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Computer Science > Multiagent Systems

arXiv:2306.17747 (cs)
[Submitted on 30 Jun 2023 (v1), last revised 3 Jul 2023 (this version, v2)]

Title:Discriminatory or Samaritan -- which AI is needed for humanity? An Evolutionary Game Theory Analysis of Hybrid Human-AI populations

Authors:Tim Booker, Manuel Miranda, Jesús A. Moreno López, José María Ramos Fernández, Max Reddel, Valeria Widler, Filippo Zimmaro, Alberto Antonioni, The Anh Han
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Abstract:As artificial intelligence (AI) systems are increasingly embedded in our lives, their presence leads to interactions that shape our behaviour, decision-making, and social interactions. Existing theoretical research has primarily focused on human-to-human interactions, overlooking the unique dynamics triggered by the presence of AI. In this paper, resorting to methods from evolutionary game theory, we study how different forms of AI influence the evolution of cooperation in a human population playing the one-shot Prisoner's Dilemma game in both well-mixed and structured populations. We found that Samaritan AI agents that help everyone unconditionally, including defectors, can promote higher levels of cooperation in humans than Discriminatory AI that only help those considered worthy/cooperative, especially in slow-moving societies where change is viewed with caution or resistance (small intensities of selection). Intuitively, in fast-moving societies (high intensities of selection), Discriminatory AIs promote higher levels of cooperation than Samaritan AIs.
Comments: This work is the result of the Complexity72h 2023 workshop
Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Dynamical Systems (math.DS); Optimization and Control (math.OC); Adaptation and Self-Organizing Systems (nlin.AO)
Cite as: arXiv:2306.17747 [cs.MA]
  (or arXiv:2306.17747v2 [cs.MA] for this version)
  https://doi.org/10.48550/arXiv.2306.17747

Submission history

From: The Anh Han [view email]
[v1] Fri, 30 Jun 2023 15:56:26 UTC (536 KB)
[v2] Mon, 3 Jul 2023 21:19:28 UTC (1,366 KB)
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