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OCR for page 324
Using Multi-theoretical Multi-leve! (MTML) Moclels to Study Adversarial Networks
Noshir S. Contractor
Departments of Speech Communication & Psychology
Research Affiliate? Beckman Institute for Advanced Science & Technology
University of Illinois at Urbana-Champaign
Peter R. Monge
Annenberg School for Communication & Marshall School of Business
University of Southern California
Abstract
This paper applies the multi-theoretical multi-level (MTML) model to study the creation,
maintenance and dissolution of adversanal communication and knowledge networks. It
begins by identifying the theoretical mechanisms that influence the dynamics and co-
evolution of communication and knowledge networks in general. Next it describes how
examining a`dversanal social networks requires an extension to the MTML framework. In
particular, we describe how community ecology theory helps us better understand how
the network linkages in a focal network can be influenced by other networks within the
same population as well as networks within other populations in the community. Finally,
we briefly describe an analytic framework that we have developed to specify (i) multi-
theoretical multi-level models for the evolution of these network, (ii) agent based
computational models in Blanche to assess the transient and lon=-term implications of
these theoretical mechanisms on the co-evolution of the networks, and (iii) p* analytic
techniques to validate these predictions using empincal data.
Some of the material in this paper has been adapted from Monge & Contractor (20031.
Theories of Com~nun~cation Networks. New York: Oxford University Press.
324
DYNAMIC SOCIAL NETWORK MODELING AND CYSTS
OCR for page 325
Using Multi-theoretical Multi-leve! (MTML) Models to Study Adversarial
Networks
Overview
To date, most network research has been limited by five major problems. First, it
tends to be atheoretical, ignoring the various social theories that contain network
implications. Second, it explores single levels of analysis rather than the multiple levels
out of which most networks are comprised. Third, network analysis has employed
limited theoretical insights from contemporary complex systems analysis and computer
simulations. Fourth, it typically uses descriptive rather than inferential statistics thus
robbing it of the ability to make theoretical claims about the larger universe of networks.
Finally, almost all the research is static and cross-sectional rather than dynamic. In our
book, Theones of Communication Networks (2003), we propose solutions to all five
problems. First, we have developed a multi-theoretical mode! that relates different social
theories with different network properties. Second, the mode! is multi-level, offering a
network decomposition that applies pertinent social theories at all network levels:
individuals, dyads, triples, groups, and the entire network. Third, the model relies on a
complex systems perspective, implementing Blanche, an agent-based network computer
simulation environment, to generate and test theories and hypotheses. Fourth, the model
utilizes the p* family, a set of innovative tools for statistical network analysis, to provide
a basis for valid multilevel statistical inferences. Finally, our mode! relates
communication networks to other networks, enabling more sophisticated study of how
dynamic organizational networks emerge.
This paper advances arguments in support of a multi-theoretical multi-level
approach to study the emergence of adversarial networks. Recent advances in digital
technologies invite consideration of organizing as a process that is accomplished by
flexible, adaptive, and ad hoc networks that can be created, maintained, dissolved, and
reconstituted with remarkable alacrity. We propose, and describe briefly, a Multi-
theoretical Multi-leve! (MTML) framework -- based on network formulations of social
theories -- to examine the mechanisms that influence people and organizations to forge
network links with other individuals, organizations, as well as r~on-human agents (such as
knowledge repositones). We extend the MTMr framework to r~vr~limitl~, q~l`,q^~= a..'
understanding of adversarial networks.
~ ^^ ~~ v, ~^ ^~ ~~ In ~v Allot QU1
Adversarial social networks are defined as the networks of multiple organizations
within a population or the networks of multiple populations of organizations within a
community (van Meter, 20011. These networks exist in the same or similar niches,
competing for the same or similar resources, and seek to Dive out their competitors? thus
dominating the community. The relations among these nodes represent ties that vary in
the extent to which they may be "adversarial." For instance, the multiple intelligence
gathering organizations within the US (such as the CLA, FBI, and the NSA) are
populations of organizations that are sometimes collectively referred to as the
"intelligence community." The relationships between these populations range from being
DYNAMIC SOCIAL N~TWORKMODELI?JG AND ANALYSIS
!
