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on Utility Models and Prospect Theory |
| By: | van Bruggen, Paul (Tilburg University, Center For Economic Research); Laeven, Roger J. A.; van de Kuilen, Gijs (Tilburg University, Center For Economic Research) |
| Keywords: | higher-order risk attitudes; prudence; temperance; risk apportionment; non-expected utility theory; Self-protection |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiucen:c566934e-eb60-4b4b-a972-4a61e5e15cac |
| By: | Ioannou, Petros; Wang, Zheyu |
| Abstract: | As urban areas grow and city populations expand, traffic congestion has become a significant problem, particularly in regions with substantial truck traffic. This study presents a coordinated freight routing system designed to optimize network utility and reduce congestion through personalized routing guidance and incentive mechanisms. The system customizes incentives and payments for individual drivers based on current traffic conditions and their specific routing preferences. Using a mixed logit model with a linear utility specification, the system captures drivers' route choice behaviors and decisions accurately. Participation is voluntary, ensuring most drivers receive a combined expected utility, including incentives, exceeding their anticipated utility under User Equilibrium (UE). This structure encourages drivers to follow suggested routes. Data collection on drivers' routing choices allows the system to update utility parameter estimates using a hierarchical Bayes estimator, ensuring routing suggestions remain relevant and effective. The system operates over defined intervals, where truck drivers submit their intended Origin-Destination (OD) pairs to a central coordinator. The coordinator assigns routes and payments, optimizing overall system costs and offering tailored incentives to maximize compliance. Experimental results on the Sioux Falls network validate the system's effectiveness, showing significant improvements in the objective function. This study highlights the potential of a coordinated routing system to enhance urban traffic efficiency by dynamically adjusting incentives based on drivers’ choice data and driver behavior. View the NCST Project Webpage |
| Keywords: | Engineering, Social and Behavioral Sciences, Congestion Reduction, Utility Learning, Travel Demand Management, Freight Routing |
| Date: | 2024–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:itsdav:qt6qb516n9 |
| By: | Jeremy Goodman; Harvey Lederman |
| Abstract: | We study social welfare relations (SWRs) on infinite utility streams. Our main result is a new characterization of a utilitarian SWR on lotteries over welfare distributions for an infinite population. We characterize this SWR as the maximally complete SWR over lotteries on such distributions which satisfies Strong Pareto, Permutation Invariance (elsewhere called "Relative Anonymity" and "Isomorphism Invariance"), Dominance, Ex Ante Indifference, and an Additive Invariance axiom. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.05851 |
| By: | Moise Blanchard; Patrick Jaillet |
| Abstract: | We study the problem in which a central planner sequentially allocates a single resource to multiple strategic agents using their utility reports at each round, but without using any monetary transfers. We consider general agent utility distributions and two standard settings: a finite horizon $T$ and an infinite horizon with $\gamma$ discounts. We provide general tools to characterize the convergence rate between the optimal mechanism for the central planner and the first-best allocation if true agent utilities were available. This heavily depends on the utility distributions, yielding rates anywhere between $1/\sqrt T$ and $1/T$ for the finite-horizon setting, and rates faster than $\sqrt{1-\gamma}$, including exponential rates for the infinite-horizon setting as agents are more patient $\gamma\to 1$. On the algorithmic side, we design mechanisms based on the promised-utility framework to achieve these rates and leverage structure on the utility distributions. Intuitively, the more flexibility the central planner has to reward or penalize any agent while incurring little social welfare cost, the faster the convergence rate. In particular, discrete utility distributions typically yield the slower rates $1/\sqrt T$ and $\sqrt{1-\gamma}$, while smooth distributions with density typically yield faster rates $1/T$ (up to logarithmic factors) and $1-\gamma$. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.10066 |
| By: | Zhengxing Zou (Beijing Jiaotong University and University of Toronto); René van den Brink (Vrije Universiteit Amsterdam); Yukihiko Funaki (Waseda University) |
| Abstract: | We propose and characterize weighted-egalitarian values for cooperative transferable utility games. Each weighted-egalitarian value divides the worth of the grand coalition into two parts and allocates them through equality and proportionality based on exogenous player weights. We characterize the family of all weighted-egalitarian values by employing the standard axioms of efficiency and linearity, in addition to two novel axioms: ω-ratio invariance for symmetric players and symmetry in weights. We then show that relaxing linearity to additivity and adding coalitional monotonicity results in a sub- family of affine combinations of equal division and weighted division values. Furthermore, using an axiom called monotonicity in weights, we characterize the family of convex combinations of equal division and weighted division values. |
| Keywords: | cooperative game, axiomatization, equal division value, weighted division value, equality |
| JEL: | C71 |
| Date: | 2024–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240021 |
| By: | Caferra, Rocco; Morone, Andrea; Pierno, Donato |
