White (but Not Black) Americans Continue to See Racism as a Zero-Sum Game; White Conservatives (but Not Moderates or Liberals) See Themselves as Losing

Norton and Sommers (2011) assessed Black and White Americans’ perceptions of anti-Black and anti-White bias across the previous six decades—from the 1950s to the 2000s. They presented two key findings: White (but not Black) respondents perceived decreases in anti-Black bias to be associated with increases in anti-White bias, signaling the perception that racism is a zero-sum game; White respondents rated anti-White bias as more pronounced than anti-Black bias in the 2000s, signaling the perception that they were losing the zero-sum game. We collected new data to examine whether the key findings would be evident nearly a decade later, and whether political ideology would moderate perceptions. Liberal, moderate, and conservative White (but not Black) Americans alike believed that racism is a zero-sum game. Liberal White Americans saw racism as a zero-sum game they were winning by a lot, moderate White Americans saw it as a game they were winning by only a little, and conservative White Americans saw it as a game they were losing. This work has clear implications for public policy and behavioral science, and lays the groundwork for future research that examines to what extent racial differences in perceptions of racism by political ideology are changing over time.

Expert Views on COVAX and Equitable Global Access to COVID-19 Vaccines

Objectives: We face the impossibility of having enough COVID-19 vaccines for everyone in the near future. This study aims to contribute to the debate on equitable global access to COVID-19 vaccines, tackling key ethical discussions and policy challenges regarding early phases of COVAX, the global cooperation mechanism for supporting fair vaccine allocation.Methods: We conducted in-depth interviews with twelve experts and a literature research on academic articles, media sources and public statements. We built a data analysis matrix and conducted a thematic analysis.Results: Our findings show, first, that interviewed experts who hold different views on vaccine allocation, including moderate nationalist perspectives, agree on joining a global cooperation mechanism. Second, incentives to join COVAX vary greatly among countries. Third, specific barriers to COVAX emerged in the early implementation phase. And fourth, countries might be trapped in a zero-sum game regarding the global vaccine supply.Conclusion: We present findings that enrich analyses of early phases of COVAX (April 2020–21), we introduce three ethical discussions that provide a common ground for equitable access to COVID-19 vaccines, and we highlight policy challenges.

Model Uncertainty and Financial Frictions: Implications for Optimal Monetary Policy

The last decades proved that policymaking without considering uncertainty is impracticable. In an environment of uncertainty, policymakers have doubts about the policy models they routinely use. This paper focuses specifically on the situation where uncertainty on the financial side of the economy leads to misspecification in the policy model. We describe a coherent strategy for policymakers who are averse to model misspecification and analyze optimal policy design in the face of Knightian uncertainty. To do so, we augment a financial dynamic stochastic general equilibrium model with model misspecification in a simple minimax framework where the central bank plays a zero-sum game versus a hypothetical evil agent. The policy is tailored to insure against the worst-case outcomes. We show that model ambiguity on the financial side requires a passive monetary policy stance. However, if the uncertainty originates from the supply side of the economy, an aggressive response of interest rate is required. We also show the impact of an additional macroprudential tool on the dynamics of the economy.

Computing Effective Mixed Strategies for Protecting Targets in Large-Scale Critical Infrastructure Networks

Most critical infrastructure networks often suffer malicious attacks, which may result in network failures. Therefore, how to design more robust defense measures to minimize the loss is a great challenge. In recent years, defense strategies for enhancing the robustness of the networks are developed based on the game theory. However, the aforementioned method cannot effectively solve the defending problem on large-scale networks with a full strategy space. In this study, we achieve the purpose of protecting the infrastructure networks by allocating limited resources to monitor the targets. Based on the existing two-person zero-sum game model and the Double Oracle framework, we propose the EMSL algorithm which is an approximation algorithm based on a greedy search to compute effective mixed strategies for protecting large-scale networks. The improvement of our approximation algorithm to other algorithms is discussed. Experimental results show that our approximation algorithm can efficiently compute the mixed strategies on actual large-scale networks with a full strategy space, and the mixed defense strategies bring the highest utility to a defender on different networks when dealing with different attacks.

Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization

We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator—that is, a measurable function of the observation—and a fictitious adversary choosing a prior—that is, a pair of signal and noise distributions ranging over independent Wasserstein balls—with the goal to minimize and maximize the expected squared estimation error, respectively. We show that, if the Wasserstein balls are centered at normal distributions, then the zero-sum game admits a Nash equilibrium, by which the players’ optimal strategies are given by an affine estimator and a normal prior, respectively. We further prove that this Nash equilibrium can be computed by solving a tractable convex program. Finally, we develop a Frank–Wolfe algorithm that can solve this convex program orders of magnitude faster than state-of-the-art general-purpose solvers. We show that this algorithm enjoys a linear convergence rate and that its direction-finding subproblems can be solved in quasi-closed form.