Social networks can sustain cooperation by amplifying the consequences of a single defection through a cascade of relationship losses. Building on Jackson et al. (2012), we introduce a novel robustness notion to characterize low cognitive complexity (LCC) networks - a subset of equilibrium networks that imposes a minimal cognitive burden to calculate and comprehend the consequences of defection. We test our theory in a laboratory experiment and find that cooperation is higher in equilibrium than in non-equilibrium networks. Within equilibrium networks, LCC networks exhibit higher levels of cooperation than non-LCC networks. Learning is essential for the emergence of equilibrium play. 

Social distancing has been one of the core public policy measures used to mitigate the economic and health impacts of the COVID-19 pandemic. Such widespread adoption of social distancing measures is wholly unprecedented, and governments have implemented a variety of policies to encourage compliance. These typically rely on financial penalties (fines) and/or informational messages (nudges). There is, however, a lack of evidence on the impact of these policies. We examine the effectiveness of fines and nudges in promoting social distancing in a web-based interactive experiment. The study involves a near-representative sample of 400 participants from the US population, and it was conducted in May 2020 at the height of the pandemic. Fines significantly promote distancing, but nudges only have a marginal impact. Political ideology also has a causal impact – progressives are more likely to practice distancing, and are marginally more responsive to fines. Further, individuals do more social distancing when they know they may be a superspreader. Our results highlight the crucial role of web-based interactive experiments in informing governments on the causal impact of policies at a time when lab and/or field-based experimental research is not feasible.

In recent years online social networks have become increasingly prominent in political campaigns and, concurrently, several countries have experienced shock election outcomes. This paper proposes a model that links these two phenomena. In our set-up, the process of learning from others on a network is influenced by confirmation bias, i.e. the tendency to ignore contrary evidence and interpret it as consistent with one’s own belief. When agents pay enough attention to themselves, confirmation bias leads to slower learning in any symmetric network, and it increases polarization in society. We identify a subset of agents that become more/less influential with confirmation bias. The socially optimal network structure depends critically on the information available to the social planner. When she cannot observe agents’ beliefs, the optimal network is symmetric, vertex-transitive and has no self-loops. We explore the implications of these results for electoral outcomes and media markets. Confirmation bias increases the likelihood of shock elections, and it pushes fringe media to take a more extreme ideology.

We investigate how the network structure of financial linkages and uncertainty about the location of a shock affect the likelihood of contagion and the formation of prices in a double auction market experiment. Core-periphery networks are highly susceptible to contagion and generate resales of assets that exacerbate financial contagion beyond the mechanical role of network structure. In contrast, contagion is minimal on circle networks and market prices remain stable even in the presence of large shocks. Uncertainty on the location of the shock has little influence. The traders' level of comprehension of the network-driven risk is predictive of their behavior and the likelihood of bankruptcy.