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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]

In this paper, two dynamic models, recently proposed to describe the adaptive repeated choices of a boundedly rational consumer, are joined together. One considers a consumer adjusting the consumption level of a given good over time according to the observed discrepancy between expected and realized utility gain and modifies the utility function according to past consumption experience, also including saturation effects when past consumption is excessive. The other one considers the same adjustment mechanism with constant preferences but with a behavioral effect that introduces a tendency (or bias) to imitate a reference group of consumers. Merging these two models, a two-dimensional nonlinear dynamical system is obtained which describes consumers that decide their next period consumption of a given good by following two different (sometimes contrasting) criteria: their own utility maximization on the one side and imitation of a reference group of consumers on the other side. This leads to a greater uncertainty with respect to the model without the behavioral bias. Such uncertainty is studied through a numerical exploration of the long-run dynamics, guided by some global dynamical features of the nonlinear model, such as the folding action of the critical curves that characterize the behavior of the iterated noninvertible map and the singularities related to the presence of a vanishing denominator, namely focal points and prefocal curves. So, the aim of the paper is twofold: on the one side, it tries to contribute to the literature on the economic theory of boundedly rational consumers represented by evolutionary and behavioral approaches; On the other side, it tries to contribute to the recent literature about the global analysis of discrete dynamical systems characterized by contact bifurcations leading to the creation of complex topological structures of the attractors and their basins of attraction. [ABSTRACT FROM AUTHOR]

The increased volume of rumors and attention on related topics during the COVID-19 pandemic have had a significant negative social impact. To combat rumors, it is crucial to study the actors involved in their spread. In this study, we first introduce prospect theory and construct an evolutionary game model between network operators and government regulators. We investigate collusion between network operators and Internet rumormongers as well as the regulatory behavior of government agencies. Second, we use prospect value to replace traditional expected utility to construct a profit prospect matrix and apply the dynamic replicator equation to analyze the equilibrium stability of the model. The stability conditions of the game between the two parties are closely related to the government's regulatory costs, and the strength of government punishment after collusion is detected. Finally, we propose relevant countermeasures against collusion problems during the network rumor control period. [ABSTRACT FROM AUTHOR]

We investigate a robust equilibrium investment-reinsurance problem for n ambiguity-averse competitive insurers, n ≥ 2 . Each insurer is allowed to purchase proportional reinsurance and invest in a risk-free asset and a risky asset. Each insurer aims to maximize the expected utility of a weighted relative terminal wealth with respect to the other competitors. In this article, the risky asset is assumed to follow a general and flexible model: the square root factor process. Following the game theory approach, we derive the closed solutions of the robust equilibrium investment-reinsurance strategies. Moreover, the verification theorem is provided in this article. Finally, we demonstrate some numerical analyses and give the economic explanations as well. [ABSTRACT FROM AUTHOR]

This paper investigates an optimal reinsurance problem for an insurance company with self-exciting claims, where the insurer's historical claims affect the claim intensity itself. We focus on a claim-dependent proportional reinsurance contact, where the term "claim-dependent" signifies that the insurer's risk retention ratio is allowed to depend on claim size. The insurer aims to maximize the expected utility of terminal wealth. By utilizing the dynamic programming principle and verification theorem, we obtain the optimal reinsurance strategy and corresponding value function in closed-form from the Hamilton–Jacobi–Bellman equation under an exponential utility function. We show that the claim-dependent proportional reinsurance is optimal among all types of reinsurance under the exponential utility maximization criterion. In addition, we present several analytical properties and numerical examples of the derived optimal strategy and provide economic insights through analytical and numerical analyses. In particular, we show the optimal claim-dependent proportional reinsurance can be considered as a continuous approximation of the step-wise risk sharing rule between the insurer and the reinsurer. [ABSTRACT FROM AUTHOR]

This paper develops a methodology to accommodate uncertainty in a GARCH model with the goal of improving portfolio decisions via Bayesian learning. Given the abundant evidence of uncertainty in estimating expected returns, we focus our analyses on the single parameter driving expected returns. After deriving an Uncertainty-Implied GARCH (UI-GARCH) model, we investigate how learning about uncertainty affects investments in a dynamic portfolio optimization problem. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize her expected utility from terminal wealth under an Affine GARCH(1,1) model. The corresponding stock evolution, and therefore, the wealth process, is treated as a Bayesian information model that learns about the expected return with each period. We explore the one- and two-period cases, demonstrating a significant impact of uncertainty on optimal allocation and wealth-equivalent losses, particularly in the case of a small sample size or large standard errors in the parameter estimation. These analyses are conducted under well-documented parametric choices. The methodology can be adapted to other GARCH models and applications beyond portfolio optimization. [ABSTRACT FROM AUTHOR]

This article provides an overview of utility functions and expected utility theory in economic decision-making. It discusses the history of expected utility theory, including the St. Petersburg paradox and the development of prospect theory as an alternative. The article also explores the concept of utility saturation and its impact on risk aversion. It introduces the use of Markov chains to analyze decision-making and concludes by acknowledging the limitations of models in understanding expected utility theory. [Extracted from the article]

