Fostering cooperation in structured populations through local and global interference strategies
Fostering cooperation in structured populations through local and global interference strategies
We study the situation of an exogenous decision-maker 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.
289-295
International Joint Conferences on Artificial Intelligence
Han, The Anh
b80447eb-91f3-449b-bfce-e5a6af0447d9
Lynch, Simon
5dc7147f-9d0b-4043-ab09-00e3c5179219
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Santos, Francisco C.
a509c59e-1995-44e5-8dca-500a37a50dc1
2018
Han, The Anh
b80447eb-91f3-449b-bfce-e5a6af0447d9
Lynch, Simon
5dc7147f-9d0b-4043-ab09-00e3c5179219
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Santos, Francisco C.
a509c59e-1995-44e5-8dca-500a37a50dc1
Han, The Anh, Lynch, Simon, Tran-Thanh, Long and Santos, Francisco C.
(2018)
Fostering cooperation in structured populations through local and global interference strategies.
In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018.
vol. 2018-July,
International Joint Conferences on Artificial Intelligence.
.
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Conference or Workshop Item
(Paper)
Abstract
We study the situation of an exogenous decision-maker 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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Published date: 2018
Venue - Dates:
27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018-07-13 - 2018-07-19
Identifiers
Local EPrints ID: 426266
URI: http://eprints.soton.ac.uk/id/eprint/426266
PURE UUID: ac03bec0-7fe8-48b0-b90e-8fcff1dd4139
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Date deposited: 21 Nov 2018 17:30
Last modified: 17 Dec 2019 01:42
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Contributors
Author:
The Anh Han
Author:
Simon Lynch
Author:
Long Tran-Thanh
Author:
Francisco C. Santos
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