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A general approach for computing a consensus in group decision making that integrates multiple ethical principles

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  • Salas-Molina, Francisco
  • Bistaffa, Filippo
  • Rodríguez-Aguilar, Juan A.
Abstract
We tackle the problem of computing a consensus according to multiple ethical principles – which can include, for example, the principle of maximum freedom associated with the Benthamite doctrine and the principle of maximum fairness associated with the Rawlsian principles – among the preferences of different individuals in the context of Group Decision-Making (GDM). More formally, we put forward a novel formalisation of the above-mentioned problem based on a multi-ℓp-norm approximation problem that aims at minimising multiple p-metric distance functions, where each parameter p represents a given ethical principle. Our contribution incurs obvious benefits from a social-choice perspective. Firstly, our approach significantly generalises state-of-the-art approaches that were limited to only two ethical principles (p=1, for maximum freedom, and p=∞, for maximum fairness). Secondly, our experimental results considering an established test case demonstrate that our approach is capable, thanks to a novel re-weighting scheme, to compute a multi-norm consensus that takes into account each ethical principle in a balanced way, in contrast with state-of-the-art approaches that were heavily biased towards the p=1 ethical principle.

Suggested Citation

  • Salas-Molina, Francisco & Bistaffa, Filippo & Rodríguez-Aguilar, Juan A., 2023. "A general approach for computing a consensus in group decision making that integrates multiple ethical principles," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
  • Handle: RePEc:eee:soceps:v:89:y:2023:i:c:s0038012123002069
    DOI: 10.1016/j.seps.2023.101694
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    References listed on IDEAS

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    1. Yeawon Yoo & Adolfo R. Escobedo, 2021. "A New Binary Programming Formulation and Social Choice Property for Kemeny Rank Aggregation," Decision Analysis, INFORMS, vol. 18(4), pages 296-320, December.
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