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on Utility Models and Prospect Theory |
| By: | Carole Bernard; Silvana M. Pesenti |
| Abstract: | We introduce a framework for preference-robust decision making when preferences over risk are modelled through generalised distortion risk measures. Unlike distributional robustness, our approach addresses ambiguity in the risk functional itself. We construct ambiguity sets on distortion (weight) functions using the Wasserstein distance and Bregman divergences, and derive closed-form expressions for the worst- and best-case distortion risk measures. We further extend the framework to rank-dependent utility, yielding preference-robust behavioural models. In particular, rank-dependent utility appears as a robustification of the expected utility model, yielding a novel way to address the Allais paradox. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.02854 |
| By: | Karen Mulligan; Drishti Baid; Jason N. Doctor; Darius N. Lakdawalla |
| Abstract: | Health technology assessment bodies and policymakers have grappled with the issue of whose preferences – the general healthy population or the patient population – should be incorporated into traditional cost-effectiveness analysis. This debate arises because healthy individuals and patients often value the same health state differently, and cost-effectiveness will undervalue health-related quality of life (HRQoL) for individuals with chronic illness or disability because it cannot account for adaptation. Prospect theory (PT) provides a framework that permits adaptation since it assumes individuals evaluate outcomes relative to a reference point. We estimate utility over HRQoL in a PT framework in a nationally representative U.S. population and compare directly with utility estimates in an EU framework from the same population. Consistent with previous studies in the monetary and health domains, we find evidence of probability weighting bias and loss aversion over health. Using the PT utility estimates, we demonstrate how PT can be incorporated into healthcare value assessment and present the resulting implications for medical decision making. |
| JEL: | D9 I1 I3 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35666 |
| By: | Paramahansa Pramanik |
| Abstract: | We develop a continuous-time stochastic choice theory with endogenous preference evolution. Unlike dynamic random utility, observed behavior affects future preferences through the conditional distribution of latent preference states, generating endogenous distributional feedback. We show that this feedback has observable behavioral implications and characterize stochastic choice by a behavioral representation consisting of contemporaneous choice and continuation behavior. This representation is identified from stochastic choice, yields a rigidity result linking structural preference dynamics to observable behavior, and characterizes exactly when distribution dependent utility is behaviorally reducible to dynamic random utility. We further prove a behavioral impossibility theorem: stochastic choice arrays exhibiting behavioral distributional feedback admit no dynamic random utility representation. On the probabilistic side, we establish existence and weak uniqueness for the underlying conditional McKean-Vlasov system with conditional law feedback. The structure unifies endogenous information, latent preference dynamics, behavioral identification, and stochastic choice within a single continuous-time model. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.06152 |
| By: | Igal Milchtaich |
| Abstract: | The paper explores a theoretical freemium model for the sale of information, drawing on mathematical tools used in the study of repeated zero-sum games and Bayesian persuasion. Unlike standard Bayesian persuasion models, the information seller (IS) is indifferent to the actions taken by the information buyer (IB) and is concerned solely with maximizing the revenue from selling information. Offering some information for free may increase the IB's willingness to pay for additional information. The information that the IB seeks is about the state of the world. Initially, the IB only knows the prior distribution over possible states. The IS supplies both free and paid information through signals whose state-dependent distributions determine the IB's posterior via Bayes' rule. The IB's utility is a function of the posterior. An optimal free signal is one that maximizes the IS's expected revenue from the subsequent paid signal. That revenue is equal to the IB's expected utility gain when moving from the posterior induced by the free signal to that induced by the paid signal. The paper characterizes the optimal free and paid signals and derives a formula for the maximal revenue in terms of the IB's utility function. It shows that a revenue gain for the IS from the provision of free information is accompanied by a loss to the IB. Whether free information can increase the IS's revenue depends on the form of the IB's utility function. In the two-state case, that dependence is fully characterized. In the general case, only necessary conditions are obtained. In particular, if the IB's utility function is convex, the IS can never profit from providing free information. This occurs, in particular, when the IB uses the information to solve a decision problem. By contrast, when the IB is engaged in a strategic interaction with a third party, the IS may benefit from providing free information. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.01468 |
