Experts vs. policymakers in the COVID-19 policy response

Area of Science:

• Game Theory
• Public Health Policy
• Epidemiology

Background:

• COVID-19 pandemic necessitated complex policy decisions balancing public health and societal impact.
• Policymakers face a dilemma between strict, unpopular measures and voluntary, less effective ones.
• Expert advice often guides policy, but policymakers also consider public consensus.

Purpose of the Study:

• To model the strategic interaction between policymakers and experts in pandemic response using evolutionary game theory.
• To investigate how differing incentives and initial conditions influence the choice between hard (strict) and soft (voluntary) pandemic management strategies.
• To explain the observed diversity of national COVID-19 policy responses.

Main Methods:

• Developed an evolutionary game-theoretic model analyzing the decision-making of policymakers and experts.
• Incorporated factors such as public consensus incentives for policymakers and reliance on expert advice.
• Examined path-dependency and the influence of initial strategy distribution, cultural factors, and epidemiological conditions.

Main Results:

• Strategy selection is path-dependent, heavily influenced by initial conditions.
• Multiple equilibria exist: unanimous endorsement of strict or soft strategies, asymmetric agreements, or cyclical dynamics.
• Model explains diverse international countermeasures during the first COVID-19 wave.

Conclusions:

• Contrasting pandemic policy responses across countries can arise endogenously from the interplay of incentives and local conditions.
• Differences in policy are not necessarily due to poor decision-making but reflect the model's predicted multiplicity of equilibria.
• The study highlights the importance of considering social, cultural, and epidemiological contexts in understanding policy divergence.