6 citations · 11 across the 4 of their papers we have counts for
5 papers
Knowing what to know: Implications of the choice of prior distribution on the behavior of adaptive design optimization
Sabina J. Sloman, Daniel Cavagnaro, Stephen B. Broomell
Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro, Myung, Pitt, & Kujala, 2010). ADO dynamically identifies stimuli that, in exp…
The Joint Weighted Average (JWA) Operator
Stephen B. Broomell, Christian Wagner
Information aggregation is a vital tool for human and machine decision making in the presence of uncertainty. Traditionally, approaches to aggregation broadly diverge into two cate…
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias
Sabina J. Sloman, Daniel M. Oppenheimer, Stephen B. Broomell +1
Bayesian adaptive experimental design is a form of active learning, which chooses samples to maximize the information they give about uncertain parameters. Prior work has shown tha…
Capturing Richer Information -- On Establishing the Validity of an Interval-Valued Survey Response Mode
Zack Ellerby, Christian Wagner, Stephen Broomell
Obtaining quantitative survey responses that are both accurate and informative is crucial to a wide range of fields. Traditional and ubiquitous response formats such as Likert and…
When is a crowd wise?
Clintin Davis-Stober, David Budescu, Jason Dana +1
Numerous studies and anecdotes demonstrate the "wisdom of the crowd," the surprising accuracy of a group's aggregated judgments. Less is known, however, about the generality of cro…