It remains a pressing concern to understand how neural computations relate to risky decisions. However, most observations of brain-behavior relationships in the risk-taking domain lack a rigorous computational basis or fail to emulate of the dynamic, sequential nature of real-life risky decision-making. Recent advances emphasize the role of neural prediction error (PE) signals. We modeled, according to prospect theory, the choices of n = 43 human participants (33 females, 10 males) performing an EEG version of the hot Columbia Card Task, featuring rounds of sequential decisions between stopping (safe option) and continuing with increasing odds of a high loss (risky option). Single-trial regression EEG analyses yielded a subjective value signal at centroparietal (300-700 ms) and frontocentral (>800 ms) electrodes and in the delta band, as well as PE signals tied to the feedback-related negativity, P3a, and P3b, and in the theta band. Higher risk preference (total number of risky choices) was linked to attenuated subjective value signals but increased PE signals. Higher P3-like activity associated with the most positive PE in each round predicted stopping in the present round but not risk-taking in the subsequent round. Our findings indicate that decreased representation of decision values and increased sensitivity to winning despite low odds (positive PE) facilitate risky choices at the subject level. Strong neural responses when gains are least expected (the most positive PE on each round) adaptively contribute to safer choices at the trial-by-trial level but do not affect risky choice at the round-by-round level. [ABSTRACT FROM AUTHOR]

We consider a distributed computing setting wherein a central entity seeks power from computational providers by offering a certain reward in return. The computational providers are classified into long-term stakeholders that invest a constant amount of power over time and players that can strategize on their computational investment. In this paper, we model and analyze a stochastic game in such a distributed computing setting, wherein players arrive and depart over time. While our model is formulated with a focus on volunteer computing, it equally applies to certain other distributed computing applications such as mining in blockchain. We prove that, in Markov perfect equilibrium, only players with cost parameters in a relatively low range which collectively satisfy a certain constraint in a given state, invest. We infer that players need not have knowledge about the system state and other players' parameters, if the total power that is being received by the central entity is communicated to the players as part of the system's protocol. If players are homogeneous and the system consists of a reasonably large number of players, we observe that the total power received by the central entity is proportional to the offered reward and does not vary significantly despite the players' arrivals and departures, thus resulting in a robust and reliable system. We then study by way of simulations and mean field approximation, how the players' utilities are influenced by their arrival and departure rates as well as the system parameters such as the reward's amount and dispensing rate. We observe that the players' expected utilities are maximized when their arrival and departure rates are such that the average number of players present in the system is typically between 1 and 2, since this leads to the system being in the condition of least competition with high probability. Further, their expected utilities increase almost linearly with the offered reward and converge to a constant value with respect to its dispensing rate. We conclude by studying a Stackelberg game, where the central entity decides the amount of reward to offer, and the computational providers decide how much power to invest based on the offered reward. [ABSTRACT FROM AUTHOR]

This study investigated the interplay between exit selection models and local pedestrian movement patterns within floor field frameworks. Specifically, this investigation analysed the performance of a multinomial logit exit choice model, incorporating both expected utility theory and cumulative prospect theory frameworks when coupled with three distinct local-level pedestrian movement models (FF-Von Neumann, FF-Moore, and NSFF). The expected utility theory framework considers the deterministic component as a linear relationship, while the cumulative prospect theory framework further considers the decision-maker's risky attitudes by transforming objective terms into subjective terms using a power value function. The core objective was to comprehend how local movement dynamics, as represented by the floor field models, influence decision-making during exit selection. Comparative analyses revealed intriguing variations between the three local models, despite their shared expected utility theory-based exit choice framework. These discrepancies stemmed from the diverse pedestrian trajectory behaviours generated by each model. Consequently, these local dynamics impacted the decision-maker's assessment of critical factors, such as the number of evacuees close to the decision-maker ( N C D M ) and the number of evacuees close to an exit ( N C E ), which the exit choice model incorporates. These assessments, in turn, significantly affected higher-level decision-making. The integration of the three models with the multinomial logit exit choice model, using either cumulative prospect theory and expected utility theory frameworks, further strengthened the observed bilateral relationship. While the specific nature of this relationship varied depending on the chosen framework and its implementation details, these consistent findings demonstrate the robustness of the results. This reinforced the influence of local-level pedestrian dynamics on higher-level exit selection, highlighting the importance of accurate crowd dynamics modelling, especially when advanced exit choice models consider local movement factors. [ABSTRACT FROM AUTHOR]

No existing normative decision theory adequately handles risk. Expected Utility Theory is overly restrictive in prohibiting a range of reasonable preferences. And theories designed to accommodate such preferences (for example, Buchak's (2013) Risk‐Weighted Expected Utility Theory) violate the Betweenness axiom, which requires that you are indifferent to randomizing over two options between which you are already indifferent. Betweenness has been overlooked by philosophers, and we argue that it is a compelling normative constraint. Furthermore, neither Expected nor Risk‐Weighted Expected Utility Theory allow for stakes‐sensitive risk‐attitudes—they require that risk matters in the same way whether you are gambling for loose change or millions of dollars. We provide a novel normative interpretation of Weighted‐Linear Utility Theory that solves all of these problems. [ABSTRACT FROM AUTHOR]

