The Formation of Networks with Local Spillovers and Limited Observability

In this paper I analyze the formation of networks in which each agent is assumed to possess some information of value to the other agents in the network. Agents derive payoff from having access to the information of others through communication or spillovers through the links between them. Linking decisions are based on network-dependent marginal payoff and a network independent noise capturing exogenous idiosyncratic effects. Moreover, agents have a limited observation radius when deciding to whom to form a link. I find that for small noise the observation radius does not matter and strongly centralized networks emerge. However, for large noise, a smaller observation radius generates networks with a larger degree variance. These networks can also be shown to have larger aggregate payoff. I then estimate the model using a network of coinventors, firm alliances and trade relationships between countries, and find that the model can closely reproduce the observed patterns. The estimates show that with increasing levels of aggregation, the observation radius is increasing, indicating economies of scale in which larger organizations are able to process greater amounts of information.diffusion, network formation, growing networks, limited observability

Looking for the Core of a Knowledge-based Sea Cluster: A Social Network Analysis in a Maritime Region

For more than two decades cluster policies have emerged as a central focus for decision-makers trying to instigate territorial development. The benefits, especially in terms of collective learning, knowledge sharing and other types of agglomeration economies and spill-over effects, are well stressed in the regional science literature. Today the relevance of maritime activities and marine resources to economic development is acknowledged. For several European countries, the Atlantic Ocean is part of their common history, identity and potential for developing advanced economic niches of excellence. There is no surprise that several regions are trying to implement their development strategies based on a broad Sea Cluster notion that encompasses a diversity of economic activities such as fisheries and aquaculture, coastal tourism, marine transports and activities based on marine sciences and maritime technologies. Based on the results of a trans-regional evaluation performed for the Atlantic Area under project KIMERAA, this paper evaluates the consolidation of the Sea Cluster in the Algarve, a Portuguese region internationally known by its coastal tourism. The region has also been experiencing a growing capacity in economic activities linked to marine sciences. This regional cluster did not emerge spontaneously and there are several initiatives to promote it. Interviews to regional actors showed light on two important issues. i) Which organization should be the main mediator to bridge science to market? ii) Who is the actor that is in a better position to assume a pivotal role in the formal consolidation of the cluster? Using social network analysis the main knowledge transfer mediator and the central actors are identified. Their roles and specific policy implications are underlined.

Evolution of networks

We review the recent fast progress in statistical physics of evolving networks. Interest has focused mainly on the structural properties of random complex networks in communications, biology, social sciences and economics. A number of giant artificial networks of such a kind came into existence recently. This opens a wide field for the study of their topology, evolution, and complex processes occurring in them. Such networks possess a rich set of scaling properties. A number of them are scale-free and show striking resilience against random breakdowns. In spite of large sizes of these networks, the distances between most their vertices are short -- a feature known as the ``small-world'' effect. We discuss how growing networks self-organize into scale-free structures and the role of the mechanism of preferential linking. We consider the topological and structural properties of evolving networks, and percolation in these networks. We present a number of models demonstrating the main features of evolving networks and discuss current approaches for their simulation and analytical study. Applications of the general results to particular networks in Nature are discussed. We demonstrate the generic connections of the network growth processes with the general problems of non-equilibrium physics, econophysics, evolutionary biology, etc.Comment: 67 pages, updated, revised, and extended version of review, submitted to Adv. Phy

Evaluation of human movement qualities: A methodology based on transferable-utility games on graphs.