325
OCR for page 326
cooperative to competitive. Also included within the "intelligence community." are
additional populations including intelligence gathering organizations of"fr~endly"
countries as well as the intelligence gathering organizations of hostile countnes and non-
sovereign entities. This conceptualization of adversanal networks includes communities
(or populations of organizations) within a variety of contexts including the
e~ecommunicat;lons Industry, the We- landscape, biological warfare, the film industry.
and even the development and distribution of retail consumer products. Arquilia and
RonfelUt (2001) identify several recent social movements and ideological campaigns that
they identify as "netwars" in which "numerous dispersed small groups using the latest
communications technologies could act conjointly across great distances" (p. 21. The
groups they consider include terronsts, criminals, separatists, drug cartels, radical
activists, non-government organizations (NGOs) and civil society advocates. They noted
similar adversarial network patterns when they examined the organizational forms of
seemingly disparate activities such as the al-Qaeda network's terrorist operations (see
also Krebs, 2001), the Chechen effort to secede from Russia, the Direct Action Network's
operations dunng the 1999 World Trade Organization summit in Seattle, Greenpeace, the
International Campaign to Ban Landmines (ICBL), and the Zapatista National Liberation
Anny. Therefore, although the notion of adversarial networks has captured a great deal of
attention in the context of te~Tonst networks, the theoretical scope is increasingly relevant
to a much wider cast of social contexts, many of which do not carry the same illegal and
illicit connotations (Bryant, Shumate, & Monge, 2002~.
_ , ~
~ _ 1 ~ 1 , , ~ ~ ~ ~ · ~ ~ ~ ~ ~
From "Networks in Organizations" to "Networks as Organization"
Network forms of organization are neither vertically organized hierarchies like
their bureaucratic predecessors nor are they unorganized marketplaces governed by
supply and demand (Powell, 1990; Williamson, 19961. Rather, network organizational
forms are built on generaZzzed structures that link people and knowledge in all parts of
the organization to one other, while simultaneously tying them to multiple external
contacts. These new forms are knowledge intensive (Badaracco, 1991), agile (Goldman,
Nagel, & Preiss, ~ 995), and are constantly adapting as new links are added and
dysfunctional ones are dropped. Thus, the evolving network form is the organization.
Knowledge networks by themselves and as organizational forms are understoocl
as general concepts. Knowledge circulates throughout network organizations in a variety
of forms: as individual and cognitive networks (CarIey, 1995~; as distnbuted work team
networks (Hollingshead, 19981; as internal organization-wide networks enabled by
Intranets (Monge & Contractor, 2001; 20031; and as external network connections via
Extranets (Bar, 19951. Further, these networks are highly interdependent and co-evolve
with the network fonts of which they are a part. In fact, because knowledge work
consists primanly of linking and integrating the venous components of knowledge
together, it is crucial for scholars to examine all parts of the entire knowledge network
concurrently over time. This includes efforts to compile, extend, test and refine current
understanding of (i) how best to characterize knowledge networks at various levels and
326
DYNAMIC SOCIAL NETWORK MODELING AND ANALYSIS
OCR for page 327
measure their state and dynamic evolution, and (ii) the theoretical mechanisms that
explain the evolution of these networks.
Multi-Theoretical Multi-leve} (MTML) Mode! for Studying the Emergence of
Networks
Monge and Contractor (2003) have proposed a multi-theoretical multi-level
(MTML) mode! to study the creation. maintenance, development, and reconstitution of
network linkages in organizational and inter-organizational contexts. In general terms, we
ask the question: "What are the social mechanisms that help us understand why
individuals (or aggregates of individuals) seek to forge, sustain, or dissolve our network
ties with other human and non-human agents?" As developments in information and
communication technologies continue to reduce or eliminate the potential logistic barriers
to our network relations. it becomes increasingly important to identify the various social
factors that enable or constrain the development of these network linkages. Mon;,e and
Contractor (2003) identify a wide array of theories that can be used to develop network
formulations. The theories and their theoretical mechanisms are summarized in Table I.
In the interest of brevity, the following summary does not include citations to the various
scholars who have contributed to these theones. Details about the theories and their
intellectual proponents can be found in Monge and Contractor (20031.
Theories of self-interest focus on how actors (people, organizations, etc.) make
choices that favor their personal preferences and desires (Bourdieu & Wacquant, 1992;
Coleman, 19861. That is, Person i's decision to forge a tie with another Person j is
motivated entirely by Person i's self-interest and ability in seeking a resource that Person
J possesses. Two primary theories in this area are the theory of social capital (Burt, 1992.
2001; Lin 2001) and transaction cost economics (Williamson, 1975, 198S). Distinct from
human capital, which describes individual personal characteristics, social capital focuses
on the properties of the communication networks in which people are embedded.
Structural holes in the network provide people opportunities to invest their information,
communication, and other social resources in the expectation of reaping profits.
Transaction cost economics examines the information and communication costs involved
in market and organizational transactions as well as ways in which to minimize these
costs. Network forms of organization provide an alternative to markets and hierarchy'
which focuses on embeddedness in complex networks (Powell, 19901. Information flows
are essential in determining to whom a firm should link and joint value maximization
offers an alternative principle to minimizing transaction costs (Zajac & Olsen, 19931.
