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In service systems with delay-sensitive customers, customer decisions to join the queue or balk influence demand, impacting service provider profits. Studies show that customers can be risk-seeking for short waits and risk-averse when waits exceed expectations. Prior research on this mixed-risk attitude is limited, focussing on optimal pricing policies from a social welfare perspective. This study fills this gap by investigating the impact of this behaviour on customer joining strategies and the provider's pricing and capacity policies. We analyse a profit-maximising firm managing an unobservable queue where customers display mixed-risk behaviour towards delay parameterised by a risk-switching point and a degree of risk propensity. Customers join or balk based on expected utility, while the firm determines the service price and rate to maximise expected profit. We derive optimal strategies for both customers and the firm under scenarios with concave or convex capacity costs. Computational experiments show that optimal pricing, capacity, and profit peak at intermediate risk-switching points. For smaller points, optimal price and capacity rise with the customers' degree of risk propensity, while larger points have minimal impact. Numerical exploration emphasises the significance of capacity cost and the effectiveness of leveraging price over controlling capacity for maximising firm profitability. [ABSTRACT FROM AUTHOR]

This homage to Danny Kahneman and Amos Tversky describes how each of them thought about psychology. It outlines the principal results of their collaborative research, which was their most original and most influential. Why? In search of an explanation it examines their joint thinking during their collaboration. [ABSTRACT FROM AUTHOR]

Expected utility theory often falls silent, even in cases where the correct rankings of options seems obvious. For instance, it fails to compare the Pasadena game to the Altadena game, despite the latter turning out better in every state. Decision theorists have attempted to fill these silences by proposing various extensions to expected utility theory. As I show in this paper, such extensions often fall silent too, even in cases where the correct ranking is intuitively obvious. But we can extend the theory further than has been done before—I offer a new extension, Invariant Value Theory, which deals neatly with those problem cases and also satisfies various desirable conditions. But other prima facie desirable conditions, including Independence, the theory violates. Is this a problem for the proposal? It may not be—in a new impossibility result, I show that no theory can satisfy Independence in full generality without violating several other conditions that together seem just as plausible. [ABSTRACT FROM AUTHOR]

We introduce a risk measure that simultaneously allows to minimize hedging costs and maximize expected utility in the presence of a risk constraint. We call it the scalarized utility-based multi-asset (SUBMA) risk measure. For the SUBMA risk measure we state the following results: If the utility function has constant relative risk aversion and the risk constraint is coherent, then the SUBMA risk measure is coherent. In a one-period financial market setup, we present a sufficient condition for the SUBMA risk measure to be finite-valued. We derive results about the existence of optimal payoffs. Finally, we present a dual representation for a map that generalizes classical risk measures and has not been analyzed so far. In particular, it gives us the dual representation for the SUBMA risk measure. [ABSTRACT FROM AUTHOR]

We argue that one is likely to accept what a speaker says when the expected utility of accepting their testimony is greater than the expected utility of continuing inquiry. One virtue of our hypothesis is that it allows us to explain why confidence in experts has declined in recent years. In a traditional media landscape expert testimony is easy to find, and alternative sources of information are relatively costly to access. Hence, practical considerations largely favour accepting expert testimony. But on social media, alternative information is easy to find, and it is often practically rational to accept this information rather than to search for (and identify) genuinely expert testimony. [ABSTRACT FROM AUTHOR]

It is shown that well-behaved notions of greater or less downside risk aversion, via utility transformations, lead not to just one, but two, dual, notions of absolute aversion to downside risk: one, the more evident but weaker condition, requires that the prudence measure be positive, given a positive Arrow–Pratt measure of risk aversion, whereas the other, stronger, but less obvious, condition requires that the prudence measure be greater than three times the corresponding Arrow–Pratt measure. The reason for the appearance of these two extreme conditions, bounding the spectrum of reasonable alternative notions of downside risk aversion, or equivalently of downside risk loving, are explained, and consequences of this divergence in the possible meanings of downside risk aversion are explored. • Greater aversion to risk of any order can always be formulated in two complementary ways, in the setting of expected utility. These formulations are in terms of transformations of utility functions. • At the second order, these two formulations coincide, but they differ at higher orders, with the one condition being stronger than the other. • The resulting greater downside aversion to risk conditions are applied when one of the utility functions is risk-neutral, yielding alternative criteria for absolute aversion to downside risk of the remaining utility function. • Both conditions of absolute aversion to downside risk are further characterized in terms of measure conditions, with the stronger measure condition being a novel one. [ABSTRACT FROM AUTHOR]

