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on Utility Models and Prospect Theory |
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Issue of 2024‒03‒11
twelve papers chosen by |
| By: | Eungik Lee; Kathleen Ngangoue; Andrew Schotter; Maryse Kathleen Ngangoue |
| Abstract: | We compare preferences for temporal resolution when uncertainty is resolved over a probability rather than a value. In various frameworks–e.g., Kreps and Porteus (1978)–, preferences over gradual versus one-shot resolution do not depend on whether values or probabilities define the main object of uncertainty. In our experiment, however, most subjects resolved uncertain values gradually but uncertain probabilities all at once–both in the gain and loss frames. This systematic discrepancy motivates an explanation for it that we call “process utility”, which highlights the importance of information processing when deducing revealed preferences for temporal resolution from choice data. |
| Keywords: | resolution of uncertainty, probability, gradual resolution, one-shot resolution, process utility, non-instrumental information, Kreps-Porteus |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10898&r=upt |
| By: | Simon Mongey; Michael E. Waugh |
| Abstract: | This paper characterizes the allocations that emerge in general equilibrium economies populated by households with preferences of the additive random utility type that make discrete consumption, employment or spatial decisions. We start with a complete markets economy where households can trade claims contingent upon the realizations of their preference shocks. We (i) establish a first and second welfare theorem, (ii) illustrate that in the absence of ex-ante trade, discrete choice economies are generically inefficient, (iii) show that complete markets are not necessary and a much smaller set of securities decentralizes the efficient allocation. We illustrate the relevance of these results in several canonical settings and for measuring how welfare changes in response to changes in prices. |
| JEL: | D5 D52 E2 F1 R13 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32135&r=upt |
| By: | Benjamin Patrick Evans; Sumitra Ganesh |
| Abstract: | Agent-based models (ABMs) have shown promise for modelling various real world phenomena incompatible with traditional equilibrium analysis. However, a critical concern is the manual definition of behavioural rules in ABMs. Recent developments in multi-agent reinforcement learning (MARL) offer a way to address this issue from an optimisation perspective, where agents strive to maximise their utility, eliminating the need for manual rule specification. This learning-focused approach aligns with established economic and financial models through the use of rational utility-maximising agents. However, this representation departs from the fundamental motivation for ABMs: that realistic dynamics emerging from bounded rationality and agent heterogeneity can be modelled. To resolve this apparent disparity between the two approaches, we propose a novel technique for representing heterogeneous processing-constrained agents within a MARL framework. The proposed approach treats agents as constrained optimisers with varying degrees of strategic skills, permitting departure from strict utility maximisation. Behaviour is learnt through repeated simulations with policy gradients to adjust action likelihoods. To allow efficient computation, we use parameterised shared policy learning with distributions of agent skill levels. Shared policy learning avoids the need for agents to learn individual policies yet still enables a spectrum of bounded rational behaviours. We validate our model's effectiveness using real-world data on a range of canonical $n$-agent settings, demonstrating significantly improved predictive capability. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.00787&r=upt |
| By: | Johannes Carow (Johannes Gutenberg University Mainz); Niklas M. Witzig (Johannes Gutenberg University Mainz) |
| Abstract: | We study the impact of time pressure on strategic risk-taking of professional chess players. We propose a novel machine-learning-based measure for the degree of strategic risk of a single chess move and apply this measure to the 2013-2023 FIDE Chess World Cups that allow for plausibly exogenous variation in thinking time. Our results indicate that time pressure leads chess players to opt for more risk-averse moves. We additionally provide correlational evidence for strategic loss aversion, a tendency for risky moves after a mistake/ in a disadvantageous position. This suggests that high-proficiency decision-makers in highstake situations react to time pressure and contextual factors more broadly. We discuss the origins and implication of this finding in our setting. |
| Keywords: | Chess, Risk, Time Pressure, Loss Aversion, Machine Learning |
| JEL: | C26 C45 D91 |
| Date: | 2024–02–22 |
| URL: | http://d.repec.org/n?u=RePEc:jgu:wpaper:2404&r=upt |
| By: | Michael Thaler |
| Abstract: | People often receive good news that makes them feel better about the world around them, or bad news that makes them feel worse about it. This paper studies how the valence of news affects belief updating, absent functional and ego-relevant factors. Using experiments with over 1, 500 participants and 5, 600 observations, I test whether people engage in motivated reasoning to overly trust good news versus bad news on valence-relevant issues like cancer survival rates, others’ happiness, and infant mortality. The estimate for motivated reasoning towards good news is a precisely-estimated null. Modest effects, of one-third the size of motivated reasoning in politics and performance, can be ruled out. Complementary survey evidence shows that most people expect good news to increase happiness, but to not systematically lead to motivated reasoning. These results suggest that belief-based utility is not sufficient in leading people to distort belief updating in order to favor those beliefs. |
| Keywords: | motivated reasoning, belief-based utility, experimental economics |
