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Confidence sharing: an economic strategy for efficient information flows in animal groups
Amos Korman1, Efrat Greenwald2, Ofer Feinerman2
1Laboratoire d'Informatique Algorithmique: Fondements et Applications (LIAFA), CNRS & University Paris Diderot, Paris, France.
Plos Computational Biology
|October 3, 2014
Summary
Animal groups can improve collective behavior by sharing confidence, a compressed form of information. This efficient communication strategy minimizes computational and memory demands while maintaining high performance.
Area of Science:
- Behavioral Ecology
- Theoretical Biology
- Information Theory
Background:
- Social animals share information for accurate environmental perception.
- Communication constraints can lead to information errors and degraded collective behaviors.
Purpose of the Study:
- To develop and analyze an efficient, economical algorithm for information sharing in animal groups.
- To investigate the role of confidence as a communication parameter.
Main Methods:
- Theoretical modeling using an algorithmic perspective.
- Development of a confidence-based active signaling algorithm.
- Application of Cramér-Rao bound and Fisher Channel Capacity for information flow quantification.
Main Results:
- The proposed confidence-sharing algorithm performs comparably to unconstrained optimal algorithms.
- The algorithm is minimal, with further communication reduction significantly impacting performance.
- Quantified information flow provides lower bounds on collective performance.
Conclusions:
- Confidence sharing is an efficient and essential strategy for animal communication.
- This algorithmic approach offers generalizable insights into collective behavior and information processing in social animals.
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