There are three interrelated components of our research program:
1. Behavior: The first involves an analysis of mathematical and computational theories of human behavior. We study the relationships between theories, the effects of noise on their predictions, and the difficulties inherent in recovering model parameters from choice data. By doing so, we hope to provide cohesive insights regarding the fundamental properties of choice behavior, and shed light on the ways in which theories of choice behavior can be appropriately tested and compared. E.g. Bhatia & Loomes (2017), He et al. (2019), He et al. (in press), He et al. (in press).
2. Process: The second component of our research program involves specifying the cognitive processes at play in behavior. We extend research on perceptual decision making, attention, and memory retrieval, to model information sampling and aggregation in domains such as multiattribute choice, risky choice, and probability judgment. We try to explain observed behavioral patterns as outcomes of the computational operations implemented by existing models, and test our explanations using process data on response time, recall, and attention. E.g. Bhatia (2013), Bhatia & Mullett (2016), Golman et al. (2020), Aka & Bhatia (in press), Zhao et al. (in press).
3. Representation: The final component of our research program involves specifying the mental representations that are sampled and aggregated as people make judgments and decisions. Here we apply methods and datasets from semantic memory research, computational linguistics, and artificial intelligence. By specifying both what people know about the world, and how they use this knowledge to form preferences and beliefs, we hope to build models that can deliberate over and respond to a large variety of everyday decision problems in a human-like manner. E.g. Bhatia (2017), Bhatia (2019), Bhatia et al. (2019), Bhatia & Richie (in press).