Risky Curves

Friedman, Daniel; Isaac, R. Mark; James, Duncan; Sunder, Shyam

doi:10.4324/9781315819891

Cited by 73 publications

(17 citation statements)

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“…By contrast, our goal is to determine whether decision-making is environment contingent—and, if so, how—for a wide range of lotteries, representing different levels of prospect complexity and choices under risk and uncertainty. Note that there is a related larger literature on whether parameters of the same decision model are consistent across different contexts (see Friedman et al, 2014, for an in-depth discussion); our focus, however, is on decision model heterogeneity across environments rather than parameter heterogeneity (although we allow for the latter in our estimation).…”

Section: Brief Reviews Of Relevant Researchmentioning

confidence: 99%

Variance, skewness and multiple outcomes in described and experienced prospects: Can one descriptive model capture it all?

Spiliopoulos¹,

Hertwig²

2023

Journal of Experimental Psychology: General

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We determined the scope of five decision models of choices across four environmental niches defined by whether outcome probabilities are described (risk) or experienced by sampling (uncertainty) and whether lotteries are simple (one or two outcomes per prospect) or complex (three or four). The majority of participants chose in accordance with cumulative prospect theory only in simple environments involving decisions from description (75%). In complex environments involving decisions from description and experience, however, skewness-preference models were more prevalent (57% and 68%, respectively). Consequently, in niches outside of simple lotteries under risk, rank dependence and nonlinear probability weighting failed to accurately describe the majority of choices. Exploiting elicited subjective beliefs in decisions from experience, we found that experienced (sampled) outcome likelihoods outperformed elicited beliefs in predicting choices and found scant evidence for two-stage models of decisions under uncertainty. Finally, we found statistically significant evidence that 90% of participants chose as if they relied on different models across environments; nonetheless, assuming as if participants used a single model across all environments to predict out-of-sample choice only minimally reduced prediction accuracy. We discuss the implications of model mimicry and task diagnosticity in light of these results in terms of both economic and statistical significance, both for model comparisons and inference.

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Section: Brief Reviews Of Relevant Researchmentioning

confidence: 99%

Variance, skewness and multiple outcomes in described and experienced prospects: Can one descriptive model capture it all?

Spiliopoulos¹,

Hertwig²

2023

Journal of Experimental Psychology: General

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“…For a start, it is unclear what it could mean to be risk seeking in the context of RPH. Indeed, Friedman et al (2014) suggest that it is inconceivable for someone to seek risk in the sense of RPH since risk here just refers to the possibility of undesirable things happening. In order to get the paper off the ground, I have to adopt a different approach.…”

Section: Some Terminologymentioning

confidence: 99%

“…The concern regarding the empirical shortcomings of the energy budget rule and risk-sensitive foraging theory more generally is mirrored by debates about the empirical adequacy of rational choice theory -understood as a descriptive theory of choice -in economics. For instance, Friedman et al (2014) take issue with the notion of risk underlying rational choice theory. They argue not only that the common sense notion of risk as the possibility of harm is prevalent in most decision making contexts but they also hypothesise that the lack of empirical success of rational choice theory, by which they include prospect theory and rank-dependent utility theory, is due to the adoption of the less intuitive idea of risk as a dispersion of outcomes.…”

Section: Empirical Adequacymentioning

confidence: 99%

“…The idea of risk as a dispersion was introduced by Bernoulli (1954) and made popular among modern decision theorists due to the work of von Neumann and Morgenstern (1944). Friedman et al (2014) demonstrate that dispersion measures of risk, such as the standard deviation, and harm-based measures of risk, such as the expected loss, can come apart for a number of prominent probability distributions. That is, standard deviation and related dispersion measures are not closely linked to the more direct measures of harm when assessing economic lotteries.…”

Section: Introductionmentioning

confidence: 99%

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The two faces of risk

Autzen

2023

Biol Philos

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The relationship between decision theory and the theory of natural selection in evolutionary biology offers a fertile ground for philosophical inquiry. A topic that has recently been addressed in the philosophical literature is the connection between decision-theoretic and biological discussions of risk. The paper adds to this literature by drawing attention to a distinction between two different notions of risk originating in the economic literature and by exploring their relationship in a biological context. More specifically, the paper shows that the two notions of risk can part ways in models of risk-sensitive foraging theory. The paper also draws attention to an important difference in contemporary explanations of the apparent lack of empirical success of rational choice theory and risk-sensitive foraging theory.

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“…In the former case, one need merely change criterion by a constant amount in the appropriate direction whenever any error is made; in the latter, one needs to calibrate those adjustments to the value of P , a more error—prone and contingent operation. Indeed, Friedman and associates said of such kinds of computations: “Expected Utility Theory and its many generalizations have not yet passed this simple test [out of sample predictions] in either controlled laboratory or field settings” (Friedman, Isaac, James, & Sunder, 2014, p. 7). A final factor in these considerations is that the differential effect of changes in one of these variables is modulated by the value of the other: d b /d O = P , and d b /d P = O .…”

Section: Correcting Errorsmentioning

confidence: 99%

Subjects adjust criterion on errors in perceptual decision tasks.

Killeen¹,

Taylor²,

Treviño³

2018

Psychological Review

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The optimal strategy in detection theory is to partition the decision axis at a criterion C, labeling all events that score above C "Signal", and all those that fall below "Noise." The optimal position of C, C*, depends on signal probability and payoffs. If observers place their criterion at some place other than C*, they suffer a loss in the Expected Value (EV) of payoffs over the course of many decisions. We provide an explicit equation for the degree of loss, where it is shown that the falloff in value will be steep in contexts of good discrimination and will be a flatter gradient in contexts of poor discrimination. It is these gradients of loss in EV that, in theory, drive C toward C*, strongly when discrimination is good, weakly when discrimination is poor. When signal probabilities or distributions variances are unequal, the basins of attraction are asymmetric, so that dynamic adjustments in C will be asymmetric, and thus, as we show, will leave it biased. We address our analysis to acquisition speed, response variability, discrimination reversal and other aspects of discriminated performance. In the final section, we develop an error correction model that predicts empirically observed deviations from C* that are inconsistent with the standard model, but follow from the proposed model given knowledge of d'. (PsycINFO Database Record

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Risky Curves

Cited by 73 publications

References 0 publications

Variance, skewness and multiple outcomes in described and experienced prospects: Can one descriptive model capture it all?

Variance, skewness and multiple outcomes in described and experienced prospects: Can one descriptive model capture it all?

The two faces of risk

Subjects adjust criterion on errors in perceptual decision tasks.

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