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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]

Purpose: To remain competitive in an unpredictable environment where the complexity and frequency of cybercrime are rapidly increasing, a cyber resiliency strategy is vital for business continuity. However, one of the barriers to improving cyber resilience is that security defense and accident recovery do not combine efficaciously, as embodied by emphasizing cyber security defense strategies, leaving firms ill-prepared to respond to attacks. The present study thus develops an expected resilience framework to assess cyber resilience, analyze cyber security defense and recovery investment strategies and balance security investment allocation strategies. Design/methodology/approach: Based on the expected utility theory, this paper presents an expected resilience framework, including an expected investment resilience model and an expected profit resilience model that directly addresses the optimal joint investment decisions between defense and recovery. The effects of linear and nonlinear recovery functions, risk interdependence and cyber insurance on defense and recovery investment are also analyzed. Findings: According to the findings, increasing the defense investment coefficient reduces defense and recovery investment while increasing the expected resilience. The nonlinear recovery function requires a smaller defense investment and overall security investment than the linear one, reflecting the former's advantages in lowering cybersecurity costs. Moreover, risk interdependence has positive externalities for boosting defense and recovery investment, meaning that the expected profit resilience model can reduce free-riding behavior in security investments. Insurance creates moral hazard for firms by lowering defensive investment, yet after purchasing insurance, expanded coverage and cost-effectiveness incentivize firms to increase defense and recovery spending, respectively. Originality/value: The paper is innovative in its methodology as it offers an expected cyber resilience framework for integrating defense and recovery investment and their effects on security investment allocation, which is crucial for building cybersecurity resilience but receives little attention in cybersecurity economics. It also provides theoretical advances for cyber resilience assessment and optimum investment allocation in other fields, such as cyber-physical systems, power and water infrastructure – moving from a resilience triangle metric to an expected utility theory-based method. [ABSTRACT FROM AUTHOR]

We imagine investors taking shares in an exogenous lognormal cash payoff with known parameters. Using a power utility (constant relative risk aversion (CRRA)) replica of Lintner's static payoffs-based constant absolute risk aversion-normal capital asset pricing model, we examine how an investor's expected utility is affected by the payoff parameters and surrounding market conditions. The market clearing asset price falls as a proportion of wealth when market wealth is higher, implying that CRRA investors hold a lower (rather than fixed) proportion of wealth in the risky asset when they are wealthier. Investors prefer conditions where they can obtain more risk, either because the risky payoff is exogenously riskier or competing investors want less. The equilibrium asset price is "disproportionately" lower when the asset is riskier, or when there are fewer willing buyers, leaving a better opportunity with higher expected utility. [ABSTRACT FROM AUTHOR]

Legal scholars highlight the tensions that exist between different classes of shareholders in startups. We model a startup owned by undiversified investors with heterogeneous capital contributions and risk preferences. A social planner runs the firm on behalf of all investors. We compare investors' expected utility with a hypothetical first-best decentralized benchmark. The startup's optimal investment policy is procyclical and a time-varying weighted average of shareholders' optimal investment policies. The optimal contracts issued to investors are tailor-made, interdependent, and include equity claims resembling preferred stock with heterogeneous payout caps, leading to a complex capitalization table as more investors join the startup. This paper was accepted by Will Cong, finance. Funding: This work was supported by the Cambridge Endowment for Research in Finance and Keynes Fellowship. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01724. [ABSTRACT FROM AUTHOR]

The newsvendor model has been extensively applied in the study of fashion products. Given that fashion retailers often need to make joint decisions on pricing and ordering, this paper extends the standard newsvendor model to a dynamic setting with price-dependent stochastic demand. It also incorporates the retailer’s fairness-concerned behavioral preferences. Using deterministic demand as a benchmark, we propose three static and dynamic models based on bounded rationality, accounting for both multiplicative and additive demand scenarios. We analyze the equilibrium points of joint decision-making within these models and investigate, both theoretically and numerically, the effects of key parameters–such as decision adjustment speeds and the fairness-concern parameter–on local stability. Numerical simulations are conducted to illustrate chaotic bifurcation diagrams, time series, and the retailer’s cumulative expected utility. Our results indicate that as the fairness-concern parameter increases, the retailer’s pricing rises while the optimal order quantity decreases. Additionally, faster adjustment speeds in pricing and ordering lead to system instability, resulting in chaotic bifurcation. This highlights the importance of maintaining a moderate adjustment speeds to ensure system stability. Furthermore, we find that multiplicative stochastic demand disturbances exert a greater influence on deterministic demand compared to additive stochastic disturbances. Finally, we offer several managerial implications based on these findings. [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 article describes the main historical facts concerning the Saint Petersburg paradox, the most important solutions proposed thus far, and the results of new experimental evidence and a simulation of the game that shed light on a solution for this paradox. The Saint Petersburg paradox has attracted the attention of important mathematicians and economists since it was first formulated 300 years ago, and it has strongly influenced the development of new concepts in the economic and social sciences. The main conclusion of this study is that the behavior of the individuals playing the game is not paradoxical at all, and the paradox is intrinsic to the game. [ABSTRACT FROM AUTHOR]

