Cover Image

PAPERBACK
$54.00



View/Hide Left Panel
Click for next page ( 134


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 133
Dynamic Network Analysis Kathleen M. Cariey Institute for Software Research International Carnegie Mellon University Abstract Dynamic network analysis (DNA) varies from traditional social network analysis in that it can handle large dynamic multi-mode, multi-link networks with varying levels of uncertainty. DNA, like quantum mechanics, would be a theory in which relations are probabilistic, the measurement of a node changes its properties, movement in one part of the system propagates through the system, and so on. However, unlike quantum mechanics, the nodes in the DNA, the atoms, can learn. An approach to DNA is described that builds DNA theory through the combined use of multi-a=,ent modeling, machine learning, and meta-matnx approach to network representation. A set of candidate metric for descnbing the DNA are defined. Then, a model built using this approach is presented. Results concerning the evolution and destabilization of networks are described. Acknowledgement The research reported herein was supported by the National Science Foundation NSF IRI9633 662, the Office of Naval Research (ONR) Grant No. N00014-97-~-0037 and Grant No. 9620. ~.l 140071, Additional support was provided by the NSF IGERT 9972762 for research and training in CASOS and by the center for Computational Analysis of Social and Organizational Systems at Carnegie Mellon University (http://www.casos.ece.cmu.edu ). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the National Science Foundation or the U.S. government. DYNAMIC SOCIAL NETWO~MODEL~G ED ^^YSIS 133

OCR for page 133
Dynamic Network Analysis Terrorist organizations have network structures that are distinct from those in typical hierarchical organizations - they are cellular and distributed. While most commanders, politicians and intelligence agents have at least an intuitive understanding of hierarchies and how to affect their behavior, they have less of an understanding of how to even go about reasoning about Dynamic networked organizations (Kontelt and Arquilla, 20011. It is even more difficult for us to understand how such networks will evolve, change, adapt and how they can be destabilized. _ 1_ ~ 1 . ~ Clearly social network analysis can be applied to the study of covert networks (Sparrow, 19911. Many are stepping forward suggesting that to understand these networks we just need to "connect the dots" and then isolate the "key actors who are often defined in terms of their "centrality" in the network. To an extent, this is right. However, it belies the difficulty of "connecting the dots" in terms of mining vast quantities of information, pattern matching on agent characteristics for people who go under multiple aliases, and still ending up with information the may be intentionally misleading, inaccurate, out-of-date, and incomplete. Further, this belies the difficulty in "knowing" who is the most central when you have at best only a sample of the network. Finally, and critically, this approach does not contend with the most pressing problem - the underlying network is dynamic. Just because you isolate a key actor today does not mean that the network will be destabilized and unable to respond. Rather, it is possible, that isolating such an actor may have the same effect as cutting off the Hydra's head; many new key actors may emerge (CarIey, Lee and Krackhardt, 2001~. To understand He dynamics of terrorist, and indeed any, network we need to understand the basic processes by which networks evolve. Moreover, we have to evaluate isolation strategies in the face of an evolving network and in the face of missing, information. To ignore either the dynamics or the lack of information is liable to lead to erroneous, and possibly devastatingly wrong, policies. Taking in to account both the dynamics and the lack of information should engender a more cautious approach in which we can ask, "if we do x what is likely to happen?" L`i~tations to Traditional SNA Traditionally, social network analysis (SNA) has focused on small, bounded networks, with 2-3 types of links (such as friendship and advice) among one type of node (such as people), at one point in time, with close to perfect information. To be sure there are a few studies that have considered extremely large networks, or two types of nodes (people and events), or unbounded networks (such as inter-organizational response teams); however, these are the exception not the norm. However, such studies are still the exception not the rule. Further, while it is understood, at least in principle how to think about multi-modal, multi-plex, dynamic networks, the number of tools, the interpretation of the measures, and the illustrative studies using such "higher order" networks are still in their infancy relative to what is available for simpler networks. Finally, many of the tools do not scale well wig the size of the network or degrade gracefully with errors in the network; e.g., they may be too computationally expensive or too sensitive to both type ~ and 2 errors. What is needed is a dynamic network analysis theory and toolkit. We are working 134 DYNAMIC SOCKS N~TWO=MODEL~G ED ISIS