One primary advantage of receiver operating characteristic (ROC) analysis is considered to be its ability to quantify classification performance independently of factors such as prior probabilities and utilities of classification outcomes. This article argues the opposite. When evaluating classification performance, ROC analysis should consider prior probabilities and utilities. By developing expected utility lines (EU lines), this article shows the connection between a classifier's ROC curve and expected utility of classification. In particular, EU lines can be used to estimate expected utilities when classifiers operate at any ROC point for any given prior probabilities and utilities. EU lines are useful across all situations—no matter if one examines a single classifier or compares multiple classifiers, if one compares classifiers' potential to maximize expected utilities or classifiers' actual expected utilities, and if the ROC curves are full or partial, continuous or discrete. The connection between ROC and expected utility analyses reveals the common objective underlying these two methods: to maximize expected utility of classification. Particularly, ROC analysis is useful in choosing an optimal classifier and its optimal operating point to maximize expected utility. Yet, choosing a classifier and its operating point (i.e., changing conditional probabilities) is not the only way to increase expected utility. Inspired by parameters involved in estimating expected utility, this article also discusses other approaches to increase expected utility beyond ROC analysis. How should humans and machines improve classification performance? Past use of receiver operating characteristic (ROC) analysis has focused on improving classification performance by improving a classifier's accuracy, which is indicated by the area under the classifier's ROC curve. This article, however, shows that ROC analysis inherently relates to expected utility analysis and the objective underlying ROC analysis is to maximize expected utility of classification. This article develops a tool to visualize a classifier's expected utility in its ROC space—expected utility lines (EU lines). Analyzing ROC curves with EU lines, this article shows how ROC analysis could be used to evaluate and compare classifiers' expected utilities for any given prior probabilities and utilities of classification outcomes. The analysis reveals that expected utility depends not only on a classifier's accuracy, but more importantly on the classifier's operating point. Therefore, ROC analysis should be used not only to choose an optimal classifier, but more importantly to locate its optimal operating point to maximize expected utility. Inspired by parameters involved in estimating expected utility, this article goes beyond ROC analysis and discusses other possible ways to increase expected utility. In addition to building important theoretical foundations for properly understanding and using ROC and expected utility analyses, this article sheds light on how researchers and practitioners can improve classification performance for policy or decision processes that involve binary classifications. [ABSTRACT FROM AUTHOR]

Overconfident behavior, the excessive willingness to bet on one's performance, may be driven by optimistic beliefs and/or ambiguity attitudes. Separating these factors is key for understanding and correcting overconfident behavior, as they call for different corrective actions. We present a method to do so, which we implement in two incentivized experiments. The first experiment shows the importance of ambiguity attitudes for overconfident behavior. Optimistic ambiguity attitudes (ambiguity seeking) counterbalanced the effect of pessimistic beliefs, leading to neither over- nor underconfident behavior. The second experiment applies our method in contexts where overconfident behavior is expected to vary: easy versus hard tasks. Our results showed that task difficulty affected both beliefs and ambiguity attitudes. However, although beliefs were more optimistic for relative performance (rank) and more pessimistic for absolute performance (score) on easy tasks compared with hard tasks, ambiguity attitudes were always more optimistic on easy tasks for both absolute and relative performance. Our findings show the subtle interplay between beliefs and ambiguity attitudes: they can reinforce or offset each other, depending on the context, increasing or lowering overconfident behavior. Yuval Rottenstreich, behavioral economics and decision analysis. Funding: This work was supported by HEC Paris research budget and Bocconi junior researchers' grants. Supplemental Material: The data and online appendix are available at https://doi.org/10.1287/mnsc.2021.00165. [ABSTRACT FROM AUTHOR]

Dybvig (1988a, 1988b) solves in a complete market setting the problem of finding a payoff that is cheapest possible in reaching a given target distribution ("cost-efficient payoff"). In the presence of ambiguity, the distribution of a payoff is, however, no longer known with certainty. We study the problem of finding the cheapest possible payoff whose worst-case distribution stochastically dominates a given target distribution ("robust cost-efficient payoff") and determine solutions under certain conditions. We study the link between "robust cost-efficiency" and the maxmin expected utility setting of Gilboa and Schmeidler (1989), as well as more generally in a possibly nonexpected robust utility setting. Specifically, we show that solutions to maxmin robust expected utility are necessarily robust cost-efficient. We illustrate our study with examples involving uncertainty both on the drift and on the volatility of the risky asset. [ABSTRACT FROM AUTHOR]

How should successive generations insure each other when the young can default on previously promised transfers to the old? This paper studies intergenerational insurance that maximizes the expected discounted utility of all generations subject to participation constraints for each generation. If complete insurance is unattainable, the optimal intergenerational insurance is history dependent even when the environment is stationary. The risk from a generational shock is spread into the future with periodic "resetting." If we interpret intergenerational insurance in terms of debt, the fiscal reaction function is nonlinear and the risk premium on debt is lower than the risk premium with complete insurance. [ABSTRACT FROM AUTHOR]

An agent's preferences depend on an ordered parameter or type. We characterize the set of utility functions with single-crossing differences (SCD) in convex environments. These include preferences over lotteries, both in expected utility and rank-dependent utility frameworks, and preferences over bundles of goods and over consumption streams. Our notion of SCD does not presume an order on the choice space. This unordered SCD is necessary and sufficient for "interval choice" comparative statics. We present applications to cheap talk, observational learning, and collective choice, showing how convex environments arise in these problems and how SCD/interval choice are useful. Methodologically, our main characterization stems from a result on linear aggregations of single-crossing functions. [ABSTRACT FROM AUTHOR]

