arXiv subjects

Edoardo Gallo

Publications and source records attributed to Edoardo Gallo.

7 recordsLinked to original sources

arXiv2026

How to Disrupt a Market

Market design research in economics naturally focusses on how to improve market efficiency. Our objective here is exactly the opposite - how to design interventions that make a market less efficient. Our research is inspired by the growth of illicit markets online where reducing their efficiency may reduce societal harm. Using a web-based experiment, we find that a partial disruption to delivery is an effective method to decrease market efficiency. The decrease is borne by sellers who sell fewer goods and have lower earnings. A consequence of a disruption to delivery, however, is an increase in market concentration because it facilitates the emergence of a dominant seller. In contrast, we find that attacks on seller ratings are ineffective at reducing market efficiency. This study paves the way for evidence-based, causally driven investigations to aid policies to disrupt cybercrime and other illicit markets.

econ.GN↗

arXiv2023

Social distancing in networks: A web-based interactive experiment

Governments have used social distancing to stem the spread of COVID-19, but lack evidence on the most effective policy to ensure compliance. We examine the effectiveness of fines and informational messages (nudges) in promoting social distancing in a web-based interactive experiment conducted during the first wave of the pandemic on a near-representative sample of the US population. Fines promote distancing, but nudges only have a marginal impact. Individuals do more social distancing when they are aware they are a superspreader. Using an instrumental variable approach, we argue progressives are more likely to practice distancing, and they are marginally more responsive to fines.

econ.GN↗

arXiv2023

Cooperation and Cognition in Social Networks

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.

econ.GN↗

arXiv2022

Experience of the COVID-19 pandemic in Wuhan leads to a lasting increase in social distancing

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.

econ.GN↗

arXiv2022

The economic and health impacts of contact tracing and quarantine programs

Contact tracing and quarantine programs have been one of the leading Non-Pharmaceutical Interventions against COVID-19. Some governments have relied on mandatory programs, whereas others embrace a voluntary approach. However, there is limited evidence on the relative effectiveness of these different approaches. In an interactive online experiment conducted on 731 subjects representative of the adult US population in terms of sex and region of residence, we find there is a clear ranking. A fully mandatory program is better than an optional one, and an optional system is better than no intervention at all. The ranking is driven by reductions in infections, while economic activity stays unchanged. We also find that political conservatives have higher infections and levels of economic activity, and they are less likely to participate in the contact tracing program.

econ.GN↗

arXiv2022

Cooperation and punishment mechanisms in uncertain and dynamic networks

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.

econ.GN↗

arXiv2020

Social networks, confirmation bias and shock elections

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.

econ.TH↗