The evolution of cooperation and diversity by integrated indirect reciprocity

• URL: http://arxiv.org/abs/2303.04467v1
• Date: Wed, 8 Mar 2023 09:37:16 GMT
• Title: The evolution of cooperation and diversity by integrated indirect reciprocity
• Authors: Tatsuya Sasaki, Satoshi Uchida, Isamu Okada, Hitoshi Yamamoto
• Abstract summary: Indirect reciprocity is one of the major mechanisms for the evolution of cooperation in human societies. We propose a new model that integrates upstream and downstream reciprocity. We show that the model can result in the stable coexistence of altruistic reciprocators and free riders in well-mixed populations.
• Score: 1.6481854696778462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Indirect reciprocity is one of the major mechanisms for the evolution of cooperation in human societies. There are two types of indirect reciprocity: upstream and downstream. Cooperation in downstream reciprocity follows the pattern, 'You helped someone, and I will help you'. The direction of cooperation is reversed in upstream reciprocity, which instead follows the pattern, 'You helped me, and I will help someone else'. In reality, these two types of indirect reciprocity often occur in combination. However, upstream and downstream reciprocity have mostly been studied theoretically in isolation. Here, we propose a new model that integrates both types. We apply the standard giving-game framework of indirect reciprocity and analyze the model by means of evolutionary game theory. We show that the model can result in the stable coexistence of altruistic reciprocators and free riders in well-mixed populations. We also found that considering inattention in the assessment rule can strengthen the stability of this mixed equilibrium, even resulting in a global attractor. Our results indicate that the cycles of forwarding help and rewarding help need to be established for creating and maintaining diversity and inclusion in a society.

Related papers

• Cross-Modal Alignment via Variational Copula Modelling [54.25504956780864]
  It is essential to develop multimodal learning methods to aggregate various information from multiple modalities.<n>Existing methods mainly rely on concatenation or the Kronecker product, oversimplifying the interaction structure between modalities.<n>We propose a novel copula-driven multimodal learning framework, which focuses on learning the joint distribution of various modalities.
  arXiv  Detail & Related papers  (2025-11-05T05:28:28Z)
• Integrating upstream and downstream reciprocity stabilizes cooperator-defector coexistence in N-player giving games [1.1381558444077822]
  We show how pay-it-forward chains and reputation systems can jointly maintain social including cooperation despite cognitive limitations and group size challenges.<n>This framework demonstrates how pay-it-forward chains and reputation systems can jointly maintain social including cooperation despite cognitive limitations and group size challenges.
  arXiv  Detail & Related papers  (2025-09-05T01:49:26Z)
• Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games [47.8980880888222]
  Multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation. We propose LASE Learning to balance Altruism and Self-interest based on Empathy. LASE allocates a portion of its rewards to co-players as gifts, with this allocation adapting dynamically based on the social relationship.
  arXiv  Detail & Related papers  (2024-10-10T12:30:56Z)
• Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents [2.1301560294088318]
  Cooperation between self-interested individuals is a widespread phenomenon in the natural world, but remains elusive in interactions between artificially intelligent agents. We introduce Reciprocators, reinforcement learning agents which are intrinsically motivated to reciprocate the influence of opponents' actions on their returns. We show that Reciprocators can be used to promote cooperation in temporally extended social dilemmas during simultaneous learning.
  arXiv  Detail & Related papers  (2024-06-03T06:07:27Z)
• InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion [53.90516061351706]
  We present InterHandGen, a novel framework that learns the generative prior of two-hand interaction. For sampling, we combine anti-penetration and synthesis-free guidance to enable plausible generation. Our method significantly outperforms baseline generative models in terms of plausibility and diversity.
  arXiv  Detail & Related papers  (2024-03-26T06:35:55Z)
• Predictive Churn with the Set of Good Models [61.00058053669447]
  This paper explores connections between two seemingly unrelated concepts of predictive inconsistency. The first, known as predictive multiplicity, occurs when models that perform similarly produce conflicting predictions for individual samples. The second concept, predictive churn, examines the differences in individual predictions before and after model updates.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Modeling Random Networks with Heterogeneous Reciprocity [9.630755176298056]
  We develop methodology to model the diverse reciprocal behavior in growing social networks. We present a preferential attachment model with heterogeneous reciprocity that imitates the attraction users have for popular users. We apply the presented methods to the analysis of a Facebook wallpost network where users have non-uniform reciprocal behavior patterns.
  arXiv  Detail & Related papers  (2023-08-19T21:21:25Z)
• Learnable Pillar-based Re-ranking for Image-Text Retrieval [119.9979224297237]
  Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Re-ranking, a popular post-processing practice, has revealed the superiority of capturing neighbor relations in single-modality retrieval tasks. We propose a novel learnable pillar-based re-ranking paradigm for image-text retrieval.
  arXiv  Detail & Related papers  (2023-04-25T04:33:27Z)
• A mechanism of Individualistic Indirect Reciprocity with internal and external dynamics [0.0]
  This research proposes a new variant of Nowak and Sigmund model, focused on agents' attitude. Using Agent-Based Model and a Data Science method, we show on simulation results that the discriminatory stance of the agents prevails in most cases. The results also show that when the reputation of others is unknown, with a high obstinacy and high cooperation demand, a heterogeneous society is obtained.
  arXiv  Detail & Related papers  (2021-05-28T23:28:50Z)
• Cooperation and Reputation Dynamics with Reinforcement Learning [6.219565750197311]
  We show how reputations can be used as a way to establish trust and cooperation. We propose two mechanisms to alleviate convergence to undesirable equilibria. We show how our results relate to the literature in Evolutionary Game Theory.
  arXiv  Detail & Related papers  (2021-02-15T12:48:56Z)
• End-to-End Learning and Intervention in Games [60.41921763076017]
  We provide a unified framework for learning and intervention in games. We propose two approaches, respectively based on explicit and implicit differentiation. The analytical results are validated using several real-world problems.
  arXiv  Detail & Related papers  (2020-10-26T18:39:32Z)
• Intrinsic Motivation for Encouraging Synergistic Behavior [55.10275467562764]
  We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.
  arXiv  Detail & Related papers  (2020-02-12T19:34:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.