Center for Disaster Management

Theories of distributed cognition, when applied to complex environments, highlight the inability for single individuals or organizations to manage rapidly evolving interdependent events. Efficient response and recovery phases during natural disasters require coordination and collaboration across a wide range of heterogeneous actors. A key problem hindering effective disaster response is a lack of information and analysis with regard to the alignment of local and national needs with the resources available from the responding organizations. Response systems, which can comprise tens of thousands of organizations from various jurisdictions, often become nearly impossible to manage. Our research demonstrates how standardization of reporting data from responding organizations and dynamic network analysis can be used as coordination and decision making tools for emergency managers.

The response systems that emerge after large-scale disasters like the Haitian Earthquake on January 12th, 2010, or the recent series of events unfolding in Japan, are defined by the complex interactions of organizations.  These response systems are often analyzed by investigating the structure of the network and the consequences of its structural properties.  While there is a growing body of work on the consequences of response networks, there is limited research on the antecedents of these networks.  This paper provides statistical inferences on the selection processes that exist among organizations entering the response system and highlights the micro-level processes that give rise to the global structure. Part of the reason for the lack of research on the antecedents of disaster response networks has been due to limitations in the statistical modeling of networks that required the researcher to investigate potential generative processes in isolation.  Recent advances in exponential random graph models (also referred to as p* models) provide a flexible statistical framework to examine multiple interdependent processes simultaneously.  This paper proposes and tests theoretically driven micro-level processes believed to be responsible for generating the global structure of the observed response networks that emerged after recent earthquakes in Indonesia, Haiti, and Japan. These findings offer fresh insight into the mechanisms driving tie formation between organizations in emergencies and provide guidance on future research into the antecedents of networks

This paper introduces and tests a novel methodology for measuring networks. Rather than collecting data to observe a network or several networks in full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals' perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology, and find it especially promising in crossnetwork settings.

Interpersonal networks are increasingly important factors for organizational learning and performance.  However, little is known about how these networks emerge. Exponential random graph models are used to explore the underlying processes of advice network formation in 15 organizations.  This study examines the influence of (i) structural effects (reciprocity, transitivity, multiplexity) (ii) actor attribute effects (job function, tenure, education, self-efficacy), and (iii) peer competition.  The results suggest employees rely more on reciprocity, closure, and similarity in job function, rather than on peer expertise or status when seeking advice.  The results also show that employees who perceive greater levels of competition with peers are significantly less likely to both seek and provide advice. As public organizations continually look toward private sector strategies that promote internal competition to improve efficiency and accountability, public managers need to be aware of the potential negative implications those strategies can have on interpersonal networks and organizational learning.

Siciliano, M.D. (2015). Advice networks in public organizations: The role of structure, internal competition, and individual attributes. Public Administration Review, 75(4), 548-559. doi: 10.1111/puar.12362

This article analyzes network features and processes that influence organizational interaction during extreme events.  Previous research on network formation under conditions of stress and uncertainty accounts for several managerial factors as well as the role played by information technology and related information infrastructures.  This article contributes to previous work by illustrating key network features and processes such as homophily, transitivity, degree distributions, and propinquity and how they relate to network formation during extreme events.  Understanding these factors is important because they influence not just network formation but subsequent information and resource flow and, ultimately, network performance.  We apply these concepts to the case of Hurricane Katrina and employ exponential random graph models to determine the key network factors that influenced formation. The article concludes by discussing implications to theory and practice.

Network studies on Cognitive Social Structures collect relational data on re-spondents' direct ties and their perception of ties among all other individuals in the network. When reporting their perception networks, respondents commit two types of errors, namely, omission (false negatives) and commission (false positives) errors. We first assess the relationship between these two error types, and their contributions on overall respondent accuracy. Next we propose a method for estimating networks based on perceptions of a random sample of respondents from a bounded social network, which utilizes the Receiver Operator Characteristic curve for balancing the tradeoffs between omission and commission errors.

