DOCSLIB.ORG

An Experimental Study of Homophily in the Adoption of Health Behavior

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Experimental Study of Homophily in the Adoption of Health Behavior REPORTS Acknowledgments: We thank the Deutsche Forschungsgemein- with the graphics, and A. Holzapfel for editorial work Figs. S1 to S5 schaft (grants WE2039/8-1, RI525/17-1, KU683/9-1, and (all from Univ. of Cologne). Tables S1 and S2 KU683/12-1) and the U.S. NSF Paleoclimate Program for References financial support, the Alfred Wegener Institute for Polar and Supporting Online Material Marine Research for logistic support, J. Rethemeyer for www.sciencemag.org/cgi/content/full/334/6060/1265/DC1 conducting part of the AMS 14C measurements, B. Weninger Materials and Methods 3 June 2011; accepted 18 October 2011 for discussing the 14C data, D. Sprenk for assisting SOM Text 10.1126/science.1209299 obese individuals both receiving less exposure An Experimental Study of Homophily to valuable health innovations and having fewer sources of positive social influence, resulting in in the Adoption of Health Behavior lower levels of adoption (8, 9, 16, 18). By con- trast, populations in which obese individuals are Damon Centola better socially connected to healthier individuals should provide greater access to health innova- How does the composition of a population affect the adoption of health behaviors and innovations? tions (5, 6, 11, 20) and more social support for Homophily—similarity of social contacts—can increase dyadic-level influence, but it can also force less adopting new behaviors (7, 8, 16, 21), thereby healthy individuals to interact primarily with one another, thereby excluding them from interactions increasing both overall levels of adoption and the with healthier, more influential, early adopters. As a result, an important network-level effect of use of health innovations among the obese homophilyisthatthepeoplewhoaremostinneed of a health innovation may be among the least population (8, 9, 21, 22). likely to adopt it. Despite the importance of this thesis, confounding factors in observational data have An empirical test of these individual and made it difficult to test empirically. We report results from a controlled experimental study on the network-level effects of homophily has proven spread of a health innovation through fixed social networks in which the level of homophily was difficult because homophily in observational data independently varied. We found that homophily significantly increased overall adoption of a new health is usually confounded with other relevant factors behavior, especially among those most in need of it. such as the topological structure of social net- works (5, 11, 23), interpersonal affect in relation- ocial networks are a primary channel for alters than by homophilous ties to similarly low- ships (12, 24), and shared history and frequency the spread of health behaviors (1–3). How- status individuals (12, 15). of interaction among connected individuals Sever, it is not just the existence of social Although these accounts of homophily may (11, 12, 24). Moreover, individual-level factors ties between individuals that matters for diffu- be in tension with one another at the dyadic level, can be difficult to distinguish from relational sion. Just as important are the demographic com- at the network level both views support the the- ones: Are obese individuals simply less likely position of the population and the distribution of sis that homophily will reduce overall adoption, to adopt a health behavior, or is it their social individual characteristics throughout the social thereby increasing health inequalities across di- environment that reduces their likelihood of network (4–6). Homophily—the tendency of so- verse populations (12–14, 16). This network- adoption (25, 26)? Addressing these difficulties cial contacts to be similar to one another—can level effect emerges from the fact that homophily requires the ability to independently control the affect the extent of a behavior’s adoption in a can result in less healthy individuals having fewer degree of homophily in a social network while population (7–12). At the dyadic level, research social ties to healthier early adopters, which lim- simultaneously holding the topology of the net- on diffusion has suggested that homophilous ties its their level of exposure to health innovations work, and the distribution of individual- and can promote the spread of behavior between (3, 7, 11, 12). Moreover, fewer ties to healthy indi- population-level parameters, constant. individuals (11–13). This is because actors are viduals also means that the exposure that the less We addressed these issues by studying the more likely to be influenced by alters who are healthy individuals do receive is less likely to come effects of homophily on adoption experimen- similar to themselves. However, research on so- from healthier members of the population—who tally. We conducted an Internet-based social net- cial influence has also suggested that the effects may be more effective at influencing others to work experiment (27) in which we manipulated of status can interact with those of homophily adopt new behaviors (7–11)—thereby reducing the the level of homophily in people’ssocialnet- (12, 14, 15). Homophily among high-status likelihood of adoption among those less healthy works. Our study focused on the spread of a individuals may help to promote diffusion, but individuals who are ultimately exposed to the in- health behavior through a World Wide Web social low-status individuals may be more likely to be novation (15, 17). networking environment composed of 710 par- influenced by heterophilous ties to high-status This network thesis has important implica- ticipants, all of whom were recruited from an tions for obese members of a population because online fitness program (28). We constructed two Sloan School, Massachusetts Institute of Technology, the homophilous “clustering” of health character- types of online social networking communities: Cambridge, MA 02142, USA. E-mail: [email protected] istics in social networks (1, 3, 18, 19) can result in (i) homophilously structured populations, in which AB C DE 6 6 6 6 6 5 5 5 5 5 4 4 4 4 4 3 3 3 3 3 2 2 2 2 2 1 1 1 1 1 0 0 0 0 0 Number of Adopters Number of 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Weeks Fig. 1. (A to E) Time series showing the number of adopters in each of the five trials. Adoption levels are shown for all homophilous (solid circles) and unstructured (open triangles) networks. www.sciencemag.org SCIENCE VOL 334 2 DECEMBER 2011 1269 REPORTS individual traits [gender, age, and body mass in- the notification allowed these neighbors to sign of adopting it, Fig. 2 compares adoption levels dex (BMI)] were “clustered” in the social network up. If they completed the sign-up procedure, this among the “obese” (i.e., BMI ≥ 30) and “non- (28, 29), and (ii) unstructured populations, which would result in each of the adopters’ neighbors obese” (BMI < 30) members of the population. had fully “integrated” neighborhoods in which also receiving notifications about the diet diary. Within the homophilous condition, a significantly participants were mixed at random regardless of At the beginning of week 1, we simultaneous- greater number of non-obese than obese individ- their individual characteristics. ly activated seed nodes in each of the 10 networks. uals adopted the behavior (P < 0.05, Mann-Whitney Each participant in the study created an anon- We then observed the spread of adoption for a U test) (Fig. 2A). This is expected because of the ymous online profile, which included the par- total of 7 weeks. high ratio of non-obese to obese individuals in ticipant’s gender, age, BMI, fitness level, diet The results (Fig. 1) show that homophilously the population (6:1) (28) (table S1). However, the preferences, and favorite exercise (28). Subjects structured social networks exhibited significantly fraction of obese adopters was significantly greater were then matched with other participants in more adoption than unstructured networks. De- than the fraction of non-obese adopters (P <0.05) the study—referred to as “health buddies”—as spite low overall levels of adoption, by the third (Fig. 2B), indicating that relative to their popula- members of an online health community. Each week of the study there was already a noticeable tion sizes, homophilous networks promoted greater participant was provided with a personalized difference between conditions: In each of the five uptake of the behavior among obese individuals online “health dashboard” that displayed real-time trials, there were greater numbers of adopters in than among non-obese individuals. By contrast, health information (e.g., daily exercise minutes) the homophilous condition than in the unstruc- in the unstructured condition, both the number as well as basic profile information (as listed tured condition. By week 4, adoption levels were (Fig. 2A) and the fraction (Fig. 2B) of non-obese above) for each participant and his or her health significantly different across experimental con- adopters was significantly greater than that of buddies (28). The health dashboards also dis- ditions (P < 0.05, Mann-Whitney U test). This obese adopters (P < 0.05). Not one obese in- played a record of any health behaviors that the difference increased in week 5 (P < 0.01) and dividual signed up for the diet diary in the un- participants and their health buddies adopted. maintained this level of significance in weeks 6 structured networks, which suggests that obese Participants arriving to the study were random- and 7. At the end of week 7, the total number of members of the population were very reluctant ized to one of the two experimental conditions—a adopters across all homophilous networks was to adopt the behavior. homophilous population condition and an unstruc- more than 3 times the total number of adopters The comparison across conditions shows that tured population condition—that were distinguished in the unstructured networks.
Load more

Recommended publications
  • The Truth About Behavioral Change
    The Truth About Behavioral Change The latest thinking on social networks explains why new technologies and innovative behaviors really spread. It’s not about “going viral.” Damon Centola Reprint #60206 https://mitsmr.com/2Owt7rS MIT SLOAN MANAGEMENT REVIEW INNOVATION The Truth About Behavioral Change DAMON CENTOLA The latest thinking on social networks explains why new technologies and innovative behaviors really spread. It’s not about “going viral.” So, what happened to transform it from another also-ran into one of the largest communication platforms in the world? Twitter seems on the surface to be the kind of technology that journalist Malcolm Gladwell and Wharton School marketing professor Jonah Berger refer to as “contagious.” 1 To jump-start Twitter’s growth, its founders decided to promote it at a South by Southwest (SXSW) Interactive conference in 2007, where it was a big hit. From there, people assume it rapidly spread across the United States through the internet, thanks to social contacts connected by what network researchers call “weak ties” and “long bridges.” 2 Two years later, in 2009, Twitter adoptions were catapulted into a global orbit when a major opinion leader, Oprah Winfrey, sent her first tweet on her talk show. When Twitter launched in March 2006, the earth did not move. Its founders and a few early funders were excited That narrative is easy to grasp and compelling. It gives about the technology, but the microblogging site was not startups, and the people who invest in them, a road map the immediate blockbuster you might imagine it was, for success.
    [Show full text]
  • Queerying Homophily 2018
    Repositorium für die Medienwissenschaft Wendy Hui Kyong Chun Queerying Homophily 2018 https://doi.org/10.25969/mediarep/12350 Veröffentlichungsversion / published version Sammelbandbeitrag / collection article Empfohlene Zitierung / Suggested Citation: Chun, Wendy Hui Kyong: Queerying Homophily. In: Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer u.a. (Hg.): Pattern Discrimination. Lüneburg: meson press 2018, S. 59–97. DOI: https://doi.org/10.25969/mediarep/12350. Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer Creative Commons - This document is made available under a creative commons - Namensnennung - Nicht kommerziell 4.0 Lizenz zur Verfügung Attribution - Non Commercial 4.0 License. For more information gestellt. Nähere Auskünfte zu dieser Lizenz finden Sie hier: see: https://creativecommons.org/licenses/by-nc/4.0 https://creativecommons.org/licenses/by-nc/4.0 [ 3 ] Queerying Homophily Wendy Hui Kyong Chun To recap, in Pattern Discrimination: 1. YOU is always singular plural: • Recognition is never at the level of the individual • You = YOUS value 2. Machines engage in deep dreaming, creating patterns from noise. • Crab in = crap out • As with the gibbering muses, interpretation and herme- neutics enter through pattern discrimination, but now through the “back door” • We live in mythic times, but without knowing we do 3. The singularity of the market = the crapularity of the world: • the dumbing down of humans • the integration of subjectivity into information technologies • the reality of paranoia 60 4. To come out, we have to come in: • we are inside when we think we are outside. • Open societies need enemies to be “open” This chapter continues these points by examining homophily—the axiom that similarity breeds connection—which grounds contem- porary network science.
    [Show full text]
  • ROBB WILLER Department of Sociology 450 Serra Mall, Bldg
    ROBB WILLER Department of Sociology 450 Serra Mall, Bldg. 120 Stanford, CA 94305 [email protected] ________________________________________________________________________ ACADEMIC POSITIONS Professor, Departments of Sociology, Psychology (by courtesy), and Graduate School of Business (by courtesy), Stanford University, 2015-present Associate Professor, Departments of Sociology, Psychology (by courtesy), and Graduate School of Business (by courtesy), Stanford University, 2013-215 Fellow, Center for Advanced Study in the Behavioral Sciences, Stanford University, 2012-13. Associate Professor, Departments of Sociology and Psychology (by courtesy), University of California, Berkeley, 2012-2013. Visiting Professor, Department of Economic and Social Psychology, University of Cologne, Germany, 2011. Director, Laboratory for Social Research, University of California, Berkeley, 2006-2013. Assistant Professor, Departments of Sociology, Psychology (by courtesy), and Cognitive Science (affiliated faculty), University of California, Berkeley, 2006- 2012. _______________________________________________________________________ EDUCATION Ph.D. Cornell University Sociology, 2006 Dissertation: “A Status Theory of Collective Action” M.A. Cornell University Sociology, 2004 B.A. University of Iowa Sociology, 1999 (with High Distinction) _______________________________________________________________________ 2 Willer ________________________________________________________________________ PUBLICATIONS (* denotes authors contributed equally) Brent Simpson,
    [Show full text]
  • The Opacity Problem in Social Contagion
    The Opacity Problem in Social Contagion George Berrya∗, Christopher J. Camerona, Patrick Parkb, and Michael Macya;c aDepartment of Sociology, Cornell University, 323 Uris Hall, Ithaca NY 14853, USA bSONIC Research Group, Northwestern University, Frances Searle Building, Evanston, IL 60208, USA cDepartment of Information Science, Cornell University Gates Hall, Ithaca NY 14853, USA ∗To whom correspondence should be addressed; E-mail: [email protected]. November 19, 2018 Abstract Many social phenomena can be modeled as cascades in networks, where nodes adopt a behavior in response to peers adopting. When studying cascades, researchers typically assume that the number of active peers when a node adopts is equivalent to the node's threshold for adoption. This assumption is rarely justified due to the \opacity problem": networked cascades reveal intervals which contain thresholds, rather than point estimates. Existing approaches take the maximum of each node's threshold interval, which biases models of social influence. Opacity is inevitable in many small graphs when using the threshold model, resulting from the networked process itself rather than data collection techniques. Using simulation, we extend this finding to the probabilistic SI (independent cascade) model. We confirm these theoretical results by studying 50 large hashtag cascades among 3.2 million Twitter users, finding that 20% of adoptions suffer from opacity. Different assumptions in response to opacity qualitatively change conclusions about peer influence. While opacity is a far- reaching problem, it can be addressed. Using information from nodes who have tightly bounded intervals allows building models to reduce error in estimating node thresholds. Keywords: social contagion, social influence, peer effects, measurement 1 1 Introduction Like epidemic diseases [29, 47, 11], social contagions are ubiquitous, highly conse- quential, and widely studied [22, 41, 42, 13, 33, 44, 45, 15, 46].
    [Show full text]
  • SOCIAL NETWORK ANALYSIS Sociology 920:571:01 (491:02) Paul Mclean
    SOCIAL NETWORK ANALYSIS Sociology 920:571:01 (491:02) Paul McLean Department of Sociology Rutgers University Fall 2011 Location and time: Davison Seminar room, Tuesdays, 9:50-12:30 Office hours: M 12:30-1:30 [LSC]; T 1:00-2:00 [Davison 049], and by appointment E-mail: [email protected] Over the last few decades, and over the last ten years or so especially, there has been an enormous increase in the attention paid to social networks as key determinants of many elements of social life, including motivations, identities, social mobility, group organization and mobilization, resource distributions, decision-making, patterns of innovation, diffusion of disease and attitudes, and the organization of belief systems. One could also say without much exaggeration that a whole new science of networks has emerged, not only within sociology but across multiple disciplines. The basic idea of social network analysis is that we must study the social order relationally: actors/events are interdependent, and structure emerges as patterns in these interdependencies. Sometimes this core idea involves the study of social interaction patterns inductively from the ground up; sometimes it involves looking for particular structural forms that are believed to shape behavior and structure the social environment. Sometimes it involves a radical re-thinking of what an actor is and where a relation can be found. Network analysis is increasingly a mainstream idea, even though the tools it uses are distinctly different from mainstream sociology. Indeed there are ways to incorporate network ideas into ‘mainstream’ research. But in its most radical formulations, the study of networks vies to become a kind of fundamental theory of social organization.
    [Show full text]
  • Damon M. Centola
    11 August 2020 Damon M. Centola Annenberg School for Communication Ph/Office: 215-898-7041 University of Pennsylvania Fax/Office: 215-898-2024 3620 Walnut Street Email: [email protected] Philadelphia, PA 19104 Research Website: https://ndg.asc.upenn.edu/ Education Ph.D. Sociology, Cornell University, 2006 MA Sociology, Cornell University, 2004 BA Philosophy/Logic, Marlboro College, 1997, summa cum laude Appointments 2019- Professor of Communication, Annenberg School for Communication Professor of Engineering (secondary), School of Engineering and Applied Sciences Professor of Sociology (secondary), School of Arts and Sciences University of Pennsylvania 2019- Senior Fellow, Penn LDI Center for Health Incentives and Behavioral Economics 2019- Faculty, Penn Population Studies Center 2015- Faculty, Penn Master of Behavioral and Decision Sciences Program 2013- Faculty, Penn Warren Center for Network and Data Sciences 2013- Director, Network Dynamics Group 2013-19 Associate Professor, Annenberg School for Communication & School of Engineering and Applied Sciences, University of Pennsylvania 2008-13 Assistant Professor, M.I.T., Sloan School of Management 2006-08 Robert Wood Johnson Scholar in Health Policy, Harvard University 2001-02 Visiting Scholar, The Brookings Institution Research Computational Social Science, Social Epidemiology, Social Networks, Internet Experiments, Collective Action and Social Movements, Innovation Diffusion, Sustainability Policy, Cultural Evolution, Agent Based Modeling Awards and Fellowships 2019 Harrison White
    [Show full text]
  • ROBB WILLER Department of Sociology 450 Serra Mall, Bldg
    ROBB WILLER Department of Sociology 450 Serra Mall, Bldg. 120 Stanford, CA 94305 [email protected] ________________________________________________________________________ ACADEMIC POSITIONS Professor, Departments of Sociology, Psychology (by courtesy), and Graduate School of Business (by courtesy), Stanford University, 2015-present Associate Professor, Departments of Sociology, Psychology (by courtesy), and Graduate School of Business (by courtesy), Stanford University, 2013-215 Fellow, Center for Advanced Study in the Behavioral Sciences, Stanford University, 2012-13. Associate Professor, Departments of Sociology, Psychology (by courtesy), and Cognitive Science (affiliated faculty), University of California, Berkeley, 2012- 2013. Visiting Professor, Department of Economic and Social Psychology, University of Cologne, Germany, 2011. Director, Laboratory for Social Research, University of California, Berkeley, 2006-2013. Assistant Professor, Departments of Sociology, Psychology (by courtesy), and Cognitive Science (affiliated faculty), University of California, Berkeley, 2006- 2012. _______________________________________________________________________ EDUCATION Ph.D. Cornell University Sociology, 2006 Dissertation: “A Status Theory of Collective Action” M.A. Cornell University Sociology, 2004 B.A. University of Iowa Sociology, 1999 (with High Distinction) _______________________________________________________________________ 2 Willer ________________________________________________________________________ PUBLICATIONS (* denotes
    [Show full text]
  • Complex Contagions: a Decade in Review
    Complex Contagions: A Decade in Review Douglas Guilbeault, Joshua Becker and Damon Centola* Annenberg School for Communication Send correspondence to: [email protected] Citation: Guilbeault, Douglas, Becker, Joshua, & Centola, Damon. 2018. Complex Contagions: A Decade in Review. In S. Lehmann & Y. Ahn (Eds.), Spreading Dynamics in Social Systems. Springer Nature. (Forthcoming). 2 Abstract Since the publication of “Complex Contagions and the Weakness of Long Ties” in 2007, complex contagions have been studied across an enormous variety of social domains. In reviewing this decade of re- search, we discuss recent advancements in applied studies of complex contagions, particularly in the domains of health, innovation diffu- sion, social media, and politics. We also discuss how these empirical studies have spurred complementary advancements in the theoretical modeling of contagions, which concern the effects of network topol- ogy on diffusion, as well as the effects of individual-level attributes and thresholds. In synthesizing these developments, we suggest three main directions for future research. The first concerns the study of how multiple contagions interact within the same network and across networks, in what may be called an ecology of contagions. The second concerns the study of how the structure of thresholds and their behav- ioral consequences can vary by individual and social context. The third area concerns the roles of diversity and homophily in the dynam- ics of complex contagion, including both diversity of demographic profiles among local peers, and the broader notion of structural diver- sity within a network. Throughout this discussion, we make an effort to highlight the theoretical and empirical opportunities that lie ahead.
    [Show full text]
  • Complex Contagion and the Weakness of Long Ties
    Complex Contagion and the Weakness of Long Ties Damon Centola and Michael Macy Cornell University September 12, 2005 Complex Contagion and the Weakness of Long Ties Abstract The strength of weak ties is that they tend to be long – they connect socially distant locations. Recent research on “small worlds” shows that remarkably few long ties are needed to give large and highly clustered populations the “degrees of separation” of a random network, in which information can rapidly diffuse. We test whether this effect of long ties generalizes from simple to complex contagions – those in which the credibility of information or the willingness to adopt an innovation requires independent confirmation from multiple sources. Using Watts and Strogatz’s original small world model, we demonstrate that long ties may not only fail to speed up complex contagions, they can even preclude diffusion entirely. Results suggest that the spread of collective actions, social movements, and risky innovations benefit not from ties that are long but from bridges that are wide enough to transmit strong social reinforcement. Balance theory shows how wide bridges might also form in evolving networks, but this turns out to have surprisingly little effect on the propagation of complex contagions. We find that hybrid contagions, which have high thresholds for some nodes and low-thresholds for others, can propagate on perturbed networks if the latter are sufficiently numerous. However, for purely complex contagions, propagation depends decisively on wide bridges, a characteristic feature of spatial networks. This may account in part for the widely observed tendency for social movements to diffuse spatially.
    [Show full text]
  • ROBB WILLER Department of Sociology 450 Serra Mall, Bldg
    ROBB WILLER Department of Sociology 450 Serra Mall, Bldg. 120 Stanford, CA 94305 [email protected] ________________________________________________________________________ ACADEMIC POSITIONS Professor, Departments of Sociology, Psychology (by courtesy), and Graduate School of Business (by courtesy), Stanford University, 2015-present Director, Polarization and Social Change Lab, 2018-present Co-Director, Center on Philanthropy and Civil Society, 2019-present Associate Professor, Departments of Sociology, Psychology (by courtesy), and Graduate School of Business (by courtesy), Stanford University, 2013-2015 Associate Professor, Departments of Sociology, Psychology (by courtesy), and Cognitive Science (affiliated faculty), University of California, Berkeley, 2012- 2013. Director, Laboratory for Social Research, University of California, Berkeley, 2006-2013. Fellow, Center for Advanced Study in the Behavioral Sciences, Stanford University, 2012-13, 2020-21. Visiting Professor, Department of Economic and Social Psychology, University of Cologne, Germany, 2011. Assistant Professor, Departments of Sociology, Psychology (by courtesy), and Cognitive Science (affiliated faculty), University of California, Berkeley, 2006- 2012. _______________________________________________________________________ EDUCATION Ph.D. Cornell University Sociology, 2006 Dissertation: “A Status Theory of Collective Action” M.A. Cornell University Sociology, 2004 B.A. University of Iowa Sociology, 1999 (with High Distinction) 2 Willer _______________________________________________________________________ PUBLICATIONS (* denotes authors contributed equally) Yuan Chang Leong, Janice Chen, Robb Willer, and Jamil Zaki. In press. “Conservative and Liberal Attitudes Drive Polarized Neural Responses to Political Content.” Proceedings of the National Academy of Sciences. Jay van Bavel, K. Baicker, P.S. Boggio, V. Capraro, A. Cichocka, M. Cikara, M.J. Crockett, A.J. Crum, K.M. Douglas, J.N. Druckman, J. Drury, O. Dube, N. Ellemers, E.J. Finkel, J.H. Fowler, M. Gelfand, S.
