Environmental preference utility and evolutionary game of collaborative innovation of asymmetric technology enterprises based on complex networks
Bibliographic record
Abstract
Based on complex networks, the innovation strategies of enterprises or governments are analyzed by using asymmetric evolutionary games. The evolutionary game model is considered to be a better way to promote collaborative innovation between large and small enterprises. First, a model is established based on the replicator dynamic equations. Then, based on complex network and computer simulation technology, a network evolutionary game model with improved strategy update rules including environmental preference utility is designed. The results show that the conclusions of the mathematical and network models of evolutionary stable strategies (ESS) are the same. The complex network model can provide more detailed information on the evolutionary processes of enterprises, and the parameter values can be adjusted to analyze the evolution sensitivity. In a uniform or non-uniform environment, the effect of environmental preference on evolution is weak, indicating that the effect of collaborative innovation on asymmetric enterprises is weak. The initial probability of enterprise collaborative innovation is the key to ESS. A dynamic model of asymmetric replication factor and an evolutionary game model composed of two participants of large technology enterprises and SMEs are established. Complex networks and environmental preferences are involved in evolutionary games to better analyze the ESS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".