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on Utility Models and Prospect Theory |
| By: | R. Emilio Muniz-Langle |
| Abstract: | This paper provides an axiomatic foundation for catastrophic thinking, a form of pessimism in which an agent evaluates uncertain alternatives by attending only to a subset of adverse outcomes. We introduce Catastrophic Attention Preferences (CAP), under which an act is evaluated by its subjective expected utility conditional on the worst outcomes, up to a subjectively determined probability threshold $q$. The resulting functional is a subjective counterpart of Expected Shortfall: both the agent's belief $\mu$ and her threshold $q$ are derived from preferences rather than assumed, without a probability distribution given as a primitive. Our main result is a complete behavioral characterization: six axioms, one of which, Catastrophic Complementarity, carries the behavioral content of catastrophic thinking, together with two standard richness conditions, are equivalent to the existence of a CAP representation, and the parameters $(\mu, q)$ are unique. The parameters are fully identified from probability equivalents of events, simple binary bets that can be elicited experimentally. We characterize comparative ambiguity aversion within the class: with common beliefs, ambiguity aversion is completely ordered by $q$; with different beliefs, we provide a necessary and sufficient condition on the two belief-threshold pairs. The model admits an equivalent multiple priors representation with a closed-form set of priors, nests subjective expected utility at $q = 1$, and converges to maxmin expected utility as $q \rightarrow 0$. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05379 |
| By: | Andries, Marianne; Eisenbach, Thomas; Schmalz, Martin |
| Abstract: | Inspired by experimental evidence, we amend the recursive utility model to let risk aversion decrease with the temporal horizon. Our pseudo-recursive preferences remain tractable and retain appealing features of the long-run risk framework, notably its success at explaining asset pricing moments. In addition, our model addresses two challenges to the standard model. Calibrating the agents’ preferences to explain the equity premium no longer implies an extreme preference for early resolutions of uncertainty. Horizon-dependent risk aversion helps resolve key puzzles in finance on the valuation of assets across maturities and captures the term structure of equity risk premia and its dynamics. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19196 |
| By: | Bhargava, Saurabh (New York University); Hyde, Timothy (Department of Economics, Oberlin College) |
| Abstract: | We study risky choice in a field setting where employees choose among goal-reward contracts resembling financial lotteries and where we observe both choices and beliefs. We find risk aversion and choice heterogeneity far exceeding expected utility predictions and unexplained by prominent behavioral motives like overconfidence, nonlinear decision weights, and loss aversion. We propose and experimentally validate a heuristic explanation for risk taking involving contingency neglect during pairwise evaluation. The heuristic fits the field and lab data better than leading alternative models, uniquely predicts the belief distortions and framing effects we document, and offers a potential explanation for empirical insurance puzzles. |
| Date: | 2026–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:cxv:wpaper:2605 |
| By: | Remy Levin; Daniela Vidart |
| Abstract: | We design a method for measuring the risk preferences of agents in the deep past. The method combines a structural model of crop choice as a portfolio allocation with machine-learning prediction of expected crop returns, using historic agronomic and climate data. We estimate county-level risk preferences for the United States and farmer-level preferences in Kansas from 1889 to 1929. More risk averse farmers leveraged less, were less likely to purchase novel WWI Liberty Bonds, and were more likely to participate in local risk-sharing institutions. We show that higher risk aversion predicts slower tractor adoption and farm mechanization during the 1920s. |
| JEL: | D81 G11 N51 N52 O13 Q12 Z10 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35634 |
| By: | Gianni Bosi; Gabriele Sbaiz; Magal\`i Zuanon |
| Abstract: | Objective functions (goodness criteria which have to be optimized) that are considered, for instance, in cluster analysis, factor analysis, (linear) structural equation modeling, (linear) regression, multidimensional scaling, choice theory, and utility theory, must be {\em adequate}, i.e. carefully adapted to the structure of the observed data. Adequateness of an objective function means, in our terminology, that (certain) transformations of its arguments, e.g. changes in the unit measures of the quantities involved, do not influence the solutions of the optimization procedure. In this paper we concentrate our attention on affine strictly increasing transformations and we also incorporate the case of continuous adequate objective functions accordingly. The characterization of adequate dissimilarity coefficients for interval scaled data shows the appropriateness of the concept of adequateness that is developed in this paper. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.20074 |
| By: | Ignas Gasparavi\v{c}ius; Andrius Grigutis |
