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Economic game theory for mutualism and cooperation
Marco Archetti1, István Scheuring, Moshe Hoffman
1Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, 26 Oxford St., Harvard University, Cambridge, MA 02138, USA. archetti.marco@googlemail.com
Ecology Letters
|October 21, 2011
Summary
Economic game theory and evolutionary biology reveal how partner choice, host sanctions, and cooperation evolve. These insights explain mutualisms and social behaviors in diverse species, from plants to microbes.
Area of Science:
- Evolutionary biology
- Game theory
- Behavioral ecology
Background:
- Mutualistic relationships and cooperation are widespread in nature.
- Understanding the evolution of these interactions is a key challenge in biology.
- Economic game theory offers powerful tools to model these complex dynamics.
Purpose of the Study:
- To review recent advances at the intersection of game theory and evolutionary biology.
- To provide new insights into the evolution of partner choice, host sanctions, partner fidelity feedback, and public goods.
- To explore applications of these theories in various biological systems.
Main Methods:
- Review of theoretical frameworks from economic game theory, including asymmetrical information and contract theory.
- Application of game theory models to explain biological phenomena like symbiont screening and cooperation.
- Analysis of existing empirical examples from mutualistic interactions.
Main Results:
- Asymmetrical information theory explains partner choice in mutualisms without signaling.
- Contract theory helps distinguish host sanctions from partner fidelity feedback.
- Public goods theory demonstrates cooperation can evolve in non-kin, one-shot interactions.
Conclusions:
- Game theory provides a robust framework for understanding the evolution of cooperation and partner choice in biological systems.
- These theoretical insights have broad applications, from plant-microbe interactions to animal social behavior.
- Further integration of economic and evolutionary approaches will yield deeper understanding of biological complexity.
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