Modeling Firm Search and Innovation Trajectory Using Swarm Intelligence

Ren Raw Chen, Cameron D. Miller, Puay Khoon Toh

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We developed a swarm intelligence-based model to study firm search across innovation topics. Firm search modeling has primarily been “firm-centric,” emphasizing the firm’s own prior performance. Fields interested in firm search behavior—strategic management, organization science, and economics—lack a suitable simulation model to incorporate a more robust set of influences, such as the influence of competitors. We developed a swarm intelligence-based simulation model to fill this gap. To demonstrate how to fit the model to real world data, we applied latent Dirichlet allocation to patent abstracts to derive a topic search space and then provide equations to calibrate the model’s parameters. We are the first to develop a swarm intelligence-based application to study firm search and innovation. The model and data methodology can be extended to address a number of questions related to firm search and competitive dynamics.

Original languageEnglish (US)
Article number72
Issue number2
StatePublished - Feb 2023


  • evolutionary economics
  • firm search
  • innovation
  • patent data
  • swarm intelligence

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Theory and Mathematics
  • Computational Mathematics


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