This paper experimentally investigates risk taking in contest schemes by implementing a stopping task based on Seel and Strack (2013). In this stylized setting, managers with contest payoffs have an incentive to delay halting projects with a negative expectation, with the induced inefficiency being highest for a moderately negative drift. The experiment systematically varies the negative drift (between-subjects) and the payoff incentives (within-subject). We find evidence for risk taking in all our treatment conditions, with the non-monotonicity at least as pronounced as predicted. Contrary to the theoretical predictions, a similar pattern of behaviour persists even without contest incentives, suggesting contest incentives are not the only driver of risk-taking behaviour. Instead, observed behaviour violates expected utility maximization and is consistent with some intrinsic motivation for taking risk and myopic reasoning about the opponent. We explore the interplay between intrinsic incentives and the payment scheme and find that contest incentives might crowd out an intrinsic inclination to risk-taking. [ABSTRACT FROM AUTHOR]

Previous literature shows that prevalent risk measures such as value at risk or expected shortfall are ineffective to curb excessive risk-taking by a tail-risk-seeking trader with S-shaped utility function in the context of portfolio optimisation. However, these conclusions hold only when the constraints are static in the sense that the risk measure is just applied to the terminal portfolio value. In this paper, we consider a portfolio optimisation problem featuring S-shaped utility and a dynamic risk constraint which is imposed throughout the entire trading horizon. Provided that the risk control policy is sufficiently strict relative to the Sharpe ratio of the asset, the trader's portfolio strategies and the resulting maximal expected utility can be effectively constrained by a dynamic risk measure. Finally, we argue that dynamic risk constraints might still be ineffective if the trader has access to a derivatives market. [ABSTRACT FROM AUTHOR]

The anticipative information refers to some information about future events that may be disclosed in advance. This information may regard, for example, financial assets and their future trends. In our paper, we assume the existence of some anticipative information in a market whose risky asset dynamics evolve according to a Brownian motion and a Poisson process. Using Malliavin calculus and filtration enlargement techniques, we derive the information drift of the mentioned processes and, both in the pure jump case and in the mixed one, we compute the additional expected logarithmic utility. Many examples are shown, where the anticipative information is related to some conditions that the constituent processes or their running maximum may verify, in particular, we show new examples considering Bernoulli random variables. [ABSTRACT FROM AUTHOR]

In this paper, we give an example of a statement concerning two-player zero-sum games which is undecidable, meaning that it can neither be proven or disproven by the standard axioms of mathematics. Earlier work has shown that there exist "paradoxical" two-player zero-sum games with unbounded payoffs, in which a standard calculation of the two players' expected utilities of a mixed strategy profile yield a positive sum. We show that whether or not a modified version of this paradoxical situation, with bounded payoffs and a weaker measurability requirement, exists is an unanswerable question. Our proof relies on a mixture of techniques from set theory and ergodic theory. [ABSTRACT FROM AUTHOR]

Artificially intelligent systems are nowadays presented as systems that should, among other things, be explainable and ethical. In parallel, both in the popular culture and within the scientific literature, there is a tendency to anthropomorphize Artificial Intelligence (AI) and reify intelligent systems as persons. From the perspective of machine ethics and ethical AI, this has resulted in the belief that truly autonomous ethical agents (i.e., machines and algorithms) can be defined, and that machines could, by themselves, behave ethically and perform actions that are justified (explainable) from a normative (ethical) standpoint. Under this assumption, and given that utilities and risks are generally seen as quantifiable, many scholars have seen consequentialism (or utilitarianism) and rational choice theory as likely candidates to be implemented in automated ethical decision procedures, for instance to assess and manage risks as well as maximize expected utility. While some see this implementation as unproblematic, there are important limitations to such attempts that need to be made explicit so that we can properly understand what artificial autonomous ethical agents are, and what they are not. From the perspective of explainable AI, there are value-laden technical choices made during the implementation of automated ethical decision procedures that cannot be explained as decisions made by the system. Building on a recent example from the machine ethics literature, we use computer simulations to study whether autonomous ethical agents can be considered as explainable AI systems. Using these simulations, we argue that technical issues with ethical ramifications leave room for reasonable disagreement even when algorithms are based on ethical and rational foundations such as consequentialism and rational choice theory. By doing so, our aim is to illustrate the limitations of automated behavior and ethical AI and, incidentally, to raise awareness on the limits of so-called autonomous ethical agents. [ABSTRACT FROM AUTHOR]

Longtermists argue we should devote much of our resources to raising the probability of a long happy future for sentient beings. But most interventions that raise that probability also raise the probability of a long miserable future, even if they raise the latter by a smaller amount. If we choose by maximising expected utility, this isn't a problem; but, if we use a risk-averse decision rule, it is. I show that, with the same probabilities and utilities, a risk-averse decision theory tells us to hasten human extinction, not delay it. What's more, I argue that morality requires us to use a risk-averse decision theory. I present this not as an argument for hastening extinction, but as a challenge to longtermism. [ABSTRACT FROM AUTHOR]

