scholarly journals You cannot accurately estimate an individual’s loss aversion using an accept-reject task

2020 ◽  
Author(s):  
Lukasz Walasek ◽  
Neil Stewart
Keyword(s):  
Prospect Theory ◽  
Loss Aversion ◽  
Model Parameters ◽  
Probability Weighting ◽  
Value Functions ◽  
Trade Off ◽  
Simple Version ◽  
The Poor ◽  
Gain Loss ◽  
Linear Probability

Prospect theory's loss aversion is often measured in the accept-reject task, in which participants accept or reject the chance of playing a series of gambles. The gambles are two-branch 50/50 gambles with varying gain and loss amounts (e.g., 50% chance of winning $20 and a 50% chance of losing $10). Prospect theory quantifies loss aversion by scaling losses up by a parameter λ. Here we show that λ suffers from extremely poor parameter recoverability in the accept-reject task. λ cannot be reliably estimated even for a simple version of prospect theory with linear probability weighting and value functions. λ cannot be reliably estimated even in impractically large experiments with participants subject to thousands of choices. The poor recoverability is driven by a trade-off between λ and the other model parameters. However, a measure derived from these parameters is extremely well recovered—and corresponds to estimating the area of gain-loss space in which people accept gambles. This area is equivalent to the number of gambles accepted in a given choice set. That is, simply counting accept decisions is extremely reliably recovered—but using prospect theory to make further use of exactly which gambles were accepted and which were rejected does not work.

scholarly journals A Prospect Theory Account of the “Hot” Columbia Card Task

2018 ◽  
Author(s):  
Andreas Pedroni ◽  
Jörg Rieskamp ◽  
Thorsten Pachur ◽  
Renato Frey ◽  
Jonathan E. Westfall ◽  
...  
Keyword(s):  
Prospect Theory ◽  
Loss Aversion ◽  
Model Comparison ◽  
Ecological Validity ◽  
Real Life ◽  
Expected Utility Theory ◽  
Risky Choice ◽  
Probability Weighting ◽  
Dynamic Task ◽  
Decisions Under Risk

The investigation of decisions under risk has mainly followed one of two approaches.One relies on observing choices between lotteries in which economic primitives (outcome magnitudes, probabilities, and domains (i.e., gains and losses)) are varied systematically, and this information is described to participants. The systematic variation of the economic primitives allows to formally describe behavior with expectation-based models such as expected utility theory or cumulative prospect theory (CPT), arguably the most prominent descriptive theories of risky choice. One drawback, however, is that lottery tasks can seem artificial, likely reducing the external or ecological validity. A second more naturalistic approach employs dynamic paradigms that mimic features of real-life risky situations and are assumed to have higher ecological validity. Because key information are often not provided to the decision maker, it is impossible to apply the same models as in the first approach. The goal of the present work is to integrate both approaches, by developing models for the "hot" Columbia Card Task (CCT), a task that combines a dynamic decision situation with systematic trial-to-trial variation in economic primitives. In a model comparison on the basis of the data of 191 participants, we identified a best-performing model that describes behavior as a function of CPT’s main components, outcome sensitivity, probability weighting, and loss aversion. Our work therefore provides a framework that allows the description of risk-taking behavior in a naturalistic dynamic task based on key psychological constructs (e.g., loss aversion, probability weighting) that are rooted in the factorial variation of economic primitives.