| Abstract: | This study explores how people learn and adapt their risk preferences using different elicitation methods, challenging the neoclassical theory that suggests preferences are fixed. Instead, we show that preferences can change. However, we aim to explain whether the observed changes are due to a real change in the measure, i.e. individuals' risk preferences, or if they are attributable to the limitations of the measurement tool, i.e. the specific risk elicitation method employed. We use a detailed experimental design to examine the stability and consistency of risk preferences using a hands-on learning experience. Our main goals are to assess how consistent risk choices are, understand how preferences remain stable or change over time, and evaluate the effectiveness of different elicitation methods like the Multiple Price List and Ordered Lottery Selection ones. On the one hand, results demonstrate that risk preferences are variable and adaptable, and this can be partly due to the role of experience-based learning. On the other hand, we observe how Multiple Price List methods, even if more complex, are more accurate in identifying risk preferences and then in improving measurement stability and accuracy. |
| Keywords: | Risk preferences; Experiments; Elicitation Methods; Learning. |
| JEL: | D81 D90 |
| Date: | 2024–06–13 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121590 |
| By: | Andrea Mazzon; Peter Tankov |
| Abstract: | Aiming to analyze the impact of environmental transition on the value of assets and on asset stranding, we study optimal stopping and divestment timing decisions for an economic agent whose future revenues depend on the realization of a scenario from a given set of possible futures. Since the future scenario is unknown and the probabilities of individual prospective scenarios are ambiguous, we adopt the smooth model of decision making under ambiguity aversion of Klibanoff et al (2005), framing the optimal divestment decision as an optimal stopping problem with learning under ambiguity aversion. We then prove a minimax result reducing this problem to a series of standard optimal stopping problems with learning. The theory is illustrated with two examples: the problem of optimally selling a stock with ambigous drift, and the problem of optimal divestment from a coal-fired power plant under transition scenario ambiguity. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.09349 |
| By: | Aleksandar Arandjelovi\'c; Julia Eisenberg |
| Abstract: | We consider an insurance company which faces financial risk in the form of insurance claims and market-dependent surplus fluctuations. The company aims to simultaneously control its terminal wealth (e.g. at the end of an accounting period) and the ruin probability in a finite time interval by purchasing reinsurance. The target functional is given by the expected utility of terminal wealth perturbed by a modified Gerber-Shiu penalty function. We solve the problem of finding the optimal reinsurance strategy and the corresponding maximal target functional via neural networks. The procedure is illustrated by a numerical example, where the surplus process is given by a Cram\'er-Lundberg model perturbed by a mean-reverting Ornstein-Uhlenbeck process. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.06168 |
| By: | Cesa-Bianchi, Nicolò (Università degli Studi di Milano); Colomboni, Roberto (Università degli Studi di Milano); Kasy, Maximilian (University of Oxford) |
| Abstract: | We consider the problem of repeatedly choosing policies to maximize social welfare. Welfare is a weighted sum of private utility and public revenue. Earlier outcomes inform later policies. Utility is not observed, but indirectly inferred. Response functions are learned through experimentation. We derive a lower bound on regret, and a matching adversarial upper bound for a variant of the Exp3 algorithm. Cumulative regret grows at a rate of T2/3. This implies that (i) welfare maximization is harder than the multi-armed bandit problem (with a rate of T1/2 for finite policy sets), and (ii) our algorithm achieves the optimal rate. For the stochastic setting, if social welfare is concave, we can achieve a rate of T1/2 (for continuous policy sets), using a dyadic search algorithm. We analyze an extension to nonlinear income taxation, and sketch an extension to commodity taxation. We compare our setting to monopoly pricing (which is easier), and price setting for bilateral trade (which is harder). |
| Keywords: | optimal taxation, multi-armed bandits, experimental design |
| JEL: | C9 H21 C73 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17186 |
| By: | Alexandre Arnout (Aix-Marseille University, CNRS, AMSE, Marseille France) |
| Abstract: | I consider an electoral competition model where each candidate is associated with an exogenous initial position from which she can deviate to maximize her vote share, a strategy known as flip-flopping. Citizens have an intrinsic preference for consistent candidates, and abstain due to alienation, i.e. when their utility from their preferred candidate falls below a common exogenous threshold (termed the alienation threshold). I show how the alienation threshold shapes candidates’ flip-flopping strategy. When the alienation threshold is high, i.e. when citizens are reluctant to vote, there is no flip-flopping at equilibrium. When the alienation threshold is low, candidates flip-flop toward the center of the policy space. Surprisingly, I find a positive correlation between flip-flopping and voter turnout at equilibrium, despite voters’ preference for consistent candidates. Finally, I explore alternative models in which candidates’ objective function differs from vote share. I show that electoral competition can lead to polarization when candidates maximize their number of votes. |
