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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-09-01
摘要
隨機 擴散 is the noisy process through which dynamics like 流行病學cs, or 智能體s like animal 物種, disperse over a larger area. These processes are increasingly important to better prepare for pandemics and as 物種 ranges shift in response to 氣候 change. Unfortunately, modelling is mostly done with expensive 計算 simulations or inaccurate deterministic tools that ignore the randomness of dispersal. We introduce 'mean-FLAME' models, tracking 隨機 dispersion using approximate master equations to follow the probability distribution over all possible states of an area of interest, up to states active enough to be approximated using a mean-field model. In the limit where we track all states, this approach is locally exact, and in the other limit collapses to traditional deterministic models. In 捕食者-獵物 systems, we
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