Averting Depletion in a Two-Player Common Pool Resource Game: Being Seen, the Expectation of Future Encounters, and Biophilia Play a Role in Cooperation

Abstract

AbstractUsing a two-player common pool resource game, we investigated the influence of multiple factors on cooperation: (1) probability of future rounds, (2) visibility of other participants, (3) biophilia, (4) future discounting, and (5) life history. In each round, participants simultaneously and independently (without conferring) decided how much of the common pool to consume. Participants (n = 116) were informed that the shared resource would be fully replenished in the next round—but only if—both players together consumed ≤ 50% of the common pool in the current round. Additionally, participants were told the probability (0–100%) of further rounds of play with the same player (this probability was not real; it was purely to manipulate the player’s expectations). To assess the effect of the probability of future rounds, we developed a mathematical model to predict the threshold that would permit a Nash Equilibrium of Conditional Cooperation (CC). To manipulate visibility, half the pairs were tested in the same room (seen condition) and half in separate rooms (unseen condition). To measure biophilia, the “Nature Relatedness” (NR) scale was used. To measure future discounting, the “consideration of future consequences” (CFC) scale was used. To measure life history, the participant’s UK postcode was obtained (indicating possible residence in a deprived neighbourhood). Participants in our study were not paid. In our results, there was a significant effect showing more cooperation in the visible than not visible condition, but no significant effects of NR, CFC, nor postcodes (but NR was significant in interaction with visibility). For predictability of future rounds, we found a number of significant effects using different tests. A notable result was that there was significantly more CC when the probability of future rounds was ≥ 69% (congruent with one of our model predictions).