OCR for page 133
to develop such a tool kit and the associated metrics and decision aids. In this paper. one such tool, DyNet is described and used to examine various isolation strategies. Dynamic Network Analysis Recently there have been a number of advances that extend SNA to the realm of dynamic analysis and multi-color networks. There are three key advances: I) the meta-matrix, 2) treating ties as probabilistic, and 3) combining social networks with cognitive science and multi-agent systems. These advances result in a dynamic network analysis. Meta-Matnx: CarIey (2002) combined knowledge management, operations research and social networks techniques together to create the notion of the meta-matnx - a multi-color, multiplex representation of the entities and the connections among them. The Meta-matr~x is an extension and generalization of the PCANS approach forwarded by Cariey and Krackhardt ( 1999) that focused on people, resources and tasks. For our purpose, the entities of interest are people, knowledge/resources, events/tasks and organizations - see table I. This defines a set of 10 inter-linked networks such that changes in one network cascade into changes in the others; relationships in one network imply relationships in another. For example, co-membership in an organization or co-attendance at an event for two people suggests a tie in the social network between these two people. A group, such as a terrorist network, can be represented in terms of an overtime sequence of such networks. In fact, any organization or group can be represented in this fashion and we have used this representation on numerous occasions to characterize actual organizations and to predict their ability to adapt. All graph theory and network measures can be defined in terms of whether they can or have been applied to which cells. Further, on the basis of this meta-matr~x new metrics can be developed that better capture the overall importance of an individual, task, or resource in the group. An example of such a metric is cognitive load - the effort an individual has to employ to hold his role in the terrorist group - and it takes in to account, who he interacts with, which events he has been at, which organizations he is a member of, the coordination costs of working with others in the same organization or at the same event or in reaming from an earlier event or training for an upcoming event. A large number of such metrics have been developed and analyzed in terTns of their ability to explain the evolution, performance, and adaptability of dynamic networks. A key difficulty from a growth of science perspective, is that as we move from SNA to DNA the number, type, complexity, and value of measures changes. A core issue for DNA is what are the appropriate metrics for describing and contrasting dynamic networks. Significant new research is needed in this regard. To date, our work suggests that a great deal of leverage can be gained in describing networks by focusing on measures that utilize more of the cells in the meta- matnx. For example, cognitive load, which measures the cognitive effort and individual has to do at one point in time has been shown to be a valuable predictor of emergent leadership (Cariey and Ren, 20011. Cognitive load is a complex measure that talces into account the number of others, resources, tasks the agent needs to manage and the communication needed to engage in such activity. In addition, we find that for any of the cells in the meta-matrix, particularly for large scale networks. many of the standard graph level measures have little inflation content as the network grows in size (Anderson, Butts and CarIey, 1999) andfor are highly correlated with each other. A set of measures that are generally not correlated, scale well, and are key in characterizing a network are the DYNAMIC SOCIAL NETWOI?KAdODEL~G~D ISIS 135