Evaluating the Role of Three Basic Factors of Prospect Theory in Decision Making

2017 ◽  
Vol 6 (2) ◽  
pp. 1-22
Author(s):  
Evanthia K. Zervoudi
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Loss Aversion ◽  
Functional Form ◽  
Empirical Evaluation ◽  
Value Functions ◽  
Optimal Solutions ◽  
Negative Exponential Function ◽  
Value Functional

The main aim of this paper is to empirically evaluate the role of three significant factors of the Prospect Theory: the S-shaped value function, the loss aversion, and the distortion of probability, in decision making. In order to do this, a general behavioral reward-risk model is firstly setup and an empirical evaluation about the role of each of these factor, separately and in interaction, on the optimal solutions of the problem follows. For the analysis, well known US equity portfolios consisting by stocks listed in NYSE, AMEX, and NASDAQ formed on investment style are employed. The findings indicate that agents differentiate their behavior according to their type of preferences and their loss aversion level but they seem to always prefer high positively skewed assets such as small and value stocks. The attractiveness of positively skewed assets is re-enforced when probability distortion is introduced in the model. The introduction of probability distortion also affects the optimal perspective values of the problem increasing significantly their magnitude. After that, results show that as loss aversion increases agents tend to follow more conservative strategies, with and without probability distortion, while the value functional form has also its role in the model; bounded value functions as the negative exponential function drives agents to more conservative behaviors while unbounded value functions as the piecewise power function give the incentive to agents to undertake great risks and follow more aggressive strategies. The examination of the interaction of these factors indicate that the combination of an unbounded value functional form with a large loss aversion index may reduce agents' aggressiveness and limit (but not alter) the value functional form effect on optimal solutions.

Evaluating the Role of Three Basic Factors of Prospect Theory in Decision Making

2020 ◽  
pp. 585-604
Author(s):  
Evanthia K. Zervoudi
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Loss Aversion ◽  
Functional Form ◽  
Empirical Evaluation ◽  
Value Functions ◽  
Optimal Solutions ◽  
Value Stocks ◽  
Value Functional

The main aim of this paper is to empirically evaluate the role of three significant factors of the Prospect Theory: the S-shaped value function, the loss aversion, and the distortion of probability, in decision making. In order to do this, a general behavioral reward-risk model is firstly setup and an empirical evaluation about the role of each of these factor, separately and in interaction, on the optimal solutions of the problem follows. For the analysis, well known US equity portfolios consisting by stocks listed in NYSE, AMEX, and NASDAQ formed on investment style are employed. The findings indicate that agents differentiate their behavior according to their type of preferences and their loss aversion level but they seem to always prefer high positively skewed assets such as small and value stocks. The attractiveness of positively skewed assets is re-enforced when probability distortion is introduced in the model. The introduction of probability distortion also affects the optimal perspective values of the problem increasing significantly their magnitude. After that, results show that as loss aversion increases agents tend to follow more conservative strategies, with and without probability distortion, while the value functional form has also its role in the model; bounded value functions as the negative exponential function drives agents to more conservative behaviors while unbounded value functions as the piecewise power function give the incentive to agents to undertake great risks and follow more aggressive strategies. The examination of the interaction of these factors indicate that the combination of an unbounded value functional form with a large loss aversion index may reduce agents' aggressiveness and limit (but not alter) the value functional form effect on optimal solutions.

scholarly journals Coordinating a Supply Chain with a Loss-Averse Retailer and Effort Dependent Demand

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Liying Li ◽  
Yong Wang
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Loss Aversion ◽  
Special Class ◽  
Numerical Study ◽  
Supply Chain Performance ◽  
Channel Coordination ◽  
Gain Loss ◽  
Decision Making Behavior ◽  
Dependent Demand

This study investigates the channel coordination issue of a supply chain with a risk-neutral manufacturer and a loss-averse retailer facing stochastic demand that is sensitive to sales effort. Under the loss-averse newsvendor setting, a distribution-free gain/loss-sharing-and-buyback (GLB) contract has been shown to be able to coordinate the supply chain. However, we find that a GLB contract remains ineffective in managing the supply chain when retailer sales efforts influence the demand. To effectively coordinate the channel, we propose to combine a GLB contract with sales rebate and penalty (SRP) contract. In addition, we discover a special class of gain/loss contracts that can coordinate the supply chain and arbitrarily allocate the expected supply chain profit between the manufacturer and the retailer. We then analyze the effect of loss aversion on the retailer’s decision-making behavior and supply chain performance. Finally, we perform a numerical study to illustrate the findings and gain additional insights.

