The outbreak of the COVID-19 pandemic led to an unprecedented rise in the use of social distancing as a means to curb the spread of infection. We examine the effectiveness of fines and informational messages (nudges) in promoting social distancing in a framed web-based interactive experiment conducted during the first wave of the pandemic on a near-representative sample of the US population. Fines promote distancing, while nudges have a smaller and less robust impact. Individuals do more social distancing when they are aware they are a superspreader. Using an instrumental variable approach, we show suggestive evidence that progressives are more likely to practice distancing, and they are somewhat more responsive to fines. 

The tension between efficiency and equilibrium is a central feature of economic systems. In many contexts, social networks mediate this trade-off: an individual's network position determines equilibrium play, and social relations allow coordination on an efficient norm. We examine this trade-off in a network game with a unique Nash equilibrium, but such that subjects can achieve a higher payoff by following a "collaborative norm". Subjects establish and maintain a collaborative norm in the circle, but the norm weakens with the introduction of one hub connected to everyone in the wheel. In complex and asymmetric networks of 15 and 21 nodes, the norm disappears and subjects' play converges to Nash on every node. We provide evidence that subjects base their decisions on their degree, rather than the overall network structure.

This paper examines experimentally how reputational uncertainty and the rate of change of the social environment determine cooperation. Reputational uncertainty significantly decreases cooperation, while a fast-changing social environment only causes a second-order qualitative increase in cooperation. At the individual level, reputational uncertainty induces more leniency and forgiveness in imposing network punishment through the link proposal and removal processes, inhibiting the formation of cooperative clusters. However, this effect is significant only in the fast-changing environment and not in the slow-changing environment. A substitution pattern between network punishment and action punishment (retaliatory defection) explains this discrepancy across the two social environments.

On 11th Jan 2020, the first COVID-19 related death was confirmed in Wuhan, Hubei. The Chinese government responded to the outbreak with a lockdown that impacted most residents of Hubei province and lasted for almost three months. At the time, the lockdown was the strictest both within China and worldwide. Using an interactive web-based experiment conducted half a year after the lockdown with participants from 11 Chinese provinces, we investigate the behavioral effects of this ‘shock’ event experienced by the population of Hubei. We find that both one’s place of residence and the strictness of lockdown measures in their province are robust predictors of individual social distancing behavior. Further, we observe that informational messages are effective at increasing compliance with social distancing throughout China, whereas fines for noncompliance work better within Hubei province relative to the rest of the country. We also report that residents of Hubei increase their propensity to social distance when exposed to social environments characterized by the presence of a superspreader, while the effect is not present outside of the province. Our results appear to be specific to the context of COVID-19 and are not explained by general differences in risk attitudes and social preferences. 

This paper proposes and tests experimentally a dynamic model of bargaining to analyze decentralized markets where buyers and sellers obtain information about past deals through their social network. There is a unique equilibrium in which every individual obtains the same outcome independent of their network position, and this outcome depends on the peripheral (least connected) individuals in the network. The main testable predictions are that networks with high density and/or low variability in the number of connections across individuals allow their members to obtain a better deal. These predictions are tested in a lab experiment through 4 treatments that vary the network that subjects are assigned to. The results of the experiment lend support to the theoretical predictions. Subjects converge to an outcome that is independent of their network position, and they fare better if they are assigned to a network that is dense and/or has low variability in number of connections across members.

The maintenance of cooperative behavior is fundamental for the prosperity of human societies. Empirical studies show that high cooperation is frequently associated with the presence of strong social ties, but they are silent on whether a causal mechanism exists, how it operates, and what features of the social environment are conducive to its emergence. Here we show experimentally that strong ties increase cooperation and welfare by enabling the emergence of a close-knit and strongly bound cooperative elite. Crucially, this cooperative elite is more prevalent in social environments characterized by a large payoff difference between weak and strong ties, and no gradation in the process of strengthening a tie. These features allow cooperative individuals to adopt an all or nothing strategy to tie strengthening based on the well-known mechanism of direct reciprocity: participants become very selective by forming strong ties only with other cooperative individuals and severing ties with everyone else. Once formed, these strong ties are persistent and enhance cooperation. A dichotomous society emerges with cooperators prospering in a close-knit, strongly bound elite, and defectors earning low payoffs in a weakly connected periphery. Methodologically, our set-up provides a framework to investigate the role of the strength of ties in an experimental setting.

