Multi-alternative, Multi-attribute Decision-making
When decision-makers are faced with a choice among multiple options that have several attributes, preferences are often influenced by how the options are related to one another. This is a salient problem in marketing where consumer preferences can be influenced and even reversed by the context defined by available products. For example, a car that appears to be a compromise between one that has great reliability and another that is very affordable might be selected more often than the two extremes. We are interested in understanding (1) how context affects choice behavior and (2) what guides the underlying dynamics of multi-alternative decision processes.
Dynamically Changing Information
When we make decisions, we are often faced with complex, changing information. Any reasonable decision-making process should be able to adjust to and integrate new information, yet little is known about how this is done. Most past decision research has been devoted to understanding stationary decisions where a choice is made on the basis of fixed, unchanging information. We are interested in understanding the impact of non-stationary information on decision processes. This research addresses the following key questions: (1) How quickly do people adapt to new information? (2) How is new information integrated into the decision process? (3) Are there commonalities in how decision processes respond to changed information across different paradigms (e.g., perceptual versus consumer choice, risky versus riskless choice)? 4) Or are adaptation and integration of new information domain specific?
In collaboration with the Department of Pathology, Microbiology, Immunology at Vanderbilt University Medical Center, we investigate the cognitive processes involved in diagnostic decision-making. Diagnosis in pathology relies on expert analysis of images to detect abnormalities. While the exact rate of diagnostic errors is unknown, consistent evidence suggests error rates are >10%. It is thus critical that we understand how people make decisions based on visual information derived from medical images in order to improve training and minimize the occurrence of misdiagnoses.
The study of how people infer the probability of unknown events is an important concern in judgment research. Subjective probability judgments guide our everyday lives from a doctor judging the likelihood of a disease to a financial analyst judging the future behavior of the stock market. Our aim is to understand how people make inference judgments and why these judgments often deviate from the normative rules of classic probability theory. To this end, we use formal models based on quantum probability theory to construct and test theories of judgment. Quantum probability theory relaxes some of the axioms or assumptions of standard probability theory in order to account for violations of the latter.