Synthetic Information Systems for Better Informing Public Health Policymakers
Virginia Polytechnic Institute and National Institute of General Medical Sciences Prime (Eubank)
This project leverages two related streams of prior research. First, it is based on the multi-theoretical multilevel (MTML) model (Contractor, 2009; Contractor & Monge, 2002; Monge & Contractor, 2003) to explain the co-evolution of individuals desire to create, maintain and dissolve network ties and their behavioral decisions. Second, it leverages what we have learned empirically about the specific MTML mechanisms and their parametric magnitudes based on empirically investigating the dynamics of networks in organizations, virtual worlds (Huang et al. 2009), emergency response (Vogenbeck et al. 2007), and scientific collaborations (Provan & Contractor, 2007). These theoretical and empirical insights will be implemented as causal models (with know parameters) in this cyber-infrastructure.
The MTML model includes seven families of theoretical mechanisms:
(1) Theories of self interest focus on how people make choices that favor their personal preferences and desires by creating ties that enable them to seek goals they wish to achieve. Two primary theories in this area are the theory of social capital (Burt, 1992) and transaction cost economics (Williamson, 1991).
(2) Theories of mutual interest and collective action examine how forging links produces collective outcomes unattainable by individual action. People create ties because they believe they serve their mutual interest in accomplishing common or complementary goals. Public goods theory (Fulk, Flanagin, Kalman, Monge, & Ryan, 1996) exemplifies this perspective, by examining the conditions that induce network members to contribute their knowledge resources to the realization of public goods such as databases or knowledge repositories.
(3) Contagion theories address questions pertaining to the spread of ideas, messages, attitudes, and beliefs through direct or indirect collaboration links (Burt, 1987). Similarly, links can be blocked by isolating parts of the network or by inoculation against infection.
(4) Cognitive theories explore the role that meaning, knowledge and perceptions play in development of networks. Decisions to forge ties with others are influenced by who or what people think others know. Transactive memory systems (Moreland, 1999) consist of knowledge networks in which people assume responsibility for mastery among various aspects of larger knowledge domains. In this way the collective is more knowledgeable than any component and creating collaboration links are critical for leveraging the collective.
(5) Exchange and dependency theories explain the emergence of network links on the basis of the distribution of information and material resources among network members (Cook, 1982). People see collaboration ties with those whose knowledge they need and who in turn seek knowledge they possess.
(6) Homophily and proximity theories account for emergence of links on the basis of the trait similarity as well as similarity of place (McPherson & Smith-Lovin, 1987). Here, ties are created ion the basis of common traits such as age, gender, tenure, place, and professional interests. The theory of electronic propinquity extends this to the realm of email, telephones and other forms of electronic communication (Rice & Aydin, 1991).
(7) Finally, co-evolutionary theory posits that linkages are typically created in the belief that they will increase individual or organizational fitness, measured as performance, survivability, adaptability, and robustness (Campbell, 1986). Co-evolutionary theory articulates how communities of organizational populations linked by intra-and-inter-population networks compete and collaborate with each other for scarce resources (Baum, 1999). In the public health “contexts” co-evolutionary theory would seek to explain collaboration as well as competition as well as competition for scarce resources between various “populations.”
In our prior research we have empirically tested MTML predictions in over three dozen knowledge networks, using recent advances in exponential random graph modeling techniques (Contractor, Wasserman, & Faust, 2006). Mathematical background can be found in Wasserman and Faust (1994) and more detailed descriptions of the random graph models (often referred to as the p* family) can be found in the chapters of Carrington, Scott, and Wasserman (2005). Our findings across the three dozen networks indicate that the individuals’ motivations to create, maintain, and dissolve ties with other individuals are a complex, but contextually systematic combination of multi-theoretical motivations.
This effort extends our prior research effort by transferring the insights we have gained from our theoretical and empirical results into casual computational models within the proposed cyber-infrastructure to explain decisions such as seeking health care, staying home from school and work, getting vaccinated and/or buying antivirals. The models will utilize noisy information about disease prevalence and drugs that comes from government sources, “news”, and peers in a social network to make predictions of individual decision. Additionally, the cyber-infrastructure will enable us to consider the extent to which the specific domains we have investigated in prior research will computational “dock” with other causal models informed by complementary areas of domain expertise.