An Agent‐Based Model of Entrepreneurial Behavior in Agri‐Food Markets
Bibliographic record
Abstract
Rapid technological innovation and globalization have led to increasingly complex agri‐food supply chains and networks, and uncertain agri‐food markets. Given this type of competitive environment, management scholars have argued that agri‐food firms that adopt capabilities for entrepreneurship will outperform firms that do not. We use agent‐based simulation methods to explore this hypothesis. Agent‐based models are particularly relevant in this study as they allow for the explicit simulation of the entrepreneurial behaviors and firm interactions that lead to wealth creation. In our analysis, we find that entrepreneurial capabilities of alertness, risk‐taking, and efficiency vary in their effect on firm performance given alternative agri‐food strategic landscape configurations. L'innovation technologique rapide et la mondialisation ont donné lieu à des chaînes d'approvisionnement agroalimentaire et à des réseaux de plus en plus complexes ainsi qu'à des marchés agroalimentaires incertains. Compte tenu de ce type d'environnement concurrentiel, les spécialistes en gestion soutiennent que les entreprises agroalimentaires qui possèdent des capacités entrepreneuriales surclasseront celles qui n'en possèdent pas. Nous avons utilisé des modèles de simulation multi‐agent pour étudier cette hypothèse. Les modèles multi‐agent sont particulièrement pertinents dans la présente étude puisqu'ils permettent la simulation explicite de comportements entrepreneuriaux et d'interactions entre firmes qui engendrent la création de richesse. Les résultats de notre analyse ont montré que les capacités entrepreneuriales, telles que la vigilance, la prise de risque et l'efficacité, ont des répercussions variées sur la performance d'une firme en raison de différentes configurations stratégiques du paysage agroalimentaire.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".