• A chat service with actively involved customers and repeating interactions is studied. • The capacity of parallel chats and the server's response effort are derived. • A queueing game for a dual-channel system of chat service and call center is analyzed. • Customer bounded rationality is considered. Many service systems in various type of industry provide services via chat technologies. This type of service system is characterized by an interesting combination of properties. First, the customer is actively involved in service execution, since the service is comprised of responses of the server and the customer to each other, until the customer's request is satisfied. In addition, in such a system, the service can be provided by a single server in a parallel manner to a number of customers. Motivated by these and others interesting properties of the chat service, we formulate and analyze the stochastic queueing system as a two-dimensional quasi-birth-and-death process and derive its steady-state probabilities using matrix geometric methods. By means of economic analysis, we provide a scheme for deriving the optimal capacity of parallel chats and the optimal response effort that should be made by the server, considering that higher effort lengthens the response time but increases the probability of successful service completion. We next investigate a queueing game that considers a multi-channel service system consisting of a chat service and a traditional multi-server call center, and we consider strategic customers under both a centralized and a decentralized scenario. We show that competition between service channels may lead to lower service utilization even though customers enjoy higher expected utility. Finally, customer bounded rationality is considered and it is shown that customers who are less rational may enjoy higher utility. Sensitivity analyses are conducted for all scenarios. [ABSTRACT FROM AUTHOR]

When determining the optimal deductible level for an insurance policy, a policyholder faces two limitations. First, uncertainty arises from the randomness of future losses. Second, the opacity of the functional forms of the policyholder's loss distribution and utility function contributes to additional limitations. While the academic literature focuses on the former, we additionally include limited information on these functional forms in our model setting to reflect real-world decision-making. That is, we draw on an expected utility framework and analyze the relationship between optimal deductible levels under limited and full information. We also derive several decision rules under limited information in order to approximate the optimal deductible level under full information. To support real-world decision-making, these rules could be easily implemented in an online decision aid offered by an insurance broker, a comparison portal for insurance contracts or a consumer protection agency. [ABSTRACT FROM AUTHOR]

We study optimal information disclosure via an ambiguous persuasion approach in a two-player contest. The designer can precommit to an ambiguous device to influence the uninformed contestant's belief about his opponent's private type. We fully characterize the optimal ambiguous information structures when players are maxmin expected utility (MMEU) maximizers. Depending on the prior, it is optimal to either induce ambiguity or fully conceal information. We provide a necessary and sufficient condition under which an effort-maximizing organizer can benefit strictly more from using ambiguous persuasion than from using the optimal Bayesian device. [ABSTRACT FROM AUTHOR]

Underground engineering projects are often characterised by significant uncertainties, leading to complex decision‐making when selecting appropriate design methods. Traditional approaches, such as the observational method (OM), have been widely used but rarely explicitly account for the integration of risk, costs, construction timeframes and engineering judgement. Successfully reaching a consensus to implement the OM on tunnelling projects can be challenging and requires ensuring stakeholders understand how the method impacts structural safety. Fostering such understanding can be an arduous process. A risk‐based decision framework is introduced using expected utility theory to aid the decision to choose the OM, or not. The framework integrates risk, cost, construction timelines and engineering judgement within an economic decision model using a probabilistic approach. It is tested on a Sydney, Australia case study. A Monte Carlo model analysis examines contingency actions and structural failure probability when applying the OM. The case study highlights the significance of considering indirect costs in complex decisions and harnesses experienced professionals' insights for a comprehensive risk and cost assessment. We conclude that merging probabilistic‐based design with OM enhances the ability to demonstrate structural safety and economic factors within a unified model. [ABSTRACT FROM AUTHOR]

AbstractThis 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]

The paper aims to provide a theoretical basis for the application of revenue-sharing contract under bounded rationality and capital constraints. We consider an uncooperative ordering model in a supplier-Stackelberg game and coordination strategy with revenue-sharing contract for a loss-averse and capital-constrained retailer. We drive the existence and uniqueness conditions of the optimal solutions under bank financing and revenue-sharing contract. We also develop a series of propositions and corollaries to determine the optimal solutions and offer some managerial insights. The key contribution of the paper is to deepen and expand the revenue-sharing contract under the risk-neutral assumption, and to provide a theoretical basis for the application of revenue-sharing contract under bounded rationality and capital constraints. We find that the revenue-sharing ratio of loss-averse and capital-constrained retailer is larger than that of neutral retailer and the expected utility of loss-averse and capital-constrained retailer is larger than that of neutral retailer under coordination strategy with revenue-sharing contract. [ABSTRACT FROM AUTHOR]

As the prevalence of electric vehicles (EVs) continues to grow, their charging and discharging behaviors pose a challenge to the stable operation of power systems. Therefore, this paper analyzes the charging demand of EV users through GPS trajectory data and proposes an EV-discharging-optimization model based on vehicle-to-grid interaction (V2G). Firstly, the spatial–temporal distribution of EV-charging demand is obtained by cleaning and mining the big data of traveling vehicles, considering dynamic energy consumption theory and users' willingness; secondly, a probabilistic model of EV users' participation in V2G-demand response is constructed based on expected utility theory, which both considers the heterogeneity of users and reflects the interactive influence of users' decisions; finally, a scheduling model of EV discharging in the regional grid is established. The results show that the proposed model can explore the potential of user participation in V2G in the study area, and the V2G response resources can reduce the grid fluctuation and enable users to obtain certain benefits, which achieves a win–win situation between the grid side and the user side. [ABSTRACT FROM AUTHOR]

