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| Main Authors: | , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Mathematical biosciences and engineering : MBE
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40676975/ |
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Table of Contents:
- Budget allocation and illegal fishing: a game theoretic approach. Sullens, Maggie R Fefferman, Nina H Game Theory Conservation of Natural Resources Animals Fisheries Budgets Humans Computer Simulation Resource Allocation Algorithms Conservation efforts are under constant threat of failure due to poaching. Efforts to combat poaching may take a number of forms, but access to each form depends on resources, and access to these resources may depend on the success of previous efforts (e.g., monetary donations from supporters could directly combat poaching, but may be more effective if partially spent on recruiting additional supporters who then also donate). We adopted a mathematical framework with inspiration from the famous colonel blotto game to model the ongoing battle between conservationists and poachers. Focusing on a marine setting as a case study, players have budgets consisting of three types of resources: monetary, non-monetary, and supporters. The heterogeneous battlefields (laws, marine reserves, and community) reflect commonly employed conservation tactics meant to limit poaching. conservationists allocate resources to limit the success of poachers, while poachers allocate resources to overcome barriers implemented by conservationists. We assumed that no action can succeed without supporters, and thus whichever player wins over all the supporters in the community (i.e., the community battlefield), wins the game. We analyzed battlefield payoffs and player budget distributions to determine overall player success. We demonstrated how initially disadvantaged players may have an opportunity to win the game, although, we found that success in the first round can be most critical under certain scenarios. By framing the question in this way, we hope to provide additional tools for decision support to guide resource allocation, improving the efficacy of conservation efforts.