Definition of a methodology to analyze and measure interactions inside Regional Innovation Systems.

The aim of the present thesis proposal is to define a methodology to measure the interactions among the agents involved in a System of Innovation, due to the fact that the literature agrees in a lack of measures in this respect. The conceptualization of Regional Innovation Systems (Cooke and Morgan, 1993) can be understood like an extension and adaptation arisen from the concept of National Innovation Systems defined in the works of Freeman (1987), Nelson (ed., 1993) and Lundvall (ed., 1992) and in the subsequent development of Edquist (ed., 1997). It consist of analyzing the existence of actors (institutions, clusters, universities, industries…) and regional competences, and the interactions into Innovation Networks among them, providing regional authorities with a tool to define policies to increase competitiveness. A first stream work in which relations and flows among the main agents of an Innovation System are shown, is the one made up by the works of Scherer, (1982), Pavitt (1984), Archibugi (1988), Galli and Teubal (1997), DeBresson (ed., 1996). Another is due to Andersen (1992, 1996) on Innovation Systems, using “graph theory” and simulation models (Andersen and Lundvall, 1997). Recently, some different research projects can be found in which relations established among the agents in Innovation Systems are studied (European Planning Studies, Vol. 8, Not. 4, 2000). Besides, diverse simulation models created to measure the characteristics of Innovation Systems in different environments (Simulating Self-Organizing innovation networks” -SEIN-) are also detailed. There is a growing need to elaborate indicators that allow to predict changes in the regional innovation capacity beyond those employed in the linear model. We have also noticed the need to measure other processes such as those related to institutional relations and the creation of networks, in order to evaluate innovation policies (Zenker, 2001; Landabaso, Oughton, Morgan, 2001; Saviotti, 1997; Archibugi, Howells and Michie, eds., 1999). This is supported by the fact that several policies fostering innovation have been defined, such as RIS, RTP, RITTS, etc… In this context, and due to the importance of co-operation practices within Regional Innovation Systems, the present research project tries to contribute with a model as well as an Indicator Scoreboard which helps quantify the interrelations that occur among the agents in an Innovation System.Regional Innovation Systems, Innovation Networks, Measures, Interactions.

Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach al-lows the representation of heterogeneous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.innovation networks, agent-based modelling, scale free networks

Using Social Simulation to Explore the Dynamics at Stake in Participatory Research

This position paper contributes to the debate on perspectives for simulating the social processes of science through the specific angle of participatory research. This new way of producing science is still in its infancy and needs some step back and analysis, to understand what is taking place on the boundaries between academic, policy and lay worlds. We argue that social simulation of this practice of cooperation can help in understanding further this new way of doing science, building on existing experience in simulation of knowledge flows as well as pragmatic approaches in social sciences.Participatory Research, Institutional Analysis and Design, Knowledge Flow, Agent Based Simulation

Analysis and measurement of interactions in European Innovation

Innovation Systems constitute an analysis framework, which allows comprehending the socio-economic structure of a territory. It consists of analyzing the existence of actors such as government institutions, clusters, universities, industries… their main competences, and the interactions into Innovation Networks among them. Thus, authorities (regional, national, local…) are endowed of a tool that allows the creation and development of competitive and efficient Innovation Systems. In this context, and due to the importance of interactions inside Innovation Systems, the present research intends to contribute a methodology which helps us to analyze and measure these interactions produced within Innovation Networks. The methodology developed will be tested in a sector which is present in several European Territories. This way, not only the measures defined but also the differences among the Networks analyzed will be observed and tested.Innovation Systems, Interactions, Innovation Networks, Measures.

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

R&D Subsidization effect and network centralization. Evidence from an agent-based micro-policy simulation

This paper presents an agent-based micro-policy simulation model assessing public R&D policy effect when R&D and non-R&D performing companies are located within a network. We set out by illustrating the behavioural structure and the computational logic of the proposed model; then, we provide a simulation experiment where the pattern of the total level of R&D activated by a fixed amount of public support is analysed as function of companies’ network topology. More specifically, the suggested simulation experiment shows that a larger “hubness” of the network is more likely accompanied with a decreasing median of the aggregated total R&D performance of the system. Since the aggregated firm idiosyncratic R&D (i.e., the part of total R&D independent of spillovers) is slightly increasing, we conclude that positive cross-firm spillover effects - in the presence of a given amount of support - have a sizeable impact within less centralized networks, where fewer hubs emerge. This may question the common wisdom suggesting that larger R&D externality effects should be more likely to arise when few central champions receive a support

Simulating the Social Processes of Science

Science is the result of a substantially social process. That is, science relies on many inter-personal processes, including: selection and communication of research findings, discussion of method, checking and judgement of others' research, development of norms of scientific behaviour, organisation of the application of specialist skills/tools, and the organisation of each field (e.g. allocation of funding). An isolated individual, however clever and well resourced, would not produce science as we know it today. Furthermore, science is full of the social phenomena that are observed elsewhere: fashions, concern with status and reputation, group-identification, collective judgements, social norms, competitive and defensive actions, to name a few. Science is centrally important to most societies in the world, not only in technical, military and economic ways, but also in the cultural impacts it has, providing ways of thinking about ourselves, our society and our environment. If we believe the following: simulation is a useful tool for understanding social phenomena, science is substantially a social phenomenon, and it is important to understand how science operates, then it follows that we should be attempting to build simulation models of the social aspects of science. This Special Section of <i>JASSS</i> presents a collection of position papers by philosophers, sociologists and others describing the features and issues the authors would like to see in social simulations of the many processes and aspects that we lump together as "science". It is intended that this collection will inform and motivate substantial simulation work as described in the last section of this introduction.Simulation, Science, Science and Technology Studies, Philosophy, Sociology, Social Processes

Modelling Fresh Strawberry Supply "From-Farm-to-Fork" as a Complex Adaptive Network

 The purpose of this study is to model and thereby enable simulation of the complete business entity of fresh food supply. A case narrative of fresh strawberry supply provides basis for this modelling. Lamming et al. (2000) point to the importance of discerning industry-specific product features (or particularities) regarding managing supply networks when discussing elements in "an initial classification of a supply network" while Fisher (1997) and Christopher et al. (2006, 2009) point to the lack of adopting SCM models to variations in products and market types as an important source of SCM failure. In this study we have chosen to move along a research path towards developing an adapted approach to model end-to-end fresh food supply influenced by a combination of SCM, system dynamics and complex adaptive network thinking...