[Paper Review] Corona Games: Masks, Social Distancing and Mechanism Design

Balázs Pejó, Gergely Biczók|arXiv (Cornell University)|Jun 11, 2020

COVID-19 epidemiological studies24 references4 citations

TL;DR

This paper frames pandemic response as a mechanism design problem, using game theory to analyze how individual incentives—such as self-interest in mask-wearing and social distancing—affect public health outcomes. It shows that without proper policy design, rational individuals may choose suboptimal behaviors, and that government interventions like contact tracing and testing significantly improve outcomes by reducing uncertainty and aligning individual with social good.

ABSTRACT

Pandemic response is a complex affair. Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing and contact tracing. We argue that some non-trivial factors behind the varying effectiveness of these measures are selfish decision-making and the differing national implementations of the response mechanism. In this paper, through simple games, we show the effect of individual incentives on the decisions made with respect to wearing masks and social distancing, and how these may result in a sub-optimal outcome. We also demonstrate the responsibility of national authorities in designing these games properly regarding the chosen policies and their influence on the preferred outcome. We promote a mechanism design approach: it is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism.

Motivation & Objective

• To investigate how individual incentives—such as self-interest in mask-wearing and social distancing—affect pandemic outcomes.
• To demonstrate that national policy design profoundly influences the equilibrium outcomes of pandemic-related behavioral games.
• To show that sub-mechanisms like contact tracing and testing are interdependent and reduce decision-making uncertainty.
• To argue that governments should treat pandemic response as a mechanism design problem to balance social good and implementation costs.
• To provide a framework for optimizing policies using game-theoretic modeling and real-world data integration.

Proposed method

• Models mask-wearing and social distancing decisions as non-cooperative games under full and incomplete information.
• Introduces Bayesian decision-making to capture uncertainty about infection status in the Mask Game.
• Incorporates parameters like mask efficiency, meeting duration, and group size in the Distancing Game to model real-world trade-offs.
• Analyzes the impact of contact tracing and testing as sub-mechanisms that reduce uncertainty and shift equilibria toward socially optimal outcomes.
• Uses real-world COVID-19 statistics to quantify rational thresholds for going out and optimal meeting parameters.
• Proposes a mechanism design framework where governments act as designers of response systems, balancing cost and social benefit.

Experimental results

Research questions

• RQ1 How do individual incentives affect mask-wearing and social distancing decisions in a pandemic?
• RQ2 Under what conditions does rational self-interest lead to suboptimal public health outcomes in pandemic response?
• RQ3 How do government policies—such as free mask distribution or unemployment benefits—influence behavioral equilibria?
• RQ4 To what extent can contact tracing and targeted testing reduce uncertainty and improve decision-making in pandemic games?
• RQ5 How can governments optimally design pandemic response mechanisms to balance social good and implementation costs?

Key findings

• In the Mask Game, the equilibrium outcome is not socially optimal even under full information, due to individual incentives favoring non-compliance.
• When players' infection status is unknown, the equilibrium is typically not to wear a mask for a wide range of parameters, despite potential benefits.
• Even with low mask efficiency, it is almost always optimal to wear a mask when facing an infectious player, due to high transmission risk.
• For social distancing, going out is only rational when the benefit is large or the cost of staying home is significant, based on current COVID-19 statistics.
• Optimal meeting duration and size for out-of-home activities can be quantified using real-world data, balancing risk and utility.
• Contact tracing enables targeted testing, which drastically reduces uncertainty and shifts behavior toward socially optimal outcomes, effectively acting as an exogenous 'health oracle'.