeling
Background:
- The threshold model explains binary decision-making based on social influence.
- Anticonformity introduces a counter-intuitive social behavior.
- Asynchronous updates mimic continuous-time dynamics in social systems.
Purpose of the Study:
- To analyze an asymmetric threshold model with anticonformity under asynchronous updates.
- To compare mean-field, Monte Carlo, and Markov chain approaches for model analysis.
- To investigate homogeneous and heterogeneous agent threshold distributions.
Main Methods:
- Mean-field approximation for large systems.
- Monte Carlo simulations for empirical testing.
- Markov chain analysis for exact, small-system results.
Main Results:
- All three methods converge for large systems.
- Heterogeneous thresholds, specifically beta distributions, are crucial.
- Specific alpha and beta values reproduce real-world social hysteresis and critical mass phenomena.
Conclusions:
- The model accurately captures complex social behaviors like innovation diffusion.
- Heterogeneous agent thresholds are key to observing phenomena like social hysteresis.
- The Markov chain approach provides exact analytical results, complementing large-system approximations.
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