Complexity Sciences Center, Physics Department, University of California Davis, Davis, California, United States of America.
PLoS Comput Biol. 2012;8(6):e1002510. doi: 10.1371/journal.pcbi.1002510. Epub 2012 Jun 7.
We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream "teacher" and then pass samples from the model to their downstream "student". It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.
我们提出了一种序贯因果推理理论，其中链中的学习者根据其上游“教师”估计结构模型，然后将模型中的样本传递给下游“学生”。它扩展了遗传漂变的群体动力学，将木村的选择中性理论重新表述为使用具有记忆的结构化群体的广义漂变过程的一个特例。我们研究了几种漂变过程的扩散和固定特性，并提出了将其应用于学习、推理和进化的方法。我们还展示了漂变过程空间的组织如何控制保真度、促进创新，并导致有记忆和无记忆的序贯学习中的信息丢失。
