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Problems in counterterrorism and corporate competition have prompted research that attempts to combine statistical risk analysis with game theory in ways that support practical decision making. This article applies these methods of adversarial risk analysis to the problem of selecting a route through a network in which an opponent chooses vertices for ambush. The motivating application is convoy routing across a road network when there may be improvised explosive devices and imperfect intelligence about their locations.

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Genome-wide characterization of the Pohlia nutans transcriptome is essential for clarifying the role of stress-relevant genes in Antarctic moss adapting to the extreme polar environment. High-throughput Illumina sequencing was used to analyze the gene expression profile of P. nutans after cold treatment. A total of 93,488 unigenes, with an average length of 405 bp, were obtained. Gene annotation showed that 16,781 unigenes had significant similarity to known functional protein-coding genes, most of which were annotated using the GO, KOG and KEGG pathway databases. Global profiling of the differentially expressed genes revealed that 3,796 unigenes were significantly upregulated after cold treatment, while 1,405 unigenes were significantly downregulated. In addition, 816 receptor-like kinases and 1,309 transcription factors were identified from P. nutans. This overall survey of transcripts and stress-relevant genes can contribute to understanding the stress-resistance mechanism of Antarctic moss and will accelerate the practical exploitation of the genetic resources for this organism.

This paper studies the design of voluntary disclosure regulations for a firm that faces a stochastic environmental hazard. The occurrence of such a hazard is known only to the firm. The regulator, if finding a hazard, collects a fine and mandates the firm to perform costly remediation that reduces the environmental damage. The regulator may inspect the firm at any time to uncover the hazard. However, because inspections are costly, the regulator also offers a reward to the firm for voluntarily disclosing the hazard. The reward corresponds to either a subsidy or a reduced fine, depending on whether it is positive or negative. Thus, the regulator needs to dynamically determine the reward and inspection policy that minimizes expected societal cost in the long run. We model this problem as a dynamic adverse selection problem with costly state verification in continuous time. Despite the complexity and generality of this setup, we show that the optimal regulation policy follows a very simple cyclic structure, which we fully characterize in closed form. Specifically, the regulator runs scheduled inspections periodically. After each inspection, the reward level decreases over time until a subsequent inspection takes place. If a hazard is not revealed, the reward level is reset to a high level, restarting the cycle. In contrast to the reward level, the mandated remediation level is constant over time. Nonetheless, when subsidies are not allowed in the industry, we show that the regulator should dynamically adjust this remediation level, which then acts as a substitute for a subsidy. Our analysis further reveals that optimal inspection frequency increases not only when the inspection accuracy decreases, but also when the penalty for not disclosing the hazard increases.

This paper explores how governments may efficiently inform the public about an epidemic to induce compliance with their confinement measures. Using an information design framework, we find the government has an incentive to either downplay or exaggerate the severity of the epidemic if it heavily prioritizes the economy over population health or vice versa. Importantly, we find that the level of economic inequality in the population has an effect on these distortions. The more unequal the disease’s economic impact on the population, the less the government exaggerates and the more it downplays the severity of the epidemic. When the government weighs the economy and population health sufficiently equally, however, the government should always be fully transparent about the severity of the epidemic. This paper was accepted by Stefan Scholtes, healthcare management.

Adversarial risk analysis (ARA) offers a new solution concept in game theory. This paper explores its application to a range of simple gambling games, enabling comparison with minimax solutions for similar problems. We find that ARA has several attractive advantages: it is easier to compute, it takes account of asymmetric information, it corresponds better to human behavior, and it reduces to previous solutions in appropriate circumstances.

The World Health Organization seeks effective ways to alert its member states about global pandemics. Motivated by this challenge, we study a public agency’s problem of designing warning policies to mitigate potential disasters that occur with advance notice. The agency privately receives early information about recurring harmful events and issues warnings to induce an uninformed stakeholder to take preemptive actions. The agency’s decision to issue a warning critically depends on its reputation, which we define as the stakeholder’s belief regarding the accuracy of the agency’s information. The agency faces then a trade-off between eliciting a proper response today and maintaining its reputation to elicit responses to future events. We formulate this problem as a dynamic Bayesian persuasion game, which we solve in closed form. We find that the agency sometimes strategically misrepresents its advance information about a current threat to cultivate its future reputation. When its reputation is sufficiently low, the agency downplays the risk and actually downplays more as its reputation improves. By contrast, when its reputation is high, the agency sometimes exaggerates the threat and exaggerates more as its reputation deteriorates. Only when its reputation is moderate does the agency send warning messages that fully disclose its private information. Our study suggests a plausible and novel rationale for some of the false alarms or omissions observed in practice. We further test the robustness of our findings to imperfect advance information, disasters without advance notice, and heterogeneous receivers. This paper was accepted by Manel Baucells, decision analysis.