We study the design of negotiation strategies when a principal and agent must decide how to split a pie while the agent's outside option changes over time. The principal's optimal strategy under commitment demonstrates a new, but intuitive, set of negotiation dynamics. When the agent is tempted to leave, the principal gradually promises a larger share (decreasing demands) and more time to explore the outside option (decreasing pressure), illustrating a complementarity between these two tools. Although the principal's expected utility is decreasing in the outside option, his expected utility and demands are increasing in the outside option's drift and volatility. (JEL D63, D82, D86) [ABSTRACT FROM AUTHOR]

In response to cases that involve tiny probabilities of huge payoffs, some argue that we ought to discount small probabilities down to zero. However, this paper shows that doing so violates Independence and Continuity, and as a result of these violations, those who discount small probabilities can be exploited by money pumps. Various possible ways of avoiding exploitation will be discussed. This paper concludes that the money pump for Independence undermines the plausibility of discounting small probabilities. Much of the discussion on Independence generalizes to other views that also violate Independence. [ABSTRACT FROM AUTHOR]

Countenancing unbounded utility in ethics gives rise to deep puzzles in formal decision theory. Here, these puzzles are taken as an invitation to assess the most fundamental principles relating probability and value, with the aim of demonstrating that unbounded utility in ethics is compatible with a desirable decision theory. The resulting theory frames further discussion of Expected Utility Theory and of principles concerning symmetries of utility. [ABSTRACT FROM AUTHOR]

Our paper presents the first optimal analytical solution for an investor maximizing both consumption and terminal wealth within expected utility theory in the realm of GARCH models. Working in a general family of affine GARCH models, we derive an affine GARCH optimal wealth process, providing analytical representations for optimal allocation, consumption and value functions. In particular, the optimal consumption ratio avoids the undesirable scenario of investors consuming all wealth prior to maturity. Our numerical study highlights the importance of formally accounting for consumption as it disrupts the level of optimal risky allocations. It also shows a larger impact of stochastic conditional variance (heteroscedasticity) on risky allocations in comparison to the impact of non-Gaussianity. We find, in a numerical study based on S&P 500 index data over a 5-years horizon, that an investor following a Gaussian GARCH strategy can achieve 10% more total consumption and at the same time 8% more terminal wealth than another investor following a constant variance (homoscedastic) strategy. [ABSTRACT FROM AUTHOR]

Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimal reinsurance and investment problem from joint interests of the insurer and the reinsurer in the framework of the mixed leadership game, where both of them can invest their wealth in the financial market, and the liabilities are correlated with the risky asset. More specifically, we assume that the insurer is the leader to determine the amount he/she invests into the risky asset but acts as the follower to decide the optimal reinsurance retention level, while the reinsurer is the leader to set the reinsurance premium price and acts as a follower to determine his/her investment strategy. Both the insurer and the reinsurer aim to maximize the expected utility of their terminal wealth, and the explicit Stackelberg-Nash equilibrium is derived by solving the corresponding Hamilton-Jacobi-Bellman (HJB) equations. In order to better understand the effect of the mixed leadership game on the optimal reinsurance and investment problem, we also consider the optimization problem within the framework of the stochastic Stackelberg differential game shown in [Bai, Y., Zhou, Z., Xiao, H., Gao, R. & Zhong, F. (2021). A Stackelberg reinsurance-investment game with asymmetric information and delay. Optimization70(10), 2131–2168.], where the reinsurer is the leader while the insurer acts as the follower for both the reinsurance and investment strategies. Some theoretical analyses and numerical examples are provided to show the economic intuition and insights of the results. [ABSTRACT FROM AUTHOR]

This paper considers a non-zero-sum stochastic differential reinsurance and investment game between two competitive insurers under the expected exponential utility. It is assumed that the surplus process of each insurer is characterized by a compound Poisson model and their claim businesses are correlated through thinning dependence structure. Each insurer can purchase proportional reinsurance and is allowed to invest in a risk-free asset and a risky asset. The insurers compete with each other and are concerned with the relative performance. By using the stochastic control technique, not only the existence and uniqueness of the optimal strategies are proved, but also the specific analyses for the constraint reinsurance strategies are provided. We find that (i) the thinning dependence structure has significant impact on optimal reinsurance strategies for both insurers and can help them reduce risks; (ii) the behavior of non-zero-sum game will reduce the proportion of purchasing reinsurance for each insurer. [ABSTRACT FROM AUTHOR]

There are some connections between aging notions, stochastic orders, and expected utilities. It is known that the DRHR (decreasing reversed hazard rate) aging notion can be characterized via the comparative statics result of risk aversion, and that the location-independent riskier order preserves monotonicity between risk premium and the Arrow–Pratt measure of risk aversion, and that the dispersive order preserves this monotonicity for the larger class of increasing utilities. Here, the aging notions ILR (increasing likelihood ratio), IFR (increasing failure rate), IGLR (increasing generalized likelihood ratio), and IGFR (increasing generalized failure rate) are characterized in terms of expected utilities. Based on these observations, we recover the closure properties of ILR, IFR, and DRHR under convolution, and of IGLR and IGFR under product, and investigate the closure properties of the dispersive order, location-independent riskier order, excess wealth order, the total time on test transform order under convolution, and the star order under product. We have some new findings. [ABSTRACT FROM AUTHOR]

