Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist

2020 ◽  
Vol 102 (3) ◽  
pp. 569-582 ◽  
Author(s):  
Yaroslav Rosokha ◽  
Kenneth Younge
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Incremental Cost ◽  
Prospect Theory ◽  
Negative Feedback ◽  
Loss Aversion ◽  
Model Uncertainty ◽  
Human Subjects ◽  
Two Dimensional ◽  
Randomized Experiments

We investigate the willingness of individuals to persist at exploration when confronted by prolonged periods of negative feedback. We design a two-dimensional maze game and run a series of randomized experiments with human subjects in the game. Our results suggest individuals explore more when they are reminded of the incremental cost of their actions, a result that extends prior research on loss aversion and prospect theory to environments characterized by model uncertainty. In addition, we run simulations based on a model of reinforcement learning that extend beyond two-period models of decision making to account for repeated behavior in longer-running, dynamic contexts.

Prospect Theory and Foreign Policy Analysis

2017 ◽  
Author(s):  
Jeffrey W. Taliaferro
Keyword(s):  
Decision Making ◽  
Foreign Policy ◽  
Policy Analysis ◽  
Prospect Theory ◽  
Loss Aversion ◽  
Reference Point ◽  
Endowment Effect ◽  
Risk Averse ◽  
Foreign Policy Analysis ◽  
Point Selection

Prospect theory is one of the most influential behavioral theories in the international relations (IR) field, particularly among scholars of security studies, political psychology, and foreign policy analysis. Developed by Israeli psychologists Daniel Kahneman and Amos Tversky, prospect theory provides key insights into decision making under conditions of risk and uncertainty. For example, most individuals are risk averse to secure gains, but risk acceptant to avoid losses (loss aversion). In addition, most people value items they already posses more than they value items they want to acquire (endowment effect), and tend to be risk averse if they perceive themselves to be facing gains relative to their reference point (risk propensity). Prospect theory has generated an enormous volume of scholarship in IR, which can be divided into two “generations”. The first generation (1990–1999) sought to establish prospect theory’s plausibility in the “real world” by testing hypotheses derived from it against subjective expected-utility theory or rational choice models of foreign policy decision making. The second generation (2000–present) began to incorporate concepts associated with prospect theory and related experimental literature on group risk taking into existing mid-level theories of IR and foreign policy behavior. Two substantive areas covered by scholars during this period are coercive diplomacy and great power intervention in the periphery as they relate to loss aversion. Both generations of prospect theory literature suffer from conceptual and methodological difficulties, mainly around the issues of reference point selection, framing, and preference reversal outside laboratory settings.

Torus Destruction and Chaos–Chaos Intermittency in a Commodity Distribution Chain

1997 ◽  
Vol 07 (06) ◽  
pp. 1225-1242 ◽  
Author(s):  
O. Sosnovtseva ◽  
E. Mosekilde
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Distribution Model ◽  
High Dimensional ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Production Distribution ◽  
Distribution Chain ◽  
Beer Production ◽  
Decision Making Behavior

The destruction of two-dimensional tori T2 and the transitions to chaos are studied numerically in a high-dimensional model describing the decision making behavior of human subjects in a simulated managerial environment (the beer production-distribution model). Two different routes from quasiperiodicity to chaos can be distinguished. Intermittency transitions between chaotic and hyperchaotic attractors are characterized, and transients in which the "system pursues the ghost" of a vanished hyperchaotic attractor are studied.

scholarly journals Three Cheers—Psychological, Theoretical, Empirical—for Loss Aversion

2005 ◽  
Vol 42 (2) ◽  
pp. 129-133 ◽  
Author(s):  
Colin Camerer
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Loss Aversion ◽  
Special Role ◽  
Open Questions ◽  
Increased Sensitivity ◽  
Psychological Foundation ◽  
Petersburg Paradox ◽  
Stable Feature

This note emphasizes the special role of prospect theory in drawing psychophysical considerations into theories of decision making with respect to risk. An example of such a consideration is the dependence of outcome value on a reference point and the increased sensitivity of loss relative to gain (i.e., loss aversion). Loss aversion can explain the St. Petersburg paradox without requiring concave utility, it has the correct psychological foundation, it is theoretically useful, and it is a parsimonious principle that can explain many puzzles. A few open questions are whether loss aversion is a stable feature of preference, whether it is an expression of fear, and what are its properties.

scholarly journals Reliability of decision-making and reinforcement learning computational parameters

2021 ◽  
Author(s):  
Anahit Mkrtchian ◽  
Vincent Valton ◽  
Jonathan P Roiser
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Prospect Theory ◽  
Computational Models ◽  
Processing Parameters ◽  
Theory Model ◽  
Individual Characteristics ◽  
Good Reliability ◽  
Reinforcement Learning Model ◽  
Insight Into

