Cognitively Plausible Models of Decision Making
The statistical models used in quantitative social science and public health research are rarely plausible models of the underlying behavior or decision-making process that gave rise to the social phenomenon under investigation. Researchers in business and marketing use much more sophisticated statistical models of how people navigate their environment and make decisions, which draw on insights from cognitive science and decision theory. But these methods have never been applied in population health to decision modeling, and they are much more difficult to master than techniques currently in use. An initial investigation into marketing statistical methods found no standard statistical software, but rather programs usually written from scratch; and no single model or methodological approach, but rather a loose “toolkit” of techniques or strategies customized for specific applications. In addition, models often rely on Bayesian estimation techniques, which require significant expertise outside of standardized software packages.
My aims in this project are to: (1) master the statistical skills involved in estimating these models, and gain a formal understanding of the underlying theories of decision making; (2) adapt the marketing statistical models to new substantive applications; (3) develop a methodological framework for linking these “cognitively plausible” models of individual decision-making with agent-based models to understand the implications of decision strategies for aggregate population dynamics; and (4) explore how this statistical framework may be applied to a broader range of decision-making applications relevant to health research. My analysis will apply the choice-modeling framework in two areas of research: mate choice (as observed on an online dating website) and neighborhood choice.
Eunice Kennedy Shriver National Institute of Child Health and Human Development
(1 K01 HD 079554 01)
Funding Period: 6/1/2014 to 10/31/2019