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on Utility Models and Prospect Theory |
| By: | Conrad Kosowsky |
| Abstract: | I model a rational agent who spends resources between the current time and some fixed future deadline. Opportunities to spend resources arise randomly according to a Poisson process, and the quality of each opportunity follows a uniform distribution. The agent values their current resource stock at exactly the sum of expected utility from all future spending opportunities. Unlike in traditional discounted expected utility models, the agent exhibits correlation aversion, static (but not dynamic) preference reversals, and monotonicity with respect to payment timing. Connecting the agent's risk and time preference is intuitive, and doing so leads to a new model of procrastination where the agent misperceives their general attitude toward spending resources. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11828 |
| By: | Laura Doval; Ran Eilat; Tianhao Liu; Yangfan Zhou |
| Abstract: | An analyst observes the frequency with which a decision maker (DM) takes actions, but does not observe the frequency of actions conditional on the payoff-relevant state. We ask when can the analyst rationalize the DM's choices as if the DM first learns something about the state before taking action. We provide a support function characterization of the triples of utility functions, prior beliefs, and (marginal) distributions over actions such that the DM's action distribution is consistent with information given the agent's prior and utility function. Assumptions on the cardinality of the state space and the utility function allow us to refine this characterization, obtaining a sharp system of finitely many inequalities the utility function, prior, and action distribution must satisfy. We apply our characterization to study comparative statics and ring-network games, and to identify conditions under which a data set is consistent with a public information structure in first-order Bayesian persuasion games. We characterize the set of distributions over posterior beliefs that are consistent with the DM's choices. Assuming the first-order approach applies, we extend our results to settings with a continuum of actions and/or states.% |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13293 |
| By: | Edward Honda; Keh-Kuan Sun |
| Abstract: | We provide and axiomatize a representation of preferences over lotteries that generalizes the expected utility model. Our representation is consistent with the violations of the independence axiom that we observe in the laboratory experiment that we conduct. The violations differ from the Allais Paradox in that they are incompatible with some of the most prominent non-expected utility models. Our representation can be interpreted as a decision-maker with context-dependent attitudes to risks and allows us to generate various types of realistic behavior. We analyze some properties of our model, including specifications that ensure preferences for first-order stochastic dominance. We test whether subjects in our experiment exhibit the type of context-dependent risk attitudes that arise in our model. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13823 |
| By: | Fernando Payr\'o; Evan Piermont |
| Abstract: | We consider an analyst whose goal is to identify a subject's utility function through revealed preference analysis. We argue the analyst's preference about which experiments to run should adhere to three normative principles: The first, Structural Invariance, requires that the value of a choice experiment only depends on what the experiment may potentially reveal. The second, Identification Separability, demands that the value of identification is independent of what would have been counterfactually identified had the subject had a different utility. Finally, Information Monotonicity asks that more informative experiments are preferred. We provide a representation theorem, showing that these three principles characterize Expected Identification Value maximization, a functional form that unifies several theories of experimental design. We also study several special cases and discuss potential applications. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11625 |
| By: | Wenyuan Wang; Kaixin Yan; Xiang Yu |
| Abstract: | This paper studies a type of periodic utility maximization problems for portfolio management in incomplete stochastic factor models with convex trading constraints. The portfolio performance is periodically evaluated on the relative ratio of two adjacent wealth levels over an infinite horizon, featuring the dynamic adjustments in portfolio decision according to past achievements. Under power utility, we transform the original infinite horizon optimal control problem into an auxiliary terminal wealth optimization problem under a modified utility function. To cope with the convex trading constraints, we further introduce an auxiliary unconstrained optimization problem in a modified market model and develop the martingale duality approach to establish the existence of the dual minimizer such that the optimal unconstrained wealth process can be obtained using the dual representation. With the help of the duality results in the auxiliary problems, the relationship between the constrained and unconstrained models as well as some fixed point arguments, we finally derive and verify the optimal constrained portfolio process in a periodic manner for the original problem over an infinite horizon. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13579 |
| By: | Federico Echenique (UC Berkeley); Matías Núñez (CREST, CNRS, Ecole Polytechnique, ENSAE) |
| Abstract: | We describe a sequential mechanism that fully implements the set of efficient outcomes in environments with quasi-linear utilities. The mechanism asks agents to take turns in defining prices for each outcome, with a final player choosing an outcome for all: Price & Choose. The choice triggers a sequence of payments, from each agent to the preceding agent. We present several extensions. First, payoff inequalities may be reduced by endogenizing the order of play. Second, our results extend to a model without quasi-linear utility, to a setting with an outside option, robustness to max-min behavior and caps on prices. |