OCR for page 133
size of the network (number of nodes), density (either as number of ties or the typical social network form number of ties/number of possible ties), homogeneity in the distribution of ties (e.g., the number of clusters or subcomponents, the variance in centrality), rate of change in nodes, and rate of change in ties. The point is not that these are the only measures needed to characterize dynamic networks. The point Is that these are a candidate set that have value and that as a field we need to develop a small set of metrics that can be applied to networks, regardless of size, to characten~:e the dynamics. Table I. Meta-Matnx . . .. People Knowledge/Res Events/Tasks ources . . Social network Knowledge Attendance network network . Information Needs network network Temporal ordering Organizations People Membership network Organizational capability Institutional support or attack Knowledge/Res ources Events/Tasks Organizations Inter- organizational network Probabilistic Ties: The ties in the meta-matnx are probabilistic. Vanous factors affect the probability, including the observer's certainty in the tie and the likelihood that the tie is manifest at that time. Bayesian updating techniques (Dombroski and CarIey, 2002), cognitive inferencing techniques, and models of social and cognitive change processes (CarIey, 2002; CarIey, Lee and Krackhardt, 2001) can be used to estimate the probability and how it changes over time. We are in the process of exploring techniques for combining the cognitive inferencing with the cognitive change process models. Multi-Agent Network Models: A major problem with traditional SNA is that the people in the networks are not treated as active adaptive agents capable of taking action, learning, en c! altering their networks. There are several basic, well known, social and cognitive processes that influence who is likely to interact with whom: relative similarity, relative expertise, and co- worker. CarIey uses multi-agent technology in which the agents use these mechanisms, learn, take part in events, do tasks to mode! organizational and social charge. The dynamic social network emerges from these actions. The set of networks linking people, knowledge, tasks and other groups or organizations co-evolve. CarIey, Lee and Krackhardt (2001) use simple learning mechanisms to dynamically adjust networks as the agents in them attended events, learned new information, or were removed from the network. In DyNet, described herein, additional mechanisms center on agent isolation are also considered. DNA has a wide ran3e of applications. For example, this approach is being used to examine the likely impact of unanticipated events in the VISTA project (Died~ch et al, forthcoming), the possible effects of biological attacks on cities in BioWar (Carley et al, 2002)? in evaluating CIO response strategies to denial of service attacks (Chen, 2002), and evaluating information security 136 DYNAMIC SOCIAL NETWO~MODELING AND ANALYSIS

OCR for page 133
within organizations - ThreatFinder Project (Cariey, 20011. See also www.casos.ece.cmu current projects and working papers. Dynamic Network Theory To move beyond representation and method, we need to ask, "How do networks change?,' What are the basic processes? From the meta-matrix perspective, the processes are easy - things that lead to the adding and dropping of nodes author relations - see table 2. Again, no claim is being made that the processes listed in table 2 cover the complete spectrum; rather, they illustrate the types of node change processes that need to be postulated. A full theory of dynamic networks needs to speak to such mechanisms. People Table 2. Basic Change Processes for Nodes in the Meta-Matrix _ Knowledge/Resources Events/Tasks Innovation Goal Change Discovery Re-en~neering Forgetting Development of new Cons umption technology Stop usage of I technology Birth Death Promotion Mobility Recruitment Incarceration Isolation Organizations Organizational birth Organizational death Mergers Acquisitions Legislation of new entity Similarly, there are a set of processes that lead to the addition and removal of relations. Basic processes are cognitive, social and political in nature. Cognitive processes have to do with learning and forgetting, the changes that occur in ties due to changes in what individuals know. Social changes occur when one agent or organization dictates a change in ties, such as when a manager re-assigns individuals to tasks. Finally, political changes are due to legislation that effect organizations and the over-arching goals. To illustrate what is meant, a limited number of such processes are described in Table 3. Fumier, and this should be obvious, processes that add or eliminate nodes also affect relations to/from that node. For example, if all individuals in a society forget a particular piece of information that knowledge node, no longer exists and all connections frown people to it are now eliminated. DYNAMIC SOCIAL NETWORK MODELING ED ANALYSIS 137

OCR for page 133
Table 3. Chance Processes for Relations in the Meta-Matrix People Knowledge/ Events/ Tasks Resources Motivation to Learning Re-assignment Interact Acquisition Change in access Discovery Innovation Analogical reasoning ~ . . Organizations People Mobility Recruitment Knowledge/ Resources Events/Tasks Organizations DyNet IP development Re-engineenng Out-sourcing Alliances Coalitions The purpose of the DyNet project is to develop the equivalent of a flight simulator for reasoning about dynamic networked organizations. Through a unique blending of computer science, social networks and organization theory we are creating a new class of tools for managing organizational dynamics. The core tool is DyNet - a reasoning support too] for reasoning under varying levels of uncertainty about dynamic networked and cellular organizations, their vuInerabilities, and their ability to reconstitute themselves. Using DyNet the analyst would be able to see how the networked organization was likely to evolve if left alone, how its performance could be affected by venous information warfare and isolation strategies, and how robust these strategies are in the face of varying levels of information assurance. 138 DYNAMIC SOCIAL NETWORK MODELING AND ANALYSIS

