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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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Collective motion of predictive swarms.
Nathaniel Rupprecht1, Dervis Can Vural1
1Department of Physics, University of Notre Dame, South Bend, Indiana, United States of America.
Plos One
|October 25, 2017
Summary
Intelligent agents predicting resource availability can gather more, but overestimating the future leads to less consumption. Predictivity benefits agents only when they don't predict too far ahead or when their numbers are small.
Area of Science:
- Theoretical ecology
- Agent-based modeling
- Collective behavior
Background:
- Traditional models assume agent motion is based on local stimuli.
- Intelligent agents can improve resource gathering by anticipating future conditions.
Purpose of the Study:
- To investigate a model where agents predict future resource distribution and agent actions.
- To determine how prediction accuracy and agent numbers affect resource consumption and competitive success.
Main Methods:
- Agent-based modeling of a diffusing resource.
- Agents predict future resource density and other agents' movements.
- Analysis of resource consumption and competitive outcomes under varying prediction horizons and proportions of predictive agents.
Main Results:
- Increased prediction horizons led to decreased resource consumption.
- Predictive agents outperformed non-predictive agents only when their proportion was very small.
- Prediction accuracy diminishes with longer future outlooks.
Conclusions:
- Predictivity in agent behavior can enhance resource acquisition.
- The benefits of prediction are contingent on a limited prediction horizon or a low proportion of predictive agents.
- Over-prediction can be detrimental to resource gathering efficiency.
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