5,237 research outputs found
Economic Performance, Inter-Firm Relations and Local Institutional Engineering in a Computational Prototype of Industrial Districts
Industrial districts can be conceived as complex systems characterised by a network of interactions amongst heterogeneous, localised, functionally, integrated and complementary firms. In a previous paper, we have introduced an industrial district computational prototype, showing that the economic performance of an industrial district proceeds to the form through which firms interact and co-ordinate each others. In this paper, we use such computational framework to experiment different options of local institutional engineering', trying to understand how specific supporting institutions' could perform macro-collective activities, such as, i.e., technology research, transfer and information, improving the technological adaptation of firms. Is a district more than a simple aggregation of localised firms? What can explain the economic performance of firms localised into the same space? Could some options of 'local institutional engineering, improve the performance of a district? Could such options set aside the problem of how firms dynamically interact? These are questions explored in this paper
Self-organising agent communities for autonomic resource management
The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes
"An Agent Based Cournot Simulation with Innovation: Identifying the Determinants of Market Concentration"
In this paper, I develop a hybrid model that contains elements of both agent based simulations (ABS) as well as analytic Cournot models, to study the effects of firm characteristics, market characteristics, and innovation on market concentration, as measured by a Herfindahl-Hirschman Index (HHI). The model accommodates the following components: multiple firms with heterogeneous marginal costs, market entry and exit, barriers to entry, low or high cost industries, changing demand, varying levels of marginal cost reducing returns-to-innovation, varying costs associated with innovation, increased returns to innovation from past experience innovating, and varying propensities to innovate within the market. The components mentioned above are commonly cited as determinants of market concentration. A sensitivity analysis which is robust to high degrees of model complexity demonstrates that the model provides results that are consistent with economic theories of markets.agent based simulation, Cournot, game, innovation, oligopoly
Developing a conceptual model for exploring emergence
Emergence is a fundamental property of complex systems and can be thought of as a new property or behaviour which appears due to non-linear interactions within the system; emergence may be considered to be the 'product' or by-product of the system. For example, within social systems, social capital, the World Wide Web, law and indeed civilization in general may be considered emergent, although all within different time scales. As our world becomes increasingly more interconnected, understanding how emergence arises and how to design for and manage specific types of emergence is ever more important. To date, the concept of emergence has been mainly used as an explanatory framework (as used by Johnson 2001), to inform the logic of action research (Mitleton-Kelly 2004) or as a means of exploring the range of emergent potential of simulation of real complex systems (Axelrod 2003). If we are to improve our ability to manage and control emergence, we need first to directly study the phenomenon of emergence, its causes and consequences across real complex systems
Biology of Applied Digital Ecosystems
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the biological processes that contribute to
these properties have not been made explicit in Digital Ecosystems research.
Here, we discuss how biological properties contribute to the self-organising
features of biological ecosystems, including population dynamics, evolution, a
complex dynamic environment, and spatial distributions for generating local
interactions. The potential for exploiting these properties in artificial
systems is then considered. We suggest that several key features of biological
ecosystems have not been fully explored in existing digital ecosystems, and
discuss how mimicking these features may assist in developing robust, scalable
self-organising architectures. An example architecture, the Digital Ecosystem,
is considered in detail. The Digital Ecosystem is then measured experimentally
through simulations, with measures originating from theoretical ecology, to
confirm its likeness to a biological ecosystem. Including the responsiveness to
requests for applications from the user base, as a measure of the 'ecological
succession' (development).Comment: 9 pages, 4 figure, conferenc
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Autonomy in the real real-world: A behaviour based view of autonomous systems control in an industrial product inspection system
The thesis presented in this dissertation appears in two sequential parts that arose from an exploration of the use of Behaviour Based Artificial Intelligence (BBAI) techniques in a domain outside that of robotics, where BBAI is most frequently used. The work details a real-world physical implementation of the control and interactions of an industrial product inspection system from a BBAI perspective. It concentrates particularly on the control of a number of active laser scanning sensor systems (each a subsystem of a larger main inspection system), using a subsumption architecture. This industrial implementation is in itself a new direction for BBAI control and an important aspect of this thesis. However, the work has also led on to the development of a number of key ideas which contribute to the field of BBAI in general. The second part of the thesis concerns the nature of physical and temporal constraints on a distributed control system and the desirability of utilising mechanisms to provide continuous, low-level learning and adaptation of domain knowledge on a sub-behavioural basis. Techniques used include artificial neural networks and hill-climbing state-space search algorithms. Discussion is supported with examples from experiments with the laser scanning inspection system.
Encouraging results suggest that concerted design effort at this low level of activity will benefit the whole system in terms of behavioural robustness and reliability. Relevant aspects of the design process that should be of value in similar real-world projects are identified and emphasised. These issues are particularly important in providing a firm foundation for artificial intelligence based control systems
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
The Standard Problem
Crafting, adhering to, and maintaining standards is an ongoing challenge.
This paper uses a framework based on common models to explore the standard
problem: the impossibility of creating, implementing or maintain definitive
common models in an open system. The problem arises from uncertainty driven by
variations in operating context, standard quality, differences in
implementation, and drift over time. Fitting work by conformance services
repairs these gaps between a standard and what is required for interoperation,
using several strategies: (a) Universal conformance (all agents access the same
standard); (b) Mediated conformance (an interoperability layer supports
heterogeneous agents) and (c) Localized conformance, (autonomous adaptive
agents manage their own needs). Conformance methods include incremental design,
modular design, adaptors, and creating interactive and adaptive agents. Machine
learning should have a major role in adaptive fitting. Choosing a conformance
service depends on the stability and homogeneity of shared tasks, and whether
common models are shared ahead of time or are adjusted at task time. This
analysis thus decouples interoperability and standardization. While standards
facilitate interoperability, interoperability is achievable without
standardization.Comment: Keywords: information standard, interoperability, machine learning,
technology evaluation 25 Pages Main text word Count: 5108 Abstract word
count: 206 Tables: 1 Figures: 7 Boxes: 2 Submitted to JAMI
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