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11:00 - 12:30
Parallel sessions 2
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11:00 - 12:30
Room: HSZ - N9
Chair/s:
Maren Mayer, Tobias Rebholz
Beyond the more traditional paradigm of advice taking, which is at the center of the symposium “New advances in advice taking research: Cognitive, social, and algorithmic perspectives”, this symposium highlights paradigms and cases in which dependency among individuals is less structured. In five talks, we present and discuss evidence from paradigms featuring dependent judgments and belief updating and highlight how others influence individuals’ judgments and beliefs in various ways.

The first contribution highlights how planned missing data designs can help us measure belief updating when no initial judgment is elicited. The second contribution draws on sequential collaboration, a method for aggregating estimates that does not include initial judgments, and examines whether contributors are influenced by social information about others. The third contribution investigates how framing and repetition of information systematically influence not only subjective truth judgments but also confidence, which in turn has been associated with reduced information seeking. In the fourth contribution, belief updating to scientific hypotheses is compared under different ordering and under either sequential or simultaneous presentation of evidence. Finally, the last contribution examines how trust in science shapes individuals’ belief updating for scientific evidence considering both source expertise and ambiguity of evidence.
Submission 252
The Effect of Explicit Prior Judgment Elicitation on Belief Change
SymposiumTalk-01
Presented by: Mark Himmelstein
Mark Himmelstein 1, David Budescu 2
1 Georgia Institute of Technology, United States
2 Fordham University, United States
In standard judge advisor system (JAS) studies a judge reports a prior belief, receives advice, and then revises their initial estimate, providing a clear and tractable measure of quantitative belief revision. However, what happens if prior beliefs are not elicited? Past research has identified clear differences in posterior beliefs depending on whether priors are elicited or not, implying the mere elicitation of a prior has a treatment-like effect. However, without prior judgments, we are restricted to studying posterior judgments, rather than belief change. We propose a new method that treats judges’ priors as planned missing data and employes imputation techniques to generate estimates of those priors, and thus estimates of how their beliefs change, without ever having to ask them to directly report their priors, thereby circumventing this treatment-like effect. We first use simulation studies to demonstrate the feasibility of our method, then apply it in two advice taking experiments. In a calorie estimation task we show judges are both more willing to consider advice and weigh it more heavily when they aren’t anchored on an explicit prior. However, in a probability forecasting task, neglecting to elicit a prior induced latent confirmation bias, causing detrimental effects on judgment accuracy.