Self-interest mechanisms are likely to foster the formation of separate adversarial
networks. These theories suggest that each network will invest its own social capita] to
expand its own network. They also suggest that each network will seek to exploit the
structural holes of its adversanes.
Theories of mutual interest and collective action examine how coordinated
activity produces outcomes unattainable by individual action (Marwell & Oliver, 19931.
DYNAMIC SOCIAL NETWORK MODELING ED ISIS
327
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That is, Individual i's and Individual j's decision to forge a tie is motivated by their
belief that it serves their mutual (or collective) interest in accomplishing common or
complementary goals. One theory that exemplifies this perspective is public goods theory
(Hardin, 1982; Samuelson, 1954), which examines the communication strategies that
enable organizers to induce members of a collective to contribute their resources to the
7"able 1. Selected Social Theories and their Theoretical Mechanisms
Theorv
Theories of Self-Interest
Social Capital
Structural Holes
Transaction Costs
Mutual Self Interest &
Collective Action
Public Good Theory
Cntical Mass Theory
Cognitive Theories
Semantic/knowledge Networks
Cognitive social structures
Cognitive Consistency
Balance theory
Cognitive Dissonance
Contagion Theories
Social Information Processing
Social Learning Theory
Institutional Theory
Structural Theory of Action
Exchange and Dependency
Social Exchange Theory
Resource Dependency
Network Exchange
Homophily & Proximity
Social Companson Theory
Social Identity
Physical proximity
Electronic Proximity
328
Theoretical Mechanism
Investments in opportunities
Control of information flow
Cost minimization
Joint value maximization
Inducements to contribute
Number of people with resources & interests
Cognitive mechanisms leading to:
Shared interpretations
Similanty in perceptual structures
Drive to avoid imbalance & restore balance
Dnve to reduce dissonance
Exposure to contact leading to:
Social influence
Imitation, modeling
Mimetic behavior
Similar positions in structure and roles
Exchange of valued resources
Equality of exchange
Inequality of exchange
Complex calculi for balance
Choices based on similarity
Choose comparable others
Choose based on own group identity
Influence of distance
Influence of accessibility
DYNAMIC SOCIAL NETWORK MODELING ED ISIS
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Theories of Network Co-evolution Vanation, Selection. Retention
Organizational ecology
NK(C)
Community ecology
Competition for scare resources
Network density and complexity
Commensalist/Syrnbiotic reins b/w populations
realization of a public good. Mutual interest often conflicts with the individual self-
interests of the members of a collective and sometimes leads to free riding (Olson, 1965)
and other social and communication dilemmas (Bonacich & Schneider, 1992; Kalman,
Monge, Fulk, & Heino, 20021. Network relations are often essential to the provision and
maintenance of the good. Mutual interest theories apply largely to the separate
adversaries, as they seek to foster their own agendas in consort with those of like minds.
Insight can be gained by analyzing what the various networks define as their collective
goods and bads, and the strategies and enticements they use to induce others in join their
networks or at least support their separate causes.
Contagion theories address questions pertaining to the spread of ideas, messages,
attitudes, and beliefs through some form of direct contact (CarIey. 1991: Contractor &
Eisenberg, 19901. For instance, Person i's decision to forge a tie with another Person j is
motivated by others in Person i's network who have forged ties with Person j. Contagion
theories are based on a disease metaphor, where exposure to communication messages
leads to "contamination." Inoculation theory (McGuire, 1966) provides strategies that can
be used to prevent contamination. Two competing contagion mechanisms have received
considerable attention in the research literature. Contagion by cohesion implies that
people are influenced by direct contact with others in their communication networks
(Erickson, 19881. Contagion by structural equivalence suggests that those who have
similar structural patterns of relationships within the network are more likely to influence
one another (Burt. 19871. Social information processing (social influence) theory (Fulk,
Schmitz, & Steinfield, 1990) suggests that the attitudes and beliefs of nanny. h~r.nme
in: .: 1 ~ .~ ~ ~1 , 1 · . . -
-
~ ~ ~ ~ ^ ,~ _ ~ ~ ~ ~ ~ ~ ~ v ~ z z _
similar to those or the others In their communication networks. Social cognitive theory
(Bandura, 1986) and institutional theory (DiMaggio & Powell, 1983: Mever & Rowan
1 f~77\ _ _ _ :~ ~4 _^ ~ ~ ~ . . · ~ ~
1~1 1) pO51t U1dL mimetic processes lean lo contagion, whereby people Ed institutions
imitate the practices of those in their relevant networks. Contagion theories apply to
adversanal networks in at least three ways. First, each contending network attempts to
extend itself by acquiring and linking new members that accept its core identity. Second,
they seek to infect the members of their adversaries'
~ ~ ~ ~ ~ . . ~ . . ~ ~ ~ .
networks with messages.
n~orrnauon, ana ~aeo~og~es Nat will undermine or destroy them. Finally' the adversaries
seek to defend themselves by inoculating the members of their own networks against
they vats' efforts to infect them.
