Adam Douglas Henry

Emergence of segregation in evolving social networks

In many social networks, there is a high correlation between the similarity of actors and the existence of relationships between them. This paper introduces a model of network evolution where actors are assumed to have a small aversion from being connected to others who are dissimilar to themselves, and yet no actor strictly prefers a segregated network. This model is motivated by Schelling’s [Schelling TC (1969) Models of segregation. Am Econ Rev 59:488–493] classic model of residential segregation, and we show that Schelling’s results also apply to the structure of networks; namely, segregated networks always emerge regardless of the level of aversion. In addition, we prove analytically that attribute similarity among connected network actors always reaches a stationary distribution, and this distribution is independent of network topology and the level of aversion bias. This research provides a basis for more complex models of social interaction that are driven in part by the underlying attributes of network actors and helps advance our understanding of why dysfunctional social network structures may emerge.

Understanding Environmental Cognition

Many research questions in environmental policy lead us to questions of environmental cognition—how do individuals structure their thinking about environmental issues, how are these cognitions learned, and how do they influence behaviors? Although these processes tend to be understudied and undertheorized, at least two theoretical perspectives are useful for illuminating different aspects of environmental cognition. The Advocacy Coalition Framework (ACF) was developed in the political science literature to explain major policy change in technically complex issue areas, and the Values-Beliefs-Norms (VBN) theory was developed in the social psychology literature to explain environmental activism among the lay public. Unfortunately, these literatures rarely communicate with one another. This article shows how integrating key aspects of the ACF and VBN can moves us toward a more robust theory to guide future empirical work on cognition, learning, and behavior in the context of environmental issues.

Context and the commons

Despite the grim view of resource overexploitation and the collapse famously described in Hardins (1) much-cited “Tragedy of the Commons,” we now know that local governance institutions have the potential to overcome common pool resource dilemmas. A growing literature (2, 3), including Chhatre and Agrawals analysis of forest commons (4) in this issue of PNAS, demonstrates the conditions of successful commons governance. This body of research augurs well for our ability to design decision-making processes that facilitate both environmental and economic sustainability and thus is of immense practical importance.

Policy Change in Comparative Contexts: Applying the Advocacy Coalition Framework Outside of Western Europe and North America

Abstract The advocacy coalition framework (ACF) is one of the most frequently applied theories of the policy process. Most applications have been in Western Europe and North America. This article provides an overview of the ACF, summarizes existing applications outside of Western Europe and North America, and introduces the special issue that features applications of the ACF in the Philippines, China, India, and Kenya. This article concludes with an argument for the continued application of the ACF outside of Western Europe and North America and a research agenda for overcoming challenges in using the ACF in comparative public policy research.

Discovery of Nodal Attributes through a Rank-Based Model of Network Structure

The structure of many real-world networks coevolves with the attributes of individual network nodes. Thus, in empirical settings, it is often necessary to observe link structures as well as nodal attributes; however, it is sometimes the case that link structures are readily observed, whereas nodal attributes are difficult to measure. This paper investigates whether it is possible to assume a model of how networks coevolve with nodal attributes, and then apply this model to infer unobserved nodal attributes based on a known network structure. We find that it is possible to do so in the context of a previously studied “rank” model of network structure, where nodal attributes are represented by externally determined ranks. In particular, we show that node ranks may be reliably estimated by examining node degree in conjunction with the average degree of first- and higher-order neighbors.

Homophily, influence and the decay of segregation in self-organizing networks

We study the persistence of network segregation in networks characterized by the co-evolution of nodal attributes and link structures, in particular where individual nodes form linkages on the basis of similarity with other network nodes (homophily), and where nodal attributes diffuse across linkages, making connected nodes more similar over time (influence). A general mathematical model of these processes is used to examine the relative influence of homophily and influence in the maintenance and decay of network segregation in self-organizing networks. While prior work has shown that homophily is capable of producing strong network segregation when attributes are fixed, we show that adding even minute levels of influence is sufficient to overcome the tendency towards segregation even in the presence of relatively strong homophily processes. This result is proven mathematically for all large networks, and illustrated through a series of computational simulations that account for additional network evolution processes. This research contributes to a better theoretical understanding of the conditions under which network segregation and related phenomenon—such as community structure—may emerge, which has implications for the design of interventions that may promote more efficient network structures.

Learning sustainability innovations

Large organizations struggle to implement new and promising sustainability practices widely, as documented extensively by social science research. Networks of informal relationships among people can contribute to the diffusion and learning of such innovations.

Empirical Models of Social Learning in a Large, Evolving Network

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals’ access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.