This study investigates ambiguity aversion within the framework of a utility-maximizing investor under a modified constant-elasticity-of-volatility (M-CEV) model for the underlying asset. We derive closed-form solutions of a non-affine type for the optimal allocation and value function via a Cauchy problem. This work generalizes previous results in non-ambiguous settings by extending existing work to Hyperbolic Absolute Risk Aversion utility (HARA), correcting some typos in the literature for Constant Relative Risk Aversion utility (CRRA). Helpful details and derivations are also included in the manuscript. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimal decision making of an insurer towards a new insurable business, whose risk is independent of the existing risk faced by the insurer. We assume that the insurer, with the objective of maximizing the expected utility of its final wealth, together with the solvency constraint and the availability of reinsurance as a risk transfer mechanism, is deciding if it is viable to underwrite a new insurance business risk. If this new business is underwritten, it is shown that a stop-loss reinsurance contract is optimal when the solvency risk is quantified by the conditional value at risk. If the regulatory regime changes to the value at risk, the optimal reinsurance form becomes more complicated; it can be either stop-loss or two-layer under the assumption that the new risk has a strictly decreasing probability density function. Numerical examples are provided to illuminate the insurer's decision making and the optimal form of the reinsurance strategy. [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. Optimization 70(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]

The negative impact of used batteries of new energy vehicles on the environment has attracted global attention, and how to effectively deal with used batteries of new energy vehicles has become a hot issue. This paper combines the rank-dependent expected utility with the evolutionary game theory, constructs an evolutionary game model based on the interaction mechanism between decision makers' emotions and decision making, and studies the recycling strategy of new energy automobile trams under the heterogeneous combination of emotions. The study shows that: (1) In addition to the establishment of effective external norms, the subjective preference of decision makers can also positively affect the recycling strategy of new energy vehicle batteries. (2) Fairness preferences can have a significant nonlinear effect on new energy vehicle battery recycling strategies by changing the utility function of decision makers. (3) When new energy vehicle manufacturers remain optimistic and new energy vehicle demanders remain rational or pessimistic, the new energy vehicle battery recycling strategy can reach the optimal steady state. [ABSTRACT FROM AUTHOR]

Behavioral welfare economics usually aims at mere means paternalism, helping agents better pursue their own goals. This paper discusses one initially promising way to inform policies addressed at agents who violate expected utility theory (EUT), namely what I call "CPT debiasing." I argue that this approach is problematic even if we grant the normative authority of EUT, the descriptive adequacy of CPT (cumulative prospect theory), and the general acceptability of means paternalism. First, it is doubtful whether the CPT utility function measures what its proponents intend. Second, by imposing risk neutrality on agents, the approach involves a more problematic paternalism. [ABSTRACT FROM AUTHOR]

Vows play a central role in Buddhist thought and practice. Monastics are obliged to know and conform to hundreds of vows. Although it is widely recognized that vows are important for guiding practitioners on the path to enlightenment, we argue that they have another overlooked but equally crucial role to play. A second function of the vows, we argue, is to facilitate group harmony and cohesion to ensure the perpetuation of the dhamma and the saṅgha. However, the prominence of vows in the Buddhist tradition seems at odds with another central part of the doctrine. For vows, like other promises, seem to involve representing a persisting self as the individual who undertakes the vow. And to explicitly appeal to a persisting self conflicts with one of the most important philosophical commitments of Buddhism – the no self view. We argue though that once we articulate the details of how vows generate behavior that conforms to them, we can see that no appeal to the self is required to internalize and act on vows. [ABSTRACT FROM AUTHOR]

• Expected utility theory is not appropriate for understanding the behavior of low-income farmers. • Lexicographic ordering has the advantages of procedural rationality and the behavioral focus on thresholds. • Lexicographic ordering is readily adaptable to the narrative that low income farmers put safety first. • Focusing on safety does not imply avoidance of variance-increasing techniques. As Richard Day explained, expected utility theory suffers from procedural irrationality. This and other problems are illustrated here in the context of decision-making among low-income farmers. Farmers in developing countries are commonly thought to underinvest in modern techniques because their low incomes make them especially risk averse. In addition to the procedural leap of faith, highly restrictive assumptions are needed to apply expected utility theory to the problem. Nor does expected utility theory, as usually prescribed, fit the narrative of loss aversion. The reader is introduced to a procedurally rational substitute called lexicographic safety first. The model is illustrated for the case of rice fertilization in the Philippines, and policy implications are drawn. To illustrate the potential appeal of lexicographic ordering for other applications involving thresholds, a lexicographic model of rational addiction is also provided. [ABSTRACT FROM AUTHOR]