| Keywords: | Efficiency, Subgame-perfect implementation, Mechanism, Prices. |
| JEL: | D71 D72 |
| Date: | 2024–05–15 |
| URL: | https://d.repec.org/n?u=RePEc:crs:wpaper:2024-15 |
| By: | Christian Belzil (CREST, CNRS, Paris Polytechnic Institute, IZA, and CIRANO); Tomáš Jagelka (University of Bonn, Dartmouth College, CREST-Ensae, and IZA) |
| Abstract: | We develop a framework for accounting for individuals’ effort and cognitive noise which confound estimates of preferences based on observed behavior. Using a large-scale experimental dataset we estimate that failure to properly account for decision errors due to (rational) inattention on a more complex, but commonly used, task design biases estimates of risk aversion by 50% for the median individual. Effort propensities recovered from preference elicitation tasks generalize to other settings and predict performance on an OECD-sponsored achievement test used to make international comparisons. Furthermore, accounting for endogenous effort allows us to empirically reconcile competing models of discrete choice. |
| Date: | 2024–11–19 |
| URL: | https://d.repec.org/n?u=RePEc:crs:wpaper:2024-13 |
| By: | Martin Dufwenberg (Department of Economics, University of Arizona); Katja Goerlitz (University of Applied Labour Studies); Christina Gravert (Department of Economics, University of Copenhagen) |
| Abstract: | Peer evaluation tournaments are common in academia, the arts, and corporate environments. They make use of the expert knowledge that academics or team members have in assessing their peers performance. However, rampant opportunities for cheating may throw a wrench in the process unless, somehow, players have a preference for honest reporting. Building on Dufwenberg and Dufwenbergs (2018) theory of perceived cheating aversion, we develop a multi-player model in which players balance the utility of winning against the disutility of being identified as a cheater. We derive a set of predictions, and test these in a controlled laboratory experiment. |
| Keywords: | psychological game, cheating, tournaments, laboratory experiment |
| JEL: | C91 |
| Date: | 2024–12–13 |
| URL: | https://d.repec.org/n?u=RePEc:kud:kucebi:2420 |
| By: | Thomas Demuynck; Tom Potoms; Morgane Rigaux |
| Abstract: | We introduce a behavioural condition, called choice dominance, which (partially) ranks individuals based on their consumption of a certain good. The notion is equivalent to a single crossing restriction on the indifference curves of the individuals. We provide a revealed preference condition, called X-GARP, to test for choice dominance and we incorporate the notion into a household framework to obtain testable restrictions on the change in household demand after an increase in the reservation utility for one of the household members. We apply our fundings to an experimental dataset and a budget survey from a conditional cash transfer programme. |
| Keywords: | Choice dominance, Single crossing indifference curves, Revealed preferences, Conditional cash transfers |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:eca:wpaper:2013/384800 |
| By: | Laura Doval; Federico Echenique Wanying Huang; Yi Xin |
| Abstract: | We study the allocation of deceased-donor lungs to patients in need of a transplant. Patients make sequential decisions in an order dictated by a priority policy. Using data from a prominent Organ Procurement Organization in the United States, we provide reduced-form evidence of social learning: because patients accept or reject organs in sequence, their decisions exhibit herding behavior, often rejecting an organ that would otherwise be accepted. We develop and estimate a structural model to quantify the impact of various policy proposals and informational regimes. Our results show that blinding patients to their position in the sequence\textemdash thereby eliminating social learning\textemdash boosts organ allocation but reduces average utility per patient. In contrast, prioritizing patients by their likelihood of acceptance exacerbates social learning, leading to fewer organ allocations. Nevertheless, it raises utility per accepted organ and expedites the allocation process. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10584 |
| By: | Sara Biadetti (Università di Roma “Tor Vergata”, Italy); Lorenzo Carbonari (DEF and CEIS, Università di Roma “Tor Vergata”, Italy); Filippo Maurici (Department of Political Sciences, Università Roma Tre, Italy) |
| Abstract: | This paper explores forward-looking ambiguity (Knightian uncertainty) in a model with homogeneous workers and credit-constrained heterogeneous entrepreneurs. Agents are ambiguity-averse, using a worst-case criterion to form expectations about future productivity. We compare our economy with one that lacks uncertainty and find that ambiguity: (i) lowers the productivity threshold for market entry, (ii) reduces the equilibrium interest rate, and (iii) shifts expenditures from entrepreneurs to workers. These results stem from persistent expectation-realization mismatches. While ambiguity does not affect stability, it alters the convergence rate to the steady state and helps explain key macroeconomic comovements. |
| Keywords: | ambiguity, collateral constraints, heterogeneous agents, transition dynamics |
| JEL: | E22 D81 D84 G14 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:rim:rimwps:24-20 |
| By: | Aronsson, Thomas (Department of Economics, Umeå University); Johansson-Stenman, Olof (Department of Economics, School of Business, Economics and Law, University of Gothenburg) |
| Abstract: | A substantial body of empirical and theoretical research suggests that individuals care about, and derive instrumental benefits from, their rank in society. This paper extends the Mirrleesian model of optimal income taxation to a framework where individuals derive utility from their perceived ability rank. Such concerns generate externalities that tend to increase the optimal marginal tax rates for both corrective and redistributive reasons. While empirical evidence on the magnitude of these concerns is limited, their potential impact on optimal income taxation could be substantial, with top marginal income tax rates potentially exceeding 90%. |
