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Frey's Scenario F simulation mentioned in account of the Democratic Party's tribulations

U-M Poverty Solutions funds nine projects

Dynarski says NY's Excelsior Scholarship Program could crowd out low-income and minority students

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Workshops on EndNote, NIH reporting, and publication altmetrics, Jan 26 through Feb 7, ISR

2017 PAA Annual Meeting, April 27-29, Chicago

NIH funding opportunity: Etiology of Health Disparities and Health Advantages among Immigrant Populations (R01 and R21), open Jan 2017

Russell Sage 2017 Summer Institute in Computational Social Science, June 18-July 1. Application deadline Feb 17.

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Next Brown Bag

Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer

Screen shot of complex data analysis

New methods for modeling decision making in social science

8/1/2014 feature story

Elizabeth Bruch and colleagues develop a framework for linking marketing statistical models to agent-based models of decision making, and apply the statistical framework to decision-making phenomena of interest in population dynamics.

More Information.

Elizabeth Eve Bruch

Project Information:

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.

Elizabeth Eve Bruch, Fred M. Feinberg

Feature Archive.