Social manifestation of guilt leads to stable cooperation in multi-agent systems

Luís Moniz Pereira, Tom Lenaerts, Luis A. Martinez-Vaquero, The Anh Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Inspired by psychological and evolutionary studies, we present here theoretical models wherein agents have the potential to express guilt with the ambition to study the role of this emotion in the promotion of pro-social behaviour. To achieve this goal, analytical and numerical methods from evolutionary game theory are employed to identify the conditions for which enhanced cooperation emerges within the context of the iterated prisoners dilemma. Guilt is modelled explicitly as two features, i.e. A counter that keeps track of the number of transgressions and a threshold that dictates when alleviation (through for instance apology and self-punishment) is required for an emotional agent. Such an alleviation introduces an effect on the payoff of the agent experiencing guilt. We show that when the system consists of agents that resolve their guilt without considering the co-player's attitude towards guilt alleviation then cooperation does not emerge. In that case those guilt prone agents are easily dominated by agents expressing no guilt or having no incentive to alleviate the guilt they experience. When, on the other hand, the guilt prone focal agent requires that guilt only needs to be alleviated when guilt alleviation is also manifested by a defecting co-player, then cooperation may thrive. This observation remains consistent for a generalised model as is discussed in this article. In summary, our analysis provides important insights into the design of multi-agent and cognitive agent systems where the inclusion of guilt modelling can improve agents' cooperative behaviour and overall benefit.

Original languageEnglish
Title of host publication16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
EditorsEdmund Durfee, Michael Winikoff, Kate Larson, Sanmay Das
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1421-1430
Number of pages10
Volume3
ISBN (Electronic)9781510855076
Publication statusPublished - 2017
Event16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 - Sao Paulo, Brazil
Duration: 8 May 201712 May 2017

Conference

Conference16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
CountryBrazil
CitySao Paulo
Period8/05/1712/05/17

Fingerprint

Multi agent systems
Game theory
Numerical methods

Keywords

  • Evolution of cooperation
  • Evolutionary game theory
  • Guilt emotion
  • Multi-agent systems

Cite this

Pereira, L. M., Lenaerts, T., Martinez-Vaquero, L. A., & Han, T. A. (2017). Social manifestation of guilt leads to stable cooperation in multi-agent systems. In E. Durfee, M. Winikoff, K. Larson, & S. Das (Eds.), 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 (Vol. 3, pp. 1421-1430). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).
Pereira, Luís Moniz ; Lenaerts, Tom ; Martinez-Vaquero, Luis A. ; Han, The Anh. / Social manifestation of guilt leads to stable cooperation in multi-agent systems. 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017. editor / Edmund Durfee ; Michael Winikoff ; Kate Larson ; Sanmay Das. Vol. 3 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2017. pp. 1421-1430
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abstract = "Inspired by psychological and evolutionary studies, we present here theoretical models wherein agents have the potential to express guilt with the ambition to study the role of this emotion in the promotion of pro-social behaviour. To achieve this goal, analytical and numerical methods from evolutionary game theory are employed to identify the conditions for which enhanced cooperation emerges within the context of the iterated prisoners dilemma. Guilt is modelled explicitly as two features, i.e. A counter that keeps track of the number of transgressions and a threshold that dictates when alleviation (through for instance apology and self-punishment) is required for an emotional agent. Such an alleviation introduces an effect on the payoff of the agent experiencing guilt. We show that when the system consists of agents that resolve their guilt without considering the co-player's attitude towards guilt alleviation then cooperation does not emerge. In that case those guilt prone agents are easily dominated by agents expressing no guilt or having no incentive to alleviate the guilt they experience. When, on the other hand, the guilt prone focal agent requires that guilt only needs to be alleviated when guilt alleviation is also manifested by a defecting co-player, then cooperation may thrive. This observation remains consistent for a generalised model as is discussed in this article. In summary, our analysis provides important insights into the design of multi-agent and cognitive agent systems where the inclusion of guilt modelling can improve agents' cooperative behaviour and overall benefit.",
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Pereira, LM, Lenaerts, T, Martinez-Vaquero, LA & Han, TA 2017, Social manifestation of guilt leads to stable cooperation in multi-agent systems. in E Durfee, M Winikoff, K Larson & S Das (eds), 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017. vol. 3, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), pp. 1421-1430, 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, Sao Paulo, Brazil, 8/05/17.