In this paper, we study the risk-sharing problem among multiple agents using lambda value at risk (ΛVaR) as their preferences via the tool of inf-convolution, where ΛVaR is an extension of value at risk (VaR). We obtain explicit formulas of the inf-convolution of multiple ΛVaR with monotone Λ and explicit forms of the corresponding optimal allocations, extending the results of the inf-convolution of VaR. It turns out that the inf-convolution of several ΛVaR is still a ΛVaR under some mild condition. Moreover, we investigate the inf-convolution of one ΛVaR and a general monotone risk measure without cash additivity, including ΛVaR , expected utility, and rank-dependent expected utility as special cases. The expression of the inf-convolution and the explicit forms of the optimal allocation are derived, leading to some partial solution of the risk-sharing problem with multiple ΛVaR for general Λ functions. Finally, we discuss the risk-sharing problem with ΛVaR+ , another definition of lambda value at risk. We focus on the inf-convolution of ΛVaR+ and a risk measure that is consistent with the second-order stochastic dominance, deriving very different expression of the inf-convolution and the forms of the optimal allocations. [ABSTRACT FROM AUTHOR]

In reality, investors are uncertain about the dynamics of risky asset returns. Therefore, investors prefer to make robust investment decisions. In this paper, we propose an α-robust utility maximization problem under uncertain parameters. The investor is allowed to invest in a financial market consisting of a risk-free asset and a risky asset. The uncertainty about the expected return rate is parameterized by a nonempty set. Different from most existing literature on robust utility maximization problems where investors are generally assumed to be extremely ambiguity averse because they tend to consider only expected utility in the worst-case scenario, we pay attention to the investors who are not only ambiguity averse but also ambiguity seeking. Under power utility, we provide the implicit function representations for the precommitted strategy, equilibrium strategy of the open-loop type, and equilibrium strategy of the closed-loop type. Some properties about the optimal trading strategies, the best-case and worst-case parameters under three different kinds of strategies, are provided. Funding: This work was supported by National Natural Science Foundation of China [Grants 12071147, 12171169, 12271171, 12371470, 71721001, 71931004, 72371256], the Shanghai Philosophy Social Science Planning Office Project [Grant 2022ZJB005], Fundamental Research Funds for the Central Universities [Grant 2022QKT001], the Excellent Young Team Project Natural Science Foundation of Guangdong Province of China [Grant 2023B1515040001], the Philosophy and Social Science Programming Foundation of Guangdong Province [Grant GD22CYJ17], the Nature Science Foundation of Guangdong Province of China [Grant 2022A1515011472], and the 111 Project [Grant B14019]. [ABSTRACT FROM AUTHOR]

Savage's famous representation theorem is based on seven postulates of rational choice. We resolve some loose ends in the literature concerning the relationship between different versions of Savage's axioms. This leads us to the present form of the representation theorem. We also discuss some issues regarding the historical development of Savage's representation theorem. [ABSTRACT FROM AUTHOR]

The study of electricity resource adequacy (i.e. quantifying the risk of future resource shortfall) typically uses standard metrics such as Loss of Load Expectation and Expected Energy Unserved. A range of possible risk model outputs are critiqued in this paper as a basis for decision analysis on capacity procurement, starting with the current standard approach (in which risk is commonly monetised as EEU multiplied by VOLL), and moving on to alternatives such as risk averse metrics and wider visualisations of risk profile. There are concerns in principle from a decision analytic perspective with using expected money as a utility function. Also, while there is interest in use of risk-averse metrics such as Conditional Value-at-Risk (CVaR), these might not capture all aspects of risk profile that are of interest. To obtain a comprehensive picture of the risk profile of a system, it is necessary to visualise a range of distributions of outcome measures, rather than using single number metrics. Providing such visualisations is likely not to provide the basis for a transparent risk standard, and options are discussed such as setting a level for a single metric that is occasionally revised according to changing profile of resources. Any such metric-based approach should also be supplemented by assessment of wider aspects of risk profile, for instance through examining distributions of outcome metrics. The inherent uncertainty, due to data limitations and imperfect representation of the system in the model, should also be acknowledged appropriately in estimation of such metrics. [ABSTRACT FROM AUTHOR]

