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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

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Pair approximations of takeover dynamics in regular population structures.

Joshua L Payne¹, Margaret J Eppstein

¹Department of Computer Science, The University of Vermont, Burlington, Vermont, USA. Joshua.Payne@uvm.edu

Evolutionary Computation

|May 6, 2009

Summary

We adapted pair approximations for analyzing takeover dynamics in evolutionary algorithms. The improved method accurately models information flow across diverse population structures, enhancing evolutionary computation predictions.

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Area of Science:

Complex adaptive systems
Evolutionary computation
Network topology

Background:

Network topology significantly influences information flow in complex adaptive systems.
Takeover time analysis quantifies selective pressure by tracking favorable mutation spread.
Existing models for takeover dynamics lack generality across population structures.

Purpose of the Study:

To adapt pair approximations for general takeover time analysis in evolutionary systems.
To address limitations of original pair approximations in modeling pre-equilibrium dynamics.
To develop a rapid and general method for approximating takeover dynamics.

Main Methods:

Reformulated takeover time analysis using the Susceptible-Infectious-Susceptible (SIS) model.
Adapted pair approximation techniques for analyzing takeover dynamics.
Parameterized the pair approximation to account for neighborhood and population size interactions.

Main Results:

The original pair approximation was insufficient for pre-equilibrium dynamics.
A parameterized pair approximation accurately models takeover dynamics across various spatial topologies.
The method accounts for population size and local neighborhood influences.

Conclusions:

The parameterized pair approximation offers a general and rapid approach for takeover dynamics.
This method enhances the analysis of selective pressure in evolutionary computation.
Potential applications exist for epidemiological and ecological modeling.