Abstract A novel computational method for the analysis of expressive full-body movement qualities is introduced, which exploits concepts and tools from graph theory and game theory. The human skeletal structure is modeled as an undirected graph, where the joints are the vertices and the edge set contains both physical and nonphysical links. Physical links correspond to connections between adjacent physical body joints (e.g., the forearm, which connects the elbow to the wrist). Nonphysical links act as \u201cbridges\u201d between parts of the body not directly connected by the skeletal structure, but sharing very similar feature values. The edge weights depend on features obtained by using Motion Capture data. Then, a mathematical game is constructed over the graph structure, where the vertices represent the players and the edges represent communication channels between them. Hence, the body movement is modeled in terms of a game built on the graph structure. Since the vertices and the edges contribute to the overall quality of the movement, the adopted game-theoretical model is of cooperative nature. A game-theoretical concept, called Shapley value, is exploited as a centrality index to estimate the contribution of each vertex to a shared goal (e.g., to the way a particular movement quality is transferred among the vertices). The proposed method is applied to a data set of Motion Capture data of subjects performing expressive movements, recorded in the framework of the H2020-ICT-2015 EU Project WhoLoDance, Project no. 688865. Results are presented: development of novel method, contribution to the scientific community with a new data corpus, application the discussed method to 100 movement recordings and creation of database archive of stimuli for further use in research studies in the framework of the WhoLoDance Project

SNACH a new framework to support business process improvement.

Business processes are central to any organisation. They coordinate activities, roles, resources, systems and constraints within and across organisational boundaries to achieve predefined business goals. The demand for dynamic business environments, customer satisfaction, global competition, system integration, operational efficiency, innovation and adaptation to market changes necessitates the need for continuous process improvement. In order to adequately respond to these demands, business processes are designed in two approaches: Business Process Re-engineering (BPR) and Business Process Improvement (BPI). This thesis follows the BPI approach which considers existing infrastructure in an organization to improve operational efficiency and achieve organisational goals. Many methodologies have been developed for conducting BPI projects, but they provide little support for the actual act of systematically improving a business process. We adopted case study as the research strategy to examine a collaborative business process, specifically the UK Higher Education Institutions (HEI) admission process. The design science research methodology was used to answer the research questions and satisfy the research objectives. The Map technique was employed to construct the new BPI artefact based on the Mandatory Elements of Method (MEM) from Method Engineering. The new BPI framework comprises of a number of elements to support analysts and practitioners in process improvement activities. We present a novel approach to BPI, the SNACH (Simulation Network Analysis Control flow complexity and Heuristics) framework that supports the actual act of process improvement using a combination of process analysis techniques with integrated quantitative measurable concepts to measure and visualize improvement in four dimensions: cost, cycle time, flexibility and complexity. A simulation technique was employed to analyse the process models in terms of time and cost; and Control Flow Complexity was used to calculate the logical complexity of the process model. A complex network analysis approach was used to provide information about the structural relationship and information exchange between process activities. Using a complex network analysis approach to reduce a process model to a network of nodes and links so that its structural properties are analysed to provide information about the structural complexity and flexibility of the network. To achieve this higher level of abstraction, an algorithm was defined and validated using four disparate process models. The complex network analysis technique is integrated into the SNACH framework and its significance lies in the study of the nature of the individual nodes and the pattern of connections in the network. These characteristics are assessed using network metrics to quantitatively analyse the structure of the network, thereby providing insight into the interaction and behavioural structure of the business process activities. To conclude the design science research process phases, the artefact was evaluated in terms of its effectiveness and efficiency to systematically improve a business process by conducting an experiment using another use case

An Application of Social Network Analysis on Military Strategy, System Networks and the Phases of War

The research developed in this study will utilize Social Network and Graph Theory terminology and methodology applied to groups of systems, rather than individuals within a given system, in order to shape strategic level goals. With regard to military operations, Social Network Analysis has been used to show that enemy networks and relationships can be accurately represented using weighted layers with weighted relationships in order to identify the key player(s) that must be influenced and/or removed so that a particular effect on the enemy might be realized. Social Network Analysis is therefore a significant tool concerning tactical level of operations that aids in developing a targeting methodology which aids tactical commanders in mission planning, however has never been applied to strategic levels of Command. Like previous key player problems, this research will utilize system attributes and global relational strengths as inputs. The output results will rank order representative systems of interest that satisfy the constraints and desired objectives within a particular Phase of War. This work will apply and extend the tools of Social Network Analysis structure and techniques to a theater level mission