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The prisoner's dilemma with image scoring on networks: How does a player's strategy depend on its place in the social network?

The prisoner's dilemma with image scoring on networks: How does a player's strategy depend on its place in the social network?
The prisoner's dilemma with image scoring on networks: How does a player's strategy depend on its place in the social network?
We investigate the evolution of cooperation in the prisoner's dilemma on different types of interaction networks. Agents interact with their network neighbours. An agent is classified by a value S is an element of [-1, 1] denoting its strategy and by its image score. It will cooperate if the opponents relative image score is above S and defect otherwise. Agents spread their strategies to their network neighbours proportionally to payoff differences. We find that network topology strongly influences the average cooperation rate; networks with low degree variance allowing for the largest amount of cooperation. In heterogeneous networks an agents place in the network strongly influences its strategy. Thus, agents on hub nodes are found to 'police' the population, while being on low degree nodes tends to favour over-generous less discriminating strategies.
978-3-540-76930-9
222-231
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7

Brede, Markus (2007) The prisoner's dilemma with image scoring on networks: How does a player's strategy depend on its place in the social network? 3rd Australian Conference on Artificial Life, Gold Coast, Australia. 04 - 06 Dec 2007. pp. 222-231 .

Record type: Conference or Workshop Item (Paper)

Abstract

We investigate the evolution of cooperation in the prisoner's dilemma on different types of interaction networks. Agents interact with their network neighbours. An agent is classified by a value S is an element of [-1, 1] denoting its strategy and by its image score. It will cooperate if the opponents relative image score is above S and defect otherwise. Agents spread their strategies to their network neighbours proportionally to payoff differences. We find that network topology strongly influences the average cooperation rate; networks with low degree variance allowing for the largest amount of cooperation. In heterogeneous networks an agents place in the network strongly influences its strategy. Thus, agents on hub nodes are found to 'police' the population, while being on low degree nodes tends to favour over-generous less discriminating strategies.

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More information

Published date: 2007
Additional Information: Event Dates: DEC 04-06, 2007
Venue - Dates: 3rd Australian Conference on Artificial Life, Gold Coast, Australia, 2007-12-04 - 2007-12-06
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 272887
URI: http://eprints.soton.ac.uk/id/eprint/272887
ISBN: 978-3-540-76930-9
PURE UUID: 6564a881-9f02-419f-9603-7b44254a898d

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Date deposited: 29 Sep 2011 16:19
Last modified: 08 Jan 2022 00:05

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Contributors

Author: Markus Brede

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