Journal Articles
Network Centralization and Collective Adaptability to a Shifting Environment (under review) preprint
Abstract
Adaptability is at the center of many important organizational challenges. In this work, we study the connection between network structure and collective adaptability to a shifting environment. Prior research has shown that network centralization — the degree to which communication flows disproportionately through one or more members of the organization rather than being more equally distributed — interferes with collective problem-solving by interfering with effective integration of existing ideas, information, and solutions in the network. We hypothesize that the mechanisms that are responsible for that poor integration of ideas, information, and solutions would prove beneficial for problems requiring adaptation to a shifting environment. We conducted a 1,620-subject randomized online laboratory experiment, testing the effect of seven network structures on problem-solving success. To simulate a shifting environment, we designed a murder mystery task and manipulated when each piece of information could be found: early information encouraged an incorrect consensus, requiring a collective shift of solution when more information emerged later. We find that when the communication network within an organization is more centralized, it achieves the benefits of social influence (learning) without the costs (herding). We also find, however, that these benefits of centralization come with a major caveat: they only materialize in networks with two-way flow of information and not when information only flows from the center of the network outwards (as can occur in hierarchical structures or digitally mediated communication). We draw on these findings to re-conceptualize theory on the impact of centralization on collective intelligence in problem-solving that demands collective adaptation by an organization’s members.
“Forum size and content contribution per person: a field experiment” (Forthcoming) Management Science (with Jiye Baek) preprint
Abstract
Promoting contribution of content is a key challenge for platforms that support the collective creation or transfer of knowledge. We study the role of forum size (number of people in a forum) on contribution of content per person with a field experiment on a massive open online course (MOOC). We find that larger forums elicit more contribution per person. The number of questions and other help-seeking threads posted per person was unchanged by size, but replies and other more conversational posts increased sharply. Most of the positive effect of size was in a subset of socially responsive subjects. The implication of social responsiveness driving our results is that the unequal distribution of contribution on online platforms is unlikely to be easily changed: if more contributions are elicited from infrequent contributors, the greatest contributors would contribute even more due to there being more to respond to.
“How intermittent breaks in interaction improve collective intelligence” (2018) Proceedings of the National Academy of Sciences 115:35, pp. 8734-8739. 8/28/2018. (with Ethan Bernstein and David Lazer) pdf
Abstract
People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving. In contrast to prior work, which has focused on how the presence and network structure of social influence affect performance, here we investigate the effects of time. We show that when social influence is intermittent it provides the benefits of constant social influence without the costs. Human subjects solved the canonical traveling salesperson problem in groups of three, randomized into treatments with constant social influence, intermittent social influence, or no social influence. Groups in the intermittent social-influence treatment found the optimum solution frequently (like groups without influence) but had a high mean performance (like groups with constant influence); they learned from each other, while maintaining a high level of exploration. Solutions improved most on rounds with social influence after a period of separation. We also show that storing subjects’ best solutions so that they could be reloaded and possibly modified in subsequent rounds—a ubiquitous feature of personal productivity software—is similar to constant social influence: It increases mean performance but decreases exploration.
“Network structure and patterns of information diversity on Twitter” (2018) MIS Quarterly 42:3, pp 849-872. (With Jiye Baek and Chrysanthos Dellarocas) preprint
Abstract
Social media have great potential to support diverse information sharing, but there is widespread concern that platforms like Twitter do not result in communication between those who hold contradictory viewpoints. Because users can choose whom to follow, prior research suggests that social media users exist in echo chambers or become polarized. We seek evidence of this in a complete cross section of hyperlinks posted on Twitter, using previously validated measures of the political slant of news sources to study information diversity. Contrary to prediction, we find that the average account posts links to more politically moderate news sources than the ones they receive in their own feed. However, members of a tiny network core do exhibit cross-sectional evidence of polarization and are responsible for the majority of tweets received overall due to their popularity and activity, which could explain the widespread perception of polarization on social media.
Shore, Jesse C. "Market formation as transitive closure: The evolving pattern of trade in music." Network Science 4, no. 2 (2016): 164-187.
Abstract
Where do new markets come from? I construct a network model in which national markets are nodes and flows of recorded music between them are links and conduct a longitudinal analysis of the global pattern of trade in the period 1976–2010. I hypothesize that new export markets are developed through a process of transitive closure in the network of international trade. When two countries' markets experience the same social influences, it brings them close enough together for new homophilous ties to be formed. The implication is that consumption of foreign products helps, not hurts, home-market producers develop overseas markets, but only in those countries that have a history of consuming the same foreign products that were consumed in the home market. Selling in a market changes what is valued in that market, and new market formation is a consequence of having social influences in common.
“Facts and figuring: an experimental investigation of network structure and performance in information and solution spaces” (2015) Organization Science 26:5, pp. 1432-1446. (With Ethan Bernstein and David Lazer) preprint
Abstract
Using data from a novel laboratory experiment on complex problem solving in which we varied the structure of 16-person networks, we investigate how an organization’s network structure shapes the performance of problem-solving tasks. Problem solving, we argue, involves both exploration for information and exploration for solutions. Our results show that network clustering has opposite effects for these two important and complementary forms of exploration. Dense clustering encourages members of a network to generate more diverse information but discourages them from generating diverse theories; that is, clustering promotes exploration in information space but decreases exploration in solution space. Previous research, generally focusing on only one of those two spaces at a time, has produced an inconsistent understanding of the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we bring disparate results under a single theoretical roof and clarify the effects of network clustering on problem-solving behavior and performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one determinant of successful problem solving may harm the other.
“Spectral goodness of fit for network models” (2015) Social Networks 43:October 2015 pp. 16-27 (With Benjamin Lubin) preprint
Abstract
We introduce a new statistic, ‘spectral goodness of fit’ (SGOF) to measure how well a network model explains the structure of the pattern of ties in an observed network. SGOF provides a measure of fit analogous to the standard R-squared in linear regression. Additionally, as it takes advantage of the properties of the spectrum of the graph Laplacian, it is suitable for comparing network models of diverse functional forms, including both fitted statistical models and algorithmic generative models of networks. After introducing, defining, and providing guidance for interpreting SGOF, we illustrate the properties of the statistic with a number of examples and comparisons to existing techniques. We show that such a spectral approach to assessing model fit fills gaps left by earlier methods and can be widely applied.
"Power laws and fragility in flow networks." Social networks 35, no. 1 (2013): 116-123. (With Catherine Chu and Matt T. Bianchi)
Abstract
What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.
"Homogenization and specialization effects of international trade: Are cultural goods exceptional?." World Development 38, no. 1 (2010): 37-47.
Abstract
In contrast to the logic that international trade leads to greater specialization and differentiation of products, cultural industries are often still protected from imports, in part, because of the worry that trade will lead instead to homogenization. Is this true for cultural goods and if so, is this different from other goods? I consider the effects of homogenization on industrial development, propose a network-based method of identifying homogenization in global trade patterns, and test a range of industries. I find evidence of homogenization in many industries, calling into question a major justification for free trade.