Risk Attitude in Decision Making: In Search of Trait‐Like Constructs

Yechiam, Eldad; Ert, Eyal

doi:10.1111/j.1756-8765.2010.01126.x

Cited by 36 publications

(39 citation statements)

References 44 publications

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“…Despite the seeming ubiquity of loss aversion, the literature suggests that decisions from experience differ strongly from decisions from description [Hertwig et al, 2004]. Moreover, Yechiam and Ert [2011] found only moderate consistency within subjects across different tasks and between description-based problems presented in different domains. We propose that loss aversion might also depend on the decision maker's goal orientation.…”

Section: Gains and Losses In Decision Making And Agingmentioning

confidence: 93%

“…Developmental expectations regarding gains and losses in different domains may underlie such domain-related differences [Mustafic & Freund, 2011]. Thus, the sensitivity to and weighing of gains and losses does not represent a domain-general trait but rather the interaction between the situation (e.g., availability of resources) and the decision maker (e.g., gain or loss orientation) [Figner & Weber, 2011;Yechiam & Ert, 2011]. Accordingly, the investigation of different decision domains that systematically vary along dimensions, such as the availability of resources, seems particularly interesting for further understanding under what conditions people are more likely to show loss aversion.…”

Section: Domain-related Differencesmentioning

confidence: 99%

“…Asymmetries in gainloss processing in decision making are well accepted: loss aversion, for example, has been called 'the most successful and widely used explanatory construct in behavioral decision research' [Brenner, Rottenstreich, Sood, & Bilgin, 2007, p. 369; for critical perspectives on when loss aversion occurs, see Hertwig, Barron, Weber, & Erev, 2004;Yechiam & Ert, 2011]. Loss aversion denotes a higher impact of losses on a choice than do equivalent gains.…”

Section: Gains and Losses In Decision Making And Agingmentioning

confidence: 99%

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Normal Aging and Decision Making: The Role of Motivation

Depping

Freund²

2011

Human Development

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The main argument of this review is that motivational development associated with normal aging affects decision making. With increasing age, the ratio of gains to losses becomes more and more unfavorable. Reflecting the increasing losses in resources, goal orientation changes from a predominant orientation towards gains in young adulthood to an increasingly stronger orientation towards the prevention of loss in older adulthood. As goals serve as reference points for the evaluation of decision outcomes, this change in goal orientation across adulthood might also affect decision making. The decision-making literature has recognized that choices are influenced by goal orientation. However, little research has been conducted on how goals influence the decision-making process in general and with regard to aging in particular. To date, findings on decision making and aging remain inconsistent and are in need of a developmental framework. With regard to applications, a better understanding of the aging decision maker can provide insight into how to improve communication efforts about issues like advance care planning, medical treatment, and housing options.

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Section: Gains and Losses In Decision Making And Agingmentioning

confidence: 93%

Section: Domain-related Differencesmentioning

confidence: 99%

Section: Gains and Losses In Decision Making And Agingmentioning

confidence: 99%

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Normal Aging and Decision Making: The Role of Motivation

Depping

Freund²

2011

Human Development

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“…For example, Yechiam and Ert (2011) Students were asked to treat the payoffs as representing outcomes in Israeli shekels (1 shekel = $0.24), which implies a much lower risk level than in Rabin and Weizsäcker's study (2009). In this case, the participants did not avoid the risky outcome that contained symmetric gains and losses, implying no loss aversion.…”

Section: Experiential Tasksmentioning

confidence: 99%

Losses as modulators of attention: Review and analysis of the unique effects of losses over gains.

Yechiam¹,

Hochman²

2013

Psychological Bulletin

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290

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It has been shown that in certain situations losses exert a stronger effect on behavior than respective gains, and this has been commonly explained by the argument that losses are given more weight in people's decisions than respective gains. However, although much is understood about the effect of losses on cognitive processes and behavior, 2 major inconsistencies remain. First, recent empirical evidence fails to demonstrate that people avoid incentive structures that carry equivalent gains and losses. Second, findings in experience-based decision tasks indicate that following losses, increased arousal is observed simultaneously with no behavioral loss aversion. To account for these findings, we developed an attention-allocation model as a comprehensive framework for the effect of losses. According to this model losses increase on-task attention, thereby enhancing the sensitivity to the reinforcement structure. In the current article we examine whether this model can account for a broad range of empirical phenomena involving losses. We show that as predicted by the attentional model, asymmetric effects of losses on behavior emerge where gains and losses are presented separately but not concurrently. Yet, even in the absence of loss aversion, losses have distinct effects on performance, arousal, frontal cortical activation, and behavioral consistency. The attentional model of losses thus explains some of the main inconsistencies in previous studies of the effect of losses.

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“…We focused on risky choice, because multiparameter models are highly popular in this domain and because it is often assumed that people's responses in risky choice tasks reflect stable individual preferences (Yechiam & Ert, 2011), making it important to know how these potential invariants can best be captured. We compare the results derived from hierarchical and nonhierarchical parameter estimation in terms of two important aspects of model generalizability: (a) the stability of the model parameters-that is, the extent to which estimates remain invariant across time-and (b) the models' predictive accuracy-that is, their ability to predict new data (i.e., data that were not used to inform the parameter estimates).…”

mentioning

confidence: 99%

Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

2014

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To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers' estimates for individuals within a group can mutually inform each other. Here, we examine Bayesian hierarchical approaches to evaluating model generalizability in the context of two prominent models of risky choicecumulative prospect theory (Tversky & Kahneman, 1992) and the transfer-of-attention-exchange model (Birnbaum & Chavez, 1997). Using empirical data of risky choices collected for each individual at two time points, we compared the use of hierarchical versus independent, nonhierarchical Bayesian estimation techniques to assess two aspects of model generalizability: parameter stability (across time) and predictive accuracy. The relative performance of hierarchical versus independent estimation varied across the different measures of generalizability. The hierarchical approach improved parameter stability (in terms of a lower absolute discrepancy of parameter values across time) and predictive accuracy (in terms of deviance; i.e., likelihood). With respect to test-retest correlations and posterior predictive accuracy, however, the hierarchical approach did not outperform the independent approach. Further analyses suggested that this was due to strong correlations between some parameters within both models. Such intercorrelations make it difficult to identify and interpret single parameters and can induce high degrees of shrinkage in hierarchical models. Similar findings may also occur in the context of other cognitive models of choice.

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Risk Attitude in Decision Making: In Search of Trait‐Like Constructs

Cited by 36 publications

References 44 publications

Normal Aging and Decision Making: The Role of Motivation

Normal Aging and Decision Making: The Role of Motivation

Losses as modulators of attention: Review and analysis of the unique effects of losses over gains.

Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

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