Theories of Communication Networks

2003 ◽  
Author(s):  
Peter R. Monge ◽  
Noshir Contractor
Keyword(s):  
Communication Networks ◽  
Computer Simulations ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Cross Sectional ◽  
Network Decomposition ◽  
Network Computer ◽  
Social Theories ◽  
Complex Adaptive ◽  
Almost All

To date, most network research contains one or more of 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 very little the insights from contemporary complex systems analysis and computer simulations. Foruth, it typically uses descriptive rather than inferential statistics, thus robbing it of the ability to make claims about the larger universe of networks. Finally, almost all the research is static and cross-sectional rather than dynamic. Theories of Communication Networks presents solutions to all five problems. The authors develop a multitheoretical model that relates different social science theories with different network properties. This model is multilevel, providing a network decomposition that applies the various social theories to all network levels: individuals, dyads, triples, groups, and the entire network. The book then establishes a model from the perspective of complex adaptive systems and demonstrates how to use Blanche, an agent-based network computer simulation environment, to generate and test network theories and hypotheses. It presents recent developments in network statistical analysis, the p* family, which provides a basis for valid multilevel statistical inferences regarding networks. Finally, it shows how to relate communication networks to other networks, thus providing the basis in conjunction with computer simulations to study the emergence of dynamic organizational networks.

scholarly journals THE ADOPTION OF MANAGEMENT CONTROLS FROM A COMPLEX ADAPTIVE SYSTEMS PERSPECTIVE

2021 ◽  
Vol 7 (1) ◽  
pp. 5-24
Author(s):  
Joshua Mandre ◽  
◽  
James Kagaari ◽  
Levi Kabagambe ◽  
Joseph Ntayi ◽  
...  
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Research Question ◽  
Manufacturing Firms ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Systems Perspective ◽  
Complex Adaptive ◽  
Management Controls ◽  
Self Administered Questionnaire

The purpose of this paper is to investigate whether self-organisation predicts of adoption of management controls in manufacturing firms. The study employed the lens of complex adaptive systems theory to investigate the research question. The study used a cross-sectional survey to collect data from 202 manufacturing firms with the use of a multi-dimensional self-administered questionnaire Data were analyzed quantitatively using PLS-SEM. The findings indicate a positive relationship between innovativeness, emergence and adoption of management controls. The hypothesis for networks of interaction was not supported.

Evolutionary and Coevolutionary Theories

2003 ◽  
Author(s):  
Peter R. Monge ◽  
Noshir Contractor
Keyword(s):  
Communication Networks ◽  
Evolutionary Theory ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Organizational Boundaries ◽  
Complex Adaptive ◽  
Quasi Experimental ◽  
Emergent Structure ◽  
Sociocultural Processes ◽  
Self Organizing

Chapter 3 discussed the emergence of communication networks from the perspective of complexity theory. Specifically, we described complexity as a network of agents, each with a set of attributes, who follow rules of interaction, which produces emergent structure. Complexity arose from the fact that there were numerous agents with extensive relations. Some complex systems but by no means all, we argued, were self-organizing, meaning that they created and sustained internal structure in response to the flow of matter and energy around them. Some readers, particularly those with some familiarity with the complex adaptive systems literature, may have noticed that the discussion of complexity in chapter 3 did not include processes of adaptation, evolution, or coevolution. The reason for this is that it is possible to view these as theoretical mechanisms that operate in at least some complex, self-organizing systems, though not necessarily all. Thus, we have chosen to treat adaptation and the coevolutionary perspective as theoretical mechanisms in the same manner as the other theoretical mechanisms we have examined in chapters 5 through 8. In the present chapter we examine adaptive and coevolutionary processes as the basis for building MTML models of emergent communication networks that form the basis for organizational populations and communities. Modern interest in evolutionary theory as a basis for studying human social processes can be traced to the work of Amos Hawley (1950, 1968, 1986). Much of the interest in applying this perspective to studying organizational structures is credited to Donald Campbell (1965, 1974). Over much of his professional life Campbell explored the application of evolutionary theory to a wide array of sociocultural processes, including organizations (Baum & McKelvey, 1999). Campbell is perhaps best known throughout the social sciences for his work on experimental and quasi-experimental design (Campbell and Stanley, 1966; Cook & Campbell, 1980) and multimethod triangulation (Campbell & Fiske, 1959). Nonetheless, McKelvey and Baum (1999) point to Campbell’s enormous influence in organizational science via the early work of Aldrich (1972) on organizational boundaries, Weick’s (1979) formulation of an evolutionary model of organizing, Hannan and Freeman’s (1977, 1983) development of population ecology theory (and inertial theory), McKelvey’s (1982) work on organizational taxonomies, and Nelson and Winter’s (1982) evolutionary theory of economics.

Tags, Interaction Patterns, and the Evolution of Cooperation

2005 ◽  
Author(s):  
Rick L. Riolo ◽  
Michael D. Cohen
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Building Blocks ◽  
Internal Models ◽  
Evolution Of Cooperation ◽  
Classifier Systems ◽  
Interaction Mechanisms ◽  
Bucket Brigade ◽  
Complex Adaptive ◽  
Almost All

There are several key ideas that appear in almost all of John Holland's writings on artificial and natural complex adaptive systems: internal models, default hierarchies, genetic (evolutionary) algorithms, and recombination of building blocks. One other mechanism, which is linked to all of those, is tag-based interaction. Perhaps the first use of tag-based interaction (though it was not so named) can be found in Holland's "broadcast system," [26] a formal specification of an architecture suitable for modeling adaptation of open-ended, parallel processes. Tag-based interaction mechanisms next played a key role in classifier systems [30, 32]. In classifier systems, a tag acts as a kind of "address" of one or more classifier rules (productions), enabling rules to send messages to selected sets of rules, and allowing rules to select which messages they will respond to. Thus, tags provide a way to structure computations, making it possible to prove that classifier systems are computationally complete [18], to various neural network architectures [8, 55] and even to abstract models of immune systems [17]. Tags also are used to form coupled chains of classifiers, to construct subroutinelike structures, and to allow Holland's Bucket Brigade algorithm to efficiently allocate credit to "stage setting" rules [9, 30, 50]. Holland has also described how tagged classifiers might be used to form default hierarchies and other more complex internal models [28, 30, 33, 46]. More generally, Holland has emphasized the key role that tag-based interaction mechanisms have in almost all complex adaptive systems (CAS), i.e., systems composed of limited capability agents who interact to generate systemlevel behavior [31]. In the context of CAS, tags are arbitrary properties or traits of agents which are visible to other agents, and which agents can detect and use to condition reactions to other tag-carrying agents. Tags can be agent features, such as surface markings, or they can be agent behaviors, from behavioral routines in animals to more complex behaviors of humans, e.g., wearing particular clothes, carrying flags, or following religious customs [3, 31, 53]. Since agents can have different tags, and since arbitrary tags can come to be associated with particular types of agents (with their own interaction and behavioral patterns), tags can take on "meanings" by virtue of the types of agents who display each particular tag, i.e., as a result of the other behavioral traits those agents tend to have.

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