In the biological and the social sciences there are few topics as important as the consequences of diversity for the functioning and transformation of ecosystems and social systems1,2,3,4. One of the most important lines of research in ecology, for instance, is the effect of diverse functional traits on the stability and efficiency of ecosystems1,5. Functional traits include physiological, morphological, and phenological traits that affect individual fitness6,7 and provide mechanistic insights into how species may respond to disturbance. A system with higher functional diversity and redundancy of functions allows ecosystems to withstand disturbance and maintain a consistent level of productivity1,5,8,9,10,11. For example, fisheries with a greater diversity of functional groups produce a higher level of fish biomass more consistently than less diverse fisheries12. While the consequences of a functional diversity of traits are well understood for ecosystems, the effects of functional traits within cognitive and social contexts on the governance of natural resources is less well developed.

Akin to functional traits in ecology, cognitive functional traits, such as general (g) and social intelligence, specifically, theory of mind (ToM), are domain general mental abilities that allow individuals to process information and adapt in social-ecological settings. g reflects the variance common to mental tests (e.g. IQ tests) and measures the ability of individuals to engage in complex reasoning and abstract thought13. ToM is the ability to model and reason about the intentions of others14,15,16. Given these definitions and the different tasks that g and ToM help individuals accomplish, we postulate that a functional diversity of intelligences improves the ability of groups to govern resources and that intelligence functional diversity is maximized when groups have high competency in both g and ToM. In particular, in this paper, we investigate the effects of cognitive functional diversity on the ability of social groups to govern a common pool resource system.

A common pool resource system is a system in which resources are non-excludable (all individuals have access) and the harvest decisions of each individual affect the availability of resources for the entire group (i.e., the resource is rivalrous). In such systems, governance entails developing rules and norms that allow individuals to harvest the resource now and, at the same time, create incentives for sharing and preserving the resource for future generations17,18. In order to assess the relationship between a group’s ability to sustain common pool resources and cognitive abilities, we conduct behavioral experiments in a spatially explicit common pool resource system to test the functional intelligences proposition (FIP)19.

The basic premise of the FIP is that g and ToM serve different functions. This statement is supported, first, by the fact that high g individuals with autism show a deficit in ToM14, and when such high ability individuals attempt to model others’ mental states, brain regions associated with ToM remain inactive14. Second, while aspects of g (e.g., cause and effect detection and transitive inference) are pervasive among vertebrates20, evidence of ToM remains rare20. The best evidence of ToM among nonhuman vertebrates comes from social animals with complex communication systems, in particular, blue jays, ravens, and chimpanzees21. These patterns suggest a widespread convergent selective pressure for the ability to detect cause and effect and reason about such relationships (aspects of g) among vertebrates, but this selective pressure does not, apparently, lead to widespread ToM abilities. In other words, the two abilities are not co-evolving in nature. Finally, direct measures of social-cognitive ToM weakly correlate with measures of g among human populations22.

Given that g and ToM serve distinct functions, both abilities should affect the performance of social groups that attempt to harvest common pool resources. This is because harvesting common pool resources requires both a cognitive mapping of resource dynamics and effective models of others’ intentions. In particular, we propose that the highest functional diversity of intelligence occurs when groups contain individuals with high g and high ToM. In this scenario, groups are made up of individuals good at mapping their biophysical environment and individuals good at mapping and communicating intentions within their social environment. In short, g and ToM should interact, and, as both abilities increase, groups should more effectively solve the collective action dilemmas that arise in a common pool resource system. However, if either capacity declines, collective action becomes more problematic and thus the sustainable management of resources more difficult.

Collective action dilemmas continually arise in a common pool resource system because of the incentives that an individual has to maximize her gains while dealing with uncertainties related to resource abundance and the behavior of other individuals in the system. Such uncertainty, along with the willingness to maximize one’s own gain, creates an incentive to over exploit resources. Following Hardin’s seminal work23, only two strategies were once thought capable of solving such dilemmas and conserving common pool resources: (1) strong, top–down state control, and (2) privatization23. However, the depletion of common pool resources does not inevitably occur absent state control or private property rights18. In fact, researchers have documented multiple cases in which groups sustainably manage common pool resources from the bottom-up17,18,24,25,26,27. These groups often display five common characteristics: They (1) adapt rules of harvesting and resource appropriation to local resource dynamics; (2) establish a proportionality between the provision and appropriation of resources; (3) monitor the resource itself; (4) sanction those who do not comply with the community rules (or the rules of the commons); and (5) clearly define who has access to harvesting resources17.

