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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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A Cognitive Computational Approach to Social and Collective Decision-Making
Alan N Tump1,2, Dominik Deffner1,2, Timothy J Pleskac3
1Center for Adaptive Rationality, Max Planck Institute for Human Development.
Summary
Understanding social influence requires examining cognitive mechanisms. This study links individual cognition and group dynamics using computational models, revealing feedback loops between personal decision-making and social behavior.
Area of Science:
- Cognitive Science
- Social Psychology
- Computational Neuroscience
Background:
- Collective dynamics are crucial for decision-making, but their outcomes (adaptive vs. maladaptive) depend on underlying cognitive mechanisms.
- Social influence can lead to accurate information spread or maladaptive social contagion.
- Linking individual cognition to collective behavior is a key challenge in understanding social dynamics.
Purpose of the Study:
- To mechanistically link individual cognitive processes with emergent collective dynamics.
- To demonstrate the utility of a cognitive computational approach for studying social influence.
- To investigate the bidirectional relationship between individual cognition and social systems.
Main Methods:
- Utilizing cognitive modeling in conjunction with interactive group experiments.
- Applying evidence-accumulation and reinforcement-learning models to analyze collective behavior.
- Simultaneously examining how individual cognition influences social systems and vice versa.
Main Results:
- Demonstrated that cognitive models can bridge individual and collective levels of analysis.
- Showcased the power of computational approaches in understanding social decision-making.
- Provided a framework for studying the dynamic feedback between individual and social cognition.
Conclusions:
- Cognitive modeling offers a powerful approach to mechanistically understand collective dynamics.
- The interplay between individual cognition and social systems is bidirectional and dynamic.
- This framework facilitates the study of adaptive and maladaptive social behaviors.
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