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Growing Silicon Valley on a landscape: an agent-based approach to high-tech industrial clusters

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Abstract.

We propose a Nelson-Winter model with an explicitly defined landscape to study the formation of high-tech industrial clusters such as those in Silicon Valley. The existing literature treats clusters as the result of location choices and focuses on how firms may benefit from locating in a cluster. We deviate from this tradition by emphasizing that high-tech industrial clusters are characterized by concentrated entrepreneurship. We argue that the emergence of clusters can be explained by the social effect through which the appearance of one or a few entrepreneurs inspire many followers locally. Agent-based simulation is employed to show the dynamics of the model. Data from the simulation and the properties of the model are discussed in light of empirical regularities. Variations of the model are simulated to study policies that are favorable to the high-tech economy.

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Authors and Affiliations

  1. Public Policy Institute of California, 500 Washington Street, 94111, San Francisco, CA, USA

    Junfu Zhang

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  1. Junfu Zhang
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Correspondence to Junfu Zhang.

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JEL Classification:

L11, R12

This paper has been presented at the 9th International Schumpeter Society Conference in Gainesville, Florida, the Western Economic Association’s 77th Annual Conference in Seattle, Washington, the 24th Annual Research Conference of the Association for Public Policy Analysis and Management in Dallas, Texas, and the Workshop on Industry and Labor Dynamics: The Agent-Based Computational Economics Approach in Turin, Italy. I would like to thank Rob Axtell, Giovanni Dosi, Olav Sorenson, and an anonymous referee for their comments, suggestions, and encouragement. I am grateful to Nikesh Patel for his superb assistance.

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Zhang, J. Growing Silicon Valley on a landscape: an agent-based approach to high-tech industrial clusters. J. Evol. Econ. 13, 529–548 (2003). https://doi.org/10.1007/s00191-003-0178-4

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  • DOI: https://doi.org/10.1007/s00191-003-0178-4

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