Cognitive theories explore the role that meaning, knowledge, and perceptions play
in communication networks. individual i's decision to forge a tie with another Person j is
motivated by who Individual i thinks Person j knows or what i thinks j knows or
possesses. Semantic networks are created on the basis of shared message content and
similarity in interpretation and understanding (CarIey, 1986; Monge & Eisenberg, 1987~.
A complementary perspective views interorganizational networks as structures of
DYNAMIC SOCIAL NETWORK HODEL~G ED CYSTS
329
OCR for page 330
knowledge (Kogut, Shan, & Walker. 1993). Creating interorganizational alliances
requires building extensive knowledge networks among prospective partners and
maintaining them among current partners. These knowledge networks are the
mechanisms though which organizations share both explicit and tacit knowl~rl~e
f ~ ~ i_^ ~ ~ . ~ ~ ^~ _ . , 1 , . ~
w~,~`cve c-c,~nrnuncc-ct`~on spruces represent one perceptions that people have about
their communication networks, that is, about who in their networks talk to whom
(Corman & Scott, 19941. These individual cognitive communication networks can be
aggregated to provide a collective or consensual view of the entire network (Krackhardt~
19871. Cognitive consistency theory examines the extent to which the attitudes, beliefs,
opinions and values of network members are governed by a Unve toward consistency or
balance (Heider, 19581. The theory suggests that network members tend toward cognitive
similarity as a function of the cognitive balance in their networks rather than alternative
mechanisms such as contagion (Davis & Leinhardt, 1972; Holland & Leinhardt, 19751.
Transactive memory systems consist of knowledge networks in which people assume
responsibility for mastery among various aspects of a larger knowledge domain. In this
way the collective is more knowledgeable than any component (Hollingshead. 2000,
Moreland, 1999; Wegner, 19871. Knowledge repositories linked to the larger knowledge
network facilitate knowledge storage and processing (Weaner, 19951. While knowledge
flow is essential to an effective knowledge network. communication dilemmas sometimes
lead people to withhold potentially useful information (Kalmarl, et al., 2002~. These
theories offer insights into adversarial networks. Competitors often develop differing
semantic networks based on ingroup-outgroup polarization. While developing their own
knowledge networks, competitors seek to undermine the knowledge networks of others,
often by disseminating misinformation and faulty knowledge to their opponents,
including their cognitive communication structures. Finally transactive memory systems
help identify where crucial expertise lies within knowledge networks thus he.lmino
1 ~ . ~ ~
auvcrsanes to Junta sneer own expertise and to mount attacks their opponents.
Exchange and dependency theories seek to explain the emergence of communication
networks on the basis of the distribution of information and material resources across the
members of a network (Emerson. 1962, 1979a, 1972b; Homans, 19501. That is, Person i's
decision to forge a tie with another Person j is motivated by i's interest in seeking a
resource that j possesses, and in exchange offering Individual j some resource that i
possesses and is of interest to j. People seek what they need from others while giving
what others also seek. The exchange form of this family of theories is based largely on
equality, assuming that giving and getting generally balances out across the network
(Bienenstock ~ Bonacich, 1992; Cook, 19821. The dependency form emphasizes
inequality and focuses on how those who are resource rich in the network tend to
dominate those who are resource poor (Benson, ~ 975; Freeman, ~ 977, ~ 9791.
Consequently, power, control, trust, and ethical behavior are central issues to both
theories (Oliver, 19911. Adversaries seek to dominate each other, perhaps to the point of
destroying, all opponents. While they are likely to utilize exchange mechanisms within
their own networks. adversarial networks are dominated by attempts to assert dependency
relations on their competitors.