| By: | Peter Carr; Stephan Sturm |
| Abstract: | We consider the question of the optimal timing of the sale of an asset with stochastic dynamics. Our analysis is based on the method of the distribution builder introduced by Sharpe, Goldstein and Blythe [SGB00] for the purpose of optimal portfolio selection. Instead of specifying a utility function or risk aversion coefficient, this tool directly elicits the target distribution of the investor. We show how the problem of an optimal asset sale is in this setting linked to the problem of finding a Skorokhod embedding of a distribution into a diffusion process. In the case where the asset process follows a geometric Brownian motion and a specific family of distributions is targeted, one can observe a risk-return tradeoff. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.18783 |
| By: | Geoffrey Heal; Marcella Lucchetta |
| Abstract: | This paper provides a novel framework for assessing the effect of ambiguity on asset values within the Klibanoff-Marinacci-Mukerji (KMM) smooth ambiguity framework. By shifting the analysis into a continuous space of prior probabilities, we establish that ambiguity leads to an adjustment of beliefs (“ambiguity-adjusted probabilities” or “distorted probabilities”) characterized by First-Order Stochastic Dominance (FSD). Leveraging this property, we introduce a systematic economic decomposition of asset valuation separating the baseline risky valuation from the structural cost of uncertainty. Our continuous framework shows that increased ambiguity aversion depresses optimal asset demand. |
| JEL: | D81 G10 G12 Q20 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35488 |
| By: | Isaiah Andrews |
| Abstract: | Representation theorems in decision theory establish that behavior satisfies certain axioms if and only if it can be rationalized by a well-defined objective. I argue that this ``if and only if'' structure provides a potentially useful foundation for label-free evaluation and regularization of LLMs and other AI systems. Axiom compliance can be checked from the model's own responses to synthetic choice problems, with no external labels or human feedback, and the penalties are readily computable. Because the axioms are necessary and sufficient, the resulting checks exhaust the implications of the relevant rationality standard for the elicited data: a model that passes cannot be rejected on rationality grounds by any further test of the same data. I discuss three instantiations: probabilistic coherence via a theorem of de Finetti, preference rationality via Afriat's theorem, and subjective expected utility via a theorem of Echenique and Saito (2015), each yielding a continuous penalty that is zero whenever behavior can be rationalized. Since coherence does not restrict which objective rationalizes behavior, these penalties complement rather than replace other evaluation and training signals. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05015 |
| By: | Yukihiko Funaki; Yukio Koriyama; Matias Nunez; Giacomo Rostagno |
| Abstract: | Extending the Price-and-Choose (P&C) mechanism of Echenique and Nunez (2025), we propose the Price-Accept-and-Choose (PA&C) mechanism, which preserves efficiency while eliminating P&C's first-mover advantage. We then analyze randomized and bidding variants and show that the resulting equilibrium payoffs correspond to standard solutions in transferable utility games: the Center of the Imputation Set value for P&C and the Shapley value for PA&C. In the randomized variants, these solutions arise in expectation; in the bidding variants, they are implemented on every equilibrium path. We further relate other efficient designs, such as balanced VCG payments, to additional solution concepts, forging an interpretable bridge between implementation theory and cooperative game theory. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.02773 |
| By: | Mauro Bambi; Ehud Lehrer; Eilon Solan |
| Abstract: | This paper investigates the strategic and welfare properties of endogenous population partitioning (secession) within large-population anonymous games featuring strategic heterogeneity. We consider a continuum-player framework with a binary action space where players are categorized either as fol- lowers, who experience positive network externalities from conformity, or as contrarians, who seek distinctiveness via anti-conformism. We fully characterize the set of Nash equilibria and establish con- ditions under which costless secession yields structural Pareto improvements. We demonstrate that in any strategically mixed society, every mixed-strategy Nash equilibrium admits a Pareto-improving se- cession. With finitely many types, secession systematically mitigates coordination frictions, enhancing both individual payoffs and aggregate utility. Furthermore, we characterize social planner configura- tions optimizing weighted aggregate utility, establishing a formal mathematical isomorphism between optimal jurisdictional design and the theory of Bayesian persuasion solved via concavification. Finally, we derive the structural conditions governing migration stability when subgroups can unilaterally re- locate across distinct societies. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.06092 |
| By: | Scott R. Baker; Justin Balthrop; Mark J. Johnson; Jason D. Kotter; Kevin Pisciotta |
