Simulating knowledge dynamics in innovation networks (SKIN)

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.

Central European Journal of Operations Research, 2011

Mathematical modeling of innovation diffusion has attracted strong academic interest since the early 1960s. Traditional diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze realworld scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of innovation diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research.

2003

ABSTRACT A multi-agent simulation embodying a theory of innovation networks has been built and used to suggest a number of policy-relevant conclusions. The simulation animates a model of innovation (the successful exploitation of new ideas) and this model is briefly described. Agents in the model representing firms, policy actors, research labs, etc. each have a knowledge base that they use to generate 'artefacts' that they hope will be innovations.

International Journal of Management and Business Research, 2014

In knowledge-based economy, knowledge has a public good and non-rivalry nature. Firms build their own knowledge stock not only by means of internal R&D and collaboration with partners, but also by means of previously spilled over knowledge of other firms and public research laboratories (such as universities). Firms based on their absorptive capacity, and level of intra-industry and extra-industry knowledge spillover could learn to produce innovative products. SKIN (Simulating Knowledge Dynamics in Innovation Networks) is an agentbase framework to study knowledge dynamics between innovative firms. The basic SKIN framework does not support absorptive capacity and knowledge spillover concepts, so this paper extends basic SKIN framework and adds absorptive capacity and knowledge spillover to it, and experiments -by simulation- the effect of these concepts on organizational learning. The results show that absorptive capacity has a moderating effect on organizational learning. This effec...

Understanding Complex Systems, 2009

Springer Complexity is an interdisciplinary program publishing the best research and academic-level teaching on both fundamental and applied aspects of complex systems-cutting across all traditional disciplines of the natural and life sciences, engineering, economics, medicine, neuroscience, social and computer science. Complex Systems are systems that comprise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous formation of distinctive temporal, spatial or functional structures. Models of such systems can be successfully mapped onto quite diverse "real-life" situations like the climate, the coherent emission of light from lasers, chemical reaction-diffusion systems, biological cellular networks, the dynamics of stock markets and of the internet, earthquake statistics and prediction, freeway traffic, the human brain, or the formation of opinions in social systems, to name just some of the popular applications. Although their scope and methodologies overlap somewhat, one can distinguish the following main concepts and tools: self-organization, nonlinear dynamics, synergetics, turbulence, dynamical systems, catastrophes, instabilities, stochastic processes, chaos, graphs and networks, cellular automata, adaptive systems, genetic algorithms and computational intelligence. The two major book publication platforms of the Springer Complexity program are the monograph series "Understanding Complex Systems" focusing on the various applications of complexity, and the "Springer Series in Synergetics", which is devoted to the quantitative theoretical and methodological foundations. In addition to the books in these two core series, the program also incorporates individual titles ranging from textbooks to major reference works.

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.

2011

Abstract 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 structure-oriented 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.

Organizations and markets can both be seen as complex systems whose aggregate dynamics cannot often be inferred from their micro building blocks ]. Different traditional modeling methods exist to support researchers to understand and predict the behavior of such complex systems. However, most of these models are developed under the assumption of a state of stability, equilibrium and linearity, whereas real world organizations and markets are dynamic, non-linear and complex. Furthermore, such traditional models are usually input-output models and therefore do not give insight into the process of getting to a state of equilibrium.

Industrial and Corporate Change, 2010

in actors, i.e. individuals or organizations, becoming more dependent on other actor's knowledge and skills to complement their own skills in the innovation process. As a result, networks have become more and more prominent in affecting the innovation process. Conceptualizing innovation as a search and learning process that results in a successful product, we introduce a two-phase model of collective innovation that accounts for the deliberateness of the innovation process.

International Journal of Production Economics, 2006

Despite the wideness of the literature on industrial districts (IDs), the driving processes of ID innovation have not still received much attention: Questions about how new innovation processes emerge, how, when and where they evolve are minimally addressed in the literature. To address these questions new theoretical approaches and methodologies are needed. In the paper we approach this topic by adopting complexity science and by using the agent-based simulation methodology. In particular, an agent-based simulation is conducted to investigate how innovation processes in IDs have to be modified to assure their survival in a highly competitive environment.