AbstractThis article considers a portfolio selection problem under the criterion of expected utility maximization for a jump-diffusion model, where the drifts of the risky assets are modulated by a continuous hidden Markov chain. Suppose that the utility function is a general concave one and there is a constraint on the wealth process. Due to this constraint, we turn to solve this problem by using the technique of martingale method. Besides, based on the filtering theory, we reduce the original partial information problem into a corresponding completely observable one and closed-form expressions of the optimal results are derived, which is the solution to a system of nonlinear equations. For the specific examples, we present optimal results for the exponential and power utility. Furthermore, some sensitivity analyses of the constraint are provided. [ABSTRACT FROM AUTHOR]

AbstractThis article investigates a trilateral stochastic differential reinsurance and investment game with heterogeneous reinsurance premiums. As the leader in the Stackelberg game, the reinsurer maximizes the expected utility of the combination of the three players’ terminal wealth other than its own utility. The degree to which a reinsurer pays attention to an insurer depends on the coefficient αi(i=1,2). The insurers spread their risks and expand their underwriting capacity by purchasing reinsurance with strategies involving relative performance in a non-zero-sum stochastic differential game. We derive explicit expressions of the Nash equilibrium strategy and prove the verification theorem using dynamic programming and backward induction methods. We discuss the parameters comparative static analysis of the equilibrium strategies through numerical examples. The numerical results show that the coefficient αi(i=1,2) is proportional to the reinsurance premium price and is inversely proportional to the reinsurance ratio. [ABSTRACT FROM AUTHOR]

We consider a service system in which customers who arrive at a service station and place an order, are not involved in the processing of their order, which can therefore be executed in their absence. Consequently, customers may leave the service station for some period of time during the processing of their order (i.e., go to orbit), and then return. While the customers are in orbit, they can utilize their time efficiently. If the service is completed before the customer's return from orbit, the ready service (RS) is stored in a designated storage facility until the customer returns and retrieves the RS from the inventory. If, however, the service is not yet completed when the customer returns, the customer can leave to orbit again. Accordingly, the policy is called "repeated orbit" (during the service). We formulate and analyze the queueing-inventory-repeated-orbit (QIRO) system using the matrix geometric method. The optimal orbiting time is calculated by maximizing the customer's expected utility. In addition, the optimal RS storage capacity and the optimal investment in preservation technologies (to store the RSs) are derived, both of which serve to increase demand and thus maximize the system's expected profit. [ABSTRACT FROM AUTHOR]

This article proposes and studies a new type of constant elasticity of volatility (CEV) model, titled LVO-CEV, where the name indicates that the excess return is linear in the volatility. The model has either strong or weak solutions, depending on the elasticity parameter, thanks to a connection to radial Ornstein-Uhlenbeck processes. Attainability of lower bounds and the existence of pricing measures are provided. The model allows for closed-form solutions in the context of expected utility theory for investors with hyperbolic absolute risk aversion (HARA) utilities on terminal wealth and consumption. We estimate and implement our model as well as other popular models of indexes and stocks, providing a fair comparison in portfolio management. [ABSTRACT FROM AUTHOR]

Accepted by: Giorgio Consigli In this paper we study the optimal reinsurance-investment problem for an insurer with constant absolute risk aversion (CARA) utility. Because the insurer needs to avoid large claims and make profits in the actual operation process, he/she is allowed to buy proportional reinsurance and invest in a risk-free bond, a stock and a default bond. Moreover, the price process of the stock follows the constant elasticity of variance (CEV) model and the correlation between the risk process and the stock's price is considered. For the objective of expected utility maximization, we establish the Hamilton–Jacobi–Bellman equation and derive the optimal reinsurance-investment strategy explicitly for the pre-default case and the post-default case via the dynamic programming. Finally, numerical examples and sensitivity analyses are provided to illustrate the effects of model parameters on the optimal strategy. We find that (i) the default risk has no impact on the optimal strategy of the stock and the parameters of the stock's price have no impact on the optimal money amount invested in the defaultable bond. (ii) The optimal reinsurance strategy depends on the volatility of the stock's price under the CEV model. (iii) The correlation between risk model and the stock's price does have effects on the insurer's optimal reinsurance-investment strategy. These findings may provide guidance to the management of insurers in practice. [ABSTRACT FROM AUTHOR]