Social manifestation of guilt leads to stable cooperation in multi-agent systems. / Pereira, Luís Moniz; Lenaerts, Tom; Martinez-Vaquero, Luis A.; Han, The Anh.

16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017. ed. / Edmund Durfee; Michael Winikoff; Kate Larson; Sanmay Das. Vol. 3 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2017. p. 1421-1430.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Social manifestation of guilt leads to stable cooperation in multi-agent systems

AU - Pereira, Luís Moniz

AU - Lenaerts, Tom

AU - Martinez-Vaquero, Luis A.

AU - Han, The Anh

PY - 2017

Y1 - 2017

N2 - Inspired by psychological and evolutionary studies, we present here theoretical models wherein agents have the potential to express guilt with the ambition to study the role of this emotion in the promotion of pro-social behaviour. To achieve this goal, analytical and numerical methods from evolutionary game theory are employed to identify the conditions for which enhanced cooperation emerges within the context of the iterated prisoners dilemma. Guilt is modelled explicitly as two features, i.e. A counter that keeps track of the number of transgressions and a threshold that dictates when alleviation (through for instance apology and self-punishment) is required for an emotional agent. Such an alleviation introduces an effect on the payoff of the agent experiencing guilt. We show that when the system consists of agents that resolve their guilt without considering the co-player's attitude towards guilt alleviation then cooperation does not emerge. In that case those guilt prone agents are easily dominated by agents expressing no guilt or having no incentive to alleviate the guilt they experience. When, on the other hand, the guilt prone focal agent requires that guilt only needs to be alleviated when guilt alleviation is also manifested by a defecting co-player, then cooperation may thrive. This observation remains consistent for a generalised model as is discussed in this article. In summary, our analysis provides important insights into the design of multi-agent and cognitive agent systems where the inclusion of guilt modelling can improve agents' cooperative behaviour and overall benefit.

AB - Inspired by psychological and evolutionary studies, we present here theoretical models wherein agents have the potential to express guilt with the ambition to study the role of this emotion in the promotion of pro-social behaviour. To achieve this goal, analytical and numerical methods from evolutionary game theory are employed to identify the conditions for which enhanced cooperation emerges within the context of the iterated prisoners dilemma. Guilt is modelled explicitly as two features, i.e. A counter that keeps track of the number of transgressions and a threshold that dictates when alleviation (through for instance apology and self-punishment) is required for an emotional agent. Such an alleviation introduces an effect on the payoff of the agent experiencing guilt. We show that when the system consists of agents that resolve their guilt without considering the co-player's attitude towards guilt alleviation then cooperation does not emerge. In that case those guilt prone agents are easily dominated by agents expressing no guilt or having no incentive to alleviate the guilt they experience. When, on the other hand, the guilt prone focal agent requires that guilt only needs to be alleviated when guilt alleviation is also manifested by a defecting co-player, then cooperation may thrive. This observation remains consistent for a generalised model as is discussed in this article. In summary, our analysis provides important insights into the design of multi-agent and cognitive agent systems where the inclusion of guilt modelling can improve agents' cooperative behaviour and overall benefit.

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KW - Evolutionary game theory

KW - Guilt emotion

KW - Multi-agent systems

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M3 - Conference contribution

VL - 3

SP - 1421

EP - 1430

BT - 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017

A2 - Durfee, Edmund

A2 - Winikoff, Michael

A2 - Larson, Kate

A2 - Das, Sanmay

PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

ER -

Pereira LM, Lenaerts T, Martinez-Vaquero LA, Han TA. Social manifestation of guilt leads to stable cooperation in multi-agent systems. In Durfee E, Winikoff M, Larson K, Das S, editors, 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017. Vol. 3. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). 2017. p. 1421-1430