330
DYNAMIC SOCIAL N~TWO=MODEL~G ED THESIS
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Homophily and proximity theories account for network emergence on the
basis of the simitanty of network members' traits (Brass, 1995) as well as their
similanty of place (Homans, 19501. Agent i's decision to forge a tie with another Agent j
is motivated by i and j sharing common traits (gender, tenure, etc.) or being proximate in
physical or electronic spaces. Traits represent a variety of personal and demographic
characteristics such as age, gender, race, professional interests, etc. (CarIey, 1991;
Coleman, 1957. Marsden, 1988) Social comparison theory suggests that people fee]
discomfort when they compare themselves to others who are different because they have
a natural desire to affiliate with those who are like themselves (Festinger, 19544. Of
course, this ignores the old adage that opposites attract, which would argue for a
heterophily mechanism. Proximity theories argue that people communicate most
frequently with those to whom they are physically closest (Monge, et al, 19851. The
theory of electronic propinquity extends this to the realm of email, telephones and other
foes of electronic communication (Contractor & Bishop, 2000;Korzenny & Bauer,
1981; WelIman, 20011. HomophiTy theory suggests that networks engaged in adversarial
relations are likely to be comprised of people and organizations who are more similar to
each other than to the members of their competitors' networks. A block mode! of all
adversanal networks (in a given adversarial community) combined into a supra-network
would reveal greater similarity within than between blocks. Proximity theory indicates
that competing adversarial networks should exist in separate physical and electronic
spaces. Yet similarity of traits and space also predicts likely points of contact between
competitors and possible locations for penetration of one network by another, both overt
and covert.
Coevolutionary' theory extends traditional evolutionary theory that posits
aspirations towards "fitness" based on mechanisms of vanation, selection, retention, and
struggle or competition (Aldrich, 1999; Campbell, 1965; Hawley, 1986~. For example,
Entity it's decision to forge a tie with Entity j is motivated entirely by ''s belief that the
network linkage will increase i's fitness (measured as performance, survivability,
adaptability, robustness, etc.; Hannan & Freeman, 19771. Random or planned variations
In organizational traits occur, which are selected and retained on the basis of their
contribution to organizational fitness and survival(Baum, 1996; Nelson & Winter, 19821.
Coevolutionary theory articulates how communities of organizational populations linked
by intra-and-inte~population networks compete and cooperate with each other for scare
resources (Astley. 1985; Kauffman, 19931. In order to survive, organizational networks
must adapt to the constantly changing environmental niches in which they find
themselves while also attempting to influence the ways in which their environments
change (McKelvey, ~ 9971.
In most social contexts, more than one of the theoretical mechanisms reviewed
above simultaneously influence people. In some cases different theones, some using
similar theoretical mechanisms, offer similar explanations but at different levels of
analysis. For instance, contagion mechanisms help explain the emergence of networks
among individuals as well as among adversarial organizations. In other cases, different
theories offer contradictory explanations for the emergence of networks. For instance,
DYNAMIC SOCIAL ME:TWORK MODELING kD CYSTS
331
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theories of self-interest would suggest that adversarial networks create links with
others who are indirectly connected to their adversaries. thereby increasing the
amount of nonredundant information that can be =leaned. On the other hand, cognitive
theories of balance suggest that people seek to be friends with enemies of their enemies
thereby increasing their cognitive need for balance.
EXTENSIONS TO MTML MODEL FOR Tom: STUDY OF ADVERSARIAL
NETWORKS: A COMMUNITY ECOLOGY PERSPECTIVE
The MTML model proposed by Monge and Contractor (2003) focused largely on
the social mechanisms that explain the creation, maintenance, and dissolution of network
linkages within single networks. It does so by examining the structural tendencies of
venous relations (such as communication linkages, knowledge linkages, trust relations)
among the actors within that network and the amputee (such as gender, level in the
hierarchy, and level of expertise) of the actors within that sane network. Also included in
that model, though not extensive explored, was the influence of other networks on the
focal network as well as the influence of the focal network at previous points in time.
These relations are summarized in Figure I. In the case of adversarial networks it is
Figure I. The MTME network structuring process
The MIME Network
Structunng Processes
1 1
Exogenous
attributes
of actors
Exogenous
relations in
networks
Structure of
the focal
network
Endogenous mechanisms
important to further explore the ways in which competitors influence each other and
convolve within the community.
332
DYNAMIC SOCIAL NETWORK MODELING AND ANALYSIS
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There are several examples of how adversarial networks influence the
stra~ctunng of ties within a focal network. For instance, Baker and Faulkner (1993)
note that the activation of ties within a covert network are often constrained by the
structure and scope of ties within the adversanes' networks. The desire to communicate,
share information, and coordinate within a covert network have to be reconciled with the
desire to shield the identity, structure and content of the focal network --- that is, the very
survival of the covert network. Indeed as Weiser (2001) says a recently uncovered
terrorist manual notes explicitly the premium put on secrecy over communication.