| JEL: | C91 D83 D91 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10915&r=upt |
| By: | Cheng Chou (University of Leicester); Geert Ridder (University of Southern California); Ruoyao Shi (Department of Economics, University of California Riverside) |
| Abstract: | In a dynamic binary choice model that allows for general forms of nonstationarity, we transform the identification of the flow utility parameters into the solution of a (linear) system of equations. The identification of the parameters, therefore, follows the usual argument for linear GMM. In particular, we show that the state transition distribution is not essential for the identification and estimation of the parameters. We propose a three-step conditional-choice-probability-based semiparametric estimator that bypasses estimation of and simulating from the state transition distribution. Simulation experiments show that our estimator gives comparable or better estimates than a competitor estimator, yet it requires fewer assumptions in certain scenarios, is substantially easier to implement, and is computationally much less demanding. The asymptotic distribution of the estimator is provided, and the sensitivity of the estimator to a key assumption is also examined. |
| Keywords: | dynamic binary choice model, Markov property, linear system, identification, semiparametric estimation |
| JEL: | C35 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:202402&r=upt |
| By: | Wisarut Suwanprasert |
| Abstract: | I develop a theoretical framework to study gains from trade and optimal tariffs in the presence of behavioral biases. I introduce a sufficient statistic, called “behavioral wedge, †that generalizes the model to capture various types of behavioral biases, including utility misperceptions and inattention. First, I explore how behavioral biases influence gains from trade, demonstrating potential welfare losses from trade for behavioral agents. Second, I characterize optimal tariffs and behavioral nudges in the presence of behavioral biases. I show that small open economies can leverage trade policy to mitigate the welfare losses from behavioral biases, whereas larger economies might use nudges to manipulate the world’s terms of trade. Finally, I discuss the role of behavioral biases in shaping public support for the 2018 China–United States trade war and Brexit. |
| Keywords: | Trade theory; Behavioral economics; Gains from trade; Optimal tariffs; Nudges |
| JEL: | D9 F1 H2 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:pui:dpaper:216&r=upt |
| By: | Guangsheng Yu; Qin Wang; Caijun Sun; Lam Duc Nguyen; H. M. N. Dilum Bandara; Shiping Chen |
| Abstract: | In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we come across an unexpected finding. That is, the current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. We propose, analyze, and implement a novel reference incentive model, which is inherently structured as a Directed Acyclic Graph (DAG)-based NFT network. This model aims to maximize connections (or references) between NFTs, enabling each isolated NFT to expand its network and accumulate rewards derived from subsequent or subscribed ones. We conduct both theoretical and practical analyses of the model, demonstrating its optimal utility. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06459&r=upt |
| By: | Elena Capatina; Gary Hansen; Minchung Hsu |
| Abstract: | This paper compares the impact of long term care (LTC) risk on single and married households and studies the roles played by informal care (IC), consumption sharing within households, and Medicaid in insuring this risk. We develop a life-cycle model where individuals face survival and health risk, including the possibility of becoming highly disabled and needing LTC. Households are heterogeneous in various important dimensions including education, productivity, and the age difference between spouses. Health evolves stochastically. Agents make consumption-savings decisions in a framework featuring an LTC statedependent utility function. We find that household expenditures increase significantly when LTC becomes necessary, but married individuals are well insured against LTC risk due to IC. However, they still hold considerable assets due to the concern for the spouse who might become a widow/widower and can expect much higher LTC costs. IC significantly reduces precautionary savings for middle and high income groups, but interestingly, it encourages asset accumulation among low income groups because it reduces the probability of meanstested Medicaid LTC. |
| Keywords: | Long Term Care, Household Risk, Precautionary Savings, Medicaid |
| JEL: | D91 E21 H31 I10 I38 J14 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:acb:cbeeco:2023-697&r=upt |
| By: | Julia M. Puaschunder (Columbia University, USA) |
| Abstract: | Behavioral economics is an innovative applied science. In the 1950s economic rational choice models came under scrutiny. A theoretical critique emerged that not all human beings strive for efficiency and rationality all the time. Behavioral economics first drew attention to deviations from rationality and discussed the non-applicability of rational choice models for depicting the actual behavior of humans. During the 1970s, Amartya Sen formalized the rational choice critique and published powerful examples of how economics needs a reality check and backtesting of its core axioms of rationality, efficiency and time consistency for actual real-world relevancy and external validity of the standard rational choice claims. By 1979, the two psychologists Daniel Kahneman and Amos Tversky presented a line of laboratory experiments at universities that proved the rational choice theory to be inaccurate to explain the real-world decision-making patterns of individuals. The following behavioral economics revolution rewrote economics for accuracy and predictability for actual human day-to-day choices and behavior. Sociologists, political scientists, psychologists created a line of research to describe how individuals actually