OCR for page 133
Database of Organ izational \ Network Scenarlos \ Profile \ Aback Characteristics ~ Scenario Of known or / Hypothetical / Individuals = Network or ~ Observe Cellular Dynamics Organization I. Critic al - DYN ET , - ~ 00 ~ \ I~._., l,(7 .. , Figure I. DYNET: A desktop too! for reasoning about dynamic networked and cellular Organlzatlons. DyNet is intended to be a desktop system that can be placed in the hands of intelligence personnel, researchers, or military strategists. Through hands-on what if analysis the analysts will be able to reason in a what -if fashion about how to build stable adaptive networks with high performance and how to destabilize networks. There are many applications for such a too! including: threat assessment, assessing information security risks in corporations; inter training; simulation of the red team in a gaming situation, and estimation of efficacy of destabilization policies. Currently an alpha version exists as a batch program (no visualization) and it has been used to evaluate simple isolation strategies. The system can handle data on real networks. The DyNet too} is a step toward understanding how networks will evolve, change, adapt and how they can be destabilized. The goal will be to incorporate all of the evolutionary mechanisms previously discussed. DyNet, which is a computer mode] of dynamic networks, can also be thought of as the embodiment of a theory of dynamic networks. The focus of this theory is on the cognitive, and to a lesser extent, social processes by which the networks in the meta-matnx evolve. The basic cognitive forces for change in DyNet are learning, forgetting, goal-setting, and motivation for interaction. The basic social forces for change are recruitment, isolation, and to a limited extent the initiation of rumors and training. The basic motivations for interaction are relative similarity, relative expertise or some combination of the two. Relative similanty is based on the fundamental finding of homophilly, the tendency of interacting partners to be similar. Arguments surrounding this fundamental process include the need for communicative ease. comfort, access, and training. Relative expertise is based on the fundamental finding that when in doubt people will turn they view as experts for information. Arguments surround this fundamental processes include the need to acquire, desire to minimize search, desire to optimize information, and so on. Other basic motivations such as the need to exhibit competence and the need to coordinate have also been identified and will be added to DyNet but are not in the current system. Among the attrition strategies are removal of the most "central" individual, removal of the individual with the highest cognitive load, and removal of individual's at random. User's can control the frequency and severity of such attrition strategies. Previous studies using this system have shown that a) it is difficult to completely destabilize a network, by that the best strategy DYNAMIC SOCL4L NT:TWORK MODELING AND ANALYSIS 139

OCR for page 133
depends on the structure of the network and c) attrition strategies vary in whether there effectiveness is enhanced or diminished by removing multiple agents at once or sequentially (CarIey, 20021. Agents can be distinguished based on fixed characteristics such as race, family and gender, and on knowledge (or training). Further, the agents can operate in a world without information technology or augmented by access to email, web pages, or manuals. Access to others can be restricted, as might be the case when operatives live in different countries. Performance metrics include task completion, accuracy, energy for tasks, information diffusion, and group cohesion. Finally, the basic networks can be extracted continually ire order to see the system evolve. Among the networks that can be extracted are the knowledge network, the overall social network, the emotive or "friendship" networks, and the acquisition or ~'advice" network. The network evolutionary strategies include learning (during interaction), forgetting, personnel attntion, misinformation, and changing task demands. DyNet offers the user the choice of entering specific networks or entering network characteristics (such as size and density). Results Using, DyNet a series of virtual experiments were run. These experiments were designed to examine the interaction between network structure, dynamics (particularly in response to isolation), and the information that the observer has on which to base the isolation strategies. In figure 2, a very high level conceptualization of these differences is shown. Three possible isolation strategies: isolating individuals at random, isolating those who are the most central (degree centrality), and isolating those with the highest cognitive toad are shown relative to a specific organization and networks within it. Given that the networks are evolving at issue is which of these strategies will be the most effective? Further, we might ask, if the social network was different, e.g., less hierarchical, would that matter? . = : . ~ ~ ~ - , ,~)Highest Centrality ~ '~"~ ~ ~] Highest Co~,nit~e Mad ~~ .--'/ sit Figure 2. Structure, Isolation and Dynamics 140 DYNAMIC SOCIAL NETWORK MODELING AND ANALYSIS Random