| Abstract: | We show that the key optimization results of the classical Markowitz portfolio selection theory, originally formulated for variance as the risk measure, remain available in explicit closed form under a broader class of strictly convex quadratic risk measures. The proposed framework replaces the covariance matrix with an arbitrary symmetric positive definite matrix and allows additional linear and constant terms, thereby containing various models arising in transaction cost optimization, benchmark relative optimization, covariance regularization, and factor models. Closed-form formulas are obtained for the efficient frontier, the global minimum risk portfolio, the maximum Sharpe ratio portfolio, the Capital Market Curve, the tangency portfolio, and the maximum utility portfolio. In contrast to the classical Markowitz model, the tangency portfolio does not coincide with the maximum Sharpe ratio portfolio, revealing a new geometric phenomenon. A numerical example confirms the derived formulas. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.24449 |
| By: | Richard Bernknopf; Leila Gonzales; Christpher Keane |
| Abstract: | Natural hazards are a nonmarket disamenity that affects an individual's search for employment resulting in a negative environmental impact that produces an economic inefficiency. We develop a seek-and-screen job search approach that uses a discrete choice simulation to examine how salary, crime, and natural hazard risk influence job choice. We model the job decision process as a series of elimination events using a Cox hazard model grounded in a Random Utility Model. We use data from the discrete choice simulation to estimate both a standard proportional hazards model and an extended specification that allows the effect of natural hazard risk to vary across decision rounds. Individuals are exposed to the dynamics of a simulated job search as they make decisions between pairs of job offers in an adaptive learning process based on income, geography, crime level, and natural hazard attributes. The results of the job choice decisions provide the input to a statistical survival analysis. The results indicate that salary and crime exert stable and economically intuitive effects on job elimination, with higher salary reducing and higher crime increasing the likelihood of removal. In contrast, natural hazard risk exhibits a time-varying effect that increases the probability of elimination in early rounds but becomes neutral or favorable in later stages of the decision process. These findings suggest that environmental risk is evaluated differently as individuals transition from initial screening to final job selection, highlighting the importance of modeling job choice as a multi-stage process. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.24811 |
| By: | Matthew O. Jackson; Benjamin S. Manning; Yutong Xie; Walter Yuan; Qiaozhu Mei |
| Abstract: | We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large language model a ``type vector'' and then prompt it to choose actions across settings in which we observe human choices. For instance, the type vector (2, 4) becomes ``You are a player characterized by the following profile: 2 out of 5 in Altruism, 4 out of 5 in Risk Aversion, '' after which it is prompted to make choices. We vary the dimensions (e.g., Altruism, Fairness, Trust, $\dots$) and values (e.g., 1--5) to minimize distance to human choices. Applying the method to 119, 147 decisions made by 78, 657 subjects from more than 35 countries across 10 classic economic game roles, we find that human behavior can be closely matched using three dimensions: Risk Aversion, Strategic Sophistication, and Trust. Moreover, the types needed to fit individuals across games cluster into fewer than a dozen groups, and can predict behavior in held-out games with different rules and available actions. The results suggest that behavior across diverse settings can be approximated by a low-dimensional, portable representation, supporting the possibility of general yet parsimonious theories across the behavioral sciences. More broadly, the method can provide insights into the structure of many human behaviors. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.18265 |
| By: | Karle, Heiko; Schumacher, Heiner; Volund, Rune |
| Abstract: | We study a simple bargaining model in which the sender can make an early offer to the receiver. Initially, the sender has private information about the value of the receiver's outside option. The receiver learns this value before she chooses between the sender's early offer and her outside option. Nevertheless, if the receiver is expectation-based loss averse, the sender can persuade her to accept an offer that is inferior to her outside option. This result is due to the interaction of two effects: the attachment effect that makes it costly for the receiver to reject an offer that she planned to accept, and the uncertainty effect which renders the acceptance of the sender's offer as the preferred plan since it creates peace of mind at an early stage. If the receiver faces uncertainty in multiple dimensions, the main result holds for all degrees of loss aversion. Thus, expectation-based loss-averse preferences imply that there is scope for persuasion through signaling even if the receiver has all payoff-relevant information at the decision stage. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19247 |
| By: | Harin, Alexander |
| Abstract: | This is the second part of the systematic introduction to the sub-interval analysis. In particular, an introduction to sub-interval images (or SI-images or S-IIs or SIIs) is presented here. Basic notions of the sub-interval images are formulated. Some concepts of SII-indexing are proposed. A short general outlook of possible use of the SI-analysis for Big Data is given. The S-IIs can be used mainly in approximations and preliminary operations such as preliminary analysis, search, and recognition in databases; in, e.g., accounting and audit, micro- and macroeconomics and, especially, in Big Data. |
| Keywords: | mathematic; databases; Big Data; macroeconomics; microeconomics; accounting; |
| JEL: | C02 C1 M4 |
| Date: | 2026–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:130725 |