Fig. 2: Results of first trained mechanism against baselines. | Nature Communications

Fig. 2: Results of first trained mechanism against baselines.

From: Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem

Fig. 2

A The surplus and Gini coefficient generated from games played under three baseline mechanisms (blue, purple and red) and the RL agent (green), for virtual players (left panel) and human participants (right panel) in Exp. 1. Each small dot is a game; the larger dot is the mean over games. B Correspondences between predicted outcomes (from virtual players, shading) and observed outcomes in Exp.1 (dots) for each baseline mechanism and the RL agent. Shown separately for games that were sustained to the end by at least one player (colours) and those where the pool was exhausted prematurely (grey). C The average Gini coefficient of the offer made to players as a function of the pool size, for individual trials (grey dots) and games (coloured dots), both for behavioural clones (upper panels) and human data in Exp.1 (lower panels). D Exclusions occur when a player receives nothing for one or more consecutive rounds. Here, we plot the duration of exclusions against the trial on which they are initiated (dots). Points on the diagonal indicate that the player was never reincluded (exclusion lasts until trial 40). The superimposed coloured histograms show the count of exclusions for each duration bin (of width 2). Note that unlike baselines the RL agent excludes frequently, but for short durations. E The offer made by each mechanism to each player as a function of the lagged contribution of that player over adjacent trials. Dots are individual coefficients; black line is the median.

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