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Xu et al find lower cognition at midlife for adults born during China's 1959-61 famine

UM's Wolfers on separating deep expertise from partisanship in analyses of economic condtions

Findings by Burgard, Kalousova, and Seefeldt on the mental health impact of job insecurity

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Apply by Jan 8 for NIA-supported PSC post-doc fellowship, to begin Sept 1, 2018

On Giving Blue Day, help support the next generation through the PSC Alumni Grad Student Support Fund or ISR's Next Gen Fund

Bailey et al. find higher income among children whose parents had access to federal family planning programs in the 1960s and 70s

U-M's campus climate survey results discussed in CHE story

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Next Brown Bag

Mon, Jan 22, 2018, noon: Narayan Sastry

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

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