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Miech on 'generational forgetting' about drug-use dangers

Impacts of H-1B visas: Lower prices and higher production - or lower wages and higher profits?

MTF data show 10% of 19-20 year-olds report bouts of drinking 10-plus alcoholic beverages

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Call for papers: Conference on computational social science, April 2017, U-M

Sioban Harlow honored with 2017 Sarah Goddard Power Award for commitment to women's health

Post-doc fellowship in computational social science for summer or fall 2017, U-Penn

ICPSR Summer Program scholarships to support training in statistics, quantitative methods, research design, and data analysis

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Next Brown Bag

Mon, Feb 13, 2017, noon:
Daniel Almirall, "Getting SMART about adaptive interventions"

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.