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on Utility Models and Prospect Theory |
| By: | Rosella Castellano (UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome]); Roy Cerqueti (GRANEM - Groupe de Recherche Angevin en Economie et Management - UA - Université d'Angers - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement) |
| Abstract: | This paper deals with an intergenerational utility maximization problem for consuming a naturally exhaustible resource. In this context, we are at odds with the unfair standard procedure of applying a time-dependent factor for discounting the utility and introducing a suitable function for penalizing overconsumption. A finite-time horizon dynamic stochastic optimization problem is presented to achieve the desired target. We provide a closed-form solution to the problem which does not lead forcefully to the classical golden rule outcome of not consuming now to save the future. The theoretical findings are validated through extensive numerical experiments, with a parameter set selected based on empirical data. Such experiments highlight that optimal consumption depends strongly on the natural resource regeneration rate and the initial value of the stock. |
| Keywords: | Stochastic optimal control, Simulations, Sustainability, Natural resources |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05108867 |
| By: | Wei Lu; Daniel L. Chen; Christian B. Hansen |
| Abstract: | Understanding how large language model (LLM) agents behave in strategic interactions is essential as these systems increasingly participate autonomously in economically and morally consequential decisions. We evaluate LLM preferences using canonical economic games, finding substantial deviations from human behavior. Models like GPT-4o show excessive cooperation and limited incentive sensitivity, while reasoning models, such as o3-mini, align more consistently with payoff-maximizing strategies. We propose a supervised fine-tuning pipeline that uses synthetic datasets derived from economic reasoning to align LLM agents with economic preferences, focusing on two stylized preference structures. In the first, utility depends only on individual payoffs (homo economicus), while utility also depends on a notion of Kantian universalizability in the second preference structure (homo moralis). We find that fine-tuning based on small datasets shifts LLM agent behavior toward the corresponding economic agent. We further assess the fine-tuned agents' behavior in two applications: Moral dilemmas involving autonomous vehicles and algorithmic pricing in competitive markets. These examples illustrate how different normative objectives embedded via realizations from structured preference structures can influence market and moral outcomes. This work contributes a replicable, cost-efficient, and economically grounded pipeline to align AI preferences using moral-economic principles. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.20796 |
| By: | Eric Donald; Masao Fukui; Yuhei Miyauchi |
| Abstract: | How do regional productivity shocks or transportation infrastructure improvements affect aggregate welfare? In a general class of spatial equilibrium models, we provide a formula for aggregate welfare changes, decomposed into terms associated with (i) technology (Fogel 1964, Hulten 1978), (ii) spatial dispersion of marginal utility, (iii) fiscal externalities, (iv) technological externalities, and (v) redistribution. We further use this decomposition to derive a general formula for optimal spatial transfers and show that, whenever optimal transfers are in place, the technology term alone captures the aggregate welfare effects of technological shocks. We apply our framework to study welfare gains from improving the US highway network. We find that changes in the spatial dispersion of marginal utility are as important as technological externalities in accounting for the deviations from the Fogel-Hulten benchmark to assess welfare gains. |
| JEL: | E0 F0 R0 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34075 |
| By: | Abeeb Olaniran (Department of Economics, University of Pretoria, South Africa); David Gabauer (Department of Financial and Business Systems, Lincoln University, New Zealand); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Onur Polat (Department of Public Finance, Bilecik Seyh Edebali University, Turkey) |
| Abstract: | Climate-related risks have become a growing source of market disruption, with potential behavioral implications for investor decision-making. This study investigates whether and how climate risks influence risk aversion among market participants. Using a quantilogram approach, we examine the predictive power of different climate risk measures, covering both physical and transition risks, for a behavioral proxy of investor risk aversion. The analysis yields three key findings. First, climate risks significantly increase risk aversion, particularly in the lower and median quantiles of climate risk and the upper quantiles of risk aversion. Second, physical risks exert a stronger influence than transition risks, with global warming and U.S. climate-related policy uncertainty emerging as the most impactful within their respective categories. Third, the observed effects remain robust after controlling for other sources of macroeconomic and financial uncertainty. These findings suggest that climate risks can dampen investor risk appetite, a result with important implications for financial market stability and the design of disaster-related financial policy interventions. |
| Keywords: | Climate-related risks, Quantilogram frameworks, Quantiles, Predictability, Risk aversion |
| JEL: | C21 C22 G32 G41 Q54 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202524 |
| By: | Giuseppe De Marco (University of Napoli Parthenope and CSEF and Università degli Studi di Napoli Federico II); Maria Romaniello (University of Campania Luigi Vanvitelli.); Alba Roviello (Department of Economics and Statistics, University of Napoli Federico II.) |
| Abstract: | This paper examines how guilt aversion affects the equilibria of symmetric 2×2 games with the same Nash equilibrium structure as the Hawk–Dove game: two asymmetric strict pure equilibria and one completely mixed-strategy equilibrium. We classify these generalized Hawk–Dove games into two subclasses, Type 1 and Type 2, based on players’ preferences over deviations toward symmetric profiles. We characterize best-reply correspondences and equilibria under guilt aversion, showing that outcomes are highly sensitive to guilt parameters. In Type 1 games, when guilt sensitivity exceeds a threshold, a new symmetric equilibrium emerges while the mixed-strategy equilibrium disappears. In Type 2 games, guilt aversion affects only the mixed equilibrium, leaving the two asymmetric equilibria unchanged. |
| Keywords: | Hawk-Dove games, equilibria, guilt aversion, psychological games, ambiguous beliefs. |
| JEL: | D81 |
| Date: | 2025–01–08 |
| URL: | https://d.repec.org/n?u=RePEc:sef:csefwp:756 |
| By: | F. Nguyen |
| Abstract: | The "free trial" followed by automatic renewal is a dominant business model in the digital economy. Standard models explain trials as a mechanism for consumers to learn their valuation for a product. We propose a complementary theory based on the rational inattention framework. Consumers know their valuation but face a cognitive cost to remember to cancel an unwanted subscription. We model this using a Shannon entropy-based cost of information processing, where a consumer's baseline attention level decays with the length of the trial period. This creates a novel trade-off for a monopolist firm: a longer trial increases "inattentive revenue" from consumers who fail to cancel, but it also lowers ex-ante consumer utility, making the initial offer less attractive. We show that this trade-off leads to an interior optimal trial length, even for products where value-learning is instantaneous. Our model, under standard assumptions about demand elasticity and the distribution of consumer valuations, generates sharp, testable predictions about the relationship between contract terms. We find that the optimal renewal price and trial length are complements: firms offering longer trials will also set higher post-trial prices. We analyze the impact of policies aimed at curbing consumer exploitation, such as "click-to-cancel" regulations. We show that such policies, by making attention effectively cheaper, lead firms to reduce trial lengths. The effect on price depends directly on the elasticity of demand from loyal subscribers. We also extend the model to include paid trials, showing that introductory prices and trial lengths act as strategic substitutes. Our framework provides a micro-founded explanation for common features of subscription contracts and offers a new lens through which to evaluate consumer protection policies in digital markets. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.06422 |