decide during the first decade of the 2000s. Behavioral insights were then used to find ways how to ‘nudge’ individuals, communities and leaders to help others make better choices in different domains, for instance such as finance, marketing, health and well-being. Around the world, governmental officials and governance experts adopted behavioral nudges and winks to create better choice architectures and decision-making patterns. This paper describes the history of behavioral economics with attention to North American roots and European interpretations in order to then prospect future trends in behavioral economics. First, given the enormous popularity behavioral economics has enjoyed in the most recent decades, a general knowledge has formed about behavioral nudges. Libertarian paternalism is – by now – limited when it comes to implicitly tricking people into making choices based on well-known insights. A common body of knowledge on behavioral aspects of choice patterns may lead to reactance if people notice manipulation. The general population should therefore be trained to make self-empowered choices that meet their individual principles, needs and wants based on their behavioral expertise. Behavioral economists should move from manipulating nudges to educating trainings of the layperson. Second, the field of behavioral sciences has experienced a deep replication crisis given major data cheating scandals and contemporary fraud allegations. General oversight mechanism between co-authors, backtesting of effects for validity and their general applicability is therefore warranted. he general population should be trained to be critical of behavioral insights presented to them and be encouraged by behavioral economists to feedback on the potential non-applicability of p-hacked results. Third, online searchplace distortion of behavioral economics results has become a sad reality for young behavioral economists in the strategic search engine results manipulation through Search Engine Disoptimization (SEDO). This implicit internet harassment calls for a democratization of information and whole-rounded inclusion of thoughts online. Behavioral economists should raise awareness for this negative competitive behavior and work together with global governance institutions, regulatory bodies but also industry professionals to curb negative internet search engine manipulation and empower the upcoming generation of behavioral economists to speak up when this is happening. Professional bodies should be informed to help those whose career has been hit by competitive internet manipulation. All these trends are speculated to lead to a revamped behavioral economics revolution that demands for behavioral economics for all. The future of behavioral economics is believed to lie in self-empowered leadership, not manipulation. A democratization of behavioral economics information leading to a general knowledge basis on actual behavioral patterns will guide a self-empowered decision-making cadre within the general population. Search for true and credible behavioral insights can lift the entire field to a more helpful stage to become a standing guidepost for wise quality decision-making. The digital millennium calling for fair internet use will hopefully prosper an inclusive and diversified information on behavioral insights to be accessible, useful and meaningful for all. |
| Keywords: | Behavioral Economics, Behavioral Finance, Behavioral Insights |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:smo:raiswp:0293&r=upt |
| By: | Guillaume Bataille (Aix-Marseille Univ., CNRS, AMSE, Marseille, France) |
| Abstract: | This paper derives closed-form solutions for a strategic, simultaneous harvesting in a predator-prey system. Using a parametric constraint, it establishes the existence and uniqueness of a linear feedback-Nash equilibrium involving two specialized fleets and allow for continuous time results for a class of payoffs that have constant elasticity of the marginal utility. Theses results contribute to the scarce literature on analytically tractable predator-prey models with endogenous harvesting. A discussion based on industry size effects is provided to highlight the role played by biological versus strategic interactions in the multi-species context. |
| Keywords: | fisheries, Dynamic games, common-pool resource, Predator-prey relationship |
| JEL: | Q22 Q57 C61 C73 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:2405&r=upt |
| By: | Sarracino, Francesco; Slater, Giulia |
| Abstract: | Countries where interpersonal trust is higher have, on average, higher gross domestic product (GDP) per capita. Does this mean that economic growth is associated to growing trust over time? We review the literature addressing this question, and provide updated empirical evidence on the effects of economic growth on trust over time, a well-established measure of social capital, widely considered in economic studies. We use country panel data from the Penn World Tables and information on people trusting others from the Survey Data Recycling (SDR) v.2.0 database, the largest source of data on trust currently available. Results confirm the positive cross-sectional relation found in previous studies. However, over time trust decreases when GDP grows. A number of robustness checks and a test of causality support this conclusion. The negative relationship between economic growth and trust over time affects prevalently unequal, rich countries. This is possible because growing income inequality increases the chances for social comparisons, which substitute trust in individuals’ utility functions. Additionally, income inequality hampers cooperation and cohesiveness in favour of competition, and increases the probability of social unrest. This suggests that the quality of growth matters: interpersonal trust decreases when economic growth is accompanied by income inequalities. |
| Keywords: | trust, economic growth, panel data, paradox, inequality |
| JEL: | I38 O15 O40 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120053&r=upt |