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

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

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

Defence firms facing liberalization: innovation and export in an agent-based model of the defence industry

The paper presents an agent-based simulation model of the defence industry. The model resembles some of the key characteristics of the European defence sector, and studies how firms in this market will respond to the challenges and opportunities provided by a higher degree of openness and liberalization in the future. The simulation analysis points out that European defence firms will progressively become more efficient, less dependent on public procurement and innovation policy support, and more prone to knowledge sharing and inter-firm collaborations. This firm-level dynamics will in the long-run lead to an increase in the industry’s export propensity and a less concentrated market.Defence industry; liberalization; EU; export; innovation; agent-based simulation model

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

R&D and knowledge dynamics in university-industry relationships in biotech and pharmaceuticals: An agent-based model

In the last two decades, University-Industry Relationships have played an outstanding role in shaping innovation activities in Biotechnology and Pharmaceuticals. Despite the growing importance and the considerable scope of these relationships, there still is an intensive and open debate on their short and long term effects on the research system in life sciences. So far, the extensive literature on this topic has not been able to provide a widely accepted answer. This work introduces a new way to analyse University-Industry Relationships (UIRs) which makes use of an agent-based simulation model. With the help of simulation experiments and the comparison of different scenario results, new insights on the effects of these relationships on the innovativeness of the research system can be gained. In particular, focusing on knowledge interactions among heterogeneous actors, we show that: (i) universities tend to shift from a basic to an applied research orientation as a consequence of relationships with industry, (ii) universities' innovative capabilities benefit from industry financial resources but not so much from cognitive resources of the companies, (iii) biotech companies' innovative capabilities largely benefit from the knowledge interaction with universities and (iv) adequate policies in terms of public basic research funding can contrast the negative effects of UIRs on university research orientation. --University-Industry Relationships,Knowledge Dynamics,University Patenting,Technology Transfer,Agent-Based Modelling

Research Design and Research Systems: An Application of Agent-Based Modelling to Research Funding

Governments nurture their multi-disciplinary innovation systems by funding several public organizations to help universities and research institutes support research projects and associated infrastructure. To study the impact of research funding, a generic stylized model is developed using Agent-Based Modelling (ABM) to simulate the outcomes. To provide context, the analysis anchors the problem in the context of Genome Canada’s research funding efforts. The process of academic research and the impact of grants on its speed and output (papers published) is simulated. To compare the outcomes for policy choices, two measures or indices are developed for the outcomes: efficiency is measured by number of papers per granted money and equity is measured by a Gini coefficient (for papers and money granted); the Matthew effect is also tested to check for effects on equity. Defining academic investigators as the main agent and having investigations and grants as subagents, along with assumptions for the procedures and parameters, an ABM is designed in which investigators conduct individual research using grant and non-grant funds. The simulation model is then tested and verified to be used for evaluation and comparison of policy scenarios. The results revealed that the instruments of allocated budget per competition, the gap between competitions, the sum granted for any proposal, and the size of the target group may be utilized to improve the efficiency and equity of the system. However, there is usually a trade-off between these two objectives and a loss in one of them is necessary to achieve a gain in the other. The tools can be combined in order to secure better results, but there are other factors that should be taken into account in making decisions. Although some lessons can be learned from such a simple model, making it applicable to policy making and to real-world issues, other factors such as investigator heterogeneity, collaborations, and grant administration complexities should be taken into account

Is Open Source about innovation? How interactions with the Open Source community impact on the innovative performances of entrepreneurial ventures

Practitioners generally assert that collaboration with the Open Source software (OSS) community enables young software firms to achieve superior innovation performance. Nonetheless, to the best of our knowledge, scholars have never extensively speculated about this assertion or rigorously tested it. In this paper, we attempt to do so. First, we root on the entrepreneurship literature and on the OSS research stream to discuss and empirically investigate whether entrepreneurial ventures collaborating with the OSS community (OSS EVs) achieve innovation performance superior to that of their non-collaborating peers. Then, we refer to the concept of absorptive capacity to determine which factors make OSS EVs better able to leverage their collaboration with the OSS community for innovation purposes. Our econometric estimates use a sample of 230 firms and indicate that OSS EVs collaborating with the OSS community achieve superior innovation. At the same time, the impact of community collaborations on innovation is stronger for EVs that are endowed with more skilled human capital, have experience with firm- OSS community collaboration, and actively contribute to the community.Entrepreneurial ventures, Open Source, firm-community collaboration, innovation performance