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NewESD-WP-2015-02 A Survey of Methods for Data Inclusion in System Dynamics Models

James Houghton
Research Associate
Sloan School of Management
Massachusetts Institute of Technology
Email: houghton@mit.edu

Michael Siegel
Principal Research Scientist
Sloan School of Management
Massachusetts Institute of Technology
Email: msiegel@mit.edu

Anton Wirsch
Research Assistant
System Design and Management
Massachusetts Institute of Technology
Email: anton@sloan.mit.edu

Allen Moulton
Research Scientist
Sociotechnical Systems Research Center
Massachusetts Institute of Technology
Email: amoulton@mit.edu

Stuart Madnick
John Norris Maguire Professor of Information Technology and Professor of Engineering Systems
MIT Sloan School of Management and MIT School of Engineering
Massachusetts Institute of Technology
Email: smadnick@mit.edu

Daniel Goldsmith
Research Affiliate
Sloan School of Management        
Massachusetts Institute of Technology
Email: goldsmith@mit.edu

In 1980, Jay Forrester enumerated three types of data needed to develop the structure and decision rules in models: numerical, written and mental data, in increasing order of importance. While this prioritization is appropriate, it is numerical data that has experienced the most development in the 25 years since Forester made his enumeration. In this paper, we’ll focus on how numerical data can be incorporated into models when written and mental data are known, and survey the techniques for doing so.

NewESD-WP-2015-01 Homophily and the Speed of Social Mobilization: The Effect of Acquired and Ascribed Traits

Jeff Alstott
Pre-doctoral IRTA
Section on Critical Brain Dynamics, National Institute of Mental Health;
and
PhD Student
Behavioral and Clinical Neuroscience Institute, Departments of Experimental Psychology and Psychiatry
University of Cambridge
Email: jja34@cam.ac.uk

Stuart Madnick
John Norris Maguire Professor of Information Technology and Professor of Engineering Systems
MIT Sloan School of Management and MIT School of Engineering
Massachusetts Institute of Technology
Email: smadnick@mit.edu

Chander Velu
Assistant Professor
Institute for Manufacturing, Department of Engineering
University of Cambridge
Email: c.velu@eng.cam.ac.uk

Large-scale mobilization of individuals across social networks is becoming increasingly prevalent in society. However, little is known about what affects the speed of social mobilization. Here we use a framed field experiment to identify and measure properties of individuals and their relationships that predict mobilization speed. We ran a global social mobilization contest and recorded personal traits of the participants and those they recruited. We studied the effects of ascribed traits (gender, age) and acquired traits (geography, and information source) on the speed of mobilization. We found that homophily, a preference for interacting with other individuals with similar traits, had a mixed role in social mobilization. Homophily was present for acquired traits, in which mobilization speed was faster when the recuiter and recruit had the same trait compared to different traits. In contrast, we did not find support for homophily for the ascribed traits. Instead, those traits had other, non-homophily effects: Females mobilized other females faster than males mobilized other males. Younger recruiters mobilized others faster, and older recruits mobilized slower. Recruits also mobilized faster when they first heard about the contest directly from the contest organization, and decreased in speed when hearing from less personal source types (e.g. family vs. media). These findings show that social mobilization includes dynamics that are unlike other, more passive forms of social activity propagation. These findings suggest relevant factors for engineering social mobilization tasks for increased speed.

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