scholarly journals Cumulative prospect theory and deferred annuities

2019 ◽  
Vol 11 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Anran Chen ◽  
Steven Haberman ◽  
Stephen Thomas
Keyword(s):  
Prospect Theory ◽  
Loss Aversion ◽  
Design Methodology ◽  
Behavioral Model ◽  
Cumulative Prospect Theory ◽  
Optimal Consumption ◽  
Content Type ◽  
Deferred Annuity ◽  
To Receive

Purpose Although it has been proved theoretically that annuities can provide optimal consumption during one’s retirement period, retirees’ reluctance to purchase annuities is a long-standing puzzle. The purpose of this paper is to use behavioral model to analyze the low demand for immediate annuities. Design/methodology/approach The authors employ cumulative prospect theory (CPT), which contains both loss aversion and probability transformations, to analyze the annuity puzzle. Findings The authors show that CPT can explain the unattractiveness of immediate annuities. It also shows that retirees would be willing to buy a long-term deferred annuity at retirement. By considering each component from CPT in turn, the loss aversion is found to be the major reason that stops people from buying an annuity while the survival rate transformation is an important factor affecting the decision of when to receive annuity incomes. Originality/value This paper identifies CPT as one of the reasons for the low demand of immediate annuities. It further suggests that long-term deferred annuities could overcome behavioral obstacles and become popular among retirees.

scholarly journals QALYs without bias? Nonparametric correction of time trade‐off and standard gamble weights based on prospect theory

2019 ◽  
Vol 28 (7) ◽  
pp. 843-854 ◽  
Author(s):  
Stefan A. Lipman ◽  
Werner B.F. Brouwer ◽  
Arthur E. Attema
Keyword(s):  
Prospect Theory ◽  
Standard Gamble ◽  
Trade Off

scholarly journals Dynamic prospect theory - two core economic decision theories coexist in the gambling behavior of monkeys

2021 ◽  
Author(s):  
Agnieszka Tymula ◽  
Yuri Imaizumi ◽  
Takashi Kawai ◽  
Jun Kunimatsu ◽  
Masayuki Matsumoto ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Prospect Theory ◽  
Weighting Function ◽  
Gambling Behavior ◽  
Theory Model ◽  
Probability Weighting ◽  
Choice Data ◽  
Mathematical Functions ◽  
Weighting Functions ◽  
Dynamic Function

Research in behavioral economics and reinforcement learning has given rise to two influential theories describing human economic choice under uncertainty. The first, prospect theory, assumes that decision-makers use static mathematical functions, utility and probability weighting, to calculate the values of alternatives. The second, reinforcement learning theory, posits that dynamic mathematical functions update the values of alternatives based on experience through reward prediction error (RPE). To date, these theories have been examined in isolation without reference to one another. Therefore, it remains unclear whether RPE affects a decision-maker's utility and/or probability weighting functions, or whether these functions are indeed static as in prospect theory. Here, we propose a dynamic prospect theory model that combines prospect theory and RPE, and test this combined model using choice data on gambling behavior of captive macaques. We found that under standard prospect theory, monkeys, like humans, had a concave utility function. Unlike humans, monkeys exhibited a concave, rather than inverse-S shaped, probability weighting function. Our dynamic prospect theory model revealed that probability distortions, not the utility of rewards, solely and systematically varied with RPE: after a positive RPE, the estimated probability weighting functions became more concave, suggesting more optimistic belief about receiving rewards and over-weighted subjective probabilities at all probability levels. Thus, the probability perceptions in laboratory monkeys are not static even after extensive training, and are governed by a dynamic function well captured by the algorithmic feature of reinforcement learning. This novel evidence supports combining these two major theories to capture choice behavior under uncertainty.

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