    [Show full text]
  • Homophily, Cultural Drift, and the Co-Evolution of Cultural Groups
    Journal of Conflict Resolution Volume 51 Number 6 December 2007 905-929 Ó 2007 Sage Publications 10.1177/0022002707307632 Homophily, Cultural Drift, http://jcr.sagepub.com hosted at and the Co-Evolution of http://online.sagepub.com Cultural Groups Damon Centola Institute for Quantitative Social Science, Harvard University, Cambridge, MA Juan Carlos Gonza´lez-Avella Vı´ctor M. Eguı´luz Maxi San Miguel Institute for Cross-Disciplinary Studies and Complex Systems, Campus Universitat de les Illes Balears, Palma de Mallorca, Spain Studies of cultural differentiation have shown that social mechanisms that normally lead to cultural convergence—homophily and influence—can also explain how distinct cultu- ral groups can form. However, this emergent cultural diversity has proven to be unstable in the face of cultural drift—small errors or innovations that allow cultures to change from within. The authors develop a model of cultural differentiation that combines the traditional mechanisms of homophily and influence with a third mechanism of network homophily, in which network structure co-evolves with cultural interaction. Results show that in certain regions of the parameter space, these co-evolutionary dynamics can lead to patterns of cultural diversity that are stable in the presence of cultural drift. The authors address the implications of these findings for understanding the stability of cul- tural diversity in the face of increasing technological trends toward globalization. Keywords: social networks; cultural diversity; homophily; cultural drift;
    [Show full text]
  • Damon M. Centola
    20 October 2020 Damon M. Centola Annenberg School for Communication Ph/Office: 215-898-7041 University of Pennsylvania Fax/Office: 215-898-2024 3620 Walnut Street Email: [email protected] Philadelphia, PA 19104 Research Website: https://ndg.asc.upenn.edu/ Education Ph.D. Sociology, Cornell University, 2006 MA Sociology, Cornell University, 2004 BA Philosophy/Logic, Marlboro College, 1997, summa cum laude Appointments 2019- Professor of Communication, Annenberg School for Communication Professor of Engineering (secondary), School of Engineering and Applied Sciences Professor of Sociology (secondary), School of Arts and Sciences University of Pennsylvania 2019- Senior Fellow, Penn LDI Center for Health Incentives and Behavioral Economics 2019- Faculty, Penn Population Studies Center 2015- Faculty, Penn Master of Behavioral and Decision Sciences Program 2013- Faculty, Penn Warren Center for Network and Data Sciences 2013- Director, Network Dynamics Group 2013-19 Associate Professor, Annenberg School for Communication & School of Engineering and Applied Sciences, University of Pennsylvania 2008-13 Assistant Professor, MIT Sloan School of Management 2006-08 Robert Wood Johnson Scholar in Health Policy, Harvard University 2001-02 Visiting Scholar, The Brookings Institution Research Computational Social Science, Social Epidemiology, Social Networks, Internet Experiments, Collective Action and Social Movements, Innovation Diffusion, Sustainability Policy, Cultural Evolution, Agent Based Modeling Awards and Fellowships 2019 Harrison White
    [Show full text]