Spike-based decision learning of nash equilibria in two-player games

Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.

Learning for sustainability - LforS: Simulation games - a creative tool for interactive learning

Description of simulation and training games as tool for awareness and capacity development in multi steakeholder processes

When social accounts work: evidence from ultimatum games

This paper examines the mitigating effect of social accounts on retaliatory behavior in a miniultimatum game setting. Results from games with 108 German high school students support the hypothesis that an ex ante informational and sensitive message can decrease an individuals’ negative perception of an unfair offer and increase the acceptance of the outcome. Furthermore, the moderating effect of gender on retaliatory behavior is investigated. We show that an informational and sensitive message makes more of a difference for women in accepting unfair distributions than it does for men.