Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 187 tok/s
Gemini 2.5 Pro 40 tok/s Pro
GPT-5 Medium 32 tok/s Pro
GPT-5 High 25 tok/s Pro
GPT-4o 104 tok/s Pro
Kimi K2 177 tok/s Pro
GPT OSS 120B 436 tok/s Pro
Claude Sonnet 4.5 38 tok/s Pro
2000 character limit reached

Coevolutionary success-driven multigames (1410.4546v1)

Published 16 Oct 2014 in physics.soc-ph, cs.SI, and q-bio.PE

Abstract: Wealthy individuals may be less tempted to defect than those with comparatively low payoffs. To take this into consideration, we introduce coevolutionary success-driven multigames in structured populations. While the core game is always the weak prisoner's dilemma, players whose payoffs from the previous round exceed a threshold adopt only a minimally low temptation to defect in the next round. Along with the strategies, the perceived strength of the social dilemma thus coevolves with the success of each individual player. We show that the lower the threshold for using the small temptation to defect, the more the evolution of cooperation is promoted. Importantly, the promotion of cooperation is not simply due to a lower average temptation to defect, but rather due to a dynamically reversed direction of invasion along the interfaces that separate cooperators and defectors on regular networks. Conversely, on irregular networks, in the absence of clear invasion fronts, the promotion of cooperation is due to intermediate-degree players. At sufficiently low threshold values, these players accelerate the erosion of defectors and significantly shorten the fixation time towards more cooperative stationary states. Coevolutionary multigames could thus be the new frontier for the swift resolution of social dilemmas.

Citations (100)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube