Prospect Theory: Contributions to Understanding Actors, Causes and Consequences of Conflict in Africa
Bibliographic record
Abstract
Despite many recognized shortcomings, Rational Choice Theory remains the dominant perspective on decision-making in the literature on African conflict, whether overtly acknowledged or not. Prospect Theory, originally derived from the field of behavioural economics, can complement and advance this perspective not only by explaining the behaviour of actors, but also by allowing for predictions and the devising of strategies to avoid or end on-going conflicts based on a set of systematic biases that influence how actors make decisions. After a brief definition of Prospect Theory, this work will begin with an overview of the existing literature on decision-making as it relates to conflict, examine how Rational Choice is inadequate in explaining much human behaviour and thus how Prospect Theory can fill this gap. It will then move on to give a fuller definition of the various hypotheses derived from Prospect Theory that pertain to the study of conflict. An example of the application of Prospect Theory to a related field in which thorough research has been conducted, Deterrence Theory, will be used to demonstrate the model’s potential for study in other areas. This will be followed by a more in-depth analysis of the ways in which Prospect Theory can contribute to understanding the behaviour of actors in war, the causes of conflict, and the consequences in the African context. It will conclude with a summary and proposition for further research that can advance this analysis.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".