Heterogeneous investments induced by emotions promote cooperation in public goods games

Emotion plays an important role in heterogeneous investments and has some direct effects on the cooperation behaviour of a player in a public goods game (PGG). How this irrational factor affects the heterogeneous investments and what level of cooperators in players with emotions are still unknown to us. Here, the heterogeneous investments induced by emotions into a PGG were introduced. The emotional index was firstly quantified by considering a memory-cumulative effect, and then an investment formula was proposed based on this emotional index. At last, the effect of emotions on the cooperation behaviour in a PGG was investigated. Results show that the heterogeneous investments induced by emotions can improve cooperation significantly in a PGG, and that an increase of the memory length, the emotional increment, or the memory discounting factor can improve the cooperation level.

The strategy method conflates confusion with conditional cooperation in public goods games: evidence from large scale replications

The strategy method is often used in public goods games to measure individuals’ willingness to cooperate depending on the level of cooperation by others (conditional cooperation). However, while the strategy method is informative, it risks being suggestive and inducing elevated levels of conditional cooperation that are not motivated by concerns for fairness, especially in uncertain or confused participants. Here we make 845 participants complete the strategy method two times, once with human and once with computerized groupmates. Cooperation with computers cannot rationally be motivated by concerns for fairness. Worryingly, 69% of participants conditionally cooperated with computers, whereas only 7% conditionally cooperated with humans while not cooperating with computers. Overall, 83% of participants cooperated with computers, contributing 89% as much as towards humans. Results from games with computers present a serious problem for measuring social behaviors.

Evolution of cooperation and consistent personalities in public goods games

The evolution of cooperation has remained an important problem in evolutionary theory and social sciences. In this regard, a curious question is why consistent cooperative and defective personalities exist and if they serve a role in the evolution of cooperation? To shed light on these questions, here, I consider a population of individuals who possibly play two consecutive rounds of public goods game, with different enhancement factors. Importantly, individuals have independent strategies in the two rounds. However, their strategy in the first round affects the game they play in the second round. I consider two different scenarios where either only first-round cooperators play a second public goods game, or both first-round cooperators and first-round defectors play a second public goods game, but in different groups. The first scenario can be considered a reward dilemma, and the second can be considered an assortative public goods game but with independent strategies of the individuals in the two rounds. Both models show cooperators can survive either in a fixed point or through different periodic orbits. Interestingly, due to the emergence of a correlation between the strategies of the individuals in the two rounds, individuals develop consistent personalities during the evolution. This, in turn, helps cooperation to flourish. These findings shed new light on the evolution of cooperation and show how consistent cooperative and defective personalities can evolve and play a positive role in solving social dilemmas.

Pandemic Threat, Ostrom Threshold and Pre-Emptive Public Goods: why East Asia performed better in the COVID-19 crisis

The COVID-19 pandemic is an eminent threat posed by nature to the survival of the whole community. The cost X it imposes upon the community can be mitigated by the community’s pre-emptive public goods: an early warning system, capacity for monitoring, contact tracing and isolating infected persons, the strength of its public health system and the cultivated readiness to cooperate with anti-COVID protocols. The community provides these public goods in a nonstrategic game N (Nature) where the probability of a “bad outcome” (being symptomatically infected) falls with the total spending on pre-emptive public goods. Aside from N, members of the community play an Economic Dilemma Game (EDG), a symmetric Prisoner’s Dilemma Game (PDG) with strategy set (C, D), where the community earns its economic income which in turn provides the financing of the pre-emptive public goods. Games EDG and N are fused into a composite game N+EDG by defining the probability of a good outcome as increasing with the level of public goods financing. N+EDG has the same strategy set (C, D) as EDG but the payoffs of players are composite: the payoff from EDG less the expected share of the pandemic cost to the members. We show that there is a threshold pandemic cost X0 (Ostrom threshold) so that if X ≥ X0, the N+EDG has dominant strategy in C. At the cooperative equilibrium, the community is at its peak strength: economic output from EDG is largest and the contribution to pre-emptive public good is highest. A severe-enough cost of the pandemic threat as perceived by the group (i) causes players to exhibit an altruistic phenotype (choosing C every time) and (ii) leads to the lowest probability of a bad outcome. We argue that previous experience with pandemics in the last two decades on top of a higher tendency to follow authority in East Asia supported both the provision of better pre-emptive public goods and the higher abidance with anti-COVID protocols. These explain better performance.

Selfish learning is more important than fair-minded conditional cooperation in public-goods games

Why does human cooperation often unravel in economic experiments despite a promising start? Previous studies have interpreted the decline as the reaction of disappointed cooperators retaliating in response to lesser cooperators (conditional cooperation). This interpretation has been considered evidence of a uniquely human form of cooperation, motivated by altruistic concerns for fairness and requiring special evolutionary explanations. However, experiments have typically shown individuals information about both their personal payoff and information about the decisions of their groupmates (social information). Showing both confounds explanations based on conditional cooperation with explanations based on individuals learning how to better play the game. Here we experimentally decouple these two forms of information, and thus these two learning processes, in public goods games involving 616 Swiss university participants. We find that payoff information leads to a greater decline, supporting a payoff-based learning hypothesis. In contrast, social information has small or negligible effect, contradicting the conditional cooperation hypothesis. We also find widespread evidence of both confusion and selfish motives, suggesting that human cooperation is maybe not so unique after all.

Computational Behavioral Models in Public Goods Games with Migration Between Groups

In this study we have simulated numerically two models of linear Public Goods Games where players are equally distributed among a given number of groups. Agents play in their group by using two simple sets of rules that are inspired by the observed behavior of human participants in laboratory experiments. In addition, unsatisfied agents have the option of leaving their group and migrating to a new random one through probabilistic choices. Stochasticity, and the introduction of two types of players in the population, help simulate the heterogeneous behavior that is often observed in experimental work. The numerical simulation results of the corresponding dynamical systems show that being able to leave a group when unsatisfied favors contribution and avoids free-riding to a good extent in a range of the enhancement factor where defection would prevail without migration. Our numerical simulation results are qualitatively in line with known experimental data when human agents are given the same kind of information about themselves and the other players in the group. This is usually not the case with customary mathematical models based on replicator dynamics or stochastic approaches. As a consequence, models like the ones described here may be useful for understanding experimental results and also for designing new experiments by first running cheap simulations instead of doing costly preliminary laboratory work. The downside is that models and their simulation tend to be less general than standard mathematical approaches.