We investigate the stability of internet platform trading mechanisms using the notion of ex-ante incentive compatible core defined by Forges et al. (2002) in the context of an exchange economy. A mechanism can be blocked by a single buyer and seller pair if they can find an interim incentive-compatible trading mechanism that gives them higher ex-ante expected utilities. Standard double auction mechanisms like the trade reduction mechanism and McAfee double auction mechanism may not be single-buyer–single-seller (SBSS) ex-ante stable. We characterize interim incentive-compatible, interim individually-rational, symmetric and revenue-maximizing mechanisms that are SBSS ex-ante stable using methods in Myerson and Satterthwaite (1983). • We study the stability of double auction mechanisms of a platform. • Standard double auctions may not be single-buyer-single-seller (SBSS) ex-ante stable. • We characterize class of revenue-maximizing mechanisms that are SBSS ex-ante stable. [ABSTRACT FROM AUTHOR]

We examine the problem of how much risk‐averse agents would be willing to bet on events where there are multiple possible winners but only one will actually win. We describe how this problem can be solved for concave utility functions and illustrate the properties of the solution. The optimal betting strategy is more aggressive than strategies derived from considering each outcome separately such as the Kelly criterion. The strategy also recommends sometimes placing bets with negative expected returns because they act as hedges against losses on other bets. While this strategy maximizes the bettor's subjective expected utility, if betting odds incorporate a profit margin and reflect underlying probabilities correctly, then this more aggressive approach loses more money and results in lower realized utility. [ABSTRACT FROM AUTHOR]

Obtaining a utility-maximizing optimal portfolio in a closed form is a challenging issue when the return vector follows a more general distribution than the normal one. In this paper, for markets based on finitely many assets, a closed-form expression is given for optimal portfolios that maximize an exponential utility function when the return vector follows normal mean-variance mixture models. Especially, the used approach expresses the closed-form solution in terms of the Laplace transformation of the mixing distribution of the normal mean-variance mixture model and no distributional assumptions on the mixing distribution are made. Also considered are large financial markets based on normal mean-variance mixture models, and it is shown that the optimal exponential utilities in small markets converge to the optimal exponential utility in the large financial market. This shows, in particular, that to reach the best utility level investors need to diversify their investments to include infinitely many assets into their portfolio, and with portfolios based on only finitely many assets they will never be able to reach the optimum level of utility.

Green innovation in the logistics industry is critical to mitigating environmental crises and ensuring sustainable development. In this study, a two-echelon logistics service supply chain (LSSC) is constructed consisting of a logistics service provider (LSP), a logistics service integrator (LSI), and customers. The LSP and the LSI collaborate to provide high-quality green logistics service products (GLSPs) to customers. Strategic cooperation is established between upstream and downstream enterprises in the form of supportive investment and cost-sharing to strengthen inter-organizational relationships; the LSI may show overconfidence in both the impact of his green innovation effort on demand expansion and control power in the vertical LSSC. By solving the LSI-led Stackelberg game, we find that: First, the innovation cost coefficients and the payment ratio affect optimal decisions, including the optimal level of GLSP design innovation, level of GLSP delivery innovation, price of the GLSP, and LSI's optimal expected utility. Second, relationship strength positively impacts optimal decisions; its impact on the LSI's optimal expected utility is related to the upper limit of the LSI's supportive investment for the LSP. Third, the LSI's overconfidence positively impacts the optimal decisions and the LSI's optimal expected utility, while it harms the LSI's optimal real utility; the impacts of the LSI's overconfidence and inter-organizational relationship strength are additive. Finally, a "green subsidy and loss-sharing" incentive contract is proposed, which can improve both the levels of design and delivery innovation, as well as the LSSC members' real utilities. The findings may provide new insights for LSSC green innovation practitioners. [ABSTRACT FROM AUTHOR]

This study proposes an agent-based quantum-like model to investigate the individual selection among three or more lotteries while incorporating the decision-making risk and uncertainty. We extend the classical expected utility functions with quantum probabilities and construct the compound belief state to compare one specific lottery belief state against others. The involved decision-making process is represented formally by the comparison operator, which can be decomposed into a few subprocesses. We give an example of individual lottery selection from three lotteries to illustrate the model. Finally, we propose ways to select from more than three lotteries. [ABSTRACT FROM AUTHOR]

Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the utility function whose expected value is maximized across a population. Although researchers typically use utilities that depend on observed outcomes alone, in many settings the decision maker's utility function is more properly characterized by the joint set of potential outcomes under all actions. For example, the Hippocratic principle to "do no harm" implies that the cost of causing death to a patient who would otherwise survive without treatment is greater than the cost of forgoing life-saving treatment. We consider optimal policy learning with asymmetric counterfactual utility functions of this form that consider the joint set of potential outcomes. We show that asymmetric counterfactual utilities lead to an unidentifiable expected utility function, and so we first partially identify it. Drawing on statistical decision theory, we then derive minimax decision rules by minimizing the maximum expected utility loss relative to different alternative policies. We show that one can learn minimax loss decision rules from observed data by solving intermediate classification problems, and establish that the finite sample excess expected utility loss of this procedure is bounded by the regret of these intermediate classifiers. We apply this conceptual framework and methodology to the decision about whether or not to use right heart catheterization for patients with possible pulmonary hypertension. for this article are available online. [ABSTRACT FROM AUTHOR]

Generative Bayesian Computation (GBC) methods are developed to provide an efficient computational solution for maximum expected utility (MEU). We propose a density-free generative method based on quantiles that naturally calculates expected utility as a marginal of posterior quantiles. Our approach uses a deep quantile neural estimator to directly simulate distributional utilities. Generative methods only assume the ability to simulate from the model and parameters and as such are likelihood-free. A large training dataset is generated from parameters, data and a base distribution. Then, a supervised learning problem is solved as a non-parametric regression of generative utilities on outputs and base distribution. We propose the use of deep quantile neural networks. Our method has a number of computational advantages, primarily being density-free and an efficient estimator of expected utility. A link with the dual theory of expected utility and risk taking is also described. To illustrate our methodology, we solve an optimal portfolio allocation problem with Bayesian learning and power utility (also known as the fractional Kelly criterion). Finally, we conclude with directions for future research. [ABSTRACT FROM AUTHOR]

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future uncertain benefits that are linked by causation. This multi-period causal process, incorporating certainty and uncertainty, replaces the single-period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modeling and real-world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. With the growing interest in natural experiments in statistics and causal machine learning (CML) across many fields, such as healthcare, economics, and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision-making models that focus only on rationality, bounded to various degrees. To be most effective, machine learning must mirror human reasoning as closely as possible, an alignment established through CEML, which represents an evolution to truly "human AI". This paper maps out how the non-linear optimization required for the CEML structural response functions can be accomplished through Sequential Least Squares Programming (SLSQP) and applied to data sets through the S-Learner CML meta-algorithm. Upon this foundation, the next phase of research is to apply CEML to appropriate data sets in various areas of practice where causality and accurate modeling of human behavior are vital, such as precision healthcare, economic policy, and marketing. [ABSTRACT FROM AUTHOR]

In the process of forest ecological benefit compensation, there are problems of information asymmetry and "misaligned incentives", which will reduce the compensation efficiency. In order to improve the compensation efficiency, based on principal–agent theory, this study constructs incentive contract models for governmental forest ecological benefit compensation under three different scenarios, namely, information symmetry, single-sided information asymmetry, and double-sided information asymmetry. The study finds that the government can design different incentive contracts to motivate forest farmers with high and low forestry capabilities. And the government's expected utility is influenced by the proportion of forest farmers with high and low forestry capabilities in reality. Due to the information gap between the government and forest farmers, it is inevitable that high-capability forest farmers will obtain an information rent. Under double-sided information asymmetry, the incentive coefficient for lower-capability forest farmers and their optimal actual public welfare forest conservation area decrease as the proportion of high-capability forest farmers increases. Furthermore, when the proportion of high-capability forest farmers exceeds a certain threshold, signing compensation contracts with low-capability forest farmers can harm the government's interests. The research conclusions provide a scientific basis for the government to formulate differentiated incentive contracts for forest ecological benefits. This can effectively align forest farmers' conservation behaviors with the improvement of public forest ecological benefits. As a result, it contributes to improving the efficiency of forest ecological benefit compensation. [ABSTRACT FROM AUTHOR]

The classic framework of Anscombe and Aumann (Ann Math Stat 34:199–205, 1963) for decision-making under uncertainty postulates both a primary source of uncertainty (the "horse race") and an auxiliary randomization device (the "roulette wheel"). It also imposes a specific timing of resolution of uncertainty as the horse race takes place before the roulette is played. While this timing is without loss of generality for Subjective Expected Utility, it forbids plausible choice patterns of ambiguity aversion. In this paper, we reverse this timing by assuming that the roulette is played prior to the horse race and we obtain an axiomatic characterization of Choquet Expected Utility that is dual to that of Schmeidler (Econometrica 57(3):571–587, 1989). In this representation, ambiguity aversion is characterized by an aversion to conditioning roulette acts on horse events which, as we argue, is more plausible. Moreover, it can be larger than in Schmeidler's model. Finally, our reversed timing yields incentive compatibility of the random incentive mechanisms, frequently used in experiments for eliciting ambiguity attitudes. [ABSTRACT FROM AUTHOR]