| Abstract: | This paper examines the rapid expansion and convergence of retail betting markets. We analyze market design elements, discuss economic utility, and highlight shared behavioral drivers of sports betting markets, prediction markets, and retail options trading. Our review underscores how technological innovation, behavioral biases, and regulatory arbitrage have shaped recent market evolution. We highlight important considerations for policy-makers facing a changing landscape and outline possibilities for further research. |
| JEL: | G11 G4 G5 G50 K20 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35520 |
| By: | Christoph Schlegel |
| Abstract: | We characterise the division rules for claims problems that satisfy equal treatment of equals, bilateral consistency, composition down, and composition up. The rules are precisely the members of a one-parameter log-exponential family $\{r^\theta\}_{\theta\in[-\infty, +\infty]}$, with constrained equal awards (CEA) and constrained equal losses (CEL) as its endpoints. For finite $\theta$, $r^\theta$ is the equal-sacrifice rule in awards for $u_\theta=\log\varphi_\theta$, where \[ \varphi_\theta(x):=\frac{e^{\theta x}-1}{\theta}\quad(\theta\ne0), \qquad \varphi_0(x):=x, \] and simultaneously the equal-sacrifice rule in losses for the dual utility $u_{-\theta}$. The proportional rule is the midpoint, $\theta=0$. Continuity of the rules are not assumed but a consequence of the other axioms. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.01489 |
| By: | Lange, Nico; Dulgeridis, Marcel; Cornelsen, Jens |
| Abstract: | Artificial intelligence applications increasingly promise efficiency gains, personalization, automation and improved user experiences. For medium-sized companies, however, the challenge is less the existence of potential use cases than the ability to prioritize them under resource constraints, heterogeneous data maturity and regulatory uncertainty. Building on a bachelor thesis on AI-supported user experience in online shops, this discussion paper reframes the developed prioritization matrix as a generic AI Impact Scoring Tool that can be applied to virtually any AI initiative. The proposed tool combines a value axis, a feasibility axis and a scenario-based sensitivity analysis. It draws on user-experience theory, technology acceptance, stimulus-organism-response reasoning, multi-criteria utility analysis and design-oriented model development. The e-commerce use case serves as a blueprint: AI-generated product imagery, personalized product recommendations and AI-based interaction systems are assessed to illustrate how different AI applications can be positioned as quick wins, strategic investments, experimental options or deprioritized initiatives. The central contribution lies in making AI investment decisions more transparent, comparable and adaptable. Rather than providing a one-time ranking, the model offers a reusable governance and decision framework for management teams that need to evaluate changing AI opportunities over time. |
| Keywords: | Artificial Intelligence, AI Governance, User Experience, E-Commerce, Prioritization Matrix, Utility Analysis, Sensitivity Analysis, Medium-Sized Enterprises |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iubhbm:343054 |
| By: | Sidharth Mallik; Waymond Rodgers |
| Abstract: | The enormous growth in datasets, both in number and size, has prompted investors to adapt to new ways for assimilating information. Normatively, the approach has been to integrate such datasets into pricing formulations and assess the performance of portfolios created thereafter. However, such approaches underestimate their influence in portfolio investments by limiting their impact to pricing only. While being theoretically valid, this results in a potential sub-optimal performance in the presence of real-life decision constraints, and a blind spot for performance attribution. We start by analysing investment decisions from a knowledge perspective, which unfurls a new structure. We then propose a FinTech process termed Knowledge Optimisation that aims to integrate the influence of knowledge components that could be related to data, models, or business units that extract information. A 3-stage process, namely, decision structure, portfolio selection, and performance assessment is designed. We present an alternative to the ex-ante Sharpe Ratio, integrating a term for knowledge units. Through scenario analysis involving portfolio investment situations, we illustrate the utility. By design, the process improves the importance of knowledge in investment decisions. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05991 |
| By: | Shaowei Ke; Mu Zhang |
| Abstract: | Choice alternatives are often multidimensional and risky. We introduce and axiomatize the \textit{structured multidimensional expected utility} representation, a unified framework that generalizes existing approaches to evaluating such alternatives. The representation uses a \textit{rooted clustered tree} to organize the joint, separate, and conditional evaluation of risk across dimensions within a common structure. We analyze the uniqueness of the representation and characterize useful special cases. We apply the representation to inequality across individuals, groups, and generations and to multisource income, characterizing the implications of bracketing for stochastic dominance and the avoidance of multidimensional risk. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05043 |
| By: | Mira Frick; Ryota Iijima; Daisuke Oyama |