| Keywords: | Redistributive taxation; ability; ordinal comparisons; externalities |
| JEL: | D62 D82 D90 H21 H23 |
| Date: | 2024–12–17 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:umnees:1031 |
| By: | Nicol\`o Cesa-Bianchi; Tommaso Cesari; Roberto Colomboni; Luigi Foscari; Vinayak Pathak |
| Abstract: | We consider a sequential decision-making setting where, at every round $t$, a market maker posts a bid price $B_t$ and an ask price $A_t$ to an incoming trader (the taker) with a private valuation for one unit of some asset. If the trader's valuation is lower than the bid price, or higher than the ask price, then a trade (sell or buy) occurs. If a trade happens at round $t$, then letting $M_t$ be the market price (observed only at the end of round $t$), the maker's utility is $M_t - B_t$ if the maker bought the asset, and $A_t - M_t$ if they sold it. We characterize the maker's regret with respect to the best fixed choice of bid and ask pairs under a variety of assumptions (adversarial, i.i.d., and their variants) on the sequence of market prices and valuations. Our upper bound analysis unveils an intriguing connection relating market making to first-price auctions and dynamic pricing. Our main technical contribution is a lower bound for the i.i.d. case with Lipschitz distributions and independence between prices and valuations. The difficulty in the analysis stems from the unique structure of the reward and feedback functions, allowing an algorithm to acquire information by graduating the "cost of exploration" in an arbitrary way. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13993 |
| By: | Adem Alparslan |
| Abstract: | This study examines conversational business analytics, an approach that utilizes AI to address the technical competency gaps that hinder end users from effectively using traditional self-service analytics. By facilitating natural language interactions, conversational business analytics aims to empower end users to independently retrieve data and generate insights. The analysis focuses on Text-to-SQL as a representative technology for translating natural language requests into SQL statements. Developing theoretical models grounded in expected utility theory, this study identifies the conditions under which conversational business analytics, through partial or full support, can outperform delegation to human experts. The results indicate that partial support, focusing solely on information generation by AI, is viable when the accuracy of AI-generated SQL queries leads to a profit that surpasses the performance of a human expert. In contrast, full support includes not only information generation but also validation through explanations provided by the AI, and requires sufficiently high validation effectiveness to be reliable. However, user-based validation presents challenges, such as misjudgment and rejection of valid SQL queries, which may limit the effectiveness of conversational business analytics. These challenges underscore the need for robust validation mechanisms, including improved user support, automated processes, and methods for assessing quality independent of the technical competency of end users. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12128 |
| By: | Sara Biadetti (Università di Roma “Tor Vergata”, Italy); Lorenzo Carbonari (DEF and CEIS, Università di Roma “Tor Vergata”, Italy); Filippo Maurici (Department of Political Sciences, Università Roma Tre, Italy) |
| Abstract: | This paper explores the effect of forward-looking ambiguity (Knightian uncertainty) on labor share distribution within a macro-development model involving entrepreneurs who are heterogeneous in productivity and wealth, and homogeneous saving workers. Ambiguity-averse agents base their decisions on worst-case labor share forecasts. As workers' expectations dominate, an endogenous hedging distribution emerges. The presence of ambiguity affects stability and induces cyclical fluctuations in labor supply, wages, and production, thereby amplifying short-term economic cycles. Capital over-accumulation arises as workers save more to hedge against uncertainty. Over time, persistent ambiguity leads to capital misallocation, reducing entrepreneurial assets while boosting aggregate capital. |
| Keywords: | ambiguity, Knightian uncertainty, heterogeneity, labor share, development |
| JEL: | E22 D81 D84 G14 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:rim:rimwps:24-18 |
| By: | Jian Guo; Saizhuo Wang; Yiyan Qi |
| Abstract: | Multi-stage decision-making is crucial in various real-world artificial intelligence applications, including recommendation systems, autonomous driving, and quantitative investment systems. In quantitative investment, for example, the process typically involves several sequential stages such as factor mining, alpha prediction, portfolio optimization, and sometimes order execution. While state-of-the-art end-to-end modeling aims to unify these stages into a single global framework, it faces significant challenges: (1) training such a unified neural network consisting of multiple stages between initial inputs and final outputs often leads to suboptimal solutions, or even collapse, and (2) many decision-making scenarios are not easily reducible to standard prediction problems. To overcome these challenges, we propose Guided Learning, a novel methodological framework designed to enhance end-to-end learning in multi-stage decision-making. We introduce the concept of a ``guide'', a function that induces the training of intermediate neural network layers towards some phased goals, directing gradients away from suboptimal collapse. For decision scenarios lacking explicit supervisory labels, we incorporate a utility function that quantifies the ``reward'' of the throughout decision. Additionally, we explore the connections between Guided Learning and classic machine learning paradigms such as supervised, unsupervised, semi-supervised, multi-task, and reinforcement learning. Experiments on quantitative investment strategy building demonstrate that guided learning significantly outperforms both traditional stage-wise approaches and existing end-to-end methods. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10496 |