Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies

The Anh Han, Simon Lynch, Long Tran-Thanh, Francisco C. Santos

Research output: Contribution to conferencePaper

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Abstract

We study the situation of an exogenous decisionmaker aiming to encourage a population of autonomous, self-regarding agents to follow a desired behaviour at a minimal cost. The primary goal is therefore to reach an efficient trade-off between pushing the agents to achieve the desired configuration while minimising the total investment. To this end, we test several interference paradigms resorting to simulations of agents facing a cooperative dilemma in a spatial arrangement. We systematically analyse and compare interference strategies rewarding local or global behavioural patterns. Our results show that taking into account the neighbourhood’s local properties, such as its level of cooperativeness, can lead to a significant improvement regarding cost efficiency while guaranteeing high levels of cooperation. As such, we argue that local interference strategies are more efficient than global ones in fostering cooperation in a population of autonomous agents.
Original languageEnglish
Pages289-295
Number of pages7
Publication statusPublished - 20 Jul 2018
Event27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence - Stockholmsmässan, Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018
http://www.ijcai-18.org/

Conference

Conference27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence
Abbreviated titleIJCAI-ECAI-18
CountrySweden
CityStockholm
Period13/07/1819/07/18
Internet address

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Autonomous agents
Costs

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Han, T. A., Lynch, S., Tran-Thanh, L., & Santos, F. C. (2018). Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies. 289-295. Paper presented at 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, Stockholm, Sweden.
Han, The Anh ; Lynch, Simon ; Tran-Thanh, Long ; Santos, Francisco C. . / Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies. Paper presented at 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, Stockholm, Sweden.7 p.
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Han, TA, Lynch, S, Tran-Thanh, L & Santos, FC 2018, 'Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies' Paper presented at 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, Stockholm, Sweden, 13/07/18 - 19/07/18, pp. 289-295.

Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies. / Han, The Anh; Lynch, Simon; Tran-Thanh, Long; Santos, Francisco C. .

2018. 289-295 Paper presented at 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, Stockholm, Sweden.

Research output: Contribution to conferencePaper

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T1 - Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies

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AU - Tran-Thanh, Long

AU - Santos, Francisco C.

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N2 - We study the situation of an exogenous decisionmaker aiming to encourage a population of autonomous, self-regarding agents to follow a desired behaviour at a minimal cost. The primary goal is therefore to reach an efficient trade-off between pushing the agents to achieve the desired configuration while minimising the total investment. To this end, we test several interference paradigms resorting to simulations of agents facing a cooperative dilemma in a spatial arrangement. We systematically analyse and compare interference strategies rewarding local or global behavioural patterns. Our results show that taking into account the neighbourhood’s local properties, such as its level of cooperativeness, can lead to a significant improvement regarding cost efficiency while guaranteeing high levels of cooperation. As such, we argue that local interference strategies are more efficient than global ones in fostering cooperation in a population of autonomous agents.

AB - We study the situation of an exogenous decisionmaker aiming to encourage a population of autonomous, self-regarding agents to follow a desired behaviour at a minimal cost. The primary goal is therefore to reach an efficient trade-off between pushing the agents to achieve the desired configuration while minimising the total investment. To this end, we test several interference paradigms resorting to simulations of agents facing a cooperative dilemma in a spatial arrangement. We systematically analyse and compare interference strategies rewarding local or global behavioural patterns. Our results show that taking into account the neighbourhood’s local properties, such as its level of cooperativeness, can lead to a significant improvement regarding cost efficiency while guaranteeing high levels of cooperation. As such, we argue that local interference strategies are more efficient than global ones in fostering cooperation in a population of autonomous agents.

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Han TA, Lynch S, Tran-Thanh L, Santos FC. Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies. 2018. Paper presented at 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, Stockholm, Sweden.