We run an experiment to elicit preferences over state-contingent timed payouts. We analyze the data using a new revealed preference method (building on Nishimura et al., 2017) that can test for consistency with utility functions that increase with a given preorder. We find that correlation neutrality, a property implied by discounted expected utility, is widely violated and there is, instead, strong evidence of intertemporal correlation averse behavior. Our results suggest that utility is not additive across both states and time and that credible models of choice need to allow people to prefer negative correlation in timed payouts. [ABSTRACT FROM AUTHOR]

Flood risk management faces challenging decisions to balance between reducing disastrous flood consequences and different societal goals such as development. The inherent complexity and limited data often lead to significant uncertainties in decision‐making, potentially resulting in suboptimal resource allocation. Consequently, there may be value in aiming to reduce uncertainty, minimizing the possibility of selecting deemed efficient decisions because of deficiencies in the current knowledge. To address this, a novel methodology is proposed, integrating Bayesian uncertainty with value of information concepts, commonly employed in healthcare economics. This methodology assesses the implications of current uncertainty and identifies worthwhile sources for resolution prior making decisions. Validation in a synthetic case study and application in a real case (Zapayan wetland in the Magdalena River, Colombia) demonstrate the method's efficacy. Results show that the proposed method can help apprising if the available information is enough to make a decision, or if more information should be obtained. For example, for the synthetic case, resolving the sources of uncertainty with extra information does not significantly improve the expected utility, so a decision could be made based on existing information. For the real case, reducing the uncertainty related to the exposed assets should be targeted first, by an information gathering activity, before deciding. [ABSTRACT FROM AUTHOR]

A subjective expected utility maximizer is usually considered to be neutral to ambiguity (Knightian uncertainty). Arguably, a decision-maker who behaves as if he or she forms probabilistic beliefs over uncertain events (but does not necessarily maximize expected utility) is also ambiguity neutral. This note proposes a simple definition of (absolute) ambiguity neutrality in choice under Knightian uncertainty: if a decision-maker bets more money on event A rather than on event B then he or she should also consistently bet less money on event B (rather than on event A). This definition is consistent with the classic Ellsberg two- and three-color paradoxes. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in the optimal investment and reinsurance strategies of an insurer who wishes to maximize the expected power utility of its terminal wealth on finite time horizon. We are also interested in the problem of maximizing the growth rate of expected power utility per unit time on the infinite time horizon. The risk process of the insurer is described by an approximation of the classical Cramér–Lundberg process. The insurer invests in a market consisting of a bank account and multiple risky assets. The mean returns of the risky assets depend linearly on economic factors that are formulated as the solutions of linear stochastic differential equations. With this setting, Hamilton–Jacobi–Bellman equations that are derived via a dynamic programming approach have explicit solution obtained by solving a matrix Riccati equation. Hence, the optimal investment and reinsurance strategies can be constructed explicitly. Finally, we present some numerical results related to properties of our optimal strategy and the ruin probability using the optimal strategy. [ABSTRACT FROM AUTHOR]

Problem definition: Motivated by the debate around workers' welfare in the gig economy, we propose a framework to evaluate current practices and possible alternatives. We study a setting in which customers seek service from workers and a platform facilitates such matches over the course of the day. The platform allocates time slots to workers using an allocation policy, and the workers are strategic agents (with respect to "when to work") who maximize their expected utility that depends on their preferred times to work, the allocated slots, and the total availability time. The platform seeks to ensure that a sufficient number of workers is available to satisfy demand, whereas the workers aim to maximize their wage-driven utility. Methodology/results: We evaluate policies on two dimensions critical to any firm: the supply of workers across the day, and the effective wages of workers. We illustrate that several families of currently deployed policies have serious limitations. We find these limitations exist because the policies do not let workers fully express their preferences and/or cannot account for heterogeneity in such preferences. We propose a new allocation policy and establish strong performance guarantees with respect to both the workers' supply and effective wages. The policy is simple and fully leverages the market information to reach better market outcomes. We supplement our theory with numerical experiments in the context of ride-hailing calibrated on various New York City data sets that illustrate performance across a range of markets. Managerial implications: We highlight a fundamental inefficiency of policies currently deployed that limit workers' ability to express their preferences. By allowing workers to express their temporal preferences, and by judiciously prioritizing "full-time" workers over "part-time" workers, we can obtain a potentially significant Pareto improvement, maintaining (or even increasing) workers' supply while increasing their effective wages. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0414. [ABSTRACT FROM AUTHOR]

We propose a multiperiod insurance model under a bonus–malus system with two rate classes and consider an insured who has purchased full insurance for her losses. To explore the potential advantage of underreporting her insurable losses, the insured follows a barrier strategy and only reports lossses above the barrier to the insurer. We obtain a unique equilibrium declaration strategy in closed form for a risk‐neutral insured who maximizes her expected wealth, and in semiclosed form for a risk‐averse insured who maximizes her expected exponential utility of wealth, both over an exogenous random horizon. We find that the equilibrium barriers for the two classes are equal and strictly greater than zero, offering a theoretical explanation for the underreporting of insurable losses, a form of ex post moral hazard. Finally, we consider the case of three rate classes and show, through numerical examples, that the equilibrium barriers are not equal. [ABSTRACT FROM AUTHOR]

