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1. On Fair Division for Indivisible Items

   https://doi.org/10.4230/LIPIcs.FSTTCS.2018.25  

   Chaudhury, Bhaskar Ray; Cheung, Yun Kuen; Garg, Jugal; Garg, Naveen; Hoefer, Martin; Mehlhorn, Kurt (December 2018, Leibniz international proceedings in informatics)

   We consider the task of assigning indivisible goods to a set of agents in a fair manner. Our notion of fairness is Nash social welfare, i.e., the goal is to maximize the geometric mean of the utilities of the agents. Each good comes in multiple items or copies, and the utility of an agent diminishes as it receives more items of the same good. The utility of a bundle of items for an agent is the sum of the utilities of the items in the bundle. Each agent has a utility cap beyond which he does not value additional items. We give a polynomial time approximation algorithm that maximizes Nash social welfare up to a factor of e^{1/{e}} ~ 1.445. The computed allocation is Pareto-optimal and approximates envy-freeness up to one item up to a factor of 2 + epsilon. 

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2. Discrete Choice under Risk with Limited Consideration

   https://doi.org/10.1257/aer.20190253  

   Barseghyan, Levon; Molinari, Francesca; Thirkettle, Matthew (June 2021, American Economic Review)

   null (Ed.)

   This paper is concerned with learning decision-makers’ preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the model’s semi-nonparametric point identification, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset on property insurance purchases. (JEL D81, D83, D91, G22, G52) 

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3. Heterogeneous Choice Sets and Preferences

   https://doi.org/10.3982/ECTA17448  

   Barseghyan, Levon; Coughlin, Maura; Molinari, Francesca; Teitelbaum, Joshua C. (January 2021, Econometrica)

   null (Ed.)

   We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity. 

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4. Measuring Ex Ante Welfare in Insurance Markets

   https://doi.org/10.1093/restud/rdaa015  

   Hendren, Nathaniel (June 2020, The Review of Economic Studies)

   null (Ed.)

   Abstract The willingness to pay for insurance captures the value of insurance against only the risk that remains when choices are observed. This article develops tools to measure the ex ante expected utility impact of insurance subsidies and mandates when choices are observed after some insurable information is revealed. The approach retains the transparency of using reduced-form willingness to pay and cost curves, but it adds one additional sufficient statistic: the percentage difference in marginal utilities between insured and uninsured. I provide an approach to estimate this additional statistic that uses only the reduced-form willingness to pay curve, combined with a measure of risk aversion. I compare the approach to structural approaches that require fully specifying the choice environment and information sets of individuals. I apply the approach using existing willingness to pay and cost curve estimates from the low-income health insurance exchange in Massachusetts. Ex ante optimal insurance prices are roughly 30% lower than prices that maximize observed market surplus. While mandates reduce market surplus, the results suggest they would actually increase ex ante expected utility. 

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5. Agent‐based Modeling to Evaluate Human–Environment Interactions in Community Flood Risk Mitigation

   https://doi.org/10.1111/risa.13854  

   Han, Yu; Mao, Liang; Chen, Xuqi; Zhai, Wei; Peng, Zhong‐Ren; Mozumder, Pallab (November 2021, Risk Analysis)

   This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management. 

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