Land tenure and conflict resolution: a game theoretic approach in the Narok district in Kenya
Bibliographic record
Abstract
Many conflicts in many parts of the developing world can be traced to disputes over land ownership, land use and land degradation. In this paper, we test the hypothesis that information asymmetries among various principals within these countries in land tenure and market systems have caused marginalization of some principals by the others. A sustained process of marginalization driven by these asymmetries has inevitably caused the disadvantaged to revolt resulting in many cases in violent clashes. In this paper, we develop a game theoretic model to test our hypothesis by analyzing the complex interdependencies existing among the various principals in the Narok District in Kenya. Violent clashes have been increasing in the district since the first outbreak in 1993. Preliminary results seem to confirm our hypothesis that asymmetrical information structures among the various principals over land and agricultural markets could have been the catalytic forces for these conflicts. In order to reduce these discrepancies, we recommend two institutional reforms. The first involves the adoption of a hybrid land tenure system whereby land ownership is based on individual titles while the use and sale of the land is governed by communal rules established by a community participatory proceeds. The second recommendation involves the formation of an information network comprising of all principals with the main objective of it being a forum for exchange of ideas and information pertaining to land use options and the opportunities offered by the market system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".