Innovation Networks-a Simulation Approach

Handbook on the Economic Complexity of Technological Change, 2011

Innovation is clearly a disequilibrium phenomenon and therefore a consideration of the path that innovation takes through the economic system is crucial to understanding the adjustment process for the economy. We have developed models of how innovation can pass through the economy calibrated to the actual relationships of businesses in different industries. These models consider not only the network characteristics but also the knowledge and competence of firms and their willingness to pass on new ideas. All of the results are calibrated to specially commissioned survey results. We consider the steps required to generate an innovation cascade and the time likely to be required to achieve this. Further, we review whether current policy programmes have the potential to be effective in fostering the effective introduction of innovation in the context of these data and models.

2021

Multi-agent based simulations provide tools andtechniques to observe and analyze the emergentbehavior that happens due to the interactions amongagents. These agents could represent the actors in a realsituation, or they could be used to test or to verifyhypothesis, theories and to perform experiments in acontrolled environment (e.g. virtual world). It is usefulin certain cases, for instance when the event and itsinner workings are hard to observe, which is common insocial simulations. The diffusion of innovation theory,presented by Everett Rogers, provides a classification ofthe individuals in a social system related to how long aninnovation takes to spread into the system. Also, itdescribes how people form clusters based on thehomophily concept. This paper brings two hypothesesfor the diffusion of innovation and puts them into testby a multi-agent based simulation, running in theSwarm multi-agent based framework. Rogers' theory,as well as multi-agent based simulations, is brief...

Journal of Artificial Societies and Social Simulation, 2011

The paper elaborates the notion of innovation as an emerging property of complex system dynamics and presents an agent-based simulation model (ABM) of an economy where systemic knowledge interactions among heterogeneous agents are crucial for the recombinant generation of new technological knowledge and the introduction of innovations. In this approach the organization of the system plays a crucial role in assessing the chances of individual firms to actually introduce innovations because it qualifies the access to external knowledge, an indispensable input, together with internal learning and research activities, into the recombinant generation of new knowledge. The introduction of innovations is analyzed as the result of systemic knowledge interactions among myopic agents that are credited with an extended procedural rationality that includes forms of creative reaction. The creative reaction of agents may lead to the introduction of productivity enhancing innovations. This takes place only when the structural, organizational and institutional characteristics of the system are such that agents, reacting to out-of-equilibrium conditions, can actually take advantage of external knowledge available within the innovation system into which they are embedded to generate new technological knowledge. The ABM enables one to explore effects of alternative organizational features of the systems, namely different configurations of the intellectual property right regimes and different architectural configurations of the regional structure into which knowledge interactions take place, on the rates of introduction of technological innovations. The results of the ABM suggest that the dissemination of knowledge favors the emergence of creative reactions and hence faster rates of introduction of technological innovations.

Multi-Agent simulation has great potential when it comes to explaining the subtle interactions of heterogeneous actors in complex social systems. In particular, it moves away from a correlative approach - based on quantitative variables - and towards a processual approach based on social mechanisms. This paper uses a recent EU funded research project (IMAGES) as a case study to explore the difficulties in building multi-agent models of social systems. The context is the simulation of innovation diffusion where the “innovation” is receiving money for new ecological farming practices. Three crucial difficulties are identified. Firstly, the absence of suitable elicitation and collection techniques for dynamic data. Secondly, the absence of adequately rigorous theories in the existing social science literature. Finally, the prevailing expectation that the purpose of Multi-Agent simulation is to enhance the predictive power of social science. (This goal will not be realistic until the first two problems are solved and perhaps not even then.) The paper also discusses some tentative solutions to these problems.

IEEE Access

Agent-based modeling has proven effective in increasing the understanding of complex systems, including social-economical systems. A goal of modeling complex systems is to distill the system into simple agents with phenotypes guided by simple rules. The model then displays the emergent behavior of these agents interacting with each other and their environment. An agent-based model of innovation and its place in a global economy or ecosystem is presented. The model utilizes simple agents to represent innovating entities such as large corporations and small companies. The results produced by this model reveal the dynamics of innovation and its role in a global economy. The results indicate a large need for partnership in innovation for those entities working within rapidly changing domains. Domains, such as high technology, have constantly changing market expectations, which force innovating entities to seek external sources of assistance to meet these expectations in a timely enough fashion so as to incur benefit. *This paper was edited by Senior Editor Joseph Nadan Section 3. Section 4 the results of this model are presented which illustrate that innovation entities operating within rapidly changing domains must seek innovation partners to cope with the rapidly changing market expectations. The results produced by the model and their contributions to innovation are discussed in Section 5. Finally, conclusions and suggestions for future research are presented in Section 6.

Prometheus, 2015

Although there is considerable attention for the role of public research organisations in driving innovation, methodologies that can be used to analyse and optimise these roles are limited. An agent-based model could potentially assess ex-post the operation of public research organisations within innovation systems, as well as allows ex-ante analysis of potential intervention effects for optimising their innovation capacity. Such concept needs to entail two perspectives. From the perspective of innovation, it should represent not only organisational capacities and capabilities required for research and development, but also integrate antecedent and subsequent processes related to the act of innovation. From the perspective of innovators, it should integrate the organisational metabolism that enables innovation, as well as contain agglomerate effects of organisational operation on the overall dynamic of innovation networks. Based on such requirements, some options for designing an agent-based model are suggested.

Computational and Mathematical Organization Theory, 2011

Modern knowledge-intensive economies are complex social systems where intertwining factors are responsible for the shaping of emerging industries: the self-organising interaction patterns and strategies of the individual actors (an agency-oriented pattern) and the institutional frameworks of different innovation systems (a structureoriented pattern). In this paper, we examine the relative primacy of the two patterns in the development of innovation networks, and find that both are important. In order to investigate the relative significance of strategic decision making by innovation network actors and the roles played by national institutional settings, we use an agent-based model of knowledge-intensive innovation networks, SKIN. We experiment with the simulation of different actor strategies and different access conditions to capital in order to study the resulting effects on innovation performance and size of the industry. Our analysis suggests that actors are able to compensate for structural limitations through strategic collaborations. The implications for public policy are outlined.

Socially Intelligent Agents: Creating Relationships with Computers and Robots, 2002

Multi-Agent Systems (MAS) have great potential when it comes to explaining the interactions of heterogeneous actors in complex social environments: the primary task of the social sciences. In this chapter, I shall argue that one factor hindering the realisation of this potential is the neglect of systematic data use and appropriate data collection techniques. I will also suggest that the ability to collect data for particular MAS may serve as a novel and effective heuristic device for design purposes. The discussion will centre on a concrete example: the properties of MAS to model innovation diffusion.