In the context of the five characteristics above, g is critical to understand the local resource dynamics13,28. Higher g should allow groups to analyze and assess resource changes, hence the higher the total group g (or the average), the more likely a group contains many individuals who model resource dynamics correctly and notice changes in local resource conditions29. However, higher g also implies an increase in individuals who rationally calculate the costs and benefits of using resources and more readily compute strategies that will yield the highest net benefit to themselves30. Thus, the effects of g on the harvest of common pool resources should remain context dependent. For example, harvesting resources in systems where boundaries, monitoring of resources and rule matching are irrelevant, high g individuals should maximize their own short-term rewards31. However, when harvesting resources in common pool resource systems, higher g individuals assess uncertainty in both the resource (abundance and dynamics) as well as in others’ behavior. Especially when uncertainty related to others’ behavior is high, high g individuals are more likely to defect in order to maximize their current benefit, potentially leading to the overharvest of resources32.

ToM is critical for individuals to model and monitor others’ mental states and social positions14,15,33. Higher ToM should increase an individual’s ability to anticipate and monitor others’ behaviors, abide by inclusive rule making and diffuse more efficiently, either through their actions or words, conflicts that may arise in common pool resource systems33,34,35,36. A higher ability to model others’ mental states is associated with more efficient social interactions and more pro-social behavior as defined by Frey37, allowing groups with higher ToM to build and maintain fair and legitimate rules that take proportionality and local circumstances into account. Intelligence research in psychology also indicates that increases in ToM improves the ability of groups to achieve a mutually beneficial goal in a static environment. For example, Woolley and colleagues36 find that the g of individuals does not predict a general group level intelligence factor but ToM does35,36. This means that groups with higher ToM perform better on a battery of tasks than groups with lower ToM scores. In contrast with g, where the sum of the ability of individuals forming a group drives the overall understanding of a system, any one individual with low ToM may jeopardize the whole group’s ability to devise effective rules to manage the system29,38, reducing a group’s ability to manage resources in the face of change and, in some cases, leading to overharvest.

In the case of environmental change that affects resources, a functional diversity of cognitive abilities should be critical for adapting to negative changes (HL treatment –discussed below), while, perhaps, not as important when environmental change improves a resource (LH treatment–discussed below). The difference in the importance of cognitive abilities may stem from the difference between resource and group dynamics when conditions improve vs. degrade. When conditions improve, there is no need for re-negotiation and one can keep behaving as she did in the past without adverse consequences. On the other hand, when conditions deteriorate, harvest behavior needs to change in accordance with the new condition of local scarcity. In this context, negotiations about resource appropriation need to happen in order for the group to continue to manage resources sustainably. For example, in repeated and environmentally stable situations, all groups may eventually find optimal solutions (i.e., learning by doing). However, when the ecological system changes, effective mental models of the underlying resource dynamics, as well as other individuals’ behavior are critical. More effective information processing improves learning and adaptation. Groups composed of individuals with higher g should better adapt to changes in their resource base than groups with lower g, as groups with higher g more readily detect changes in a system and devise rules to match such changes. However, changes in the biophysical resource system often require the re-negotiation of social rules and the communication of new knowledge. Hence, higher ToM should increase a group’s ability to work towards a common goal.

In sum, the combination of high g and high ToM should lead to groups who are better at solving collective action dilemmas and, thus, managing a common pool resource system19. Figure 1 summarizes the predicted interaction between g and ToM. In the lower right quadrant, groups composed of many individuals with high g should effectively monitor a resource and develop rules that match the dynamics of the resource. However, such groups also have low ToM and should experience more conflict, especially when a system changes. The costs associated with conflict should nullify the gains from higher g. Another way to think about this prediction is that ToM affects how efficiently individuals cooperate in groups and may lead to the emergence of a general group intelligence factor39. When ToM is low, a group has a lot of individual cognitive capital relevant to understanding the resource, but conflict and difficulty communicating blunt the emergence of a group level intelligence that harnesses the individual level capital to maintain good governance over time. Similarly, in the upper left quadrant, groups with high ToM but low g should lack individuals with effective mental models of how a resource system works, and this may nullify the benefits of more amicable groups. In the lower left quadrant, low g and ToM groups should find it difficult to understand the resource system and effectively work together, which should lead to poor governance. Finally, in the upper right quadrant, high g and ToM should improve governance, especially in a system susceptible to negative changes in a resource base over space and time. Groups that contain individuals with high g and high ToM understand the system well and more efficiently work together to govern a resource.

Fig. 1 Predicted interaction effects of g and ToM on the ability of groups to harvest resources and adapt to changes in resource growth Full size image

Our results indicate that groups with high g and ToM better adapt to deteriorating environmental conditions. Such groups are less likely to deplete resources and harvest more resources, as these groups have a better understanding of how the system works and are also able to negotiate and communicate effectively. Conversely, our results also indicate that when conditions improve, groups with high competency in g more effectively reap the benefit of the positive change. In fact, high g, along with reciprocity, is sufficient for groups to perform well when resource conditions improve, as conflict situations are less likely to arise when resources are plentiful. In this situation the discriminating variable between group performances is how well each group understands the resource system.