Background: Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative that computational measures capture individual characteristics reliably. To date, this issue has received little consideration. Methods: Here we examine the reliability of canonical reinforcement learning and economic models derived from two commonly used tasks. Healthy individuals (N=50) completed a restless four-armed bandit and a calibrated gambling task twice, two weeks apart. Results: Reward and punishment processing parameters from the reinforcement learning model showed fair-to-good reliability, while risk/loss aversion parameters from a prospect theory model exhibited good-to-excellent reliability. Both models were further able to predict future behaviour above chance within individuals. Conclusions: These results suggest that reinforcement learning, and particularly prospect theory measures, represent relatively reliable decision-making mechanisms, which are also unique across individuals, indicating the translational potential of clinically-relevant computational parameters for precision psychiatry.

scholarly journals Flexibility to contingency changes distinguishes habitual and goal-directed strategies in humans

2017 ◽  
Author(s):  
Julie J. Lee ◽  
Mehdi Keramati
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Human Subjects ◽  
Relative Influence ◽  
Optimal Decision ◽  
Hybrid Strategy ◽  
Model Free ◽  
Optimal Decision Making ◽  
Value Changes ◽  
Change Task

AbstractDecision-making in the real world presents the challenge of requiring flexible yet prompt behavior, a balance that has been characterized in terms of a trade-off between a slower, prospective goal-directed model-based (MB) strategy and a fast, retrospective habitual model-free (MF) strategy. Theory predicts that flexibility to changes in both reward values and transition contingencies can determine the relative influence of the two systems in reinforcement learning, but few studies have manipulated the latter. Therefore, we developed a novel two-level contingency change task in which transition contingencies between states change every few trials; MB and MF control predict different responses following these contingency changes, allowing their relative influence to be inferred. Additionally, we manipulated the rate of contingency changes in order to determine whether contingency change volatility would play a role in shifting subjects between a MB and MF strategy. We found that human subjects employed a hybrid MB/MF strategy on the task, corroborating the parallel contribution of MB and MF systems in reinforcement learning. Further, subjects did not remain at one level of MB/MF behavior but rather displayed a shift towards more MB behavior over the first two blocks that was not attributable to the rate of contingency changes but rather to the extent of training. We demonstrate that flexibility to contingency changes can distinguish MB and MF strategies, with human subjects utilizing a hybrid strategy that shifts towards more MB behavior over blocks, consequently corresponding to a higher payoff.Author SummaryTo make good decisions, we must learn to associate actions with their true outcomes. Flexibility to changes in action/outcome relationships, therefore, is essential for optimal decision-making. For example, actions can lead to outcomes that change in value – one day, your favorite food is poorly made and thus less pleasant. Alternatively, changes can occur in terms of contingencies – ordering a dish of one kind and instead receiving another. How we respond to such changes is indicative of our decision-making strategy; habitual learners will continue to choose their favorite food even if the quality has gone down, whereas goal-directed learners will soon learn it is better to choose another dish. A popular paradigm probes the effect of value changes on decision making, but the effect of contingency changes is still unexplored. Therefore, we developed a novel task to study the latter. We find that humans used a mixed habitual/goal-directed strategy in which they became more goal-directed over the course of the task, and also earned more rewards with increasing goal-directed behavior. This shows that flexibility to contingency changes is adaptive for learning from rewards, and indicates that flexibility to contingency changes can reveal which decision-making strategy is used.

scholarly journals Conformist social learning inoculates against adverse risk aversion

2021 ◽  
Author(s):  
Wataru Toyokawa ◽  
Wolfgang Gaissmaier
Keyword(s):  
Decision Making ◽  
Social Learning ◽  
Risk Aversion ◽  
Negative Feedback ◽  
Risk Taking ◽  
Human Subjects ◽  
Informational Cascades ◽  
Minority Influence ◽  
Positive And Negative Feedback ◽  
Error Learning

AbstractGiven the ubiquity of potentially adverse biases incurred by trial-and-error learning, it seems paradoxical that improvements in decision-making performance through conformist social learning, a process widely considered to be bias amplification, still prevail in animal behaviour. Here we show, through model analyses and online experiments with 467 adult human subjects, that conformity can promote favourable risk taking in repeated decision making, even though many individuals are systematically biased towards suboptimal risk aversion owing to the myopia of reinforcement learning. Although positive feedback conferred by conformity could result in suboptimal informational cascades, our dynamic model of behaviour identified a key role for negative feedback that arises when a weak minority influence undermines the inherent behavioural bias. This ‘collective behavioural rescue’, emerging through coordination of positive and negative feedback, highlights a benefit of social learning in a broader range of environmental conditions than previously assumed and resolves the ostensible paradox of adaptive collective flexibility through conformity.