Delegation contracts with conventional linear benchmarking cannot motivate institutions to acquire information, which deteriorates price informativeness and increases return volatility. This study investigates performance-based contracts in which the benchmark is a nonlinear (quadratic) function of the benchmark portfolio return. In a unified model incorporating both information acquisition and investment decisions, we show that delegation contracts with the nonlinear benchmark can overcome the weakness of conventional benchmarked contracts. Specifically, they can incentivize information acquisition, enhance price informativeness, lower return volatility, and, when penalty intensity is relatively low, increase institutions' expected utility and reduce fixed delegation costs. The impact of the contract's incentive component on the equilibrium price and price informativeness depends on the average incentive slope. Further analysis finds that delegated investment by informed institutional investors can improve price informativeness. This effect is more pronounced under nonlinear benchmarked contracts than under non-benchmarked or linear benchmarked contracts. [ABSTRACT FROM AUTHOR]

We examine the effect of expected competition on markups in a random utility model where the number of competing firms may differ across consumers. Firms observe consumers' utility shocks and set prices using personalized pricing. We derive a precise condition under which the expected markup across consumers can be represented by a simple expression involving consumers' expected utility and the expected demand. This delivers a general condition under which greater expected competition is price‐increasing. Whether this condition holds depends on the distribution of utility shocks, consumers' outside option, the expected number of competing firms, and the distribution of the number of firms competing for each consumer. [ABSTRACT FROM AUTHOR]

This paper analyzes the robust long-term growth rate of expected utility and expected return from holding a leveraged exchange-traded fund. When the Markovian model parameters in the reference asset are uncertain, the robust long-term growth rate is derived by analyzing the worst-case parameters among an uncertainty set. We compute the growth rate and describe the optimal leverage ratio maximizing the robust long-term growth rate. To achieve this, the worst-case parameters are analyzed by the comparison principle, and the growth rate of the worst-case is computed using the Hansen–Scheinkman decomposition. The robust long-term growth rates are obtained explicitly under a number of models for the reference asset, including the geometric Brownian motion, Cox–Ingersoll–Ross, 3/2, and Heston and 3/2 stochastic volatility models. Additionally, we demonstrate the impact of stochastic interest rates, such as the Vasicek and inverse GARCH short rate models. This paper is an extended work of Leung and Park (Int J Theor Appl Finance 20(6):1750037, 2017). [ABSTRACT FROM AUTHOR]

To address the conflict of interest between the government and enterprises regarding urban emergency transportation resources in unconventional emergencies and to enhance resource allocation and response efficiency. This paper proposes a collaborative government–enterprise model for emergency transport capacity reserves and develops an incentive model based on principal–agent theory. First, by comprehensively considering enterprise characteristics, high-quality enterprises are selected to collaborate with the government in building an emergency capacity pool of social vehicles. Second, to address potential conflicts of interest between the government and enterprises within the emergency capacity pool, this paper uses principal–agent theory to analyze the interest game process under information asymmetry, constructs a corresponding incentive model, and determines the government's optimal incentive coefficient, the enterprise's optimal actual capacity supply ratio, and the benefit distribution between both parties. Finally, numerical simulations and sensitivity analyses were used to verify the model's applicability. The findings reveal that transport effort cost, economic requisition compensation, and government supervision cost influence the optimal decisions and outcomes in government–enterprise interactions. This study provides theoretical guidance and managerial insights for coordinating emergency transport scheduling between the government and enterprises during unconventional emergencies. [ABSTRACT FROM AUTHOR]

This paper establishes a previously unexplored connection between expected utility theory and potential games. Starting with a decision problem with a complete preference relation over lotteries on a finite set of alternatives, we construct a two-person symmetric game using a payoff function that represents the preference relation, and show that if the preference relation satisfies the von Neumann-Morgenstern expected utility axioms then the constructed game is a potential game. Conversely, starting with a two-player symmetric game, we (uniquely) construct a (complete) preference relation over the lotteries using the first player's payoffs in the game, and show that if the game is a potential game then the resulting preference relation satisfies the expected utility axioms. [ABSTRACT FROM AUTHOR]

