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

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

Chapter 22 Simulation Modeling as a Policy Tool

This chapter describes, justifies, presents the pros and cons of and illustrates the use of simulation modeling as a handy, cost-effective and agile tool for policymakers. Simulation modeling is flexible enough to accommodate different levels of detail, precision and time frameworks. It also serves the purpose of a concrete communication platform that facilitates scenario analysis, what-if alternatives and forward looking. We specifically define agent-based modeling within the larger simulation domain, provide a brief overview of other computation modeling methodologies and discuss the concepts of multiple models, verification, validation and calibration. The conceptual framework section closes with a discussion of advantages and disadvantages of using simulation modeling for policy at various stages of implementation. Finally, we present a panorama of actual applications of simulation modeling in policy, with an emphasis on economic analysis

United we stand, divided we fall : essays on knowledge and its diffusion in innovation networks

Knowledge is a key resource, allowing firms to innovate and keep pace with national and international competitors. Therefore, the management of this resource within firms and innovation networks is of utmost importance. As the collection and generation of (new) knowledge gives such competitive advantage, there is a strong interest of firms and policy makers on how to foster the creation and diffusion of new knowledge. Within four studies, this doctoral thesis aims at extending the literature on knowledge diffusion performance by focussing on the effect of different network structures on diffusion performance as well as on knowledge types besides mere techno-economic knowledge. Study 1 analyses the effect of different structural disparities on knowledge diffusion by using an agent-based simulation model. It focuses on how different network structures influence knowledge diffusion performance. This study especially emphasizes the effect of an asymmetric degree distribution on knowledge diffusion performance. Study 1 complements previous research on knowledge diffusion by showing that (i) besides or even instead of the average path length and the average clustering coefficient, the (symmetry of) degree distribution influences knowledge diffusion. In addition, (ii) especially small, inadequately embedded agents seem to be a bottleneck for knowledge diffusion in this setting, and iii) the identified rather negative network structures on the macro level seem to result from the myopic linking strategies of the actors at the micro level, indicating a trade-off between optimal structures at the network and at the actor level. Study 2 uses an agent-based simulation model to analyse the effect of different network properties on knowledge diffusion performance. In contrast to study 1, this study analyses this relationship in a setting in which knowledge is diffusing freely throughout an empirical formal R&D network as well as through four benchmark networks. In addition, the concept of cognitive distance and differences in learning between agents in the network are taken into account. Study 2 complements study 1 and further previous research on knowledge diffusion by showing that (i) the (asymmetry of) degree distribution and the distribution of links between actors in the network indeed influence knowledge diffusion performance to a large extend. In addition, (ii) the extent to which a skewed degree distribution dominates other network characteristics varies depending on the respective cognitive distance between agents. Study 3 analyses how so called dedicated knowledge can contribute to the transformation towards a sustainable, knowledge-based Bioeconomy. In this study, the concept of dedicated knowledge, i.e. besides mere-techno economic knowledge also systems knowledge, normative knowledge and transformative knowledge, is first introduced. Moreover, the characteristics of dedicated knowledge which are influencing knowledge diffusion performance are analysed and evaluated according to their importance and potential role for knowledge diffusion. In addition, it is analysed if and how current Bioeconomy innovation policies actually account for dedicated knowledge. This study complements previous research by taking a strong focus on different types of knowledge besides techno-economic knowledge (often overemphasized in policy approaches). It shows, that i) different types of knowledge necessarily need to be taken into account when creating policies for knowledge creation and diffusion, and ii) that especially systems knowledge so far has been insufficiently considered by current Bioeconomy policy approaches. Study 4 analyses the effect of different structural disparities on knowledge diffusion by deducing from theoretical considerations on network structures and diffusion performance. The study tries to answer whether the artificially generated network structures seem favourable for the diffusion of both mere techno-economic knowledge as well as dedicated knowledge. Study 4 especially complements previous research on knowledge diffusion by (i) analysing an empirical network over a long period of time, and (ii) by indicating a potential trade-off between structures favourable for the diffusion of mere techno-economic