预测的热力学
The thermodynamics of prediction
摘要：
响应随机驱动信号的系统可以被解释为通过其动力学计算环境变量的隐式模型。系统的状态保留了关于过去环境波动的信息，这些信息的一小部分可以预测未来的波动。剩余的非预测性信息反映了模型的复杂性，它不能提高预测能力，因此代表了模型的无效性。我们揭示了这个模型的低效率和热力学低效率之间的基本等价性，耗散测量。我们的结果持有任意远离热力学平衡，并适用于广泛的系统，包括生物分子机器。他们强调了信息的有效利用和高效热力学运行之间的深刻联系：任何被构造来保持其环境记忆并以最大能量效率运行的系统都必须是可预测的。
英文摘要：
A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system's state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive.
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