In How States Think, John Mearsheimer and Sebastian Rosato argue that expected-utility maximization is too subjective to serve as the basis for making rational decisions in the realm of national security. They claim that rationality in security studies should instead be defined by whether leaders conduct deliberative, theory-driven reasoning. This essay explains why Mearsheimer and Rosato's critique of expected-utility theory is unpersuasive, and how their conception of theory-driven reasoning ignores key aspects of decision-making that national security officials can feasibly address. Lyndon Johnson's decision to take the United States to war in Vietnam provides a vivid example of how leaders can meet Mearsheimer and Rosato's criteria for rationality despite clear shortfalls of analytic reasoning. [ABSTRACT FROM AUTHOR]

We study a reinsurer who faces multiple sources of model uncertainty. The reinsurer offers contracts to n insurers whose claims follow compound Poisson processes representing both idiosyncratic and systemic sources of loss. As the reinsurer is uncertain about the insurers' claim severity distributions and frequencies, they design reinsurance contracts that maximise their expected wealth subject to an entropy penalty. Insurers meanwhile seek to maximise their expected utility without ambiguity. We solve this continuous-time Stackelberg game for general reinsurance contracts and find that the reinsurer prices under a distortion of the barycentre of the insurers' models. We apply our results to proportional reinsurance and excess-of-loss reinsurance contracts, and illustrate the solutions numerically. Furthermore, we solve the related problem where the reinsurer maximises, still under ambiguity, their expected utility and compare the solutions. [ABSTRACT FROM AUTHOR]

How people plan is an active area of research in cognitive science, neuroscience, and artificial intelligence. However, tasks traditionally used to study planning in the laboratory tend to be constrained to artificial environments, such as Chess and bandit problems. To date there is still no agreed-on model of how people plan in realistic contexts, such as navigation and search, where values intuitively derive from interactions between perception and cognition. To address this gap and move towards a more naturalistic study of planning, we present a novel spatial Maze Search Task (MST) where the costs and rewards are physically situated as distances and locations. We used this task in two behavioral experiments to evaluate and contrast multiple distinct computational models of planning, including optimal expected utility planning, several one-step heuristics inspired by studies of information search, and a family of planners that deviate from optimal planning, in which action values are estimated by the interactions between perception and cognition. We found that people's deviations from optimal expected utility are best explained by planners with a limited horizon, however our results do not exclude the possibility that in human planning action values may be also affected by cognitive mechanisms of numerosity and probability perception. This result makes a novel theoretical contribution in showing that limited planning horizon generalizes to spatial planning, and demonstrates the value of our multi-model approach for understanding cognition. Author summary: We present a computational study of spatial planning under uncertainty using a novel Maze Search Task (MST), in which people search mazes for probabilistically hidden rewards. The MST is designed to resemble real-life spatial planning, where costs and rewards are physically situated as distances and locations. We found that people's spatial planning is best explained by planners with limited planning horizon, as opposed to both myopic heuristics or the optimal expected utility, showing that a limited planning horizon can generalize to spatial planning tasks. We also find that our results do not exclude the possibility that in human planning action values may be affected by cognitive mechanisms of numerosity and probability perception. [ABSTRACT FROM AUTHOR]

People's names are challenging to learn at all ages. Because people somewhat know this, they might spontaneously use cost-efficient encoding strategies and devote more resources to learn names that are most likely to be useful. To test this hypothesis, we created a pseudo-incidental learning situation in which young and older participants were exposed to 12 characters from a TV show and reviewed face-name-instrument triplets. Characters' probability of appearance was specified via importance labels (main or secondary characters, bit parts). A surprised cued recall test showed that young adults performed better than older ones, and that semantic information was better recalled than names. Consistent with cost-efficient encoding strategies, participants in both groups recalled names and semantic information about most important characters better. Interestingly, there were large individual differences: people who reported using cost-efficient strategies performed better. At the individual level, memory advantages for most important characters' names and semantic information correlated. [ABSTRACT FROM AUTHOR]

