Professor of Sociology, Founding Co-Director of the Computational Social Science Institute
This paper replicates and investigates recent findings of order effects in social network data collection, where later names on a roster receive fewer nominations. We model order effects as biases in nomination choices and demonstrate observational and experimental methods for assessing these biases and illuminating their mechanisms.
Liu, Shuyin, Nolin, David, and James A. Kitts. “Name Order Effects in Measuring Adolescent Social Networks Using Rosters” Social Networks. 76: 68-78, 2024.
This paper reviews two threads of research in Computational Social Science, one that studies face-to-face interaction using sensors or other electronic devices, and another that studies computer-mediated interaction via social media or telecommunication technology. This chapter extends recent efforts (Kitts, 2014; Kitts & Quintane, 2020; Lewis, 2022) to enrich the dialogue between the new CSS data sources and conventional social network concepts and theories.
Kitts, James A., Grogan, Helene, and Kevin Lewis. “Social Networks and Computational Social Science.” In McLevey, John, Carrington, Peter J., and John Scott (Eds.) Sage Handbook of Social Networks (Second Edition), 2023.
This study investigates the meaning of friendship for eight diverse cohorts of sixth graders, challenging ubiquitous assumptions that friendships represent liking and social interaction, friendships are directed, and friendships are equivalent to one another. Adolescents primarily construe friendship as relational norms, expectations for mutual behavior, along with mutual liking and interaction. Boys and girls weight these dimensions differently in defining friendship.
Kitts, James A. and Diego F. Leal. “What Is(n’t) a Friend? Dimensions of the Friendship Concept Among Adolescents.” Social Networks. 66: 161-170, 2021.
This paper reviews and discusses two revolutions in the science of social networks: In the first revolution, new telecommunications and sensor technologies allow researchers to collect data on interaction events with unprecedented volume and granularity. In the second revolution, innovations in statistical methods are uniquely fit to make sense of these streaming data, by explicitly modeling temporal interdependence of relational events. This paper interrogates the mapping between conventional social network theories and these new forms of streaming interaction data, developing empirical frontiers for the analysis of interaction event histories.
Kitts, James A. and Eric Quintane. “Rethinking Social Networks in the Era of Computational Social Science.” In Light, Ryan and James Moody (Eds.) Oxford Handbook of Social Network Analysis. pp 71-97. Oxford: Oxford University Press, 2020.
This paper pursues an analytic framework for social influence, reviewing general models of the diffusion of behaviors on networks. For an audience of practitioners, we contribute to the development of a framework that will help users diagnose a situation and identify potent levers for effective influence campaigns. Our framework integrates and builds on the best available research in social psychology, organizational behavior, and network science.
Kitts, James A. and Yongren Shi. “Toward an Analytical Framework of Social Influence: Behavioral Diffusion” National Academy of Sciences Report: Exploring the Development of Analytic Frameworks. 2018.
Previous research on interaction behavior among organizations has typically aggregated those behaviors over time as a network of organizational relationships. This paper instead studies structural-temporal patterns in organizational exchange. Applying this lens to a community of Italian hospitals during 2003–7, the authors observe two mechanisms of interorganizational reciprocation: organizational embedding and resource dependence, and show how these two mechanisms operate on distinct time horizons and operate differently in competitive and non-competitive contexts. Results shed light on the evolution of generalized exchange or status hierarchies at the population level.
Kitts, James A., Lomi, Alessandro, Mascia, Daniele, Pallotti, Francesca, and Eric Quintane. “Investigating the Temporal Dynamics of Interorganizational Exchange: Patient Transfers Among Italian Hospitals.” American Journal of Sociology. 123(3): 850-910, 2017.
This paper depicts the evolution of cooperation on regular lattices, with strategies propagating locally by relative fitness. The underlying dilemma arises from two distinct dimensions—the gains for exploiting cooperative partners (Greed) and the cost of cooperating with exploitative partners (Fear). This paper uses computational experiments to show that embedding interaction in networks generally leads Greed and Fear to have divergent, interactive, and highly nonlinear effects on cooperation at the macro level, even when individuals respond identically to Greed and Fear. We then replicate our experiments on inter-organizational network data derived from links through shared directors among 2,400 large US corporations.
Kitts, James A., Leal, Diego F., Jones, Thomas M., Felps, Will, and Shawn L. Berman. “Greed and Fear in Network Reciprocity: Implications for Cooperation among Organizations” PLoS ONE 11(2), 2016.
Researchers employ four versions of the social network concept: A social network is seen as a set of socially constructed role relations (e.g., friends, business partners), a set of interpersonal sentiments (e.g., liking, trust), a pattern of behavioral social interaction (e.g., conversations, citations), or an opportunity structure for exchange. Researchers conventionally assume these conceptualizations are interchangeable as social ties, but important discrepancies appear, especially for non-ties. Investigating the interplay across the four definitions is timely because emerging tools of computational social science — wearable sensors, logs of telecommunication, online exchange, or other interaction — now allow us to observe the fine-grained dynamics of interaction over time. Cutting-edge lenses allow us to move beyond reified notions of social ties (and non-ties) and instead directly observe and analyze the dynamic and structural interdependencies of social interaction behavior.
Kitts, James A. “Beyond Networks in Structural Theories of Exchange: Promises from Computational Social Science” Advances in Group Processes. Vol 31, 2014.
Experimental and field research has demonstrated a pervasive tendency toward pairwise conformity among individuals connected by positive social ties, and work using formal models has shown that opinions on connected influence networks should thus converge toward uniformity. Observing that diversity persists even in small scale groups and organizations, we investigate two empirically grounded mechanisms of social differentiation that may account for this persistence: First, actors may dislike or disrespect peers who diverge too much from their own views, and may change their opinions or behaviors to distance themselves further from those negative referents. Second, when surrounded by similar others, actors may try to maintain a sufficient sense of uniqueness by exploring new opinions or behaviors. Using computational experiments, we demonstrate that these two mechanisms lead to different patterns of polarization, radicalization, and factionalism and also investigate the conditions under which integration occurs. In closing, we discuss the implications for cultural dynamics in organizations.
Mäs, Michael, Flache, Andreas, and James A. Kitts. “Cultural Integration and Differentiation in Groups and Organizations.” Perspectives on Culture and Agent-Based Simulations: Integrating Cultures, Springer, 2014.
Small-scale human societies are a leap in size and complexity from those of our primate ancestors. We propose that the behavioral predispositions which allowed the evolution of small-scale societies were also those that allowed the cultural evolution of large-scale sociality, in the form of multiple transitions to large-scale societies. Although sufficient, the cultural evolutionary processes that acted on these predispositions also needed a unique set of niche parameters, including ecological factors, guiding norms, and technologies of social control and coordination. Identifying the regularities and patterns in these factors will be the empirical challenge for the future.
Carel van Schaik, Pieter Francois, Herbert Gintis, Daniel Haun, Daniel J. Hruschka, Marco A. Janssen, James A. Kitts, Laurent Lehmann, Sarah Mathew, Peter J. Richerson, Peter Turchin, Polly Wiessner. “Cultural Evolution of the Structure of Human Groups” Cultural Evolution. Cambridge, MIT Press, 2014.