This paper begins by contrasting the caricatures 'homo and femina economicus' with 'homo and femina realitus'. Against this backdrop, the paper considers three 'apparently falsified' empirical predictions of the standard expected utility model of individual decision-making concerning participation in fiscal crimes: that tax evasion and benefit fraud can be treated identically; fiscal crimes should be endemic; and that all individuals, depending on parameter values, should be either honest or dishonest. A utility function relating to decisions with a moral dimension is used to offer insight into the rationalization of the predictions and involves defining an individual's 'optimal honesty' in the context of fiscal crimes. The policy implications of the approach are briefly explored. [ABSTRACT FROM AUTHOR]

We introduce vendor-managed inventory (VMI) modes and retailer-managed inventory modes and use conditional value-at-risk to quantify the inventory manager's risk aversion. Then, we compare the supply chain (SC) members' optimal decisions and expected utilities when the online retail platform or the supplier conducts sales effort under different inventory modes. The cost-sharing contracts are introduced to optimize the SC performance. Some interesting findings are achieved: for VMI SC, SC members' sales-effort preferences change with the wholesale price; the inventory manager's risk-aversion degree has a significant impact on the SC members' preferences for inventory management modes; there always exists appropriate cost-sharing ratio regions in which the SC members' expected utilities can be improved. The corresponding research results can help to improve the operational effectiveness of different kinds of SCs. [ABSTRACT FROM AUTHOR]

This paper presents a novel optimization model that explores the optimal investment strategies for DC pension plans with return of premium clauses. We have assumed that the financial market consists of a risk-free asset and a risky asset, where the price of the risky asset follows the CEV model. Under the expected utility criterion, the optimal investment strategies were derived by employing stochastic optimal control theory and the Legendre transformation method. Explicit expressions of the optimal investment strategy were provided when the utility function was specified as exponential, power, or logarithmic. Finally, numerical analysis was conducted to examine the impact of factors such as interest rate, return rate, and volatility of the risky asset on the optimal strategies.

We make a novel investigation of welfare costs associated with various suboptimal decisions made by retirees, both analytically and numerically. We utilize a unique framework that incorporates recursive utility with housing, and also encompasses expected utility and recursive utility without housing as special cases. Our findings indicate that under-investment in stocks incurs lower welfare costs compared to an equivalent over-investment. Suboptimal allocations in bond holdings result in higher costs than similar misallocations in stocks. Choosing not to participate in the stock market is less detrimental than avoiding the bond market. Should retirees opt to simplify their decision-making by investing solely in one type of asset, it is less costly for them to invest exclusively in bonds. Overconsumption of housing is less costly than an equivalent underconsumption. Suboptimal consumption imposes the highest welfare cost. Decisions regarding consumption, housing, and savings are found to be more crucial than the choice of how to distribute liquid savings between stocks and bonds. Additionally, recursive utility model better captures retirees' preference for bonds over stocks than expected utility model. Our results, which are consistent across various parameter settings, provide valuable insights for academics and policymakers aiming to enhance retiree welfare. [ABSTRACT FROM AUTHOR]

AbstractIn this article, we analyze a robust optimal reinsurance and investment problem in a model with default risks and jumps for a general company which holds shares of an insurer and a reinsurer. The insurer’s surplus is characterized by a diffusion model and the insurer has the opportunity to buy proportional reinsurance from the reinsurer. Moreover, the insurer can invest in a risk-free asset (bank account), a risky asset (stock), and a defaultable bond. Meanwhile, the reinsurer can also invest in a risk-free asset and another risky asset. The price dynamics of the risky asset is assumed to follow a jump-diffusion model. What is more, the general company is concerned about the ambiguity and aims to find a robust optimal reinsurance and investment strategy. The objective of the general company is to maximize the minimal expected exponential utility of the weighted sums of the insurer’s and the reinsurer’s terminal wealth. Applying stochastic dynamic programming approach, we first establish the robust HJB (Hamilton-Jacobi-Bellman) equations for the post-default case and the pre-default case, respectively. Furthermore, we obtain the robust optimal strategy explicitly and present the corresponding verification theorem. Finally, we give some numerical examples to demonstrate the influences of model parameters on the robust optimal strategy. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal investment strategy for a defined contribution pension plan under the jump diffusion model and S-shaped utility. We assume that pension plan members can invest their pension fund in the financial market consisting of a risk-free asset and a risk asset whose price process follows a jump diffusion process. The goal of pension plan managers is to maximize the expected utility of the real terminal wealth under the jump diffusion model and S-shaped utility. We apply Lagrange dual method, concavification technique and martingale method to derive the closed-form expressions of the optimal wealth process and the optimal investment strategy. Finally, we also try to use some numerical analysis to explain the impacts of model parameters on the optimal terminal wealth value and trading strategy. [ABSTRACT FROM AUTHOR]

The article discusses the concept of Mobility as a Service (MaaS), which is a digital platform that allows users to plan, book, and pay for various types of transportation services. MaaS is seen as a way to achieve sustainability objectives by reducing private car ownership and traffic congestion. However, there is limited evidence on how MaaS has contributed to these objectives. The article highlights the challenges facing MaaS, including the need to incentivize users and make the alternative modes of transportation more attractive. The authors suggest that involving non-transport providers and implementing a tendering authority could help address these challenges. Overall, the future of MaaS is uncertain and further research is needed. [Extracted from the article]