| Keywords: | flip-flopping, turnout, electoral competition, alienation, polarization |
| JEL: | D72 C72 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:aim:wpaimx:2423 |
| By: | Fabian R. Pieroth; Tuomas Sandholm |
| Abstract: | In practice, most auction mechanisms are not strategy-proof, so equilibrium analysis is required to predict bidding behavior. In many auctions, though, an exact equilibrium is not known and one would like to understand whether -- manually or computationally generated -- bidding strategies constitute an approximate equilibrium. We develop a framework and methods for estimating the distance of a strategy profile from equilibrium, based on samples from the prior and either bidding strategies or sample bids. We estimate an agent's utility gain from deviating to strategies from a constructed finite subset of the strategy space. We use PAC-learning to give error bounds, both for independent and interdependent prior distributions. The primary challenge is that one may miss large utility gains by considering only a finite subset of the strategy space. Our work differs from prior research in two critical ways. First, we explore the impact of bidding strategies on altering opponents' perceived prior distributions -- instead of assuming the other agents to bid truthfully. Second, we delve into reasoning with interdependent priors, where the type of one agent may imply a distinct distribution for other agents. Our main contribution lies in establishing sufficient conditions for strategy profiles and a closeness criterion for conditional distributions to ensure that utility gains estimated through our finite subset closely approximate the maximum gains. To our knowledge, ours is the first method to verify approximate equilibrium in any auctions beyond single-item ones. Also, ours is the first sample-based method for approximate equilibrium verification. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11445 |
| By: | Kshama Dwarakanath; Svitlana Vyetrenko; Tucker Balch |
| Abstract: | We present an agent-based simulator for economic systems with heterogeneous households, firms, central bank, and government agents. These agents interact to define production, consumption, and monetary flow. Each agent type has distinct objectives, such as households seeking utility from consumption and the central bank targeting inflation and production. We define this multi-agent economic system using an OpenAI Gym-style environment, enabling agents to optimize their objectives through reinforcement learning. Standard multi-agent reinforcement learning (MARL) schemes, like independent learning, enable agents to learn concurrently but do not address whether the resulting strategies are at equilibrium. This study integrates the Policy Space Response Oracle (PSRO) algorithm, which has shown superior performance over independent MARL in games with homogeneous agents, with economic agent-based modeling. We use PSRO to develop agent policies approximating Nash equilibria of the empirical economic game, thereby linking to economic equilibria. Our results demonstrate that PSRO strategies achieve lower regret values than independent MARL strategies in our economic system with four agent types. This work aims to bridge artificial intelligence, economics, and empirical game theory towards future research. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.12038 |
| By: | J. Lucas Reddinger |
| Abstract: | Is an option especially tempting when it is both immediate and certain? I test the effect of risk on the present-bias factor given quasi-hyperbolic discounting. In my experiment workers allocate about thirty to fifty minutes of real-effort tasks between two weeks.I study dynamic consistency by comparing choices made two days in advance of the work-day with choices made when work is imminent. My novel design permits estimation of present bias using a decision with a consequence that is both immediate and certain. I find greater present bias when the consequence is certain.This finding has implications for any economic decision involving a present-biased decision-maker, including labor contracting and consumer good pricing. I offer a methodological remedy for experimental economists. |
| Keywords: | present bias, dynamic inconsistency, quasi-hyperbolic discounting, timepreferences, risk preferences, immediacy effect, certainty effect, experimental economics |
| JEL: | C91 D80 D90 |
| Date: | 2023–11 |
| URL: | https://d.repec.org/n?u=RePEc:pur:prukra:1338 |
| By: | Su Jung Jee; Kerstin H\"otte; Caoimhe Ring; Robert Burrell |
| Abstract: | This study investigates the controversial role of Intellectual Property Rights (IPRs) in climate technology transfer and innovation in developing countries. Using a systematic literature review and expert interviews, we assess the role of IPRs on three sources of climate technology: (1) international technology transfer, (2) adaptive innovation, and (3) indigenous innovation. Our contributions are threefold. First, patents have limited impact in any of these channels, suggesting that current debates over IPRs may be directed towards the wrong targets. Second, trademarks and utility models provide incentives for climate innovation in the countries studied. Third, drawing from the results, we develop a framework to guide policy on how IPRs can work better in the broader context of climate and trade policies, outlining distinct mechanisms to support mitigation and adaptation. Our results indicate that market mechanisms, especially trade and demand-pull policies, should be prioritised for mitigation solutions. Adaptation differs, relying more on indigenous innovation due to local needs and low demand. Institutional mechanisms, such as finance and co-development, should be prioritised to build innovation capacities for adaptation. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.12338 |