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on Utility Models and Prospect Theory |
| By: | G. Charles-Cadogan |
| Abstract: | This paper develops a decision-theoretic interpretation of local expected utility on Abstract Wiener space. The framework is intended for economic acts that are naturally stochastic paths, such as income paths, consumption paths, investment payoffs, insurance losses, or laboratory stimuli unfolding over a continuum of contingencies. In this setting the object of choice is an act or payoff path, while Hermite functions serve as state-feature coordinates rather than objects of preference. Abstract Wiener space supplies a Gaussian reference measure on the Banach space of paths and a Hilbert subspace on which local utility is represented by orthogonal coefficients. The associated Wiener-integral weights are generally signed stochastic functionals; they become admissible decision weights only after a specified nonnegative normalization. This distinction clarifies the relation between the model, state-dependent utility, subjective expected utility, rank-dependent utility, and cumulative prospect theory. The main representation result gives finite primitive conditions under which projected path-valued acts admit a normalized local-utility representation. The corresponding elicitation result identifies composite utility-weight coefficients from projected acts and recovers normalized decision weights when the local utility scale is independently elicited. The paper also states testable restrictions: normalized weights must be nonnegative, add to one, preserve monotonicity, and satisfy additional rank or gain-loss restrictions when the subjective expected utility, rank-dependent utility, or cumulative prospect theory submodels are imposed. Numerical illustrations show how the representation operates in finite approximations. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.25026 |
| By: | Masahiro Kato |
| Abstract: | This study considers the problem of portfolio choice, where we recommend a portfolio to an investor to maximize the expected utility of their wealth. Our goal is to construct an asymptotically optimal portfolio choice rule in terms of expected utility regret, the difference between the expected utility of an oracle investor and that achieved by a portfolio chosen from data. We propose the Expected Utility Regret (EUR) rule, which jointly selects a portfolio class and estimates its weights. In a regular parametric return model, a single EUR rule attains both the minimax and the Bayes lower bounds, including their leading constants, without using the prior distribution that defines the Bayes criterion. We then derive the mean--variance and risk-parity portfolios as special cases of this framework. Under smooth increasing and concave utility, the EUR rule and the sample mean--variance portfolio attain the same leading expected regret when expected excess returns approach zero sufficiently fast. When the returns divided by their volatilities have a joint distribution that does not depend on the order of the assets, the EUR rule and the risk-parity portfolio coincide. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.02290 |
| By: | Luigi Montrucchio; Lorenzo Stanca |
| Abstract: | We derive sufficient conditions for the existence, non-existence, uniqueness and global attractivity of stochastic recursive utilities. Our approach uses abstract order-theoretic fixed-point arguments, allowing us to accommodate a large class of Markov consumption-growth models with unbounded per-period consumption and a broad range of recursive preferences, including non-expected utility specifications. We illustrate the usefulness of the framework through macro-finance applications to widely used consumption-growth models under several preference specifications, including Epstein--Zin preferences, multiple priors, smooth ambiguity, and Hansen--Sargent robust-control preferences. In each application, our general conditions reduce to a simple, economically interpretable parameter test that shows how fundamental parameters influence existence and uniqueness. While uniqueness of recursive utility can be difficult to guarantee in unbounded environments, in a Markov setting with non-unit elasticity we establish uniqueness within the class of recursive utilities having a so-called separability property. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.03157 |
| By: | Carmelo Rodríguez à lvarez (ICAE Instituto Complutense de Análisis Económico, Depto. Análisis Económico y Economía Cuantitativa, Universidad Complutense de Madrid) |
| Abstract: | We study social choice functions that select sets of alternatives, taking voters' preferences over sets as input, when voters evaluate sets according to conditional expected utility obtained from Bayesian updating of a prior. Barberà et al. (2001) shows that under a uniform common prior both dictatorial and bidictatorial rules satisfy strategy-proofness and unanimity. We show that this multiplicity is a knife-edge case. If at most one voter holds a uniform initial prior, dictatorship is the unique rule satisfying these properties. The argument links set-valued rules to random dictatorship and shows that the probability ratios imposed by Bayesian updating from non-uniform priors are incompatible with combining the top choices of multiple voters. |
| Keywords: | Strategy-proofness; Social choice functions over sets; Random dictatorship; Probability assessments. |
| JEL: | C71 D82 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ucm:doicae:2602 |
| By: | Wei Ma |
| Abstract: | Experimental evidence reveals that, in aggregating time preferences, a decision maker exhibits mild aversion to inequality across individuals' evaluations of a stream. Neither the utilitarian nor the maxmin aggregation rule can account for this finding. Building on principles concerning the comparability of intergenerational utilities and intergenerational fairness, this paper axiomatizes a family of aggregation rules that allow the decision maker to display varying degrees of inequality aversion. These rules fit well with experimental data. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.36744 |
| By: | Moeen Nehzati |
| Abstract: | We show that distinct pure-strategy Nash equilibria typically have different utilitarian welfare values in sufficiently rich classes of $C^2$ games with open strategy sets. Here, typical means both generic and finitely prevalent, while rich means that the class admits a finite-dimensional family of perturbations that can independently move payoff values and first derivatives in any direction at any two distinct strategy profiles (for example, polynomial perturbations of degree at most three). Consequently, a welfare-maximizing equilibrium, if one exists, is unique. The result extends to each fixed $C^1$ payoff aggregator whose derivative with respect to the payoff vector is everywhere nonzero. In the full $C^2$ utility space, we also treat scalar criteria depending on strategies, payoffs, and payoff derivatives, subject to a nonvanishing condition in payoff-level or cross-player derivative directions. Finally, adjoining arbitrarily small perturbations from a suitable finite-dimensional space makes welfare separation typical in the augmented family, with separation holding for almost every perturbation of each fixed base game. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.08661 |