OCR for page 133
The Structure of the Network Matters The first finding, and it is quite robust, is that the structure of the network matters. That is, random networks in which the relations are distributed in an independent and identical fashion? hierarchies, and cellular networks all evolve quite differently, require different strategies to destabilize, have different abilities to diffuse information, and exhibit different performance for the same task. In Figures 3 and 4. this difference is illustrated with respect to the networks ability to recover from isolation strategies. In figure 3 we see the impact of the three isolation strategies on a random lid network and in figure 4 the impact of the same strategies on a cellular network. As an aside, the particular cellular network simulated here is one whose features map onto available information about covert networks, such as the cells are completely connected internally and cell size ranges from 3-10 members. In these figures not only do we see that the isolation strategies vary in their effectiveness based on the structure of the network they are attacking, but in addition, cellular networks are able to recover from the attacks. l ~ <~ 6 In_ i ~ be- . .. If. ................ ~ ... .iitili434:..i di i~l.Ji[~161Ili1~li`,,l:llJ.~. 46...~`i;.lilliij.iCliil. i]tiJil~ 1 ~ ~ off I~ (D LO d ~ ~ ~ 1 CkJ Cry IS) ~ (A ~ Co lo) ~ lo) I ~ ~ ~ ~ ~ Rg^3 ~~m mans ~ '''''-'-''a -a:: ._ __ _ _._ _..''' ' ' ' '' - , ___._ _ _ ___ _ _ _ at, l ~ _ Networks Can Heal Themselves F;41e4..~1~ng~ya ok co I. , ~ ... _ .. _ _._ _ .. ... ... _ I >~ ~-~ ~$ O~ ....... .................................. ........................................................... ........ c' . ........ ........................... . '''''''' i l I) , .~.,,,,4~ i~,, i~;i ~ .li~.J.i;;t3,~,, Ji.k,- i.~.~ .dil -,,,,,*' 1;~~,,. ~ 0 A) C\1 O) CO ~ O 1` d- ~ 0 l CD ~ CO 00 0 Cut CY) ~ ~ 0 . . . Cur I R3~1 one ! A second key findin, is that networks are generally able to heal themselves. That is isolation of a node that links disparate groups together typically does not leave those groups disconnected. Rather the basic social and cognitive processes outlined lead individuals to seek alternative contact points to interact with. For example in Figure 5 we see on the left a network where the person with the highest cognitive [load, the emergent leader was isolated. A consequence is that multiple new leaders emerge, each of whom ends up being more directive than the original leader. Healing is not guaranteed and in fact depends on the underlying structure, the cultural basis for interaction? the degree of isolation, the frequency of isolation, and the strategy for isolation. For example, as was seen in Figure 4, cellular networks he e] themselves regardless of which isolation strategy is used against it. in this case, the cell structure of the network enables DYNAMIC SOCKS NETWO=MODEL~G ED TRYSTS 141