Research on decision-making behavior of crowdsourcing task based on loss aversion and incentive level

Kybernetes ◽  
2019 ◽  
Vol 49 (5) ◽  
pp. 1507-1528
Author(s):  
Liang Wang ◽  
Tingjia Xu ◽  
Jie Chen
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Loss Aversion ◽  
Simulation Analysis ◽  
Content Type ◽  
Effort Level ◽  
Incentive Level ◽  
Decision Making Models ◽  
Decision Making Behavior ◽  
The Impact

Purpose The purpose of this paper is to study the decision-making behavior of the initiator and the participant under innovative and project-based tasks, respectively. It further explores the impact of the participant’s loss aversion and the initiator’s incentive level on the participant’s optimal effort level to reveal the implicit managerial mechanism. Design/methodology/approach This paper uses the Principal-agent Theory, Prospect Theory and Game Theory to study the decision-making behavior in crowdsourcing tasks. First, according to the return at the reference point, it establishes the utility function models of the participant and the initiator. Second, based on diverse loss aversion coefficient and incentive coefficient, it constructs the decision-making models of two types of task respectively. Third, it verifies the validity of models through simulation analysis. Findings For innovative task, the participant’s optimal effort level increases with the increment of loss aversion and incentive level, but decreases with the increase of his effort cost. For project-based task, the participant’s optimal effort level rises with the decrease of loss aversion; if the initiator does not take appropriate incentives, information asymmetry will lead to the task becoming a low-level innovation approach. Moreover, under innovative task, when the participant has loss aversion (or loss aversion reversal), his optimal effort level is higher (or lower) than that with no loss aversion, while the result under project-based task is just the opposite. Originality/value This paper characterizes two types of crowdsourcing task. Based on the prospect theory, it develops the decision-making models of the participant and the initiator under innovative and project-based tasks, thus exploring the impact of loss aversion and incentive level on their decision-making behavior. According to the findings in this paper, the initiator may effectively speculate the participant’s effort level and adopt reasonable monetary incentive measures to optimize the crowdsourcing return. In addition, this study can provide some reference for the design of incentive mechanism in crowdsourcing tasks and improve the relevant research of crowdsourcing.

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.

When risky decisions are not surprising: An application of prospect theory to the Israeli war decision in 2006

2016 ◽  
Vol 51 (4) ◽  
pp. 484-503 ◽  
Author(s):  
Anat Niv-Solomon
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Loss Aversion ◽  
Prime Minister ◽  
Decision Makers ◽  
Military Operation ◽  
Israeli Government ◽  
Risky Decisions

On 12 July 2006, Hezbollah operatives crossed into Israel and attacked a military patrol, killing three soldiers and kidnapping two more. In retaliation to this incident Israel launched a military operation that resulted in 34 days of fighting between Hezbollah and Israel. The Israeli retaliation has been deemed to be severe and surprising. Furthermore, a public investigation commission established by the Israeli government implicated key decision-makers, and especially Prime Minister Olmert, as guilty of hasty and irresponsible decision-making. This article views this case through the lens of prospect theory, showing how the decision was made at the framing stage, and suggesting that this decision was not hasty but, rather, was consistent with the logic of loss-aversion.

scholarly journals Non-Deterministic Policies in Markovian Decision Processes

2011 ◽  
Vol 40 ◽  
pp. 1-24 ◽  
Author(s):  
M. Milani Fard ◽  
J. Pineau
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Human Subjects ◽  
Sequential Decision ◽  
Current Form ◽  
Web Navigation ◽  
Markovian Processes

Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made to apply methods from reinforcement learning to construct decision support systems for action selection in Markovian environments. Although conventional methods in reinforcement learning have proved to be useful in problems concerning sequential decision-making, they cannot be applied in their current form to decision support systems, such as those in medical domains, as they suggest policies that are often highly prescriptive and leave little room for the user's input. Without the ability to provide flexible guidelines, it is unlikely that these methods can gain ground with users of such systems. This paper introduces the new concept of non-deterministic policies to allow more flexibility in the user's decision-making process, while constraining decisions to remain near optimal solutions. We provide two algorithms to compute non-deterministic policies in discrete domains. We study the output and running time of these method on a set of synthetic and real-world problems. In an experiment with human subjects, we show that humans assisted by hints based on non-deterministic policies outperform both human-only and computer-only agents in a web navigation task.

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