Simulation of the Clash between Cultural Values in Heterogeneous Society using Numerical Methods
Bibliographic record
Abstract
Cultural value conflicts, which have their origins in different moral codes, traditions, and social standards, are a reliable source of social friction in communities that are comprised of people from different backgrounds. When it comes to effectively forecasting or controlling the dynamics of such disputes, traditional qualitative techniques often provide inadequate results. In this study, a mathematical framework is presented for the purpose of simulating cultural value conflicts via the use of numerical approaches that are based on differential equation modelling and agent-based systems. We construct a conflict index function that simulates interactions between cultural groups across time. This function is based on Hofstede's cultural dimensions theory as well as Inglehart–Welzel's cultural map. The quantification of cultural resilience and conflict escalation in hypothetical multicultural configurations is accomplished by the enhanced use of finite difference techniques and interaction models inspired by the Lotka–Volterra model. In order to undertake empirical validation, census-based demographic data and World Values Survey (WVS) datasets from Canada and the Netherlands, two of the most notable multicultural countries in the world, are used. The findings indicate that there are non-linear patterns of cultural convergence and divergence that occur under different integration approaches and population changes for different populations. The data that we have obtained provide a quantitative foundation for policy choices that are intended to improve social cohesiveness and reduce the amount of cultural polarization that exists. This research marks a big step forward in the process of incorporating numerical simulation into the investigation of sociocultural conflicts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".