OCR for page 133
the network as a to engage in what appears as "meta-learning," i.e., learning how to recover from unanticipated attrition. Cellular networks, which are the structure most like those used by terrorist organizations, are very difficult to destabilize. The reasons are complex, but a key factor is that such network structures are able to heal relatively faster than other structure both in teens of the re-emergence of leaders and in teens of performance recoveries after personnel have been removed. Full Information is Not Necessary In the foregoing two examples, we saw the impact of destabilization strategies on network without considering "how is it that we know what we know?" Or in other words, "if we are not sure what the underlying network looks like, how confident can we be in our predictions about how to destabilize it?" Notice, that in traditional SNA, typically we have close to full information. For covert networks we do not. Information may be missing because we don't know some of the nodes - the people involved, or because we don't know some of the relations. C? _~ .. . ... ... .. _ .. _ . ~ (5,_ /~ ~ (c ~~ ~~3:- o~ . . .- - - ~0 (5) v ,. ~ Figure 5. The network before (left) and after (right) the isolation of the leader. Figure 6. The impact of incomplete information about who is in the network. In Figure 6 we see the impact of not knowing al the nodes. Here we see the . ~ comparison ot no attack, versus the average impact n o - is o la tic n of isolating personnel 31 30 2 9 '_ _ l 2 8 25 142 Ad: it' Pe rf 0 r m a n c e perfect 90% people 75% people 50% people know ledge DYNAMIC SOCIAL NETWORK MODELING AND ANALYSIS

OCR for page 133
(across the three isolation strategies under conditions of full information, knowing 90% of the nodes, 75~o and only 50%. Clearly having close to perfect or perfect knowledge means that more effective isolation strategies are found. Note, however, that any isolation is better than none, assuming our goal is to degrade the performance and that we don't need perfect information to be quite effective. Now, consider the case where we don't have perfect information about the relations. One way for this to occur is if we don't know all the knowledge or resources that are available to the network. In figure 7 we see the impact of having imperfect knowled Be of the relations as a function of how much do we know about the other entities, in this case what there is for the other to know. Here we see that we actually do better knowing less. This is due to the interaction between what we know and the isolation strategy. Essentially, when we don't really know the underlying social and knowledge network we may overestimate the primacy of a person. who although not the key in terms of degree centrality, is more central in terms of cognitive load. Thus, in effect, less knowledge makes both the centrality and the cognitive load strategies more similar resulting in on average lower performance due to the fact that cellular networks are more devastated by the extraction of such emergent leaders, at least in the short run. Further, reduced information about relations makes all isolation strategies more mixed thus inhibiting the ability of the opponent to engage in meta-learning. Figure 7. The impact of incomplete information about what people know. 30 29 27 26 r - ~r~~ ..._.. _.... __ ....... _._..._..... ...._.._ _ ......_ ._ _ __.._ .. _ _..._...._ _...___ ___ ... _......... ~......_... _....... ... ~ _.......... _... _........... _ ... Perform ance 31 -- 1~ 2: ~~e~(-l~ Perform anon no-isolation perfect knowledge 90% knowledge Summary 75% knowledge 50% knowledge , . Thinking about networks from a dynamic perspective is absolutely essential to understanding the modern world. An approach toward dynamic networks has been outlined. There are several distinctive hallmarks to this approach. First, in contrast to other multi-agent work, the agents we describe are in actual social networks. Here, the networks and the agents co-evolve. Secondly, the web of affiliations connects not just agents. but agents and other entities such as knowledge, tasks and organizations. The agents described here in are more cognitively realistic than the typical a-life agents. They are also more socially realistic in teIms of interaction than the typical e-commerce agents as the agents we use are boundedly rational rather than optimizers. Another distinction compared to most systems is that DyNet can take real networks as input. DYNAMIC SOCIAI~ N~TWORKMOD~L~G ED CYSTS 143

