Qi Su

Designing incentives that promote cooperation is a central problem in public policy. Such incentives are typically evaluated by the stable cooperation level, which implicitly assumes that they last forever. In practice, incentives are costly and must end, after which the cooperation they built may erode. Since cooperation counts for as long as it lasts, a more practically relevant question is how to spend a limited budget to maximize the cooperation accumulated over the evolutionary process. Here, we develop a framework for maximizing cumulative cooperation in evolving populations through rewards or punishments. For general social dilemmas, including the prisoner’s dilemma, snowdrift game, and stag-hunt game, we show that reaching a target state at minimum cumulative cost follows a minority-control principle: reward minority cooperators or punish minority defectors. Furthermore, under a fixed control budget, to maximize the cumulative cooperation, the optimal strategy is to apply this principle to drive the population to a desired state, then maintain that state until the budget is exhausted, and finally allow natural evolution. Our results shift the focus from maximizing cooperation at a single time point to maximizing cooperation accumulated over time.

Multitrophic interactions can strongly influence the structure and functioning of ecosystems, but how plant diversity influences the direction and predictability of multitrophic interactions across agricultural and natural ecosystems remains unclear. Using 149 field studies across five continents, we found that, on average, increasing plant diversity tended to exert differential top-down and bottom-up effects in croplands versus grasslands and forests. Organic and nonorganic croplands exhibited 846 and 148% higher invertebrate natural enemy-to-herbivore abundance ratios under increased plant diversity, consistent with top-down control patterns where predator gains cause herbivore declines, enhancing crop outcomes. In grasslands and forests, increasing plant diversity was associated with bottom-up effects where enhanced productivity increased both herbivore and predator populations, with the enemy-to-herbivore ratio increasing 4.73% for grasslands and 21.2% for forests. Our findings suggest that biodiversity effects on productivity are not solely explained by direct plant-plant interactions and the resulting biodiversity-productivity relationship. Rather, they reveal patterns consistent with the framework of top-down and bottom-up effects, the relative balance of which may vary depending on ecosystem and management type. The magnitude of the effects of diversified farming on crop pests suggests that crop diversification may be an important avenue for managing crop pests preventatively and thereby enhancing agricultural sustainability.

Cooperation is typically seen as the ideal outcome in a social dilemma. Because cooperators are vulnerable to exploitation, much of the literature has focused on mechanisms, such as spatial structure, that support prosocial behaviour and the production of public goods^1-11. Yet the rules for distributing these goods also shape behaviour and long-term prosperity. Here we study policies for allocating public goods, comparing equitable allocation, in which returns are proportional to potential contributions, with uniform allocation, in which all individuals receive equal shares. For most social networks, we find that uniform allocation facilitates the spread of cooperation compared with equitable allocation. But this success comes with a cost. Uniform allocation concentrates resources in a small number of highly connected individuals¹², whereas peripheral individuals receive fewer benefits and may even be worse off than in a non-cooperative society. We develop a theoretical analysis of the tension between cooperation and equality, and we identify this conflict across diverse empirical social networks. Our results show that inequality may be an unavoidable consequence of allocation policies designed to foster cooperation in spatially heterogeneous populations. The question of how to promote cooperation is therefore incomplete: because policies that facilitate cooperation can also generate social stratification, we must weigh the benefits of cooperation against the inequality that accompanies it.

Understanding the emergence of cooperation in social networks has advanced through pairwise interactions, but the corresponding theory for group-based public goods games (PGGs) remains less explored. Here, we provide theoretical conditions under which cooperation thrives in PGGs on arbitrary population structures, which are accurate under weak selection. We find that a class of networks that would otherwise fail to produce cooperation, such as star graphs, are particularly conducive to cooperation in PGGs. More generally, PGGs can support cooperation on almost all networks, which is robust across all kinds of model details. This fundamental advantage of PGGs derives from self-reciprocity realized by group separations and from clustering through second-order interactions. We also apply PGGs to empirical networks, which shows that PGGs could be a promising interaction mode for the emergence of cooperation in real-world systems.

A collaborative group can often outperform a single individual in complex problem solving, even when information is limited. This phenomenon, called collective intelligence, can be achieved by engineering a central planner who assigns subtasks distributed across the group. But such algorithms cannot explain how natural populations, which often lack sophisticated central control, can nonetheless evolve collective intelligence. In fact, the process of social learning by imitating successful peers will typically reduce diversity and inhibit collective intelligence. Here, we consider a prediction task where the true outcome each round is a continuous quantity that depends linearly on a large number of random causal factors. Each individual can observe only one factor, and the collective prediction is generated by aggregating personal predictions across individuals. We propose two classes of reward structures that guarantee the emergence of collective intelligence through social learning. One scheme provides greater rewards to those individuals (called experts) whose personal predictions are more accurate. The other scheme provides greater rewards to those individuals (called reformers) whose predictions have greater potential to reduce the collective error, even though their personal predictions may be far from the truth. Although both of these payoff structures can provably maintain diversity and establish collective intelligence, we show that rewards based on collective error are more robust to diverse problem settings than rewards based on personal accuracy. Our results show that identifying reformers is more effective than identifying experts in promoting the emergence of collective intelligence.