knowledge and those for the diffusion of other types of dedicated knowledge. Summing up, it is impossible to make general statements that allow for valid policy recommendations on network structures optimal for knowledge diffusion. Without knowing the exact structures and context, politicians will hardly be able to influence network structures. Especially if we call for knowledge enabling transformations as the transformation towards a sustainable knowledge-based Bioeconomy, creating structures for the creation and diffusion of this knowledge is quite challenging and needs for the inclusion and close cooperation of many different actors on multiple levels.Wissen ist eine Schlüsselressource. Daher ist die Verwaltung dieser Ressource in Unternehmen und Innovationsnetzwerken von größter Bedeutung. Da das Sammeln und Generieren von (neuem) Wissen einen derartigen Wettbewerbsvorteil bietet, besteht ein starkes Interesse seitens Unternehmen und politischen Entscheidungsträgern, die Schaffung und Verbreitung von neuem Wissen zu fördern. Im Rahmen dieser Doktorarbeit soll in vier Studien die Literatur zu Wissensdiffusion erweitert werden, indem die Auswirkungen verschiedener Netzwerkstrukturen sowie die verschiedener Wissensarten in den Mittelpunkt gestellt werden. Studie 1 analysiert die Auswirkungen struktureller Unterschiede auf die Wissensdiffusion unter Verwendung eines agentenbasierten Simulationsmodells. Der Fokus liegt hierbei darauf, wie verschiedene Netzwerkstrukturen die Leistung der Wissensverbreitung beeinflussen. Diese Studie betont insbesondere den Effekt einer asymmetrischen Gradverteilung auf die Wissensdiffusionsleistung. Studie 1 ergänzt bisherige Arbeiten zu Wissensdiffusion, indem sie zeigt, dass (i) neben oder sogar anstelle der durchschnittlichen Pfadlänge und des durchschnittlichen Clustering-Koeffizienten die (Symmetrie der) Gradverteilung die Wissensdiffusion stark beeinflusst. Außerdem scheinen (ii) besonders kleine, unzureichend eingebettete Akteure ein Engpass für die Wissensverbreitung in diesem Umfeld zu sein. Studie 2 verwendet ein agentenbasiertes Simulationsmodell, um die Auswirkungen verschiedener Netzwerkeigenschaften auf die Leistung der Wissensdiffusion zu analysieren. Im Gegensatz zu Studie 1 analysiert diese Studie die freie Verbreitung von Wissen in einem empirischen, formalen FuE-Netzwerk sowie in vier Benchmark-Netzwerken. Darüber hinaus werden das Konzept der kognitiven Distanz und Unterschiede beim Lernen zwischen Agenten im Netzwerk berücksichtigt. Studie 2 ergänzt Studie 1 und weitere Forschung, indem sie zeigt, dass (i) die (Asymmetrie) der Gradverteilung und die Verteilung der Verbindungen zwischen den Akteuren des Netzwerks tatsächlich die Leistung der Wissensdiffusion stark beeinflussen. (ii) Das Ausmaß, in dem eine Verteilung der Verbindungen andere Netzwerkcharakteristiken dominiert, variiert in Abhängigkeit von der jeweiligen kognitiven Entfernung zwischen den Agenten. In Studie 3 wird analysiert, wie sogenanntes dediziertes Wissen zur Transformation hin zu einer nachhaltigen, wissensbasierten Bioökonomie beitragen kann. In dieser Studie wird zunächst das Konzept des dedizierten Wissens eingeführt, d. H. neben rein techno-ökonomischem Wissen auch Systemwissen, normatives Wissen und transformatives Wissen. Darüber hinaus werden die Merkmale des dedizierten Wissens analysiert und entsprechend ihrer Bedeutung und möglichen Rolle für die Wissensverbreitung bewertet. Darüber hinaus wird analysiert, ob und wie die aktuelle Innovationspolitik der Bioökonomie tatsächlich dediziertes Wissen berücksichtigt. Diese Studie ergänzt die bisherigen Forschungsarbeiten, indem sie neben technoökonomischem Wissen (das in politischen Ansätzen oft überbewertet wird) einen starken Fokus auf verschiedene Arten von Wissen legt. Es zeigt sich, dass i) unterschiedliche Arten von Wissen notwendigerweise bei der Erstellung von Strategien zur Schaffung und Verbreitung von Wissen berücksichtigt werden müssen, und ii) dass insbesondere Systemwissen bislang in aktuellen Bioökonomiepolitikstrategien nicht ausreichend berücksichtigt wurde. Studie 4 analysiert die Auswirkungen verschiedener struktureller Ungleichheiten auf die Wissensdiffusion, indem aus theoretischen Überlegungen die Diffusionsleistung hergeleitet wird. Die Studie versucht zu beantworten, ob die künstlich erzeugten Netzwerkstrukturen für die Verbreitung von rein technoökonomischem Wissen sowie von dediziertem Wissen günstig erscheinen. Studie 4 ergänzt insbesondere die bisherigen Forschungsarbeiten, indem sie (i) ein empirisches Netzwerk über einen langen Zeitraum analysiert und (ii) einen potenziellen Zielkonflikt aufzeigt zwischen Strukturen, die für die Verbreitung von rein technoökonomischem Wissen günstig sind, und solchen für die Verbreitung anderer Arten von dediziertem Wissen. Zusammenfassend lässt sich sagen, dass es nicht möglich ist, allgemein gültige Aussagen und politische Handlungsempfehlungen zu optimalen Netzwerkstrukturen zu treffen. Ohne die genauen Strukturen und Zusammenhänge zu kennen, kann die Politik kaum Einfluss auf Netzwerkstrukturen und Wissensverbreitung nehmen. Insbesondere wenn wir nach Wissen verlangen, das eine Transformation zu einer nachhaltigen, wissensbasierten Bioökonomie ermöglicht, stellt die Schaffung von Strukturen für die Verbreitung dieses Wissens eine große Herausforderung dar und erfordert die Einbindung und enge Zusammenarbeit vieler verschiedener Akteure auf mehreren Ebenen