When allocating study time for the English sections of the National College Entrance Examination or the Postgraduate Entrance Examination, learners often encounter value-test likelihood trade-offs, where questions of similar difficulty may have different points and different likelihoods of being tested. This research explored how individuals allocated study time and whether this process exhibited bounded rationality by examining the risky choice framing effects in study time allocation. The research set up two types of items: 1-point items with a 90% likelihood and 9-point items with a 10% likelihood. Each type of item had the same test likelihood but was expressed in different framings. For the 90% likelihood items, the test framing emphasized that they had a 90% likelihood of being tested. Meanwhile, the non-test framing emphasized that they had a 10% likelihood of not being tested. A total of 41 college students participated in the study, and they were allowed to self-regulate their study time for each type of item. The results showed that learners' study time allocation differed under the two equivalent framings. This indicates that the process of study time allocation is not completely rational, but is rather boundedly rational, which is inconsistent with the expected utility theory. [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]

We study information aggregation in a dynamic trading model. We show theoretically that separable securities, introduced by Ostrovsky in the context of Expected Utility, no longer aggregate information if some traders have imprecise beliefs and are ambiguity averse. Moreover, these securities are prone to manipulation as the degree of information aggregation can be influenced by the initial price set by the uninformed market maker. These observations are also confirmed in our laboratory experiment using prediction markets. We define a new class of strongly separable securities, which are robust to the above considerations and show that they characterize information aggregation in both strategic and non-strategic environments. We derive several testable predictions, which we are able to confirm in the laboratory. Finally, we show theoretically that strongly separable securities are both sufficient and necessary for information aggregation but, strikingly, there does not exist a security that is strongly separable for all information structures. [ABSTRACT FROM AUTHOR]

We derive analytical expressions for the risk of an investor's expected utility under parameter uncertainty. In particular, our analysis focuses on characterizing the out-of-sample utility variance of three portfolios: the classic mean-variance portfolio, the minimum-variance portfolio, and a shrinkage portfolio that combines both. We then use our analytical expressions to study a robustness measure that balances out-of-sample utility mean and volatility. We show that neither the sample mean-variance portfolio nor the sample minimum-variance portfolio exhibits maximal robustness individually, and one needs to combine both to optimize portfolio robustness. Accordingly, we introduce a robust shrinkage portfolio that delivers an optimal tradeoff between out-of-sample utility mean and volatility and is more resilient to estimation errors. Our results highlight the importance of considering out-of-sample performance risk in designing and evaluating investment strategies and stochastic discount factor models. This paper was accepted by Kay Giesecke, finance. Funding: N. Lassance gratefully acknowledges financial support by the Fonds de la Recherche Scientifique [Grant J.0115.22]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00178. [ABSTRACT FROM AUTHOR]

To stay competitive, firms regularly invest in innovation by supporting internal capital projects (funded and executed in-house) that explore new products and operational improvements. Each year, in a highly competitive process, managers from different functional units of the firm submit proposals (that include estimated costs and benefits) for such projects. Managers, because of their domain knowledge and expertise, are naturally better informed about the costs and benefits of their respective projects and can use this information strategically to secure funding. An example of such behavior is the under-reporting of the cost estimate of a project and subsequently requesting additional funding during the execution phase. Such strategic behavior not only affects the firm's ability to fund the best projects but is also costly. Motivated by this challenge of deciding the funding of such projects at a global agribusiness firm, we seek a mechanism that is both provably near-optimal for the firm and guarantees truthful reporting from managers. Our setting consists of a principal and multiple agents. In each time period, over an infinite horizon, each agent requests funding for a potential project from the principal. Before submitting his proposal, each agent privately estimates the project's cost and its benefit. If funded, each project is executed in one period. The principal's funding decisions are binary; that is, each project is either funded in full or not funded. The actual cost (benefit) of a funded project is realized on its completion and incurred (earned) by the principal. The agents earn utility from the experience and reputation gained in completing projects. In each period, the principal desires to keep the total spend below a budget but can borrow external money at a cost. Our main contribution is a practically appealing dynamic nonmonetary mechanism for internal capital allocation under which, for any ϵ>0 , (a) truth-telling forms an ϵ-Bayesian Nash equilibrium and (b) the principal's expected utility is within ϵ of the expected utility in the first-best setting. This paper was accepted by Karan Girotra, operations management. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01121. [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]

This paper considers the expected utility portfolio optimization problem with initial-time and intermediate-time Value-at-Risk constraints on terminal wealth. We derive the closed-form solutions which are optimal among all feasible controls at initial time, i.e., precommitted strategies. Moreover, the precommitted strategies are also optimal at the intermediate time for "bad" market states. A contingent claim on Merton's portfolio is constructed to replicate the optimal portfolio. We find that risk management with intermediate-time risk constraints is prudent in hedging "bad" intermediate market states and performs significantly better than the one terminal-wealth risk constraint solutions under the relative loss ratio measure. [ABSTRACT FROM AUTHOR]