Traditional conceptions of state-sponsored cyber economic espionage suggest that countries with different product profiles should experience high levels of espionage between them. However, this is not what we observe empirically. Incidence of economic espionage tends to be prevalent between countries with similar product and manufacturing profiles. This suggests that we may be missing critical parts of what drives state-sponsored cyber economic espionage. To help unravel this puzzle, I develop a novel theoretical framework that proposes that because attackers seek to maximize the expected utility of stolen information, they target countries that possess similar productive capabilities as themselves. Consequently, countries with dissimilar product profiles should avoid targeting each other for espionage. I test this argument using data on states' product complexities and cyber economic espionage for a global sample of countries in a dyadic analytical framework. The results robustly show that for any pair of countries, as the complexities of their products diverge, they become significantly less likely to aim espionage attempts at each other. This study thus contributes new insights to explain why cyber economic espionage appears restricted to only a small number of advanced economies. It also illustrates the utility of large-N dyadic approaches in studying state-sponsored cyber espionage. [ABSTRACT FROM AUTHOR]

This study considers the optimal debt ratio and dividend payment policies for an insurer concerned about model misspecification. We assume that the insurer can invest all of its asset to the financial market and the ambiguity may exist in the risky asset. Taking into account the ambiguous situation, the insurer aims to maximize the expected utility of a discounted dividend payment until it ruins. Under some assumption, we prove that there exists classical solutions of the optimal debt ratio, dividend payment policies, and value functions that show that the existence of ambiguity can affect the optimal debt ratio and dividend policies significantly. [ABSTRACT FROM AUTHOR]

In a recent paper Winsberg (2021) argued in favor of research into geoengineering by relying on Good's theorem, which states that conducting research maximizes one's expected utility. However, this result sometimes fails for risk-avoidant agents (Buchak 2010). Since risk avoidance captures some of the "precautionary" intuitions that critics of geoengineering share, it is important to see if geoengineering research would maximize one's utility if risk avoidance is taken into account. I show that under some conditions conducting geoengineering research would not maximize risk-weighted expected utility. [ABSTRACT FROM AUTHOR]

For decades, eliminating preventable error has been presented as an audacious, responsible, even noble goal for healthcare organizations. Bosk proposed to show the folly of this aspiration. This essay moves Bosk's work forward by placing ideas about reducing error in the context of other attempts to routinize work, on the one hand, and casting doubt of the viability of such programs, on the other. Other scholars have reshaped our understanding of human behavior by revising key assumptions to make them more psychologically and sociologically realistic. Using Simon's revision of global rationality and Kahneman's revision of expected utility theory as examples, this essay takes a first stab at suggesting which assumptions of the preventable error paradigm need revising. Because we do not fully understand how we gather and process information about each other, this essay argues that we should preserve opportunities for more holistic information gathering, both by ethnographers and medical staff. Importantly, those include occasions when medical workers (not algorithms) encounter, observe, and listen to patients as full human beings (not their digital twins). [ABSTRACT FROM AUTHOR]

The goal of core-satellite investing is to optimally balance the portfolio allocation between a core and satellite investment. This paper provides an explicit solution when the investor's optimality criterion is the third-order and fourth-order expansion of the expected utility function, respectively. Based on a numeric example, we document the sensitivity of the proposed weights to coskewness and cokurtosis components. Finally, we use ETFs to examine the portfolio performance of the coresatellite strategy with higher order moments. We document that integrating the higher order moment in core-satellite investing can improve the financial performance of a portfolio. [ABSTRACT FROM AUTHOR]

Summary: A treatment regime is a sequence of decision rules, one per decision point, that maps accumulated patient information to a recommended intervention. An optimal treatment regime maximises expected cumulative utility if applied to select interventions in a population of interest. As a treatment regime seeks to improve the quality of healthcare by individualising treatment, it can be viewed as an approach to formalising precision medicine. Increased interest and investment in precision medicine has led to a surge of methodological research focusing on estimation and evaluation of optimal treatment regimes from observational and/or randomised studies. These methods are becoming commonplace in biomedical research, although guidance about how to choose among existing methods in practice has been somewhat limited. The purpose of this review is to describe some of the most commonly used methods for estimation of an optimal treatment regime, and to compare these estimators in a series of simulation experiments and applications to real data. The results of these simulations along with the theoretical/methodological properties of these estimators are used to form recommendations for applied researchers. [ABSTRACT FROM AUTHOR]

How to improve the quality of the new retail service supply chain (RSSC) has become a hot topic for enterprises and consumers. Considering the influence of the new RSSC enterprises' emotional attitude on the decision-making of quality improvement, the theory of rank-dependent expected utility (RDEU) is combined with an evolutionary game, constructing an evolutionary game model of collaborative quality improvement of new RSSC, and analyzing the game strategy choice of each participant in collaborative quality improvement of new RSSC. The study shows that when only one party is emotional, the rationality of retail service integrators will promote the synergistic improvement of the quality of the new RSSC more than functional service providers. Moreover, pessimism and optimism have an inverted U-shaped effect on quality decisions. When both parties have emotions, functional service providers remain optimistic, and retail service integrators remain pessimistic or rational can promote the collaborative improvement of new RSSC quality. In addition, the effects of quality preference, peer mechanism, feedback mechanism, and risk mechanism on the collaborative quality improvement of new RSSC are analyzed. Based on the research findings, relevant countermeasures are proposed to incentivize new retailers to conduct collaborative improvement in quality in terms of establishing an open mechanism for negotiation and consultation, strengthening the emotion management of new retail node firms, and adjusting the transmission of quality signals, with a view to realizing the quality collaborative improvement of the new RSSC. [ABSTRACT FROM AUTHOR]