Media Coverage: Financial Times, USA Today, NYMag.com, Entrepreneur, LSE Business Review, Academy of Management Insights

This research investigates the link between rivalry and unethical behavior. We propose that people will be more likely to engage in unethical behavior when competing against their rivals than when competing against non-rival competitors. Across an archival study and a series of experiments, we found that rivalry was associated with increased unsporting behavior, use of deception, and willingness to employ unethical negotiation tactics. We also explore the psychological underpinnings of rivalry in order to illuminate how it differs from general competition and why it increases unethical behavior. The data reveal a serial mediation pathway whereby rivalry heightens the psychological stakes of competition (by increasing actors’ contingency of self-worth and status concerns), which leads to the adoption of a stronger performance-approach orientation, which then increases unethical behavior. These findings highlight the importance of rivalry as a widespread, powerful, yet largely unstudied phenomenon with significant organizational implications. They also help to inform when and why unethical behavior occurs within organizations, and demonstrate that the effects of competition are dependent upon relationships and prior interactions between actors.

We study individual ability to memorize and recall information about friendship networks using a combination of experiments and survey-based data. In the experiment subjects are shown a network, in which their location is exogenously assigned, and they are then asked questions about the network after it disappears. We find that subjects exhibit three main cognitive biases: (i) they underestimate the mean degree compared to the actual network; (ii) they overestimate the number of rare degrees; (iii) they underestimate the number of frequent degrees. We then analyze survey data from two ‘real’ friendship networks from a Silicon Valley firm and from a University Research Center. We find, somewhat remarkably, that individuals in these real networks also exhibit these biases.

Media Coverage: ScienceDaily, Phys.org, Tendencias21, Nectunt blog, Cambridge News

The emergence and sustenance of cooperative behavior is fundamental for a society to thrive. Recent experimental studies have shown that cooperation increases in dynamic networks in which subjects can choose their partners. However, these studies did not vary reputational knowledge, or what subjects know about other’s past actions, which has long been recognized as an important factor in supporting cooperation. They also did not give subjects access to global social knowledge, or information on who is connected to whom in the group. As a result, it remained unknown how reputational and social knowledge foster cooperative behavior in dynamic networks both independently and by complementing each other. In an experimental setting, we show that global reputational knowledge is crucial to sustaining a high level of cooperation and welfare. Cooperation is associated with the emergence of dense and clustered networks with highly cooperative hubs. Global social knowledge has no effect on the aggregate level of cooperation. A community analysis shows that the addition of global social knowledge to global reputational knowledge affects the distribution of cooperative activity: cooperators form a separate community that achieves a higher cooperation level than the community of defectors. Members of the community of cooperators achieve a higher payoff from interactions within the community than members of the less cooperative community.

Individuals learn by chit-chatting with others as a by-product of their online and offline activities. Social plugins are an example in the online context: they embed information from a friend, acquaintance or even a stranger on a web page and the information is usually independent of the content of the web page. We formulate a novel framework to investigate how the speed of learning by chit-chat depends on the structure of the environment. A network represents the environment that individuals navigate to interact with each other. We derive an exact formula to compute how the expected time between meetings depends on the underlying network structure and we use this quantity to investigate the speed of learning in the society. Comparative statics show that the speed of learning is sensitive to a mean-preserving spread of the degree distribution (MPS). Specifically, if the number of individuals is low (high), then a MPS of the network increases (decreases) the speed of learning. The speed of learning is the same for all regular networks independent of network connectivity. An extension explores the effectiveness of one agent, the influencer, at influencing the learning process.

Media Coverage: Guardian, International Business Times, The Voice, ABC Radio, BBC Radio (audio), Economy, Weekendavisen (in Danish).

We present the first empirical study to reveal the presence of implicit discrimination in a non-experimental setting. By using a large dataset of in-match data in the English Premier League, we show that white referees award significantly more yellow cards against non-white players of oppositional identity. We argue that this is the result of implicit discrimination by showing that this discriminatory behaviour: (i) increases in how rushed the referee is before making a decision, and (ii) it increases in the level of ambiguity of the decision. The variation in (i) and (ii) cannot be explained by any form of conscious discrimination such as taste-based or statistical discrimination. Moreover, we show that oppositional identity players do not differ in their behaviour from other players along several dimensions related to aggressiveness and style of play providing further evidence that this is not statistical discrimination.

Individuals' cognitive knowledge of their social networks is affected by systematic biases. This paper investigates the role of the mean degree bias, i.e. the tendency to underestimate the number of connections of others, in determining the structure of stable networks. It develops a strategic network formation model where agents have heterogeneous knowledge of the network: cognizant agents know the whole network, while ignorant ones are less knowledgeable and biased. For a broad range of parameters, all cognitively stable (CS) networks are small world networks with segregation patterns and brokers. There are also some CS networks with one hub.