OCR for page 133
In contrast to traditional SNA, DNA considers the role of the agent in terms of processes and not just position. That is, the agents can do things - communicate, store information, learn. Further, the networks are dynamic and changing even as the agents change. The links ar probabilistic the networks multi-colored and multi-plex to the extent that the set of networks combine in to one complex system where changes in one sub-network inform and constrain charges in the others, often leading to error cascades. Finally, DNA explores the sensitivity of the measures and the impacts to error. The approach, theory, and results described here are illustrative. Clearly much work needs to be done before we have a complete understanding of network dynamics. Are there likely to be other change mechanisms than those currently in DyNet - to be sure. However, since all human action is cognitively mediated - it is unlikely that such mechanisms will not be denvable, at a basic level from what the physical and physiological constraints, what the agent knows, the basic learning and information processing mechanisms, and the way in which groups. organizations and institutions store such information. To create a truly dynamic network theory we need to create the equivalent of a quantum dynamics for the socio-cognitive world, where the fundamental entities, the people, unlike atoms, have the ability to learn. References Anderson. Brigham S., Carter Butts & Kathleen M. CarIey, 1999, "The Interaction of Size and Density with Graph-Leve! Measures" to Social Networks. " Social Networks. 21: 239-267. Butts, C. 2000. "Network Inference Error. and Informant (In1Acc~rnov A Rnv~.cinn Annrr`~rh " to appear in Social Networks _ _ a___ ~ ~ ~ ~~ MA ~^ ~ ^~4 ~ ~~~ Carley K. (1990). ``Group Stability: A Socio-Cognitive Approach'' in Lawler E.' Markovsky B.' Ridgeway C. and Walker H. (Eds.), AdvancesinGroup Processes}, JAIPress, Greenwich, CN. CarIey, Kathleen M. & Yuqing Ren, 2001, "Tradeoffs Between Performance and Adaptability for C31 Architectures." In Proceedings of the 2001 Command and Control Research and Technology Symposium. Conference held in Annapolis, Maryland, June, 2001. Evidence Based Research, Vienna, VA. Carley, Kathleen M. 1999, "On the Evolution of Social and Organizational Networks." In Steven B. Andrews and David Knoke (Eds.) Vol. 16 special issue of Research in the Sociology of Organizations. on "Networks In and Around Organizations." Greenwhich, CN: JAl Press, Inc. Stamford, CT, pp. 3-30. Carley, Kathleen M. 2002, "Inhibiting Adaptation" In Proceedings of the 2002 Command and Control Research and Technology Symposium. Conference held in Naval Postgraduate School, Monterey, CA. Evidence Based Research, Vienna, VA. Cariey, Kathleen M. 2002, "Smart Agents and Organizations of the Future'. The Handbook of New Media. Edited by Leah Lievrouw & Sonia L`ivingstone, Ch. 12 pp. 206-220, Thousand Oaks, CA, Sage. CarIey, Kathleen M. Ju-Sun:, Lee and David Krackhardt, 2001, Destabilizing Networks Connections 24~31:31-34. Diedrich, Frederick J., Kathleen M. Cariey, Jean MacMillan, Keith Baker, MA; Jerry L`. Schiabach, LTC I, Victor Fink, forthcoming, VISuaTization of Threats and Attacks (VISTA) in Urban Environments" Military intelligence Professional Bulletin 144 DYNAMIC SOCKS HETWO=MODE~ - G ED ^^YSIS

OCR for page 133
Krackhardt, David & Kathleen M. Carley, 1998, "A PCANS Model of Structure in Organization" Pp. 1 13-119 in Proceedings of the 1998 International Symposium on Command and Control Research and Technology. Conference held in June. Monterray. CA. Evidence Based Research, Vienna, VA. Ronfeldt, D. and J. Arquilla. September 21, 2001. "Networks, Netwars. and the Fight for the Future," First Monday, Issue 6 No. 10. online: http://f~rstmonday.org/issues/issue6_ 1 0/ronfelUt/index.htm. Skvoretz, J. 1990. "Biased Net Theory: Approximations, Simulations, and Observations," Social Networks, Vol. 12. pp. 217-238 Sparrow, M. (l991). The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks 13:25 I-274. DYNAMIC SOCIAL fJETWORKMODEL~G~D^~YSIS 145