Probability Matching, a classical violation of expected utility maximization, refers to people's tendency to randomize, or even match their choice frequency to the outcome probability, when choosing over binary lotteries that differ only in their probabilities. Why? I present an experiment designed to distinguish between several broad classes of explanations: (1) models of Correlation-Invariant Stochastic Choice — randomizing due to factors orthogonal to the correlation between lotteries, such as non-standard preferences or errors, and (2) models of Correlation-Sensitive Stochastic Choice — deliberately randomizing due to misperceived hedging opportunities, especially when lotteries are negatively correlated. My experimental design differentiates between their testable predictions by varying the correlation between lottery outcomes. The findings indicate that the first class, despite being home to most existing theories, has limited explanatory power. Using additional treatment, I rule out Similarity Heuristics as a competing explanation with the second class. The results indicate that a vast majority of individuals deliberately randomize due to misperceived hedging opportunities. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal management of an aggregated defined benefit pension plan in a stochastic environment. The interest rate follows the Ornstein-Uhlenbeck model, the benefits follow the geometric Brownian motion while the contribution rate is determined by the spread method of fund amortization. The pension manager invests in the financial market with three assets: cash, a zero-coupon bond and a stock. Regardless of the initial status of the plan, we suppose that the pension fund may become underfunded or overfunded in the planning horizon. The optimization goal of the manager is to maximize the expected utility in the overfunded region minus the weighted solvency risk in the underfunded region. By introducing an auxiliary process and related equivalent optimization problems and using the martingale method, the optimal wealth process, optimal portfolio and efficient frontier are obtained under four cases (high tolerance towards solvency risk, low tolerance towards solvency risk, a specific lower bound, and high lower bound). Moreover, we also obtain the probabilities that the optimal terminal wealth falls in the overfunded and underfunded regions. At last, we present numerical analyzes to illustrate the manager's economic behaviors. [ABSTRACT FROM AUTHOR]

We focus on analyzing the risk preferences of suppliers regarding two types of financing: partial credit guarantee (PCG) and trade credit financing (TCF). Using the conditional value-at-risk (CVaR) criterion, we study each supply chain member's equilibrium solution and optimal financing strategy under the assumption of demand distributions with an increasing failure rate. Initially, we present an analytical solution of the risk-averse supplier's wholesale price, and prove that the optimal wholesale price decreases in the risk-averse level and initial capital, and increases in the credit guarantee ratio. Furthermore, we derive optimal financing strategies for both the supplier and the retailer under various circumstances. However, it is important to note that the results reveal a potential trade-off associated with PCG. While risk-averse suppliers may be more inclined to provide PCG compared to risk-neutral suppliers, this financing option can negatively impact the supplier's utility and the expected profits of the retailers. Finally, we illustrate how the optimal financing strategies shift in response to changes in the risk-averse level and credit guarantee ratio, and present the win-win situations for the supplier and the retailer. [ABSTRACT FROM AUTHOR]

The paper analyses optimal spending of an endowment fund. The purpose is to find a spending rule which is optimal for the owners and which secures that the fund will last "forever". This we do by finding closed form solutions of the optimal consumption to wealth ratio. We solve this problem using the life cycle model, where the agent can have preferences represented by expected utility or recursive utility. We apply our results to a sovereign wealth fund, and demonstrate that the optimal spending rate is significantly lower than the fund's expected real rate of return, a rule which is in common use. Employing the latter as the spending rate, implies that the fund's value deteriorates both in probability and in expectation, as time goes. For both kinds of long term convergence we find closed form threshold values. Spending below these values secures a sustainable fund. [ABSTRACT FROM AUTHOR]

This paper considers a class of growth models with idiosyncratic human capital risk and private information about individual effort choices (moral hazard). Households are infinitely-lived and have preferences that allow for a time-additive expected utility representation with a one-period utility function that is additive over consumption and effort as well as logarithmic over consumption. Human capital investment is risky due to idiosyncratic shocks that follow a Markov process with transition probabilities that depend on effort choices. The production process is represented by an aggregate production function that uses physical capital and human capital as input factors. We show that constrained optimal allocations are simple in the sense that individual effort levels and individual consumption growth rates are history-independent. Further, constrained optimal allocations are the solutions to a recursive social planner problem that is simple in the sense that exogenous shocks are the only state variables. We also show that constrained optimal allocations can be decentralized as competitive equilibrium allocations of a market economy with a simple tax- and transfer scheme. Finally, it is always optimal to subsidize human capital investment in the market economy. [ABSTRACT FROM AUTHOR]

