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• W2284660070 abstract "If we are to enact better policy, fight crime anddecrease poverty, we will need better computational models of howsociety works. In order to make computational social science auseful reality, we will need generative models of how socialinfluence sprouts at the interpersonal level and how it leads toemergent social behavior. In this thesis, I take steps atunderstanding the predictors and conduits of social influence byanalyzing real-life data, and I use the findings to create ahigh-accuracy prediction model of individuals' future behavior. Thefunf dataset which comprises detailed high-frequency data gatheredfrom 25 mobile phone-based signals from 130 people over a period of15 months, will be used to test the hypothesis that people whointeract more with each other have a greater ability to influenceeach other. Various metrics of interaction will be investigatedsuch as self-reported friendships, call and SMS logs and Bluetoothco-location signals. The Burt Network Constraint of each pair ofparticipants is calculated as a measure of not only the directinteraction between two participants but also the indirectfriendships through intermediate neighbors that form closed triadswith both the participants being assessed. To measure influence,the results of the live funf intervention will be used wherebehavior change of each participant to be more physically activewas rewarded, with the reward being calculated live. There werethree variants of the reward structure: one where each participantwas rewarded for her own behavior change without seeing that ofanybody else (the control), one where each participant was pairedup with two 'buddies' whose behavior change she could see live butshe was still rewarded based on her own behavior, and one whereeach participant who was paired with two others was paid based ontheir behavior change that she could see live. As a metric forsocial influence, it will be considered how the change in slope andaverage physical activity levels of one person follows the changein slope and average physical activity levels of the buddy who sawher data and/or was rewarded based on her performance. Finally, alinear regression model that uses the various types of directionand indirect network interactions will be created to predict thebehavior change of one participant based on her closeness with herbuddy. In addition to explaining and demonstrating the causes ofsocial influence with unprecedented detail using network analysisand machine learning, I will discuss the larger topic of using sucha technology-driven approach to changing behavior instead of thetraditional policy-driven approach. The advantages of thetechnology-driven approach will be highlighted and the potentialpolitical-economic pitfalls of implementing such a novel approachwill also be addressed. Since technology-driven approaches tochanging individual behavior can have serious negative consequencesfor democracy and the free-market, I will introduce a noveldimension to the discussion of how to protect…" @default.
• W2284660070 created "2016-06-24" @default.
• W2284660070 creator A5022017615 @default.
• W2284660070 date "2013-01-01" @default.
• W2284660070 modified "2023-09-28" @default.
• W2284660070 title "Understanding social influence using network analysis and machine learning" @default.
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