论文标题
因果关系的物理基础
Physical grounds for causal perspectivalism
论文作者
论文摘要
我们将特殊开放和不可逆转的物理系统的内部物理状态中因果关系的不对称性（一种因果剂）扎根。因果剂是一种自主物理系统，保持稳定状态，远离热平衡，具有特殊的子系统：传感器，执行器和学习机器。使用反馈，学习机纯粹由热力学约束驱动，改变其内部状态，以学习传感器和执行器记录之间相关性固有的概率功能关系。我们认为这些功能关系只是代理学到的因果关系，因此这种因果关系只是因果因素的内部物理状态之间的关系。我们表明，学习是由热力学原理驱动的：当消散功率最小化时，错误率将最小化。尽管因果因素的内部状态必然是随机的，但所有机器都共享了学识渊博的因果关系，并以相同的硬件嵌入在同一环境中。我们认为，这种因果关系对这种“硬件”的依赖性是因果观点的新颖展示。
We ground the asymmetry of causal relations in the internal physical states of a special kind of open and irreversible physical system, a causal agent. A causal agent is an autonomous physical system, maintained in a steady state, far from thermal equilibrium, with special subsystems: sensors, actuators, and learning machines. Using feedback, the learning machine, driven purely by thermodynamic constraints, changes its internal states to learn probabilistic functional relations inherent in correlations between sensor and actuator records. We argue that these functional relations just are causal relations learned by the agent, and so such causal relations are simply relations between the internal physical states of a causal agent. We show that learning is driven by a thermodynamic principle: the error rate is minimised when the dissipated power is minimised. While the internal states of a causal agent are necessarily stochastic, the learned causal relations are shared by all machines with the same hardware embedded in the same environment. We argue that this dependence of causal relations on such `hardware' is a novel demonstration of causal perspectivalism.