Further, as Rothenberg (200 I, p. 41~ states, the viability of terrorist networks is
specifically enhanced by other networks in the community - "the American democratic
system that they seek to destroy, a system that pel:l~its open movement, freedom of
choice, and respects privacy." As descnbed earlier, many of the MTML mechanisms
(such as self-interest, social exchange, and proximity) can account for how competing
networks configure themselves to advance their position in the community while
decreasing the viability of their competitors.
Community ecology theory (Aldrich, 1999; Baum & Singh, ;1994; Hawley, 1950)
provides a framework to explain the coevolution of populations of adversarial networks
within a community. Community ecology examines multiple populations of differing
organizations as well as the venous niches in which they occur. Organizations must
typically compete with others in their own populations to acquire the resources they nerd
to survive in their selected environments.
, ·, . . . ~ ~
~ ^ ~ _r ~ Shiv ~~ ~_~& _ Em_ ~ _~JA~O Lab_) 11~
- ~ ~ For example, Barnett (1990) studied the
compenuon among the members or the population of the telephone companies in
PennsyI>rania from 1879-1934, until one company AT&T dominated the market. And
Staber (1989) studied the emergence or worker and consumer coops showing how they
competed for customer loYaltY. Likewise members of the population of US intelligence
organizations (such as the FBI, CLA, NSA) compete with each other for resources,
jurisdiction, and legitimacy But internal competition is not the only challenge that
populations face. They must deal with the members of other populations of friendly and
adversanal intelligence organizations outside the US as well as of non-governmental
organizations that coexist in their niche. For example, Haveman (1992) studied the
competition between banks and savings and loan associations for customers and their
funds under changing regulatory conditions. And Carroll and Swaminathan (1992)
examined the emergence of microbrewer~es and brewpubs who competed with the mass
producers. Often organizations must compete, but under some conditions organizations
from different populations can also cooperates seeking mutually beneficial outcomes, a
fact that Kauffman (1995~ p. 215) saYs is much more commonly recognized now than in
earlier theonzing.
interdependence Unve
, (, _
Aldrich (1999, citing Hawley, 1950) argues that two types of
community dynamics: commensalism and symbiosis.
"ComntensaZisn~ refers to competition and cooperation between similar units, whereas
symbiosis refers to mutual interdependence between dissimilar units' (p. 29S, italics in
the original). Thus, in large part, relations both within and among populations govern
communities. The first relation is the degree to which similar populations in the same
DYNAMIC SOCIAL N~TWO=MODEL~G ED ISIS
333
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niches compete or cooperate and the second is the degree to which different
populations in the same or different niches support each other.
An important issue that has arisen with regard to community ecology is how to
define a community (Aldrich, 1999; DiMag=,io, 19941. Hawley's (1950) original
sociological work on community ecology focuses relationships within geographically and
temporally bound communities. As community ecology has been refitted for
organizational scholarship, the definitions of community have taken a more functional
approach (Ruef, 20001. That is not to say, however, that organizational scholars
completely agree on how community should be defined, operationalized, or analyzed.
Astley's (1985) organizational mode! of community focuses on the technology-based
interrelationships between populations. ~
Earnest and hIS colleagues (Barrett, 1994;
Barnett & Carroll, 1987; Barnett, Mischke, & Ocasio, 9000) define community on the
basis of commensatist and symbiotic relationships between organizations. Hannan and
Ca~Toll (1995) broaden the scope of this definition, asserting that community '-refers to
the broader set of organizational populations whose interactions have a systemic
character, often caused by functional differentiation'' (p. 30~. Rosenkopf and Tushman
(1994) add that context is important, in their case the technological context. Aldrich
(1999) and Ruef (2000) add that the populations in a community should be organized
around a "core," whether it be technological, normative, functional, or legal-regulatory.
Ruef (2000) goes about organizing his community of health care populations by focuses
on four main functions of the health care field. Aldrich (1999, p. 301) proffers this
succinct definition: "An organizational community is a set of coevolving organizational
populations joined by ties of commensalism and symbiosis through their orientation to a
common technology, normative order, or legal-regulatory regime.~' (p. 3011.
Aldrich (1999, p. 302) proposes a taxonomy of eight possible relations between
organizational populations based on the effects these relations have on each of the
population. Aldrich proposes 6 types of commensalist relations (those that occur among
similar units): full competition (where each population negatively impacts the other);
partial competition (where only one of the populations has a negative effect on the other);
predatory competition (where one population has a positive effect on the other while the
latter has a negative impact on the fonder); neutrality (where neither population has a
positive or negative impact on the other); partial mutualism (where one has a positive
effect on the other, but the latter has no impact on the formerly full mutualism (where
both have positive impacts on each other). While full mutuaTism is clefined as a
commensalist relation (between similar populations), symbiosis is defined as a mutually
beneficial relation between dissimilar populations. Finally, Aldrich (1999; p. 302) defines
a dominance relation where a "dominant population controls the How of resources to
other populations (Hawley, 19501. The structure of the community and the convolution of
the populations of networks that comprise it den~nd on the. tom Of r~mmPncalictir
and symbiotic relations."