Complexity of the problem of choosing among uncertain acts is a salient feature of many of the environments in which departures from expected utility theory are observed. I study a class of Generalized Simple Bounds preferences in which acts that are complex from the perspective of the decision maker are bracketed by "simple" acts to which they are related by statewise dominance. I then study a refinement of the model in which the size of the partition with respect to which an act is measurable arises endogenously as a measure of subjective complexity. Finally, I consider choice behavior characterized by a "cautious completion" of Simple Bounds preferences, and discuss the relationship between this model and models of ambiguity aversion. I develop general comparative statics results, and explore applications to portfolio choice and insurance choice. • Problems of choice problems under uncertainty are complex. • From behavioral axioms, I characterize the set of acts that are well-understood. • Decision makers use acts they understand well to bracket those they do not. • The model is tractable, and admits rich comparative statics. • The model helps explain observed choice patterns in applications. [ABSTRACT FROM AUTHOR]

We test the touchstones of economic rationality—utility maximization, stochastic dominance, and expected-utility maximization—of elite students in the U.S. and in Africa. The choices of most students in both samples are generally rationalizable, but the U.S. students' scores are substantially higher. Nevertheless, the development gap in economic rationality in incentivized risk choices between these future elites is much smaller than the difference in performance on a non-incentivized canonical cognitive ability test, often used as a proxy for economic decision-making ability in studies of economic development and growth. We argue for the importance of including consistency with economic rationality in studies of decision-making ability. [ABSTRACT FROM AUTHOR]

Precision medicine currently relies on a mix of deep phenotyping strategies to guide more individualized healthcare. Despite being widely available and information-rich, physiological time-series measures are often overlooked as a resource to extend insights gained from such measures. Here we have explored resting-state hemoglobin measures applied to intact whole breasts for two subject groups – women with confirmed breast cancer and control subjects – with the goal of achieving a more detailed assessment of the cancer phenotype from a non-invasive measure. Invoked is a novel ordinal partition network method applied to multivariate measures that generates a Markov chain, thereby providing access to quantitative descriptions of short-term dynamics in the form of several classes of adjacency matrices. Exploration of these and their associated co-dependent behaviors unexpectedly reveals features of structured dynamics, some of which are shown to exhibit enzyme-like behaviors and sensitivity to recognized molecular markers of disease. Thus, findings obtained strongly indicate that despite the use of a macroscale sensing method, features more typical of molecular-cellular processes can be identified. Discussed are factors unique to our approach that favor a deeper depiction of tissue phenotypes, its extension to other forms of physiological time-series measures, and its expected utility to advance goals of precision medicine. [ABSTRACT FROM AUTHOR]

We study the role of attention and working memory in choices where options are presented sequentially rather than simultaneously. We build a model where a costly attention effort is chosen, which can vary over time. Evidence is accumulated proportionally to this effort and the utility of the reward. Crucially, the evidence accumulated decays over time. Optimal attention allocation maximizes expected utility from final choice; the optimal solution takes the decay into account, so attention is preferentially devoted to later times; but convexity of the flow attention cost prevents it from being concentrated near the end. We test this model with a choice experiment where participants observe sequentially two options. In our data the option presented first is, everything else being equal, significantly less likely to be chosen. This recency effect has a natural explanation with appropriate parameter values in our model of leaky evidence accumulation, where the decline is stronger for the option observed first. Analysis of choice, response time and brain imaging data provide support for the model. Working memory plays an essential role. The recency bias is stronger for participants with weaker performance in working memory tasks. Also activity in parietal areas, coding the stored value in working, declines over time as predicted. [ABSTRACT FROM AUTHOR]

In response to the current situation in which an imbalance in educational redistribution has led to an inefficient and slow educational environment in the long-term, difficulty in satisfying the interests of subjects, and a lack of effective targeting of school policy measures, an SD evolutionary game model targeting educational reform, with schools, administrative institutions, and professors as the three parties, was established. Based on expected utility theory and Lyapunov’s first law, a feedback regulation simulation study was conducted on subjects’ decision-making behavior. The research results show that: the evolutionary trajectory of the subjects in the game is affected by the redistribution of education funding benefits, but the evolution of the final system is in a state of stable equilibrium (push, reform, reform); the decision-making behavior of the pushing party plays a decisive role in the stability of the system, and if the education subsidies of university professors and the income from the value of research results are greater than the education subsidies before the education reform, evolution over time is conducive to prompting the professor group to prefer the education reform strategy, while the effect of school push on the stability of the educational reform system is significant in schools with lower returns on the value of research funding. [ABSTRACT FROM AUTHOR]

