Strategic Solutions: Game Theory Perspectives on Climate Change in South Asian Cli-Fi
Bibliographic record
Abstract
Climate change is a significant concern to South Asia, requiring innovative policy development and public participation approaches. This study explores the application of game theory to analyze climate strategies depicted in two significant South Asian climate fiction (cli-fi) novels: The works of Amitav Ghosh's "The Hungry Tide" and Indra Sinha's "Animal's People." To examine how game theoretic analysis of cli-fi narratives can enable their contribution to climate policy and activism for South Asia. The approach adopted in this study involves the combined textual analysis of the selected novels, game theoretic modelling of climate scenarios described within, and comparative analysis of fictional approaches to climate problem solving with real-world climate policies. The analysis focuses on three key game theoretic concepts: common pool resource problems, negotiation games, and behavioural games. Both novels relegate intricate game theoretic environments for environmental decision-making and climate activism. Both "The Hungry Tide" and "Animal's People" explore the problem of common pool resources (CPR) within the Sundarbans ecosystem and the negotiation dynamics between victims of disaster and corporation’s post-disaster. Both narratives stress the need for local knowledge and community-based tools to deal with climate vulnerabilities. Through a game theoretic analysis, the critical insights from the game theoretic analysis provide essential guidance for formulating climate policy, including the importance of transparency, adaptive management strategies and robust legal frameworks to hold corporations accountable
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 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".