Innovation creation and diffusion in a social network: an agent based approach
AbstractMarket is not only the result of the behaviour of agents, as we can find other forms of contact and communication. Many of them are determined by proximity conditions in some kind of space: in this paper we pay a particular attention to relational space, that is the space determined by the relationships between individuals. The paper starts from a brief account on theoretical and empirical literature on social networks. Social networks represent people and their relationships as networks, in which individuals are nodes and the relationships between them are ties. In particular, graph theory is used in literature in order to demonstrate some properties of social networks summarised in the concept of Small Worlds. The concept may be used to explain how some phenomena involving relations among agents have effects on multiple different geographical scales, involving both the local and the global scale. The empirical section of the paper is introduced by a brief summary of simulation techniques in social science and economics as a way to investigate complexity. The model investigates the dynamics of a population of firms (potential innovators) and consumers interacting in a space defined as a social network. Consumers are represented in the model in order to create a competitive environment pushing enterprises into innovative process (we refer to Schumpeter’s definition): from interaction between consumers and firms innovation emerges as a relational good.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 445.
Date of creation: 27 Apr 2004
Date of revision: 20 Oct 2006
Innovation; small world; computational economics; network; complexity;
Find related papers by JEL classification:
- L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
- L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- O33 - Economic Development, Technological Change, and Growth - - Technological Change; Research and Development; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
- D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-12-04 (All new papers)
- NEP-CBE-2006-12-04 (Cognitive & Behavioural Economics)
- NEP-CMP-2006-12-04 (Computational Economics)
- NEP-INO-2006-12-04 (Innovation)
- NEP-IPR-2006-12-04 (Intellectual Property Rights)
- NEP-KNM-2006-12-04 (Knowledge Management & Knowledge Economy)
- NEP-MIC-2006-12-04 (Microeconomics)
- NEP-NET-2006-12-04 (Network Economics)
- NEP-SOC-2006-12-04 (Social Norms & Social Capital)
- NEP-URE-2006-12-04 (Urban & Real Estate Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind and Society: Cognitive Studies in Economics and Social Sciences, Fondazione Rosselli, vol. 1(1), pages 57-72, March.
- Cowan, Robin & Jonard, Nicolas, 2004.
"Network structure and the diffusion of knowledge,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 28(8), pages 1557-1575, June.
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