A Parametric Analysis of Choice Under Risk


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A Parametric Analysis of Choice Under Risk, is a well researched Social and Behavioral Sciences Master’s Thesis topic, it is to be used as a guide or framework for your Academic Research.


The study of choice under risk cuts across scientific disciplines. ‘Choice under risk’
means that the probabilities of all relevant outcomes are known quantities that are less than 1.0, and at least one of these outcomes is undesirable to the individual making the decision (Angner, 2016).

Examples of these kinds of choices include drug use, unsafe sex, and gambling. Often in the real world these risky choices do not have precise known probabilities but have been correlated with laboratory measures of choice under risk (Anderson & Mellor, 2008; Lejeuz et al., 2002).

Choice under risk is a central focus of prospect theory, the dominant theoretical
framework in behavioral economics. Prospect theory facilitates prediction of choice under risk by describing how the probability of an outcome relates to its subjective value.

Similarly, ecological studies have identified variables that affect how organisms make decisions under risk. There have been efforts within behavior analysis to extend these findings to humans (e.g., Pietras & Hackenberg, 2001; Pietras, Locey, & Hackenberg, 2003).

This work has significant translational potential. For example, assessing how people make choices under risk may allow health care professionals to make better treatment decisions. (Bowling & Ebrahim, 2001).

There are several assessments that measure propensity to engage in risky behaviors which have been used in clinical settings with promising results (Harrison, Young, Butow, Salkeld, & Soloman, 2005).

Improving our understanding of the factors that underlie risky choice could in turn yield
better clinical assessment tools. Factors that affect risky choice that have been identified in previous research include the magnitude of the outcome (Bornovalova et al. 2009),

whether the payoff is variable or fixed (Bateson & Kacelnik, 1995; Meyer, Schley, & Fantino, 2011; O’Daly, Case, Fantino. 2006), the delay to reinforcement (Bateson & Kacelnik, 1995; O’Daly et al., 2006), the amount of opportunities or time to engage in risky choice (Goldshmidt & Fantino, 2004; Pietras & Hackenberg, 2001; Pietras et al. 2003), level of deprivation (Barnard & Brown, 1984; Caraco, Martindale, & Whittam, 1980), gain versus loss and probability (Kahneman & Tversky, 1979; Kuhberger, 1998; Lattimore, Baker, & Witte, 1992).

The diverse perspectives and expertise  among the behavioral sciences investigating these phenomena are evident from the
methodological heterogeneity that characterizes the field.

Within behavioral economics, hypothetical choice procedures are commonly used to
investigate risky choice. Hypothetical choice experiments assess people’s preferences by asking them what they would do in a proposed scenario.

Hypothetical choices do not result in the actual
consequences of that choice. Hypothetical choice experiments have been used by behavioral economists to discover links between preferences and variables such as probability of an outcome,

framing of the outcome as a gain or loss, and delay of the outcome (Kahneman & Tversky, 1979; Myerson & Green, 1995). Hypothetical choices are limited in their usefulness.

Hypothetical choice assessments are susceptible to endogeneity, when explanatory variables are correlated with the error term of an economic model (Guevara, 2015).

This translates to difficulty in determining the causation of shifts in preferences. Within the context of clinical assessment, the inability to consistently identify the controlling variables of choice under risk limits the clinical utility of an assessment tool.

This issue is overcome simply by using real consequences as part of the clinical assessment of risky choice.


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Master's Thesis


Social and Behavioral Sciences

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