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1. Anticipatory decision-making for cholera in Malawi

   https://doi.org/10.1128/mbio.00529-23  

   Jutla, Antarpreet; Usmani, Moiz; Brumfield, Kyle D.; Singh, Komalpreet; McBean, Fergus; Potter, Amy; Gutierrez, Angelica; Gama, Samuel; Huq, Anwar; Colwell, Rita R. (December 2023, mBio)

   Hazen, Terry C. (Ed.)

   Climate change raises an old disease to a new level of public health threat. The causative agent,Vibrio cholerae, native to aquatic ecosystems, is influenced by climate and weather processes. The risk of cholera is elevated in vulnerable populations lacking access to safe water and sanitation infrastructure. Predictive intelligence, employing mathematical algorithms that integrate earth observations and heuristics derived from microbiological, sociological, and weather data, can provide anticipatory decision-making capabilities to reduce the burden of cholera and save human lives. An example offered here is the recent outbreak of cholera in Malawi, predicted in advance by such algorithms. 

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2. Distributed Corruption Detection in Networks

   https://doi.org/10.4086/toc.2020.v016a001  

   Alon, Noga; Mossel, Elchanan; Pemantle, Robin (January 2020, Theory of Computing)

   We consider the problem of distributed corruption detection in networks. In this model each node of a directed graph is either truthful or corrupt. Each node reports the type (truthful or corrupt) of each of its outneighbors. If it is truthful, it reports the truth, whereas if it is corrupt, it reports adversarially. This model, first considered by Preparata, Metze and Chien in 1967, motivated by the desire to identify the faulty components of a digital system by having the other components checking them, became known as the PMC model. The main known results for this model characterize networks in which all corrupt (that is, faulty) nodes can be identified, when there is a known upper bound on their number. We are interested in networks in which a large fraction of the nodes can be classified. It is known that in the PMC model, in order to identify all corrupt nodes when their number is t, all in-degrees have to be at least t. In contrast, we show that in d regular-graphs with strong expansion properties, a 1 - O(1/d) fraction of the corrupt nodes, and a 1 - O(1/d) fraction of the truthful nodes can be identified, whenever there is a majority of truthful nodes. We also observe that if the graph is very far from being a good expander, namely, if the deletion of a small set of nodes splits the graph into small components, then no corruption detection is possible even if most of the nodes are truthful. Finally we discuss the algorithmic aspects and the computational hardness of the problem. 

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3. Reconciling complexity and deep uncertainty in infrastructure design for climate adaptation

   https://doi.org/10.1080/23789689.2019.1708179  

   Helmrich, Alysha M.; Chester, Mikhail V. (January 2020, Sustainable and Resilient Infrastructure)

   As climate change is emerging as a major challenge for man-made systems in the coming century, there has been significant effort to understand how to position infrastructure to adapt and deliver services reliably. Particularly, the climate is changing faster than the expected lifetime of critical infrastructure, resulting in situations well beyond the intended design conditions of a stationary climate. This study assesses how well existing infrastructure design approaches – traditional fail-safe, armoring, low regret, safe-to-fail, and adaptive management – account for climate-related complexity and uncertainty through an application of the Cynefin and Deep Uncertainty Frameworks. The results indicate that existing infrastructure design approaches have varying levels of validity for addressing climate change across spatial and temporal scales. The most common infrastructure design approaches undertake lower levels of complexity and uncertainty than climate change demands, indicating the potential of approaches that address complexity and deep uncertainty have not been fully realized. KEYWORDS: Climate change, infrastructure, deep uncertainty, complexity, adaptation 

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4. Random Ambiguity

   Lu, Jay (May 2021, Theoretical economics)

   Spiegler, Ran (Ed.)

   We introduce a model of random ambiguity aversion. Choice is stochastic due to unobserved shocks to both information and ambiguity aversion. This is modeled as a random set of beliefs in the maxmin expected utility model of Gilboa and Schmeidler (1989). We characterize the model and show that the distribution of ambiguity aversion can be uniquely identified using binary choices. A novel stochastic order on random sets is introduced that characterizes greater uncertainty aversion under stochastic choice. If the set of priors is the Aumann expectation of the random set, then choices satisfy dynamic consistency. This corresponds to an agent who knows the distribution of signals but is uncertain about how to interpret signal realizations. More broadly, the analysis of stochastic properties of random ambiguity attitudes provides a theoretical foundation for the study of models of random non-linear utility. 

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5. Effective Premium Discrimination for Designing Cyber Insurance Policies with Rare Losses

   https://doi.org/10.1007/978-3-030-32430-8_16  

   Khalili, M.; Zhang, X.; Liu, M. (October 2019, Conference on Decision and Game Theory for Security (GameSec))

   null (Ed.)

   Cyber insurance like other types of insurance is a method of risk transfer, where the insured pays a premium in exchange for coverage in the event of a loss. As a result of the reduced risk for the insured and the lack of information on the insurer’s side, the insured is generally inclined to lower its effort, leading to a worse state of security, a common phenomenon known as moral hazard. To mitigate moral hazard, a widely employed concept is premium discrimination, i.e., an agent/insured who exerts higher effort pays less premium. This, however, relies on the insurer’s ability to assess the effort exerted by the insured. In this paper, we study two methods of premium discrimination that rely on two different types of assessment: pre-screening and post-screening. Pre-screening occurs before the insured enters into a contract and can be done at the beginning of each contract period; the result of this process gives the insurer an estimated risk on the insured, which then determines the contract terms. The post-screening mechanism involves at least two contract periods whereby the second-period premium is increased if a loss event occurs during the first period. Prior work shows that both pre-screening and post-screening are generally effective in mitigating moral hazard and increasing the insured’s effort. The analysis in this study shows, however, that the conclusion becomes more nuanced when loss events are rare. Specifically, we show that post-screening is not effective at all with rare losses, while pre-screening can be an effective method when the agent perceives them as rarer than the insurer does; in this case pre-screening improves both the agent’s effort level and the insurer’s profit. 

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