2,736 research outputs found
Ising-like agent-based technology diffusion model: adoption patterns vs. seeding strategies
The well-known Ising model used in statistical physics was adapted to a
social dynamics context to simulate the adoption of a technological innovation.
The model explicitly combines (a) an individual's perception of the advantages
of an innovation and (b) social influence from members of the decision-maker's
social network. The micro-level adoption dynamics are embedded into an
agent-based model that allows exploration of macro-level patterns of technology
diffusion throughout systems with different configurations (number and
distributions of early adopters, social network topologies). In the present
work we carry out many numerical simulations. We find that when the gap between
the individual's perception of the options is high, the adoption speed
increases if the dispersion of early adopters grows. Another test was based on
changing the network topology by means of stochastic connections to a common
opinion reference (hub), which resulted in an increment in the adoption speed.
Finally, we performed a simulation of competition between options for both
regular and small world networks.Comment: 23 pages and 5 figure
Knowledge transfer in a tourism destination: the effects of a network structure
Tourism destinations have a necessity to innovate to remain competitive in an
increasingly global environment. A pre-requisite for innovation is the
understanding of how destinations source, share and use knowledge. This
conceptual paper examines the nature of networks and how their analysis can
shed light upon the processes of knowledge sharing in destinations as they
strive to innovate. The paper conceptualizes destinations as networks of
connected organizations, both public and private, each of which can be
considered as a destination stakeholder. In network theory they represent the
nodes within the system. The paper shows how epidemic diffusion models can act
as an analogy for knowledge communication and transfer within a destination
network. These models can be combined with other approaches to network analysis
to shed light on how destination networks operate, and how they can be
optimized with policy intervention to deliver innovative and competitive
destinations. The paper closes with a practical tourism example taken from the
Italian destination of Elba. Using numerical simulations the case demonstrates
how the Elba network can be optimized. Overall this paper demonstrates the
considerable utility of network analysis for tourism in delivering destination
competitiveness.Comment: 15 pages, 2 figures, 2 tables. Forthcoming in: The Service Industries
Journal, vol. 30, n. 8, 2010. Special Issue on: Advances in service network
analysis v2: addeded and corrected reference
Opinion dynamics: models, extensions and external effects
Recently, social phenomena have received a lot of attention not only from
social scientists, but also from physicists, mathematicians and computer
scientists, in the emerging interdisciplinary field of complex system science.
Opinion dynamics is one of the processes studied, since opinions are the
drivers of human behaviour, and play a crucial role in many global challenges
that our complex world and societies are facing: global financial crises,
global pandemics, growth of cities, urbanisation and migration patterns, and
last but not least important, climate change and environmental sustainability
and protection. Opinion formation is a complex process affected by the
interplay of different elements, including the individual predisposition, the
influence of positive and negative peer interaction (social networks playing a
crucial role in this respect), the information each individual is exposed to,
and many others. Several models inspired from those in use in physics have been
developed to encompass many of these elements, and to allow for the
identification of the mechanisms involved in the opinion formation process and
the understanding of their role, with the practical aim of simulating opinion
formation and spreading under various conditions. These modelling schemes range
from binary simple models such as the voter model, to multi-dimensional
continuous approaches. Here, we provide a review of recent methods, focusing on
models employing both peer interaction and external information, and
emphasising the role that less studied mechanisms, such as disagreement, has in
driving the opinion dynamics. [...]Comment: 42 pages, 6 figure
An Information Theoretic Investigation Of Complex Adaptive Supply Networks With Organizational Topologies
Supply networks exist throughout society in manufacturing and knowledge-intensive industries as well as many service industries. Organizations have been noted to behave as complex adaptive systems or information supply networks with both formal and informal structures. Thoroughly understanding supply network structure and behavior are critical to managing such organizations effectively, but their properties of complex adaptive systems make them more difficult to analyze and assess, forcing researchers to rely on unrealistic data or assumptions of behavior. This research proposes an information theoretic methodology to discover such complex network structures and dynamics while overcoming the difficulties historically associated with their study. Indeed, this was the first application of an information theoretic methodology as a tool to study complex adaptive supply networks. Moreover, managing these complex networks with formal and informal structures poses additional challenges because the effects of intervention can result in even more unpredictable effects. Noting that two primary functions of organizational networks are to transfer information between nodes and store information in the network, this research quantifies the effects of increased and decreased node performance on the ability of multiple organizational network topologies to accomplish these tasks. Multiple qualitative observations from previous researchers are quantitatively analyzed using information theoretic modeling and simulation. Results show an increased ability in local teams to store information within the network as well as a decreased ability by core-periphery networks to respond to increased information rates
Engineering simulations for cancer systems biology
Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions
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