论文标题
Lévy步行源自非平稳环境中的贝叶斯决策模型
Lévy walks derived from a Bayesian decision-making model in non-stationary environments
论文作者
论文摘要
在各种生物的迁徙行为模式中发现了莱维步道，而这种现象的原因进行了广泛讨论。我们使用模拟证明，学习在非平稳环境中的决策过程中导致置信水平的变化，并导致lévy-walk样模式。一种涉及信心的推论算法是贝叶斯推断。我们提出了一种算法，该算法介绍了学习和忘记进入贝叶斯推论的效果，并模拟了一个模仿游戏，其中两个决策代理结合了算法，从对手的观察数据中估算了彼此的内部模型。为了忘记而没有学习，由于缺乏有关对应的信息和布朗人步行的信息，代理信心水平仍然很低。相反，当引入学习时，即使以高忘记的速度，偶尔也会发生高置信度，而布朗尼人的步行普遍成为莱维（Lévy）的步行，穿越了高信任和低信心状态的混合物。
Lévy walks are found in the migratory behaviour patterns of various organisms, and the reason for this phenomenon has been much discussed. We use simulations to demonstrate that learning causes the changes in confidence level during decision-making in non-stationary environments, and results in Lévy-walk-like patterns. One inference algorithm involving confidence is Bayesian inference. We propose an algorithm that introduces the effects of learning and forgetting into Bayesian inference, and simulate an imitation game in which two decision-making agents incorporating the algorithm estimate each other's internal models from their opponent's observational data. For forgetting without learning, agent confidence levels remained low due to a lack of information on the counterpart and Brownian walks occurred for a wide range of forgetting rates. Conversely, when learning was introduced, high confidence levels occasionally occurred even at high forgetting rates, and Brownian walks universally became Lévy walks through a mixture of high- and low-confidence states.
