Complexity, Networks, and Non-Uniqueness

Symbolic Interaction, 2010

In this article I consider the potential importance of social networks for symbolic interactionism. Specifically, I argue that symbolic interactionism operates with an underdeveloped and often tacit conception of networks; that network analysis offers us various tools for further developing and operationalizing this conception, empirically; and that doing so would be to our considerable advantage. In addition, I argue that a stronger focus on networks would give interactionism an inroad into important contemporary debates on "complexity, " building on an obvious but again underdeveloped affinity between these two academic domains. Moreover, tackling complexity raises important and central sociological concerns of structure/agency and micro/macro, with the additional payoff that interactionism can develop and demonstrate its strengths, as an approach, in addressing the problems often associated with these concerns.

Routledge Handbook of Systems Thinking, 2021

Network theory has broad application in the physical, natural, and social sciences. The study of complex networks , and their applications to the study of complex systems, have focused predominantly on: (1) the elemental vertex-edge structure (a.k.a., nodes and relationships), and subsequently (2) the dynamics that occur as a result of this basic structure (e.g., diameter, distribution , small world, contagion, etc). This network theory methodology has provided powerful quantitative tools in the interdisciplinary study of complex systems, and has enriched our thinking about them. However, the simplifying assumptions of the network theory framework have also informed the field of systems thinking, sometimes to its detriment. Network-thinking tends to shoehorn a number of important elemental structures of complex systems into node-edge relational structure. By assigning these elemental structures to edges, both elemental and emergent complexity can be lost. This paper articulates how DSRP Theory can enrich network thinking about complex systems by: (1) identifying the elemental structures that are typically hidden in network models, (2) quantifying their nature and abundance, and (3) explicating their potential contribution to the intrinsic function and emergent complexity of systems. Specifically , we detail several DSRP heuristics for determining how many elements potentially exist in any network model, demonstrating the effectiveness of DSRP as a "universal cognitive grammar" for identifying and analyzing the structural potentials in complex systems.

arXiv (Cornell University), 2015

2004

Abstract Networks are all around us, all the time. From the biochemistry of our cells to the web of friendships across the planet. From the circuitry of modern electronics to chains of historical events. A network is the result of the forces that shaped it. Thus the principles of network formation can be, to some extent, deciphered from the network itself. All such information comprises the structure of the network. The study of network structure is the core of modern network science.

Interdisciplinary scientific efforts toward understanding the phenomenon of life, in particular the focus of work known as the study of artificial life encounter a number of problems related to the need to work out a common understanding of the issues under consideration. In particular, the researcher's efforts are geared towards defining terms such as agent, agency, life, purposefulness and similar definitions, which are intuitively understandable but difficult to formalize.

2013

Titl e N e t w o r k s: o p e n , clo s e d o r c o m pl ex. Co n n e c ti n g p hilo s o p hy, d e si g n a n d in n ov a tio n, p a r t 3

Physics Reports, 2006

2007

Recent approaches on the study of networks have exploded over almost all the sciences across the academic spectrum. Over the last few years, the analysis and modeling of networks as well as networked dynamical systems have attracted considerable interdisciplinary interest. These efforts were driven by the fact that systems as diverse as genetic networks or the Internet can be best described as complex networks. On the contrary, although the unprecedented evolution of technology, basic issues and fundamental principles related to the structural and evolutionary properties of networks still remain unaddressed and need to be unraveled since they affect the function of a network. Therefore, the characterization of the wiring diagram and the understanding on how an enormous network of interacting dynamical elements is able to behave collectively, given their individual non linear dynamics are of prime importance.

Networks and Spatial Economics

Economic complexity highlights the relationship between interdependence (a positional characteristic of elements belonging to a given network or structure) and connectivity (a functional characteristic of elements belonging to a given field of interaction). Positional interdependence (as the one between pieces in a jigsaw puzzle) is central to studies investigating the architecture of a complex system (Simon) while connectivity is central to the analysis of responsiveness patterns in social networks and strategic action fields. This paper discusses the fundamentals of a structural approach to economic and spatial complexity by highlighting the hierarchical arrangement of network elements as a distinctive feature of system identity. The positional distribution of network elements is a fundamental characteristic of complex networks and a central condition constraining the dynamics of those networks through the principle of relative structural invariance. The paper investigates the rol...