| Abstract: | We study the strategic impact of ambiguity through the channel of higher-order beliefs. We show that even small amounts of prior ambiguity about game payoffs can generate arbitrarily large amounts of higher-order ambiguity. This gives rise to a novel form of contagion: vanishingly small payoff ambiguity can select ``secure'' actions (e.g., non-participation) as the unique equilibrium outcome even when those actions are almost dominated. We highlight two main implications. First, classical robustness results under probabilistic uncertainty break down under ambiguity when players can deviate to secure actions. Second, if a designer can introduce small amounts of payoff ambiguity into a game, this can serve as a powerful tool for unique implementation. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.24560 |
| By: | Giovanni Dispoto; Marcello Restelli; Carmine Ventre |
| Abstract: | Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinforcement Learning (RL) approaches typically optimize for a single ESG provider, neglecting the significant divergence in rating methodologies across the industry and the unintuitive nature of manually weighting conflicting objectives. This paper addresses these limitations by formulating ESG-aware portfolio optimization as a Multi-Objective Reinforcement Learning (MORL) problem that simultaneously incorporates ratings from three distinct ESG agencies. To bridge the gap between high-dimensional algorithmic trade-offs and human decision-making, we integrate a Preference Elicitation framework using Gaussian Processes. This system enables practitioners to infer their latent utility functions through intuitive pairwise comparisons of candidate portfolios based on their Sharpe ratios and aggregate ESG scores. We systematically evaluate our framework by employing Large Language Model (LLM) personas to simulate Portfolio Managers operating under varied regional contexts. Empirical results using historical market data reveal that regional backgrounds fundamentally shift the derived preference weights. For instance, European-based personas tend to prioritize ESG alignment over financial returns, while Texas-based personas favor risk-adjusted performance. This work offers a highly adaptable framework that successfully aligns multi-objective algorithmic trading with diverse, real-world human sustainability preferences. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.02677 |
| By: | Yan Dolinsky |
| Abstract: | We study exponential-utility maximization for high-frequency trading in a discretized fractional Brownian motion model. Using spectral methods for stationary Gaussian sequences, we derive the asymptotic growth rate of the optimal certainty equivalent. We also show that the suitably rescaled optimal positions converge in finite-dimensional distributions to a Gaussian white-noise-type field. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05357 |
| By: | Muqiao Huang; Ruodu Wang; Yiyun Wang |
| Abstract: | We study equilibria in a closed, fee-free constant-function market maker (CFMM) economy with two assets and two traders. An interior state is a unilateral no-trade equilibrium exactly when the CFMM marginal price equals both traders' marginal rates of substitution. For an interior initial state, individually rational unilateral equilibria are Pareto optimal relative to the fixed CFMM invariant. A weak representative agent is obtained at each fixed equilibrium by weighted sup-convolution, whereas a state-independent strong representative agent exists exactly when traders share a common homothetic preference. Every interior feasible state is reachable through finitely many valid trades, and alternating utility-maximizing trades converge to a Pareto optimal unilateral equilibrium. We also derive conditions under which trading order produces a first-mover advantage or disadvantage in the first round. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.23915 |
| By: | Jaramillo Abad Gleymang Yubert (Osaka School of International Public Policy, the University of Osaka); Uwasu Michinori (Graduate School of Economics, The University of Osaka) |
| Abstract: | Socio-environmental issues are often studied by focusing on enforcement or compliance in isolation, with emphasis on sanction design. This study proposes a joint compliance–enforcement framework, in which sustainable governance emerges from the balanced, cyclical interaction between firms’ compliance decisions and authorities’ institutional capacities. The holder’s compliance decision is modeled as an expected-utility maximization problem. We formally derive that the post-detection probability—as a measure of institutional credibility—exerts a stronger marginal influence on holders’ compliance than the raw detection probability. This result holds through two independent channels: Arrow–Pratt global risk-aversion and information-theoretic foundations. Sustainable incentives are modeled as a positive integral payoff, distinct from the illicit-benefit deterrence. The governance multiplier result shows that improving post-detection probability simultaneously amplifies both deterrence and incentive channels. The framework is illustrated using stylized facts from large-scale fisheries in Peru and yields three testable implications with falsification conditions on compliance effort and repercussions. |
| Keywords: | Compliance, Environmental governance, Institutional credibility, Regulatory enforcement, Sustainability |
| JEL: | Q58 K42 D81 D82 L51 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:osp:wpaper:26e010 |