Business Network Simulation: Combining Research Cases and Agent-Based Models in a Robust Methodology
Bibliographic record
Abstract
This paper describes the use of an agent-based model (ABM) with the purpose of simulating interaction processes in business networks. Such an ABM must be able to capture time and process in networks while retaining a level of comprehension and overview. The ABM rests on the assumption that business networks can be viewed as complex adaptive systems (CAS) and draws on the opportunities to build and use ABMs to simulate processes in such systems. This paper outlines the conceptual foundations and the methodological challenges and opportunities associated with such an endeavor with special reference to issues concerning the modeling of the complexities of business networks. Specifically it discusses the use of ABM and research case in combination in an effort to produce a robust methodology. It contributes with suggestions on how one concretely can handle these issues and challenges when using ABMs to model non-linear dynamic interaction processes in networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".