Operationalising Rawlsian Ethics for Fairness in Norm-Learning Agents
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909435359657984 |
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| author | Woodgate, Jessica Marshall, Paul Ajmeri, Nirav |
| author_facet | Woodgate, Jessica Marshall, Paul Ajmeri, Nirav |
| contents | Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subjugation of certain agents. We present RAWL-E, a method to create ethical norm-learning agents. RAWL-E agents operationalise maximin, a fairness principle from Rawlsian ethics, in their decision-making processes to promote ethical norms by balancing societal well-being with individual goals. We evaluate RAWL-E agents in simulated harvesting scenarios. We find that norms emerging in RAWL-E agent societies enhance social welfare, fairness, and robustness, and yield higher minimum experience compared to those that emerge in agent societies that do not implement Rawlsian ethics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15163 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Operationalising Rawlsian Ethics for Fairness in Norm-Learning Agents Woodgate, Jessica Marshall, Paul Ajmeri, Nirav Multiagent Systems Artificial Intelligence Machine Learning Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subjugation of certain agents. We present RAWL-E, a method to create ethical norm-learning agents. RAWL-E agents operationalise maximin, a fairness principle from Rawlsian ethics, in their decision-making processes to promote ethical norms by balancing societal well-being with individual goals. We evaluate RAWL-E agents in simulated harvesting scenarios. We find that norms emerging in RAWL-E agent societies enhance social welfare, fairness, and robustness, and yield higher minimum experience compared to those that emerge in agent societies that do not implement Rawlsian ethics. |
| title | Operationalising Rawlsian Ethics for Fairness in Norm-Learning Agents |
| topic | Multiagent Systems Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.15163 |