Prospect theory reflects selective allocation of attention.

Pachur, Thorsten; Schulte‐Mecklenbeck, Michael; Murphy, Ryan O.; Hertwig, Ralph

doi:10.1037/xge0000406

Cited by 108 publications

(135 citation statements)

References 84 publications

(154 reference statements)

Supporting

Mentioning

115

Contrasting

Order By: Relevance

“…Recent evidence indicates aversion or tolerance to loss is associated with mental information accumulation (Clay et al, 2017) and attention paid to losses and gains (Bhatia and Golman, 2015;Yechiam and Hochman, 2013). The clearest evidence for the role of attention is that visual attention to losses correlates modestly (0.31) with the loss aversion parameter inferred from choices (Pachur et al, 2018). In addition, exogenously manipulating attention by visually presenting it for an extra 0.6 seconds increased estimated loss-aversion by about 10%.…”

Section: Discussionmentioning

confidence: 99%

Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)

Chapman¹,

Snowberg²,

Wang³

et al. 2018

View full text Add to dashboard Cite

Wilcox, and the participants of seminars and conferences for their useful comments and suggestions. Judah Okwuobi and Michelle Filiba provided excellent research assistance. Camerer and Snowberg gratefully acknowledge the financial support of NSF Grant SMA-1329195. This paper subsumes the earlier, largely methodological, working paper, "Dynamically Optimized Sequential Experimentation (DOSE) for Estimating Economic Preference Parameters," (2010). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

show abstract

Section: Discussionmentioning

confidence: 99%

Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)

Chapman¹,

Snowberg²,

Wang³

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The dynamic nature of the model and its connection to process tracing procedures like mouse tracking will also surely allow it to be supplemented with eye tracking data. Such an integration would permit us to more directly measure attention and feed it into the price accumulation process (as in the work of Krajbich & Rangel, 2011;Krajbich et al, 2012) as well as evaluate what parts of a gamble, product, or environment are considered in constructing price in the first place (Pachur et al, 2018;Fiedler & Glöckner, 2012;Shi et al, 2013;Vachon & Tremblay, 2014).…”

Section: Common Mechanisms Despite Preference Reversalsmentioning

confidence: 99%

“…As a result, they fail to predict the outcomes of any process measures, such as response times or process tracing data. Pachur et al (2018) sought to remedy this issue by pursuing a link between eye tracking data and the parameters of prospect theory, and took the first steps toward relating the static structure of past models to the dynamic structure of cognitive processes, but stopped short of constructing a generative model of value or price. As we show in this paper, such a model is critical to understanding price because the time allocated to constructing a price response can influence the distribution of prices that is eventually assigned to a choice option.…”

mentioning

confidence: 99%

A distributional and dynamic theory of pricing and preference

Kvam¹,

Busemeyer²

2019

Preprint

View full text Add to dashboard Cite

Theories that describe how people assign prices and make choices are typically based on the idea that both of these responses are derived from a common static, deterministic function used to assign utilities to options. However, preference reversals -- where prices assigned to gambles conflict with preference orders elicited through binary choices -- indicate that the response processes underlying these different methods of evaluation are more intricate. We address this issue by formulating a new computational model that assumes an initial bias or anchor that depends on type of price task (buying, selling, or certainty equivalents) and a stochastic evaluation accumulation process that depends on gamble attributes. To test this new model, we investigated choices and prices for a wide range of gambles and price tasks, including pricing under time pressure. In line with model predictions, we found that price distributions possessed stark skew that depended on the type of price and the attributes of gambles being considered. Prices were also sensitive to time pressure, indicating a dynamic evaluation process underlying price generation. The model out-performed prospect theory in predicting prices, and additionally predicted the associated response times, which no prior model has accomplished. Finally, we show that the model successfully predicts out-of-sample choices and choice response times. This price accumulation model therefore provides a superior account of the distributional and dynamic properties of price, leveraging process-level mechanisms to provide a more complete account the valuation processes common across multiple methods of eliciting preference.

show abstract

“…Information processing in risk-reward environments Pachur et al 2013Pachur et al , 2018Stewart et al 2006). In environments in which probability and payoff information are correlated, however, people may exploit this relationship to simplify processing and ease computational demands.…”

Section: Simplification Hypothesis (H1): Participants In Correlated Rmentioning

confidence: 99%

Do people exploit risk–reward structures to simplify information processing in risky choice?

et al. 2019

Self Cite

View full text Add to dashboard Cite

The high rewards people desire are often unlikely. Here, we investigated whether decision-makers exploit such ecological correlations between risks and rewards to simplify their information processing. In a learning phase, participants were exposed to options in which risks and rewards were negatively correlated, positively correlated, or uncorrelated. In a subsequent risky choice task, where the emphasis was on making either a 'fast' or the 'best' possible choice, participants' eye movements were tracked. The changes in the number, distribution, and direction of eye fixations in 'fast' trials did not differ between the risk-reward conditions. In 'best' trials, however, participants in the negatively correlated condition lowered their evidence threshold, responded faster, and deviated from expected value maximization more than in the other risk-reward conditions. The results underscore how conclusions about people's cognitive processing in risky choice can depend on risk-reward structures, an often neglected environmental property. Keywords Risk-reward • Decisions under risk • Ecological rationality • Eye tracking • Drift-diffusion model JEL Classification D91 • D81 Monetary lotteries have been playing a lead role in studies of how people should and do make decisions under risk (e.g.

show abstract

Prospect theory reflects selective allocation of attention.

Cited by 108 publications

References 84 publications

Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)

Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)

A distributional and dynamic theory of pricing and preference

Do people exploit risk–reward structures to simplify information processing in risky choice?

Contact Info

Product

Resources

About