In this paper, we provide two novel expected utility theorems by suitably adjusting the independence and continuity axioms. Our first theorem characterizes expected utility preferences using weak versions of the independence axiom (with varying mixture weights) and a new weak continuity axiom. Our second theorem characterizes these preferences using weaker versions of the independence axiom (with mixture weights fixed at 1/2) and a strong topological continuity axiom. We provide useful examples to illustrate the tightness of these characterizations. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the relationship between behavioral biases, and demand decisions for life insurance products in Uganda. Design/methodology/approach: Data were collected from 351 life insurance policyholders in Uganda. The authors used a cross-sectional survey by applying a structured questionnaire. Descriptive analysis was conducted and hypothesized relationships between the constructs were evaluated through the use of structural equation modeling. Findings: Results indicate that, behavioral biases are significant predictors of life insurance demand among Ugandan policyholders. Also, the two behavioral bias variables (heuristic bias and prospect bias) are significant predictors of demand decisions for life insurance products. Practical implications: These results are helpful for both insurers and regulators. For insurers, it is now evident that demand decisions for life insurance products are not fully rational. It is imperative for insurers to simplify life insurance product information (heuristics), integrate product education and widen dissemination of product information (prospect bias) to allow policyholders to come up with optimal demand decisions. While for insurance policymakers, the study provides an understanding of behavioral biases. With such insights, policymakers can identify exploitative and deceptive information that target policyholders to better guide life insurance documentation and product designs. Originality/value: This study is the first to offer insights into behavioral biases' influence on demand decisions for life insurance products in a developing country like Uganda. By integrating prospects and expected utility theory, this study examines rationality and irrationality in demand decisions for life insurance products. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0201 [ABSTRACT FROM AUTHOR]

We investigate the impact of money illusion on the investment strategy and retirement outcomes of pre-retirees. Money illusion refers to the tendency of individuals to overlook the effects of inflation and focus on nominal rather than real terms. We solve and compare the optimal investment strategies for a pre-retiree who exhibits money illusion and aims to maximize the expected power utility of wealth at retirement, subject to a minimum guarantee constraint. While money illusion leads to welfare losses, implementing a minimum guarantee helps suppress these losses. However, guarantee constraints set under money illusion are ineffective in meeting inflation-adjusted constraints. Our findings emphasize the significant impact of money illusion on pre-retirees' investment strategy and retirement outcomes in the form of utility loss and the risk of falling short of the minimum guarantee. [ABSTRACT FROM AUTHOR]

This paper studies the optimal consumption and investment for an agent, considering statistical arbitrary opportunities caused by mispriced stocks. The agent exhibits consumption habit formation and has access to a risk-free asset, a market index, and a pair of mispriced stocks. The optimization problem is to find the optimal consumption and investment strategies to maximize the expected utility from consumption and terminal wealth. The utility of consumption stems from the difference between the consumption and habit levels. Based on the dynamic programming method, a verification theorem is provided, and the analytical solution of the optimization problem is obtained. The numerical results show the behaviors of our formulas and are used to make practical recommendations. By studying the sensitivity of consumption and investment strategies to habit formation, mispricing, and a delta-neutral arbitrage strategy, we uncover and analyze the behaviors of the agent. Meanwhile, we define and discuss the wealth-equivalent utility loss in three cases, including ignoring habit formation, ignoring mispricing, and adopting the delta-neutral arbitrage strategy. [ABSTRACT FROM AUTHOR]

Chinese farmers, especially small ones, always sell their food at low prices during harvest season rather than storing it for a better price. Based on a theoretical framework of expected utility, this paper examines the mechanism by which risk perception affects farmers' timing choices of food sales and the role played by risk preference, utilizing data from the 2019 China Family Database and the China Household Finance Survey of farmers in six provinces of the main wheat-producing regions. This study shows that farmers with a high risk perception are more likely to choose current sales compared with intertemporal sales. The channel and mechanism analysis finds that increased risk perception leads to risky returns from intertemporal sales lower than certain returns from current sales in utility comparisons. It is further found that risk preference has a substitution effect on risk perception in farmers' intertemporal food sales. [ABSTRACT FROM AUTHOR]

This paper investigates the question of how subjective probability should relate to binary belief. We propose new distance minimization methods, and develop epistemic decision-theoretic accounts. Both approaches can be shown to get "close" to the truth: the first one by getting "close" to a given probability, and the second by getting expectedly "close" to the truth. More specifically, we study distance minimization with a refined notion of Bregman divergence and expected utility maximization with strict proper scores. Our main results reveal that the two ways to get "close" to the truth can coincide. [ABSTRACT FROM AUTHOR]

The portfolio optimization problem can be reformulated by a regression model. Mean-variance portfolios, which are constructed using the sample mean and covariance matrix of asset returns can be considered as a function of the ordinary least squares estimator of the linear regression coefficients. It is well known that this estimator has several drawbacks. In particular many researchers have pointed out its high sensitivity in the presence of outliers and its loss of efficiency in the presence of small deviations from the normality assumption. In this paper we propose a novel regression approach for optimizing portfolios by means of quantile regression models. The proposed portfolios are constructed using certain robust and distribution-free estimators and can be obtained by solving a single linear program, where estimation and portfolio optimization are performed in a single step. Our numerical results on simulated and empirical data confirm that the proposed portfolios behave much better than the traditional mean-variance portfolio, in terms of the Sharpe ratio and expected loss in utility. [ABSTRACT FROM AUTHOR]

We characterize conditions under which a better consumption distribution implies higher welfare. Specifically, here "better consumption" means first‐order stochastic dominance, and "higher welfare" means higher expected utility for every subpopulation of individuals with the same utility function. Although this implication seems natural, we first provide a counterexample wherein better consumption risk allocation outweighs lower consumption. We then show that higher expected utility results from higher consumption in different settings, including fixed dependence (fixed copula) between consumption and individual risk preferences, or alternatively using the rank invariance assumption from the treatment effects literature. These are discussed in several real‐world examples. [ABSTRACT FROM AUTHOR]

