More Like this

1. Quantifying Confidence

   https://doi.org/10.3982/ECTA13079  

   Angeletos, George-Marios; Collard, Fabrice; Dellas, Harris (January 2018, Econometrica)
2. Influencing Confidence: Testing Ways to Increase or Decrease Confidence in Knowledge

   https://doi.org/10.1002/bdm.70048  

   Stone, Eric R; Parker, Andrew M; Somerville, Annie H; Nixon, Brooke; Kemmerly, Rowan; Bongard, Michelle M (December 2025, Journal of Behavioral Decision Making)

   ABSTRACT This paper examines approaches for influencing people's confidence in their knowledge without influencing knowledge. Three studies examined the relative effectiveness of training and false feedback approaches. Participants chose which of two IKEA products they thought was more expensive and indicated their confidence in that judgment for 50 product pairs. In Study 1, participants took part in one of five conditions designed to manipulate their confidence: false feedback‐increasing, false feedback‐decreasing, training‐increasing, training‐decreasing, or control. For false feedback, we told participants they did very well or poorly on the task. For training‐increasing, we gave participants information about IKEA pricing that appeared useful but was difficult to implement. For training‐decreasing, we developed an automated calibration training technique that provided personalized calibration feedback consisting of a calibration diagram accompanied by textual summary information and advice. Neither the false feedback nor training approach increased confidence on 50 subsequent knowledge‐confidence judgments. However, both manipulations designed to reduce confidence were successful, with a substantially larger effect in the calibration training condition. In Study 2, we adapted the calibration training approach to provide false feedback indicating participants were either underconfident or overconfident. Both the original calibration training pproach and the new false feedback approach indicating overconfidence reduced confidence, and the false feedback approach indicating underconfidence increased confidence. Study 3 tested the effectiveness of this new false feedback approach on an on‐line rather than student sample, finding essentially the same results as those in Study 2. Throughout the three studies, the effects of the manipulations extended to overconfidence, overall calibration, and the Brier score. The results provide a potential tool for research and practice regarding confidence in knowledge. 

   more » « less
3. Approximate Confidence Distribution Computing

   https://doi.org/10.51387/23-NEJSDS38  

   Thornton, Suzanne; Li, Wentao; Xie, Minge (January 2023, The New England Journal of Statistics in Data Science)

   Approximate confidence distribution computing (ACDC) offers a new take on the rapidly developing field of likelihood-free inference from within a frequentist framework. The appeal of this computational method for statistical inference hinges upon the concept of a confidence distribution, a special type of estimator which is defined with respect to the repeated sampling principle. An ACDC method provides frequentist validation for computational inference in problems with unknown or intractable likelihoods. The main theoretical contribution of this work is the identification of a matching condition necessary for frequentist validity of inference from this method. In addition to providing an example of how a modern understanding of confidence distribution theory can be used to connect Bayesian and frequentist inferential paradigms, we present a case to expand the current scope of so-called approximate Bayesian inference to include non-Bayesian inference by targeting a confidence distribution rather than a posterior. The main practical contribution of this work is the development of a data-driven approach to drive ACDC in both Bayesian or frequentist contexts. The ACDC algorithm is data-driven by the selection of a data-dependent proposal function, the structure of which is quite general and adaptable to many settings. We explore three numerical examples that both verify the theoretical arguments in the development of ACDC and suggest instances in which ACDC outperform approximate Bayesian computing methods computationally. 

   more » « less
4. CONFNET: PREDICT WITH CONFIDENCE

   Sheng Wan, Tung-Yu Wu (April 2018, IEEE International Conference on Acoustics, Speech and Signal Processing)
5. Confidence intervals for seroprevalence

   https://doi.org/https://doi.org/10.1093/restud/rdab020  

   DiCiccio, T; Ritzwoller, D; Romano, J; Shaikh, A. (January 2022, Statistical science)