The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., “People you may know” or “Whom to follow” suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

Summary When individuals face collective action problems, their expectations about others' willingness to contribute affect their motivation to cooperate. Individuals, however, often misperceive the cooperation levels in a population. In the context of climate action, people underestimate the pro-climate positions of others. Designing incentives to enable cooperation and a sustainable future must thereby consider how social perception biases affect collective action. We propose a theoretical model and investigate the effect of social perception bias in non-linear public goods games. We show that different types of bias play a distinct role in cooperation dynamics. False uniqueness (underestimating own views) and false consensus (overestimating own views) both explain why communities get locked in suboptimal states. Such dynamics also impact the effectiveness of typical monetary incentives, such as fees. Our work contributes to understanding how targeting biases, e.g., by changing the information available to individuals, can comprise a fundamental mechanism to prompt collective action.

14From biofilms to whale pods, organisms have repeatedly converged on sociality as a strategy to improve individual fitness. Yet, it remains challenging to identify the most important drivers-and by extension, the evolutionary mechanisms-of sociality for particular species. Here, we present a conceptual framework, literature review, and model demonstrating that the direction and magnitude of the response of group size to sudden resource shifts provides a strong indication of the underlying drivers of sociality. We catalog six functionally distinct mechanisms related to the acquisition of resources, and we model these mechanisms' effects on the survival of individuals foraging in groups. We find that whether, and to what degree, optimal group size increases, decreases, or remains constant when resource abundance declines depends strongly on the dominant mechanism. Existing empirical data support our model predictions, and we demonstrate how our framework can be used to predict the dominant social benefit for particular species. Together, our framework and results show that a single easily measurable characteristic, namely, group size under different resource abundances, can illuminate the potential drivers of sociality across the tree of life. 15

We study the evolution of fairness in a multiplayer version of the classical Ultimatum Game in which a group of N Proposers offers a division of resources to M Responders. In general, the proposal is rejected if the (average) proposed offer is lower than the (average) response threshold in the Responders group. A motivation for our work is the exchange of flexibilities between smart energy communities, where the surplus of one community can be offered to meet the demand of a second community. In the absence of any Responder selection criteria, the co-evolving populations of Proposers and Responders converge to a state in which proposals and acceptance thresholds are low, implying an unfair exchange that favors Proposers. To circumvent this, we test different rules which determine how Responders should be selected, contingent on their declared acceptance thresholds. We find that selecting moderate Responders optimizes overall fairness. Selecting the lowest-demanding Responders maintains unfairness, while selecting the highest-demanding individuals yields a worse outcome for all due to frequent rejected proposals. These results provide a practical message for institutional design and the proposed model allows testing policies and emergent behaviors on the intersection between social choice theory, group bargaining, competition, and fairness elicitation.