A simple rule for the evolution of cooperation on graphs and social networks

By H. Ohtsuki, C. Hauert, Erez Lieberman, Martin A. Nowak. 2006

2,101 citations recorded by the source corpus.

Subfields: Genetics, Management Science and Operations Research, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Management Science and Operations Research

In Sociology and Political Science

Citations in the corpus

Relational inductive biases, deep learning, and graph networks (2018, background): “, 2017), and exploring multi-agent learning and interaction as a key catalyst for advanced intelligence (Nowak, 2006; Ohtsuki et al., 2006).”
Multi-agent Reinforcement Learning in Sequential Social Dilemmas (2017, background): “, direct reciprocity [4, 5, 6, 7], indirect reciprocity [8], norm enforcement [9, 10], simple reinforcement learning variants [3], multiagent reinforcement learning [11, 12, 13, 14, 15], spatial structure [16], emotions [17], and social network effects [18, 19].”
Heterogeneous Aspirations Promote Cooperation in the Prisoner's Dilemma Game (2010, background): “Notably, a simple rule for the evolution of cooperation on graphs and social networks is that natural selection favors cooperation if b/c > k, where b is the benefit of the altruistic act, c is its cost, while k is the average number of neighbors [30].”
Imitation dynamics of vaccination behaviour on social networks (2011, background): “It is widely known that population structure can promote the evolution of cooperative behav-219 ior (Hauert & Doebeli 2004, Nowak & May 1992, Nowak 2006b, Nowak et al. 2010, Ohtsuki et al. 2006, Tarnita et al. 2009a;b).”
Animal social networks: an introduction (2009, methodology): “More recent “evolutionary graph theory” models (for example Ohtsuki et al. 2006; Santos et al. 2008) use networks to quantify social heterogeneity, and account for it, in models of, for example, the evolution and maintenance of cooperation.”
Evolutionary games on graphs (2006, background): “search towards these models in recent years has started to provide a more complete picture about the emergence of cooperation in social dilemmas in general (Santos et al., 2006b; Hauert et al., 2006; Ohtsuki et al., 2006)). 106 One of the most striking features of evolutionary games is the occurrenc”
Graph Convolutional Reinforcement Learning (2018, background): “The development of human cooperation is facilitated by the underlying graph of human societies (Ohtsuki et al., 2006; Apicella et al., 2012), where the mutual interplay between humans is abstracted by their relations.”
Dynamic social networks promote cooperation in experiments with humans (2011, result): “This result is also consistent with theoretical predictions (11), given that the average number of neighbors exceeds the benefit-to-cost ratio of cooperation.”
Evolutionary dynamics on small-order graphs (2009, background): “We consider all possible20 connected undirected graphs of orders three through eight.”
Cooperation Prevails When Individuals Adjust Their Social Ties (2006, background): “None of the previous results on strong [7] and weak [8] selection on graphs is capable of explaining how cooperation thrives on such social networks.”
THE EVOLUTION OF EUSOCIALITY (2010, background): “Assumption (1) also holds for Moran type processes (Moran 1962) with either Death-Birth (DB) or Birth-Death (BD) updating (Ohtsuki et al 2006, Ohtsuki & Nowak 2006).”