Prospect evaluation as a function of numeracy and probability denominator

Millroth, Philip; Juslin, Peter

doi:10.1016/j.cognition.2015.01.014

Cited by 20 publications

(16 citation statements)

References 27 publications

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“…This is in line with the results of Patalano et al (2015), who found that people with high numeracy scores provided more consistent certainty equivalents, and those of Pachur et al (2017), who showed that more numerate decision makers exhibited more consistent choice patterns. Our results also conform with previous research demonstrating that more linear PWFs and/or utility functions better describe the behavior of more numerate participants (e.g., Millroth & Juslin, 2015; Pachur et al, 2017; Petrova et al, 2014; Schley & Peters, 2014; Traczyk & Fulawka, 2016).…”

Section: Discussionsupporting

confidence: 91%

“…In judgment and decision‐making research, objective statistical numeracy (Cokely et al, 2012; Lipkus et al, 2001) has been shown to have a unique predictive power independent of levels of fluid intelligence and working memory capacity (Cokely et al, 2012; Ghazal, Cokely, & Garcia‐Retamero, 2014; Traczyk et al, 2018). More numerate decision makers exhibit a higher sensitivity to probability changes than less numerate people (Millroth & Juslin, 2015; Patalano, Saltiel, Machlin, & Barth, 2015; Traczyk & Fulawka, 2016). Additionally, high numeracy seems to be associated with greater affective sensitivity to probabilities (Petrova, Traczyk, & Garcia‐Retamero, 2019; Petrova, van der Pligt, & Garcia‐Retamero, 2014) and adaptive use of affective information (Traczyk et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Consistency in probability processing as a function of affective context and numeracy

Traczyk

Fuławka

Lenda

et al. 2020

Behavioral Decision Making

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Processing information about probabilities is an integral part of decision making under risk. Even when objective probabilities are explicitly provided, people tend to distort them, which is reflected by an inverted S‐shaped probability weighting function. Such distortions depend on different factors such as numeracy and affect. The present study contributes to the understanding of how people use probabilities in risky decision making by introducing the concept of consistency in probability processing―a measure of how coherent people are in using objective probabilities. We hypothesized that consistency would depend on factors similar to those that influence the shape of the probability weighting function. Moreover, we predicted that probability processing consistency would be related to better decision outcomes in an experimental betting task. In three experiments, participants were presented with the probability of a potential gain/loss and had to place a bet on a given chance to maximize their total earnings. We defined probability processing consistency as the variance of bets placed on the same probability value, with higher variance indicating lower consistency. We found that consistency in probability processing was lower in relatively affect‐rich conditions and greater for people with higher numeracy. Additionally, people who exhibited more consistent processing of probabilities gained higher earnings from the experimental task irrespective of whether their betting strategy was optimal and of their risk preference. Our findings imply that consistency in processing probabilities may be an important factor in understanding betting strategies and the quality of decisions.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Consistency in probability processing as a function of affective context and numeracy

Traczyk

Fuławka

Lenda

et al. 2020

Behavioral Decision Making

View full text Add to dashboard Cite

show abstract

“…For example, it has been demonstrated that people with high numeracy are more consistent in their risk preference (Ashby, 2017), more sensitive to variations in EVs (Jasper, Bhattacharya, Levin, Jones, & Bossard, 2013), and less prone to framing effects and the influence of irrelevant information (Johnson & Tubau, 2013; Peters & Levin, 2008). Their pricing of monetary gambles is closer to expected values (Millroth & Juslin, 2015), which is reflected in more linear utility function (Schley & Peters, 2014) and less distorted probability weighting (Patalano, Saltiel, Machlin, & Barth, 2015). Intriguingly, people with high numeracy seem to be flexible in employing choice strategies depending on the task structure and decision environment (Traczyk, Sobkow, et al, 2018).…”

Section: The Role Of Numeracy In Paradigmatic Risk Tasksmentioning

confidence: 99%

Numeracy and Risk Literacy: What Have We Learned so Far?

et al. 2019

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Numerical skills are essential to make informed decisions in our daily life. Unfortunately, many people lack basic numeracy, which limits their ability to accurately interpret risks (i.e., risk literacy). In this paper, we provide an overview of research investigating the role of numeracy in two prominent domains, where most research was concentrated, health and finance. We summarize what has been learned so far in these domains and suggest promising venues for future research. We conclude that it is important to conduct interventions to improve numeracy in less numerate individuals and to help them make informed decisions and achieve better life outcomes.

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“…Several studies have provided evidence for different psychological mechanisms that may explain more accurate judgments and better decisions made by people with high numeracy. For example, such individuals process probabilities (Millroth & Juslin, 2015;Petrova et al, 2014Petrova et al, , 2019Traczyk & Fulawka, 2016) and values (Schley & Peters, 2014) in a more linear and consistent (Traczyk, Fulawka, et al, 2020) way; have a better memory for outcomes and numerical information (Peters & Bjalkebring, 2015;Shoots-Reinhard et al, 2020;Sobkow, Olszewska, et al, 2020); deliberate more and explore a decision problem to a greater extent (Ashby, 2017;Cokely & Kelley, 2009;Jasper et al, 2017;Traczyk, Lenda, et al, 2018), employing their experience for judgments and choices (Traczyk, Lenda, et al, 2018;Traczyk, Sobkow, et al, 2020); use decision strategies adaptively (Jasper et al, 2013;; tend to draw different (generally stronger or more precise) affective meaning from numbers and numerical comparisons (Peters, 2012;Peters et al, 2006). Interestingly, statistical numeracy is positively related to other facets of numerical abilities (Sobkow et al, 2019), such as subjective numeracy/numerical confi dence (Fagerlin et al, 2007) and approximate numeracy (Peters & Bjalkebring, 2015), but it does not mean that it is a unitary construct always predicting decision-related variables in the same direction.…”

Section: Numeracymentioning

confidence: 99%

Sensitivity to interventions and the relationship with numeracy

Dzieżyk¹,

Hetmańczuk²,

Traczyk³

2020

Decyzje

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The main goal of this research was to investigate whether people exhibit algorithm aversion—a tendency to avoid using an imperfect algorithm even if it outperforms human judgments—in the case of estimating students’ percentile scores on a standardized math test. We also explored the relationships between numeracy and algorithm aversion and tested two interventions aimed at reducing algorithm aversion. In two studies, we asked participants to estimate the percentiles of 46 real 15-year-old Polish students on a standardized math test. Participants were offered the opportunity to compare their estimates with the forecasts of an algorithm—a statistical model that predicted real percentile scores based on fi ve explanatory variables (i.e., gender, repeating a class, the number of pages read before the exam, the frequency of playing online games, socioeconomic status). Across two studies, we demonstrated that even though the predictions of the statistical model were closer to students’ percentile scores, participants were less likely to rely on the statistical model predictions in making forecasts. We also found that higher statistical numeracy was related to a higher reluctance to use the algorithm. In Study 2, we introduced two interventions to reduce algorithm aversion. Depending on the experimental condition, participants either received feedback on statistical model predictions or were provided with a detailed description of the statistical model. We found that people, especially those with higher statistical numeracy, avoided using the imperfect algorithm even though it outperformed human judgments. Interestingly, a simple intervention that explained how the statistical model works led to better performance in an estimation task

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Prospect evaluation as a function of numeracy and probability denominator

Cited by 20 publications

References 27 publications

Consistency in probability processing as a function of affective context and numeracy

Consistency in probability processing as a function of affective context and numeracy

Numeracy and Risk Literacy: What Have We Learned so Far?

Sensitivity to interventions and the relationship with numeracy

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