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Michael Gabbay

Senior Principal Physicist






Dr. Gabbay's current research involves the development of mathematical models and computational simulations of network dynamics, focusing on social and political systems. He has also conducted research in the areas of nonequilibrium pattern formation, coupled oscillator dynamics, sensor development, and data analysis algorithms. His work has appeared in physics, engineering, biology, and political science publications. Dr. Gabbay received a B.S in physics from Cornell University and a Ph.D. in physics from the University of Chicago with a specialization in nonlinear dynamics.


2000-present and while at APL-UW

Frame-induced group polarization in small discussion networks

Gabbay, M., Z. Kelly, J. Reedy, and J. Gastil, "Frame-induced group polarization in small discussion networks," Social Psychol. Q., 81, 248–271, doi:10.1177/0190272518778784, 2018.

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1 Sep 2018

We present a novel explanation for the group polarization effect whereby discussion among like-minded individuals induces shifts toward the extreme. Our theory distinguishes between a quantitative policy under debate and the discussion’s rhetorical frame, such as the likelihood of an outcome. If policy and frame position are mathematically related so that frame position increases more slowly as the policy becomes more extreme, majority formation at the extreme is favored, thereby shifting consensus formation toward the extreme. Additionally, use of a heuristic frame can shift the frame reference point away from the policy reference, yielding differential polarization on opposing policy sides. We present a mathematical model that predicts consensus policy given group member initial preferences and network structure. Our online group discussion experiment manipulated policy side, disagreement level, and network structure. The results, which challenge existing polarization theory, are in qualitative and quantitative accord with our theory and model.

Leadership network structure and influence dynamics

Gabbay, M., "Leadership network structure and influence dynamics," in Handbook of Research Methods in Complexity Science, E. Mitleton-Kelly, A. Paraskevas, and C. Day, eds., 459-478 (Cheltenham, UK: Edward Elgar Publishing, 2018).

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26 Jan 2018

This chapter describes a quantitative methodology for the analysis and modelling of leadership networks which leverages research in complex systems, mainly nonlinear dynamical systems theory and network science. A prototype software package, PORTEND, is introduced implementing the methodology and using data from expert analysts to assess policy and factional outcomes with respect to the internal dynamics of a system of political actors. The methodology includes structural analysis methods, such as algorithms for analysing issue positions and community structure, and a simulation of nonlinear social influence dynamics. PORTEND's capabilities are illustrated for an application to Iran involving fifteen leadership elites and seven issues. The factional structure of the Iranian leadership group is analysed first based on their issue positions, then with respect to the network of inter-actor influence relationships, and finally by a synthesis of the issue and network data. An application of the nonlinear social influence simulation to the nuclear issue is presented and its implications are discussed with respect to Iranian decision-making concerning the 2013–2015 nuclear negotiations. Publication of record: https://www.elgaronline.com/view/9781785364419.00033.xml

Social network analysis in the study of terrorism and insurgency: From organization to politics

Zech, S.T., and M. Gabbay, "Social network analysis in the study of terrorism and insurgency: From organization to politics," Int. Stud. Rev., 18, 214-243, doi:10.1093/isr/viv011, 2016.

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1 Jun 2016

Research using social network analysis to study terrorism and insurgency has increased dramatically following the 9/11 attacks against the United States. This research emphasizes the importance of relational analysis and provides a variety of concepts, theories, and analytical tools to better understand questions related to militant group behavior and outcomes of terrorism and insurgent violence. This paper defines key network concepts, identifies important network metrics, and reviews theoretical and empirical research on network analysis and militant groups. We find that the main focus of existing research is on organizational analysis and its implications for militant group operational processes and performance. Few studies investigate how differences in network structure lead to divergent outcomes with respect to political processes such as militant group infighting, their strategic use of violence, or how politically salient variables affect the evolution of militant cooperative networks. Consequently, we propose a research agenda aimed at using network analysis to investigate the political interactions of militant groups within a single conflict and provide illustrations on how to pursue this agenda. We believe that such research will be of particular value in advancing the understanding of fragmented civil wars and insurgencies consisting of multiple, independent militant groups.

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Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center