A time-dependent agent-based model of an eco-product market with social interactions and dynamic game pricing schemes
Bibliographic record
Abstract
We present an agent-based model of an eco-product market from a system design perspective. The aim of our work is to investigate practicable ways in which such a market can be made to emerge and develop. The model takes an existing static formulation of a differentiated products market and generalizes it to include social interactions among different consumer classes, as well as time evolution over a finite horizon. In particular, we examine how an existing market changes in response to various influences, such as new (eco-) products becoming available. Social interactions play an important role in these changes. The analysis of the model is conducted considering multiple ¿personality¿ types of consumers, ranging from early to reluctunct adopters of the new product. The simulations show various consumer distribution outcomes over the product space and give insight as to how consumer demands for the environmentally friendly products can be influenced/increased over time. We also consider a dynamic game analysis perspective for pricing schemes of eco-products on markets simulated as above.
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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 | 0.002 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".