Abstract
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 language | English (US) |
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Article number | 72 |
Journal | Algorithms |
Volume | 16 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2023 |
Keywords
- evolutionary economics
- firm search
- innovation
- patent data
- swarm intelligence
ASJC Scopus subject areas
- Theoretical Computer Science
- Numerical Analysis
- Computational Theory and Mathematics
- Computational Mathematics