While much of the research on learning in the public sector examines collaborative arrangements and interorganizational networks, it often stresses the importance of individual dialogue and social interaction as primary methods of learning. Given the collective focus, the current emphasis on organizational interaction has left the individual mechanisms by which learning occurs unspecified and understudied. This study takes as its unit of analysis the dyadic, advice seeking ties that operate as the pathways by which knowledge is transferred and developed among organizational members. Rather than exploring a single advice or knowledge sharing network, this study offers a novel approach for examining the social dynamics of organizational learning by linking two different types of task-relevant knowledge to the social networks that emerge when employees search for that knowledge. By measuring where expertise resides in each knowledge specific network and by modeling the processes and conditions affecting knowledge transfer, this study is able to analyze how efficiently organizations tap into the resources and skills that already exist among their members. The findings suggest that employees tend to ignore the experts when seeking tacit information, and instead rely on those they feel most comfortable with and with whom are most accessible.  Several important distinctions to the patterns of network formation are noted between public and private organizations.  Overall, the results suggest that public managers need to pay attention to the costs of knowledge sharing as much as its benefits in order to promote organizational learning.

This study explores the role of knowledge access and peer influence as mechanisms by which networks may shape teacher self-efficacy. The basic premise is twofold: (i) that peer interaction provides opportunities to access teaching relevant knowledge and thus may reduce uncertainty, and (ii) that self-efficacy beliefs may be shaped by the efficacy beliefs of the peers one is directly connected to in the advice network. The results suggest that both mechanisms may shape teacher self-efficacy and that the quality of ties, rather than the quantity, may have a stronger influence on self-efficacy. These findings offer new insight into the potential ways in which social networks and collegial interaction influence teacher beliefs.

This study explores the role of knowledge access and peer influence as mechanisms by which networks may shape teacher self-efficacy. The basic premise is twofold: (a) that peer interaction provides opportunities to access teaching relevant knowledge and thus may reduce uncertainty and (b) that self-efficacy beliefs may be shaped by the efficacy beliefs of the peers one is directly connected to in the advice network. The results suggest that both mechanisms may shape teacher self-efficacy and that the quality of ties, rather than the quantity, may have a stronger influence on self-efficacy. These findings offer new insight into the potential ways in which social networks and collegial interaction influence teacher beliefs.

Public administration has produced a significant body of literature on public sector networks, examining network drivers, dynamics, and outcomes. However, this literature has yet to examine networks in terms of intervention, where network mechanisms are manipulated to promote behavioral change and improve social, organizational, and community outcomes. Building on Valente’s (2012) typology, we develop a framework for strategies of public sector network intervention. First, we describe the core concepts in the theory of network interventions found primarily in public health. Next, we expand the existing typology of interventions to include macro-institutional strategies suited to public administration settings. We then identify extant public sector literatures that provide the building blocks for a framework for intervention. Finally, we classify the modes of intervention along three dimensions and discuss how each strategy might be implemented in practice. This typology categorizes ...

Governments have long-standing interests in preventing market failures and enhancing innovation in strategic industries. Public policy regarding domestic technology is critical to both national security and economic prosperity. Governments often seek to enhance their global competitiveness by promoting private sector cooperative activity at the inter-organizational level. Research on network governance has illuminated the structure of boundary-spanning collaboration mainly for programs with immediate public or nonprofit objectives. Far less research has examined how governments might accelerate private sector cooperation to prevent market failures or to enhance innovation. The theoretical contribution of this research is to suggest that government programs might catalyze cooperative activity by accelerating the preferential attachment mechanism inherent in social networks. We analyze the long-term effects of a government program on the strategic alliance network of 451 organizations...

We report on a measurement of the Υ(1S+2S+3S)→e+e- cross section at midrapidity in p+p collisions at s=200GeV. We find the cross section to be 114±38(stat+fit)-24+23(syst)pb. Perturbative QCD calculations at next-to-leading order in the color evaporation model are in agreement with our measurement, while calculations in the color singlet model underestimate it by 2σ. Our result is consistent with the trend seen in world data as a function of the center-of-mass energy of the collision and extends the availability of Υ data to RHIC energies. The dielectron continuum in the invariant-mass range near the Υ is also studied to obtain a combined yield of e+e- pairs from the sum of the Drell-Yan process and b-b¯ production.