Ph.D. in Quantitative Psychology
BioI am originally from the greater Cincinnati, Ohio area and grew up in a small town called Madeira.
EducationB.S. in Mathematics from THE Ohio State University. M.S. in Statistics and a Ph.D. in Quantitative Psychology from the University of Illinois. Post-doctoral fellowship through the Intelligence Community, working at the College of Information Sciences and Technology at Penn State University.
I study judgment and decision making under uncertainty. I have found that there are many contexts where observations (scientific or intuitive) provide far less information than is realized because of veiled violations of assumptions. My research approach is to formalize this disconnect by recasting such observations as measurements, and using psychometric theory to identify what assumptions are likely to hold, and highlighting the problematic role of violated assumptions for judgment, inference, and choice. Any investigation of judgment and choice under uncertainty will require robust methodology that can account for such disconnects between observation and theory. Failure to account for this problem will lead to inaccurate inferences from observation, for both researchers and DMs.
Theoretical Work on Global-local Incompatibility
Under uncertainty, we gather observations from the environment to generate judgments. Such everyday judgments can fall prey to a disconnect between theory and observation. I have identified contexts where proximal observations are incompatible with the theoretical target of judgment (e.g., attempting to judge global warming, economic stability, etc.). In such contexts, this global-local incompatibility serves to reduce the amount of information in forming a judgment, while simultaneously increasing perceived confidence in this judgment. My research program uses judgment of uncertain scientific results as a test bed for understanding the role of the environment in judgment (Broomell & Kane, 2017). One arm of this research program has investigated judgments of climate change, linking them to personal experiences (Broomell, Budescu, & Por, 2015; Broomell, Winkles, & Kane, 2017). Another arm has also applied this overall approach to investigating perceptions of tornado danger (Dewitt, Fischhoff, Davis, & Broomell, 2015), an uncertain context where DMs tend to ignore official warnings. More recent work in progress (funded by NOAA), is building further on investigating the cues in the environment that generate perceptions of risk in the public, and whether those cues are valid for indicating tornado danger or not. Together, these projects demonstrate the strong role that local perceptions play when attempting to evaluate large scale variables (such as global warming or natural hazards).
Methodological Work on Model Fitting
I also address the disconnect between theory and observation in my methodological work on choice modeling. For example, I have shown that the experimental stimuli used for modeling can cause serious inferential problems, for both model comparison (Broomell, Budescu, & Por, 2011) and parameter estimation (Broomell & Bhatia, 2014). This is summarized in Broomell, Sloman, Blaha, & Chellen (2019).
Broomell, S. B., Sloman, S., Blaha, L. M., & Chelen, J. (2019). Interpreting Model Comparison Requires Understanding Model-Stimulus Relationships. Computational Brain & Behavior.
Dewitt, B., Fischhoff, B., Davis, A. L., Broomell, S. B., Roberts, M., & Hanmer, J. (2019). Exclusion criteria as measurements I: Identifying invalid responses. Medical Decision Making.
Dewitt, B., Fischhoff, B., Davis, A. L., Broomell, S. B., Roberts, M., & Hanmer, J. (2019). Exclusion criteria as measurements II: Effects on utility and health policy implications. Medical Decision Making.
Fischhoff, B. & Broomell, S. B. (In Press). Judgment and Decision Making. Annual Review of Psychology.
Broomell, S. B., Winkles, J.F, & Kane, P. B. (2017). The Perception of Daily Temperatures as Evidence of Climate Change. Weather, Climate, and Society, 9, 563-574.
Broomell, S. B. & Kane, P. B. (2017). Public Perception and Communication of Scientific Uncertainty. Journal of Experimental Psychology: General, 146(2), 286-304.
Broomell, S. B., Budescu, D. V., & Por, H. H. (2015). Personal experience with climate change predicts intentions to act. Global Environmental Change, 32, 67-73. DOI: 10.1016/j.gloenvcha.2015.03.001.
Dewitt, B., Fischhoff, B., Davis, A., & Broomell, S. B. (2015). Environmental risk perception from visual cues: Caution and sensitivity in evaluating tornado risks. Environmental Research Letters, 10(12), 124009.
Broomell, S. B. & Bhatia, S. (2014). Parameter Recovery for Decision Modeling Using Choice Data. Decision, 1, 252-274.
Broomell, S. B., Budescu, D. V., & Por, H. (2011). Pair-wise Comparisons of Multiple Models. Judgment and Decision Making, 6, 821-831.
Broomell, S. B., & Budescu, D. V. (2009). Why are experts correlated? Decomposing correlations between judges. Psychometrika, 74 (3), 531-553.