The emergence of self-organisation in social systems: the case of the geographic industrial clusters

The objective of this work is to use complexity theory to propose a new interpretation of industrial clusters. Industrial clusters constitute a specific type of econosphere, whose driving principles are self-organisation, economies of diversity and a configuration that optimises the exploration of diversity starting from the configuration of connectivity of the system. This work shows the centrality of diversity by linking complexity theory (intended as "a method for understanding diversity"') to different concepts such as power law distributions, self-organisation, autocatalytic cycles and connectivity.I propose a method to distinguish self-organising from non self-organising agglomerations, based on the correlation between self-organising dynamics and power law network theories. Self-organised criticality, rank-size rule and scale-free networks theories become three aspects indicating a common underlying pattern, i.e. the edge of chaos dynamic. I propose a general model of development of industrial clusters, based on the mutual interaction between social and economic autocatalytic cycle. Starting from Kauffman's idea(^2) on the autocatalytic properties of diversity, I illustrate how the loops of the economies of diversity are based on the expansion of systemic diversity (product of diversity and connectivity). My thesis provides a way to measure systemic diversity. In particular I introduce the distinction between modular innovation at the agent level and architectural innovation at the network level and show that the cluster constitutes an appropriate organisational form to manage the tension and dynamics of simultaneous modular and architectural innovation. The thesis is structured around two propositions: 1. Self-organising systems are closer to a power law than hierarchical systems or aggregates (collection of parts). For industrial agglomerations (SLLs), the closeness to a power law is related to the degree of self-organisation present in the agglomeration, and emerges in the agglomeration’s structural and/or behavioural properties subject to self-organising dynamic.2. Self-organising systems maximise the product of diversity times connectivity at a rate higher than hierarchical systems

The Role of Niche Signals in Self-organization in Society

This dissertation is concerned with the emergence of social patterns. The ability of groups of humans to bring order to both the physical and abstract realms may be our species’ most distinguishing characteristic. It is dependent upon our willingness to cooperate and otherwise coordinate, yet willingness alone is not sufficient for achieving coordinated outcomes on a large-scale because the informational demands of bottom-up organizing are high. Understanding the emergence of social order then requires, in part, understanding how information flows are structured in ways that allow groups to meet the informational demands of self-organization. Of particular importance in this regard are the patterns of person-to-person interactions. In contemporary social network research these interactions are often described as the conduits through which information flows, but person-to-person interactions are also the site and source of the coordination problem needing to be solved. To resolve this tension, network interactions must be patterned in ways that allow for the free flow of information, yet social networks most often exhibit high degrees of clustering, a characteristic which can impede the free flow of information and, thus, large-scale coordination. Does this mean bottom-up processes do not drive coordination within large groups? Is resolution by fiat the only way? Many have made the argument we create and tolerate authorities for precisely this reason, but is that the only viable mechanism for the establishment of large-scale coordination? Inspired by stigmergy, a form of communication used by social insects to coordinate hive activities, this dissertation explores the value of signals occurring outside or alongside of the person-to-person interactions studied using social network analysis. Social life features an abundance of small signals—often in the form of verbal or written communication, but also physical objects and even sounds and smells—potentially freighted with meanings or embedded knowledge. Several research traditions have regarded these signals as part of the fabric of social life, but is the information these signals yield patterned in a way that can help overcome the challenges of large-scale coordination? To begin to answer whether these signals can play a role in mass coordination, this dissertation takes three distinct approaches. The first analyses coupled differential equations describing a system in which a common resource environment is structured by the ongoing actor-to-actor interactions. This system is a modification of a canonical model of molecular self-organization, the hypercycle, and succeeds in organizing vastly more complex sets of interactions than the original. This confirms the information embedded in the environment can indeed be a powerful source of information for coordination. The second paper takes this formal insight into the lab to test whether the addition of a small number of extra-network signals can enable the emergence of conventions in a large, networked group of human participants. It can, and the probability of it happening depends on the strength of the extra-network signal and the topological features of the network. The final paper uses a unique dataset and topic modeling in an attempt to track the emergence of consensus around the themes in works of fiction. While there can be movement in the direction of consensus, the path lengths of the underlying network are too long to support large-scale consensus, a finding consistent with results of the experiment. Implications of these three findings are discussed in the conclusion.PHDSociologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138683/1/atwell_1.pd

The Origin and the Evolution of Firms

The firms and markets of today's complex socio-economic system developed in a spontaneous process termed evolution, in just the same way as the universe, the solar system, the Earth and all that lives upon it. Darwin's theory of evolution clearly demonstrated that evolution involved increasing organization. As we began to explore the molecular basis of life and its evolution, it became equally clear that it depended on the processing and communication of information. This book develops a consistent theory of evolution in its wider sense, examining the information based laws and forces that drive it. Exploring subjects as diverse as economics and the theories of thermodynamics, the author revisits the paradox of the apparent conflict between the laws of thermodynamics and evolution to arrive at a systems theory, tracing a continuous line of evolving information sets that connect the Big-Bang to the firms and markets of our current socio-economic system

Scientific-Philosophical Base of Darwin's and Wallace's Theory of Evolution

If Darwin's and Wallace's theory of evolution is reduced to "eat and be eaten" misunderstanding and rejection arise. From a didactic point of view, a scientific and philosophical examination of the theory is necessary. It can create understanding and acceptance. Epistemologically, the theory of evolution describes a cognition and innovation process that corresponds to scientific working methods. The philosophical analysis shows that ethical behaviour emerges in evolution. The basic concept of this article is the assumption of the unity of spirit and matter (monism) and the parallelism of ethics and mechanics (Elome concept). There is no principal contradiction between the theory of evolution and religious ideas but to the magical-mythical worldview. An understanding of the scientific method is a prerequisite for a deeper understanding of the evolutionary process. It is not about a branch of biology, but about a world view

Modelling the dynamics of the innovation process : a data-driven agent-based approach

Decision making on innovation is difficult because innovation involves large numbers of and constantly changing interactions between actors and their activities. Decision makers lack information about these complex interactions. This makes it hard for them to predict the relationships between decisions and the outcomes. But now, with the easy availability of large amounts of data via internet, it is possible to get down to the details underlying innovation processes and to investigate patterns among these interactions to provide decision support. Under this background, this research explores the following Problem Statement (PS): To what extent can the new available big amounts of data be used to improve decision making on innovations? In order to answer the PS, Chapter 2 provides a new data-driven modelling method to analyse the innovation process data; Chapter 3 develops a more advanced innovation process model that provides decision makers with a good understanding of the overall structure of innovation processes; Chapter 4 investigates the underlying mechanism of emergence which provides decision makers with valuable insights into the interaction patterns on the micro level of innovation processes; Chapter 5 simulates the emergence to support decision making. This research contributes to data science, innovation management, and their cooperation.Algorithms and the Foundations of Software technolog

On security analysis of periodic systems: expressiveness and complexity

Development of automated technological systems has seen the increase in interconnectivity among its components. This includes Internet of Things (IoT) and Industry 4.0 (I4.0) and the underlying communication between sensors and controllers. This paper is a step toward a formal framework for specifying such systems and analyzing underlying properties including safety and security. We introduce automata systems (AS) motivated by I4.0 applications. We identify various subclasses of AS that reflect different types of requirements on I4.0. We investigate the complexity of the problem of functional correctness of these systems as well as their vulnerability to attacks. We model the presence of various levels of threats to the system by proposing a range of intruder models, based on the number of actions intruders can use