This paper considers a two-player game where each player chooses a resource from a finite collection of options. Each resource brings a random reward. Both players have statistical information regarding the rewards of each resource. Additionally, there exists an information asymmetry where each player has knowledge of the reward realizations of different subsets of the resources. If both players choose the same resource, the reward is divided equally between them, whereas if they choose different resources, each player gains the full reward of the resource. We first implement the iterative best response algorithm to find an ϵ -approximate Nash equilibrium for this game. This method of finding a Nash equilibrium may not be desirable when players do not trust each other and place no assumptions on the incentives of the opponent. To handle this case, we solve the problem of maximizing the worst-case expected utility of the first player. The solution leads to counter-intuitive insights in certain special cases. To solve the general version of the problem, we develop an efficient algorithmic solution that combines online convex optimization and the drift-plus penalty technique. [ABSTRACT FROM AUTHOR]

Achieving Lifelong Open-ended Learning Autonomy (LOLA) is a key challenge in the field of robotics to advance to a new level of intelligent response. Robots should be capable of discovering goals and learn skills in specific domains that permit achieving the general objectives the designer establishes for them. In addition, robots should reuse previously learnt knowledge in different domains to facilitate learning and adaptation in new ones. To this end, cognitive architectures have arisen which encompass different components to support LOLA. A key feature of these architectures is to implement a proper balance between deliberative and reactive processes that allows for efficient real time operation and knowledge acquisition, but this is still an open issue. First, objectives must be defined in a domain-independent representation that allows for the autonomous determination of domain-dependent goals. Second, as no explicit reward function is available, a method to determine expected utility must also be developed. Finally, policy learning may happen in an internal deliberative scale (dreaming), so it is necessary to provide an efficient way to infer relevant and reliable data for dreaming to be meaningful. The first two aspects have already been addressed in the realm of the e-MDB cognitive architecture. For the third one, this work proposes Perceptual Classes (P-nodes) as a metacognitive structure that permits generating relevant "dreamt" data points that allow creating "imagined" trajectories for deliberative policy learning in a very efficient way. The proposed structure has been tested by means of an experiment with a real robot in LOLA settings, where it has been shown how policy dreaming is possible in such a challenging realm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimal consumption and investment problem under the expected utility maximization criterion. It is supposed that the financial market consists of a risky asset and a risk-free asset, and the risky asset prices follow the 4/2 Cox–Ingersoll–Ross (CIR) stochastic hybrid model. The investment objective is to obtain an optimal consumption–investment strategy by maximizing the objective function. The closed-form expression of the optimal consumption–investment strategy is obtained by using optimal control theory and the corresponding Hamilton–Jacobi–Bellman (HJB) equation under the power utility function. In addition, we present a numerical example to illustrate the influence of model parameters on the optimal consumption–investment strategy. [ABSTRACT FROM AUTHOR]

The support for controversial policies in the area of transport often increases after real-world implementation. In this paper, we provide a comprehensive overview of the literature surrounding the implementation of controversial policies in transport, particularly in the context of road pricing. We then discuss various theories and mechanisms that contribute to an increase in support for such policies after real-world implementation. One significant factor is a change in attitude towards a policy. However, there are also other important factors, for example the utility experienced by people is not necessarily the same as the expected utility. In addition, people may be loss-averse relative to their reference point (often: their current situation). The disadvantages of a policy considered for implementation may be clearer than the advantages. Other factors discussed are the effect of the expected distribution of gains and losses, their magnitude, and perceptions of the processes being fair and competent. We then examine the implications of increased support after implementation, important lessons being that policymakers can anticipate growing support after implementation and that communication is key in the process of deciding on a controversial policy. In this regard, we propose that having a champion, or visionary, can be particularly effective in getting controversial policies accepted. Finally, we suggest avenues for future research. • Controversial transport policies: support often increases after implementation. • Attitude changes are an important explanation. • Champions can be helpful for the implementation of controversial policies. [ABSTRACT FROM AUTHOR]

We consider an optimal investment problem to maximize expected utility of the terminal wealth, in an illiquid market with search frictions and transaction costs. In the market model, an investor's attempt of transaction is successful only at arrival times of a Poisson process, and the investor pays proportional transaction costs when the transaction is successful. We characterize the no-trade region describing the optimal trading strategy. Our asymptotic analysis implies that the effects of the transaction costs are more pronounced (more widening effect of the no-trade region and more diminishing effect of the value function) in the market with less search frictions. [ABSTRACT FROM AUTHOR]

This paper studies the dynamically optimal consumption, investment and life-insurance strategies for a wage earners under inside information and inflation. Assume that the wage earner can invest in a risk-free asset, a risky asset and an inflation-indexed bond and that the wage earner can obtain some additional information on the risky asset from the financial market. By maximizing the expected utility of the wage earner's consumption, inheritance and terminal wealth, we obtain the dynamically optimal consumption, investment and life-insurance strategies for the wage earner. The method of this paper is mainly based on (dynamical) stochastic control theory and the technique of enlargement of filtrations. Moreover, sensitivity analysis is carried out, which reveals that a wage earner with inside information tends to increase his/her consumption and investment, while reducing his/her purchase of life insurance. [ABSTRACT FROM AUTHOR]