~-~~~~~ ~- - -rid ~~~ ~ ~~ ~.~.1~.~~
, ~
Aldnch's (1999) review illustrates several research examples for each of these
intra- and inter-population relationships. However, most of the extant research in this area
has examined only two populations at a time. Clearly, the community ecology theory
334
DYNAMIC SOCIAL NETWORK MODELING ED THESIS
OCR for page 335
posits an examination of multiple (not just two) populations within the community
and their co-evolving influences on one another. Social network analysis in general,
and the MTML framework proposed here, is particularly well-suited to this task. For
instance, we can begin to hypothesize and empirically test how the populations within the
community co-evolve when relations among the competing populations are considered at
the triadic, sub-groups, and global levels. To what extent is the creation of ties within
adversarial networks influenced by their common technology, normative order, or legal-
regulatory regime? And, to what extent are the creation, maintenance, and dissolution of
network relations within focal adversarial networks influenced by the venous
commensalist relations (within other organizations in the same population) and symbiotic
relations with other populations within the community? Thus community ecology theory
provides an important addition to the previously described MTML mechanisms in
explaining the emergence of adversarial networks.
ANALYTIC FRAMEWORK FOR STUDYING MTML MODELS
The previous section has described theoretical mechanisms that offer explanations
for the co-evolution of communication and knowledge networks. The schematic in the
following figure describes a comprehensive analytic methodology developed by
Contractor et ~ (1999) as part of our ongoing NSF-funded research to computationally
model. empincally assess, and statistically validate the multiple theories that explain co-
evolution of knowledge networks. These include changes resulting from the interventions
of technologies. The schematic shows the relationship among the key elements of the
analytic framework approach: (~) theory building/hypothesis formulation about
mechanisms of network co-evolution; (2) computational modeling/simulahon of those
mechanisms and how they produce emergent behavior; (3) collection and analysis of
empirical data, and (4) development and deployment of novel "community-ware"
knowledge network enabling tools. The new kinds of data analysis and theory validation
are enabled by (5) advances in p* statistical techniques for modeling and analyzing
network data.
Table 2 provides an illustration of how pa network analytic techniques can be
used to simultaneously test multiple theories at multiple levels (Contractor, Wasserman,
& Faust, 20021. Table 2 summarizes venous genres of network hypotheses in terms of the
probabilities of graph realizations exhibiting the hypothesized relational property. In
each case, the hypothesis is that graph realizations with the h~noth~i7f=~1 nr~n~rt`' hn`~-
larger probabilities of being observed.
acre vets In, ARMY_
In other words, the probability of ties being
present or absent in the graph reflects the hypothesized relational property. The table
begins by distinguishing endogenous and exogenous variables that influence the
probability of ties being present or absent in the focal network. It should be noted that the
exogenous-endogenous distinction being made here is not equivalent to similar
terminology used in the development of causal models in general and structural equation
models in particular. Unlike its use in causal modeling endo~enous variables here are not
DYNAMIC SOCKS N~TWO~MODELING AND ISIS
335
OCR for page 336
predicted by exogenous variables. Here, both explain structural tendencies of the
network.
Endogenor~s variables (Rows ~ through 4 in Table 2) refer to venous relational
properties of the focal network itself that influence the probability of ties being present or
absent in the same network. From a meta-theoretical perspective, these endogenous
variables capture the extent to which relational properties of the network influence its
self-organization. Exogenous variables (Rows 5 through ~ ~ in Table 2) refer to various
properties outside the specific relation within the focal network that influence the
probability of ties being present or absent in the focal network. Hence exogenous
variables include the attributes of the actors in the network, additional network relations
among the actors, the same network relation at previous points in time, as well as other
networks within the same population or other populations. Within each of these two
categories (i.e., endogenous and exogenous variables), the table offers a further sub-
ciassification based on the extent to which the probability of ties being present or absent
in the network are influenced by properties at the actor, dyed. triads and global levels. In
addition to including genres of network hypotheses, the third column in Table ~ also
Figure 2. Analytic framework to study the coevolution of adversarial networks
New hypotheses
Micro-behavioral theories
2. Computabonally model the
theoretical mechanisms of MTML to
predict convolution of adversarial
networks and their performance
Model predictions of network
evolution and meso-level
or~aniz.ational performance
<
Refinements to theory
I ~ Mulu-level hypotheses and
Technology au.cmen Cation concerts to be measured
. . ~ _ . ~
4. Use "community- ~ ~ 3.-Collect empirical data
ware" web-based tools to :~ ~~ ~ I: on co evolution of
enable the co-evolution of ~ :-: -~ ~~: ~adversanal networks
adversanal networks
.:.. ~
:~,
.. . .