Knowledge networks in the German bioeconomy : network structure of publicly funded R&D networks

Aiming at fostering the transition towards a sustainable knowledge-based Bioeconomy (SKBBE), the German Federal Government funds joint and single research projects in predefined socially desirable fields as, for instance, in the Bioeconomy. To analyse whether this policy intervention actually fosters cooperation and knowledge transfer as intended, researchers have to evaluate the network structure of the resulting R&D network on a regular basis. Using both descriptive statistics and social network analysis, I investigate how the publicly funded R&D network in the German Bioeconomy has developed over the last 30 years and how this development can be assessed from a knowledge diffusion point of view. This study shows that the R&D network in the German Bioeconomy has grown tremendously over time and thereby completely changed its initial structure. While from a traditional perspective the development of the network characteristics in isolation seems harmful to knowledge diffusion, taking into account the reasons for these changes shows a different picture. However, this might only hold for the diffusion of mere techno-economic knowledge. It is questionable whether the artificially generated network structure also is favourable for the diffusion of other types of knowledge, e.g. dedicated knowledge necessary for the transformation towards an SKBBE

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc

A dinâmica da criação de empresas impulsionada por instituições de ensino superior em redes de inovação

Na época de intensa globalização e de forte competição em que se vive, a criação de empresas contribui para a introdução no sector empresarial de novas tecnologias, novos produtos/serviços e de novas formas de organização, revelando-se um dos factores fundamental para o crescimento económico, criação de emprego, eficiência dos mercados, renovação da estrutura económica, difusão de inovação, bem como para a melhoria da competitividade global das empresas e dos países. Paralelamente, constata-se que as redes de inovação, facilitadoras da redução das incertezas através da cooperação entre os agentes, visam a produção e partilha de conhecimentos e recursos em falta, a partilha de custos e de risco, ganhos de eficiência devido à divisão do trabalho, entre outros benefícios. Nestas redes de inovação, as instituições de ensino superior (IES) assumem um papel de destaque, dado que permitem fomentar e difundir os diversos contributos proporcionados pela rede, não só a nível local e regional, como também a nível nacional e global. O objectivo principal desta investigação visa identificar se as IES impulsionam a criação de empresas, através de redes de inovação. Assim, nesta investigação desenvolveu-se um suporte teórico assente nas actuais abordagens de referência sobre criação de empresas e modelos de redes de inovação. Nele se apresenta a importância das redes de inovação no processo de criação de empresas, dado que estas permitem colmatar debilidades, reforçar os aspectos positivos e, consequentemente, influenciar o processo de criação de empresas. Para a recolha de dados foi efectuado um questionário junto de potenciais empreendedores nascentes, pertencentes às IES, tendo sido recolhidas 241 respostas. Os resultados obtidos, através de análise empírica efectuada, mostram que a cooperação e o desenvolvimento de relacionamentos com outros agentes da rede de inovação surgem como a principal forma das IES estimularem a criação de empresas, também evidenciam que a atitude da IES sobre a criação de empresas influencia a decisão dos potenciais empreendedores nascentes desenvolverem o processo de criação de empresas. Relativamente à identificação dos factores que facilitam a criação de empresas suportada em redes de inovação, destacam-se como principais os actores da rede e os recursos organizacionais; no que concerne à identificação dos factores que dificultam a criação de empresas suportada em redes de inovação, evidenciam-se os factores de conhecimento e a localização. As principais conclusões da presente tese realçam a relevância que a IES possui para o fenómeno da criação de empresas, quando inserida numa rede de inovação