Existing research shows that having numerous options can be overwhelming. Health economists have also demonstrated that individuals regularly fail to choose health insurance plans that minimize out‐of‐pocket costs or maximize expected utility. These failures are commonly attributed to choice overload, among other explanations. However, less has been written on how well people make choices when faced with only two options, particularly in the health insurance industry. In this article, we seek to add to this discussion by utilizing a proprietary database to analyze how well people choose between only two health insurance plans that are very similar in structure. Our findings indicate that many individuals still fail to choose health insurance plans that minimize out‐of‐pocket costs or maximize expected utility, even with this small and relatively simple choice set. This suggests that more research on nonstandard preferences, low insurance competence, consumer search tools, and smart defaults is needed to better understand the decision‐making process of health‐insurance consumers. [ABSTRACT FROM AUTHOR]

Following Markowitz's pioneering work on mean–variance optimization (MVO), such approaches have permeated nearly every facet of quantitative finance. In the first part of the article, the authors argue that their widespread adoption can be attributed to the universality of the mean–variance paradigm, wherein the maximum expected utility and mean–variance allocations coincide for a broad range of distributional assumptions of asset returns. Subsequently, they introduce a formal definition of mean–variance equivalence and present a novel and comprehensive characterization of distributions, termed mean–variance-equivalent (MVE) distributions, wherein expected utility maximization and the solution of an MVO problem are the same. In the second part of the article, the authors address common myths associated with MVO. These myths include the misconception that MVO necessitates normally distributed asset returns, the belief that it is unsuitable for cases with asymmetric return distributions, the notion that it maximizes errors, and the perception that it underperforms a simple 1/n portfolio in out-of-sample tests. Furthermore, they address misunderstandings regarding MVO's ability to handle signals across different time horizons, its treatment of transaction costs, its applicability to intraday and high-frequency trading, and whether quadratic utility accurately represents investor preferences. [ABSTRACT FROM AUTHOR]

The investment horizon plays a crucial role in portfolio selection: For horizons approximately up to a year, one can safely employ the mean–variance (M-V) rule. Moreover, if investment consultants use monthly rates of return to derive the M-V efficient set and the investor horizon is longer but smaller than one year, the economic cost induced by this horizon mismatch is negligible. For longer horizons, the M-V rule deviates substantially from expected utility maximization and the economic cost induced by employing the M-V rule is substantial. For relatively long horizons (say 20 or 30 years), despite the argument that with myopic preference the horizon does not matter, small stocks dominate large stocks by the maximum geometric mean (MGM) rule and, in practice, also by expected utility for all economically relevant preferences, as there is almost first-degree stochastic dominance (AFSD). [ABSTRACT FROM AUTHOR]

This paper investigates the moral hazard problem in finite horizon with both continuous and lump‐sum payments, involving a time‐inconsistent sophisticated agent and a standard utility maximizer principal: Building upon the so‐called dynamic programming approach in Cvitanić et al. (2018) and the recently available results in Hernández and Possamaï (2023), we present a methodology that covers the previous contracting problem. Our main contribution consists of a characterization of the moral hazard problem faced by the principal. In particular, it shows that under relatively mild technical conditions on the data of the problem, the supremum of the principal's expected utility over a smaller restricted family of contracts is equal to the supremum over all feasible contracts. Nevertheless, this characterization yields, as far as we know, a novel class of control problems that involve the control of a forward Volterra equation via Volterra‐type controls, and infinite‐dimensional stochastic target constraints. Despite the inherent challenges associated with such a problem, we study the solution under three different specifications of utility functions for both the agent and the principal, and draw qualitative implications from the form of the optimal contract. The general case remains the subject of future research. We illustrate some of our results in the context of a project selection contracting problem between an investor and a time‐inconsistent manager. [ABSTRACT FROM AUTHOR]

Active learning can improve the efficiency of training prediction models by identifying the most informative new labels to acquire. However, non-response to label requests can impact active learning's effectiveness in real-world contexts. We conceptualise this degradation by considering the type of non-response present in the data, demonstrating that biased non-response is particularly detrimental to model performance. We argue that biased non-response is likely in contexts where the labelling process, by nature, relies on user interactions. To mitigate the impact of biased non-response, we propose a cost-based correction to the sampling strategy–the Upper Confidence Bound of the Expected Utility (UCB-EU)–that can, plausibly, be applied to any active learning algorithm. Through experiments, we demonstrate that our method successfully reduces the harm from labelling non-response in many settings. However, we also characterise settings where the non-response bias in the annotations remains detrimental under UCB-EU for specific sampling methods and data generating processes. Finally, we evaluate our method on a real-world dataset from an e-commerce platform. We show that UCB-EU yields substantial performance improvements to conversion models that are trained on clicked impressions. Most generally, this research serves to both better conceptualise the interplay between types of non-response and model improvements via active learning, and to provide a practical, easy-to-implement correction that mitigates model degradation. [ABSTRACT FROM AUTHOR]