/
Web-based surveys and read time
COmDUter-C3Dt~red rI,lt.n
5. Develop and deploy
p* stahstica1 methods
to validate the predictions of
computational models with
empirical data
E~cloge~zous variables (Rows ~ through 4 in Table 2) refer to venous relational properties
of the focal network itself that influence the probability of ties being present or absent in
the same network. From a meta-theoretical perspective, these endogenous variables
336
DYNAMIC SOCKS NETWORK MODELING ED ^4YSIS
OCR for page 337
capture the extent to which relational properties of the network influence its self-
organization. Exogenous variables (Rows 5 through ~ ~ in Table 2) refer to venous
properties outside the specific relation within the focal network that influence the
probability of ties being present or absent in the focal network. Hence exogenous
variables include the attributes of the actors in the network, additional network relations
among the actors, the same network relation at previous points in time, as well as other
networks within the same population or other populations. Within each of these two
cate,ones (i.e., endogenous and exogenous variables), the table offers a further sub-
cIassification based on the extent to which the probability of ties being present or absent
in the network are influenced by properties at the actor. dyed. triad. and global levels. In
addition to including genres of network hypotheses, the third column in Table 2 also
includes specific theones, previously discussed in the MTME models that might provide
support for hypotheses. As such it demonstrates a close coupling between the MTML
model and pa as a confirmatory analytic technique for empirically testing the model.
DYN~IC SOCIAL HE:TWORKHODEL~G~D ISIS
337
OCR for page 338
Table 2. Summary of a multilevel mulh-theoretical framework to test hypotheses about adversarial
networks (Variable of interest: Probability of the realizations of a graph)
Independent
variable
1. Endogenous
(same network):
Actor level
2. Endo~,enous
(same network):
Dyad level
3. Endogenous
(same network):
Triad level
4. Endogenous
(same network):
Global level
5. Exogenous: Actor
attributes (Actor
level)
6. Exogenous: Actor
attributes (Dyad
level)
7. Exogenous: Actor
attributes (Triad
1level)
S. Exogenous: Actor
attributes (Global
[~1)
9. Exogenous:
Network fOther
relationsJ
10. Exogenous:
Network (Same
relation at previous
point in time)
Il. Exogenous:
Other networks
(Within the same
population and in
other populations)
338
Examples of Hypotheses: Graph realizations where there is
specific measures greater likelihood of ...
Actor centrality, ... high actor centrality have higher
structural autonomy. probabilities of occurring (e.g., Theory of
structural holesJ
. _ _
Mutuality, ... high mutuality have higher probabilities of
Reciprocation occurring (e.g., Exchange Theory)
Transitivity' ... high cyclicaTity have higher probabilities of
cyclicality occurring (e.g.' Balance Tl~eo'y)
Network density, . . . high centralization have higher probabilities
centraTization of occurnng. (e.g., Collective action theoryJ
Ages gender, ... ties between actors with similar attributes
organization type' have higher probabilities of occurring (e.g.,
education Theories of homophily)
Differential ... mutual ties between actors with similar
mutuality and attributes have higher probabilities of
reciprocation occuITing (e.g., Exchange Theory)
Differential ... transitive (or cyclical) ties between actors
transitivity and with similar attributes have higher probabilities
cyclicaTity of occurring. (e. g., Balance TheoryJ
a_ . ~
Differential network ... network centralization among actors with
density, similar attributes have higher probabilities of
centralization occurnng. (e.g., Collective action tI'eoryj
.
Advice, friendship communication ties co-occurung with ties
network on a second relation have higher probabilities
of occurring. (e.g.' Cognitive theories)
. . . _
Communication ... ties between actors co-occurring with ties at
network preceding points in time have higher
probability of occurring. (e.gEvolutionary
theories)
._-
Actor, dyed, triad, ... ties between actors co-occurring due to
and global metrics structural properties of other networks within
in other networks the same population and other populations
within the community (e.g., Community
_ Ecology theory)
DYNAMIC SOCIAL NETWORK MODE AND CYSTS
OCR for page 339
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344
DYNAMIC SOCIAL NETWO=MODEL~G ED ^^YSIS
Representative terms from entire chapter:
dynamic social
