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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.