| By: | Kohmei Makihara |
| Abstract: | We study how to allocate a good with positive externalities among agents in an information network, without monetary transfers. Each agent observes their own valuation and those of their neighbors. A principal seeks to allocate the good to the highest-valuation agent through a mechanism robust to agents' heterogeneous belief hierarchies, leveraging the network structure and the partial incentive alignment created by allocative externalities. We characterize the networks for which a robustly efficient mechanism exists: it exists if and only if at least two agents are connected to all others. Relaxing robustness to rule out weakly dominated strategies, one universally connected agent suffices. We generalize the results to a broader class of utility functions, show that robust welfare maximization is impossible unless the network is complete, and establish that an ex post incentive compatible mechanism that is efficient under truthful reports exists if and only if no agent is isolated. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.35907 |
| By: | Kexin Chen; Tao Lin; Jianwei Huang |
| Abstract: | AI advice increasingly enters human decision workflows before costly information acquisition and final action. This paper studies how such advice affects decision quality, measured by the gross utility of the final action. Advice shifts beliefs before the decision maker chooses an acquisition experiment with uniformly posterior-separable (UPS) costs. The analysis characterizes gross effects through the continuation gross value generated by net-optimal acquisition. Convexity of this value function is necessary and sufficient for every AI signal to weakly improve gross utility at every prior, while nonconvexity allows a binary signal to cause gross harm. The criterion gives tight state-space results. Every AI signal is weakly gross-improving in binary-state problems under arbitrary actions, payoffs, and UPS costs. A minimal three-state problem can generate gross harm through concentrated shutdown, where valuable acquisition stops. For classification problems with Shannon acquisition costs, the criterion becomes an active-set slope test. It guarantees weak gross improvement for homogeneous classification and all three-state weighted classification problems, while a minimal four-state failure arises from attention dilution, where acquisition shifts away from high-stake distinctions. Along a Blackwell-increasing mixture path, gross utility can first decrease and then increase. With endogenous interpretation, cheaper interpretation can reduce gross utility. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.35137 |
| By: | Lei Huang (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA); Juanjuan Zhang (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA) |
| Abstract: | When supply incentives are driven by demand, the natural force of the market may suppress the provision of products that are valuable to some consumers, leading to market failure. This paper explores a solution. Focusing on content platforms, we design a recommendation mechanism that maximizes total user utility by strategically allocating demand to sustain valuable content production. Theoretically, we prove that the optimal recommendation mechanism considers not only consumption utility, as in standard recommender systems, but also creator sensitivity, which captures how easily a creator can be incentivized by demand, and creator contribution, which reflects a creator’s importance to users overall. Computationally, we develop PixSet, a novel data transformation framework that converts variable-length sets (e.g., numbers of creators of various types) into fixed-size tensors (e.g., density distribution over pixels in a type space), which simplifies estimation as a computer vision problem with 99.9% gain in computational efficiency. The proposed recommendation mechanism is validated by both an observational-data analysis and a field experiment on Tencent WeChat, one of the world's largest content platforms. This mechanism has since been deployed as the default recommendation system at WeChat, serving billions of users and tens of millions of creators each day. |
| Keywords: | mechanism design; recommender system; automation; creator economy |
| JEL: | D47 L11 C55 C61 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:net:wpaper:2608 |
| By: | Dongshuang Hou (School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, China); Aymeric Lardon (Université Jean Monnet Saint-Etienne, Université Lumière Lyon 2, emlyon business school, GATE UMR 5824, F-42023 Saint-Etienne, France) |
| Abstract: | We introduce similarity Gaussian games, transferable-utility games in which the worth of a coalition is the total pairwise similarity among its members, measured by a Gaussian kernel. Similarity Gaussian games are convex weighted graph games, so the Shapley value is always a core allocation; it also has two complementary interpretations: it is weighted degree centrality in the associated similarity network and, up to a common factor, a Gaussian kernel density estimate evaluated at each player’s characteristic. We derive the Shapley value in closed form and study how it changes with the bandwidth. For large bandwidths, we establish an exact asymptotic affine relationship between the Shapley values of similarity Gaussian games and squared deviations games, providing a precise link between local similarity and dispersion. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:gat:wpaper:2609 |
| By: | Arcia, Gustavo (Human Capital and Education Returns Lab); Macdonald, Kevin (Human Capital and Education Returns Lab); Patrinos, Harry (University of Arkansas, Fayetteville) |
| Abstract: | Publicly funded school choice is examined as a form of education public-private partnership (PPP), with cross-national differences interpreted through institutional design rather than private provision alone. A novel theoretical framework derives two indicators – alignment and coherence – from a formal utility-theoretic model of PPP maturity, applied here for the first time to education. Across seven systems, the results reveal substantial differences in how competition, autonomy, accountability, and information are jointly structured. The findings highlight institutional architecture and complementarity as central to the design of effective education PPPs. |
| Keywords: | school choice, public-private partnerships (PPPs), education vouchers, competition in education, autonomy, accountability |
| JEL: | I21 H42 H52 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18972 |