English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Collective action demands that individuals efficiently coordinate how much,where, and when to cooperate. Laboratory experiments have extensively exploredthe first part of this process, demonstrating that a variety ofsocial-cognitive mechanisms influence how much individuals choose to invest ingroup efforts. However, experimental research has been unable to shed light onhow social cognitive mechanisms contribute to the where and when of collectiveaction. We build and test a computational model of human behavior in Clean Up,a social dilemma task popular in multi-agent reinforcement learning research.We show that human groups effectively cooperate in Clean Up when they canidentify group members and track reputations over time, but fail to organizeunder conditions of anonymity. A multi-agent reinforcement learning model ofreputation demonstrates the same difference in cooperation under conditions ofidentifiability and anonymity. In addition, the model accurately predictsspatial and temporal patterns of group behavior: in this public goods dilemma,the intrinsic motivation for reputation catalyzes the development of anon-territorial, turn-taking strategy to coordinate collective action.

A multi-agent reinforcement learning model of reputation and cooperation in human groups