Tootfinder

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
Raphael Koster, Miruna P\^islar, Andrea Tacchetti, Jan Balaguer, Leqi Liu, Romuald Elie, Oliver P. Hauser, Karl Tuyls, Matt Botvinick, Christopher Summerfield
https://arxiv.org/abs/2404.15059<…

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciprocation that sustain the commons? Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design an allocation mechanism that endogenously promotes sustainable contributions from human participants to a common pool reso…

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems
Meghdad Saeedian, Chengyi Tu, Fabio Menegazzo, Paolo D'Odorico, Sandro Azaele, Samir Suweis
https://arxiv.org/abs/2403.10938

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems
Games with environmental feedback have become a crucial area of study across various scientific domains, modelling the dynamic interplay between human decisions and environmental changes, and highlighting the consequences of our choices on natural resources and biodiversity. In this work, we propose a co-evolutionary model for human-environment systems that incorporates the effects of knowledge feedback and social interaction on the sustainability of common pool resources. The model represents …