Francisco C. Santos’s research while affiliated with Inesc-ID and other places

Do emotion expressions impact the evolution of cooperation? Indirect Reciprocity offers a solution to the cooperation dilemma with prior work focusing on the role of social norms in propagating others’ reputations and contributing to evolutionarily stable cooperation. Recent experimental studies, however, show that emotion expressions shape pro-social behaviour, communicate one’s intentions to others, and serve an error-correcting function; yet, the role of emotion signals in the evolution of cooperation remains unexplored. We present the first model of IR based on evolutionary game theory that exposes how emotion expressions positively influence the evolution of cooperation, particularly in scenarios of frequent errors. Our findings provide evolutionary support for the existence of emotion-based social norms, which help foster cooperation among unrelated individuals.

The sustainable governance of Global Risky Commons (GRC)—global commons in the presence of a sizable risk of overall failure—is ubiquitous and requires a global solution. A prominent example is the mitigation of the adverse effects of global warming. In this context, the Collective Risk Dilemma (CRD) provides a convenient baseline model which captures many important features associated with GRC type problems by formulating them as problems of cooperation. Here we make use of the CRD to develop, for the first time, a bottom-up institutional governance framework of GRC. We find that the endogenous creation of local institutions that require a minimum consensus amongst group members—who, in turn, decide the nature of the institution (reward/punishment) via an electoral process—leads to higher overall cooperation than previously proposed designs, especially at low risk, proving that carrots and sticks implemented through local voting processes are more powerful than other designs. The stochastic evolutionary game theoretical model framework developed here further allows us to directly compare our results with those stemming from previous models of institutional governance. The model and the methods employed here are relevant and general enough to be applied to a variety of contemporary interdisciplinary problems.

Worldwide cooperation is necessary to mitigate the effects of climate change. Many previous investigations employed the so-called collective risk dilemma, where the risk of losing everything whenever a target is not met was fixed from the outset, rendering predictions dependent on snapshot values assumed for this parameter, whose importance was found to be paramount. Here we couple risk with the overall success of mitigation, investigating the co-evolution of risk and cooperation in a world where countries are partitioned in two different wealth classes, allowing us to further assess the impact of wealth inequality and homophily on the co-evolutionary dynamics. We show that the stochastic dynamics is dominated by a global attractor, typically located in a region of low risk, where most developed countries cooperate most of the time while developing countries cooperate to a lesser extent. This scenario assumes no homophily which, when moderate, can contribute to increase overall cooperation, more so when combined with the presence of a small fraction of developing countries that opt for an unconditional cooperative behavior.

Understanding the conundrum of human cooperation has been declared one of the century’s grand challenges. Traditionally, the evolution of cooperative action in nature is analyzed through the lens of Evolutionary Game Theory, specifically, using the social learning framework, a model for Darwinian competition. However, more complex individuals may resort to more sophisticated learning rules such as Counterfactual Thinking (CT). Given these individuals’ cognitive empowerment, the question of how the presence of counterfactuals influences the evolution of cooperation in a hybrid population of these complex agents and social learners. Here we explore how cooperation emerges from the interplay of different strategy revision paradigms by analyzing large-scale Markov processes. We find that increasing the prevalence of CT individuals can promote cooperation, but such an increase is non-monotonous. Moreover, whereas counterfactual reasoning generally fosters cooperation, it fails to promote such behaviour among counterfactuals. Lastly, we find that increasing the population’s heterogeneity level enhances cooperation among social learners, but again not among counterfactuals. This indicates that, under certain circumstances, the presence of more sophisticated agents may help promote cooperation in hybrid populations. The proposed study may come as a starting point for a more profound understanding of agents’ counterfactual rationality impact on hybrid populations.

It is widely known how the human ability to cooperate has influenced the thriving of our species. However, as we move towards a hybrid human-machine future, it is still unclear how the introduction of artificial agents in our social interactions affect this cooperative capacity. In a one-shot collective risk dilemma, where enough members of a group must cooperate in order to avoid a collective disaster, we study the evolutionary dynamics of cooperation in a hybrid population. In our model, we consider a hybrid population composed of both adaptive and fixed behavior agents. The latter serve as proxies for the machine-like behavior of artificially intelligent agents who implement stochastic strategies previously learned offline. We observe that the adaptive individuals adjust their behavior in function of the presence of artificial agents in their groups to compensate their cooperative (or lack of thereof) efforts. We also find that risk plays a determinant role when assessing whether or not we should form hybrid teams to tackle a collective risk dilemma. When the risk of collective disaster is high, cooperation in the adaptive population falls dramatically in the presence of cooperative artificial agents. A story of compensation, rather than cooperation, where adaptive agents have to secure group success when the artificial agents are not cooperative enough, but will rather not cooperate if the others do so. On the contrary, when risk of collective disaster is low, success is highly improved while cooperation levels within the adaptive population remain the same. Artificial agents can improve the collective success of hybrid teams. However, their application requires a true risk assessment of the situation in order to actually benefit the adaptive population (i.e. the humans) in the long-term.

Polarization is common in politics and public opinion. It is believed to be shaped by media as well as ideologies, and often incited by misinformation. However, little is known about the microscopic dynamics behind polarization and the resulting social tensions. By coupling opinion formation with the strategy selection in different social dilemmas, we reveal how success at an individual level transforms to global consensus or lack thereof. When defection carries with it the fear of punishment in the absence of greed, as in the stag-hunt game, opinion fragmentation is the smallest. Conversely, if defection promises a higher payoff and also evokes greed, like in the prisoner’s dilemma and snowdrift game, consensus is more difficult to attain. Our research thus challenges the top-down narrative of social tensions, showing they might originate from fundamental principles at individual level, like the desire to prevail in pairwise evolutionary comparisons.

Even though the Theory of Mind in upper primates has been under investigation for decades, how it may evolve remains an open problem. We propose here an evolutionary game theoretical model where a finite population of individuals may use reasoning strategies to infer a response to the anticipated behavior of others within the context of a sequential dilemma, i.e., the Centipede Game. We show that strategies with bounded reasoning evolve and flourish under natural selection, provided they are allowed to make reasoning mistakes and a temptation for higher future gains is in place. We further show that non-deterministic reasoning co-evolves with an optimism bias that may lead to the selection of new equilibria, closely associated with average behavior observed in experimental data. This work reveals both a novel perspective on the evolution of bounded rationality and a co-evolutionary link between the evolution of Theory of Mind and the emergence of misbeliefs.

When speaking of moral judgment, we refer to a function of recognizing appropriate or condemnable actions and the possibility of choice between them by agents. Their ability to construct possible causal sequences enables them to devise alternatives in which choosing one implies setting aside others. This internal deliberation requires a cognitive ability, namely that of constructing counterfactual arguments. These serve not just to analyse possible futures, being prospective, but also to analyse past situations, by imagining the gains or losses resulting from alternatives to the actions actually carried out, given evaluative information subsequently known. Counterfactual thinking is in thus a prerequisite for AI agents concerned with Law cases, in order to pass judgement and, additionally, for evaluation of the ongoing governance of such AI agents. Moreover, given the wide cognitive empowerment of counterfactual reasoning in the human individual, namely in making judgments, the question arises of how the presence of individuals with this ability can improve cooperation and consensus in populations of otherwise self-regarding individuals. Our results, using Evolutionary Game Theory (EGT), suggest that counterfactual thinking fosters coordination in collective action problems occurring in large populations and has limited impact on cooperation dilemmas in which such coordination is not required.