Interpersonal trust modelling through multi-agent Reinforcement Learning
Many existing approaches to model and compute trust in a quantitative way rely on ranking, rating or assessments of agents by other agents. Even though reputation is related with trust, it does not capture all its characteristics. In parallel, many works in neuroscience shows evidence about interper...
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| Published in | Cognitive systems research Vol. 83; p. 101157 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.01.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1389-0417 1389-0417 |
| DOI | 10.1016/j.cogsys.2023.101157 |
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| Abstract | Many existing approaches to model and compute trust in a quantitative way rely on ranking, rating or assessments of agents by other agents. Even though reputation is related with trust, it does not capture all its characteristics. In parallel, many works in neuroscience shows evidence about interpersonal trust being an associative learning process encoded in the human brain. Inspired by other subjects such as Cognitive Processing/Dopamine, where Reinforcement Learning algorithms have served to model those phenomena, we propose a model for trust dynamics based on a multi-agent RL algorithm. We corroborate some trust concepts developed in social sciences within a quantitative framework. We do also propose and assess some metrics for a better understanding about the relation between the trust behaviour and the performance of the agents. Finally, we show that Trust, as described by our proposal, can serve to accelerate learning. |
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| AbstractList | Many existing approaches to model and compute trust in a quantitative way rely on ranking, rating or assessments of agents by other agents. Even though reputation is related with trust, it does not capture all its characteristics. In parallel, many works in neuroscience shows evidence about interpersonal trust being an associative learning process encoded in the human brain. Inspired by other subjects such as Cognitive Processing/Dopamine, where Reinforcement Learning algorithms have served to model those phenomena, we propose a model for trust dynamics based on a multi-agent RL algorithm. We corroborate some trust concepts developed in social sciences within a quantitative framework. We do also propose and assess some metrics for a better understanding about the relation between the trust behaviour and the performance of the agents. Finally, we show that Trust, as described by our proposal, can serve to accelerate learning. |
| ArticleNumber | 101157 |
| Author | Frey, Vincent Martinez, Julian |
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| Cites_doi | 10.25080/Majora-92bf1922-00a 10.1613/jair.1.11396 10.1109/MCSE.2007.55 10.1038/s41586-019-1924-6 10.1007/s10462-004-0041-5 10.1016/j.tics.2021.09.002 10.1145/2815595 10.1006/game.1995.1027 10.1037/0033-295X.88.2.135 10.1109/ACCESS.2019.2917999 10.1016/j.chb.2014.08.013 10.1038/nn.4239 10.1016/j.dss.2005.05.019 10.21105/joss.03021 10.1152/jn.1998.80.1.1 10.1111/1468-0297.00609 10.1017/S0269888904000116 10.1038/s41586-020-2649-2 10.1371/journal.pcbi.1004305 10.3389/neuro.01.014.2008 10.1073/pnas.1715227115 10.1038/nature14855 |
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