Unexpectedness is a central concept in Simplicity Theory, a theory of cognition relating various inferential processes to the computation of Kolmogorov complexities, rather than probabilities. Its predictive power has been confirmed by several experiments with human subjects, yet its theoretical basis remains largely unexplored: why does it work? This paper lays the groundwork for three theoretical conjectures. First, unexpectedness can be seen as a generalization of Bayes' rule. Second, the frequentist core of unexpectedness can be connected to the function of tracking ergodic properties of the world. Third, unexpectedness can be seen as constituent of various measures of divergence between the entropy of the world (environment) and the variety of the observer (system). The resulting framework hints to research directions that go beyond the division between probabilistic and logical approaches, potentially bringing new insights into the extraction of causal relations, and into the role of descriptive mechanisms in learning.

翻译：意外性是简约理论的核心概念，该认知理论将各类推理过程与柯尔莫哥洛夫复杂性的计算（而非概率）相关联。其预测能力已通过多项人类受试者实验得到验证，但理论基石仍未充分探索：为何它如此有效？本文为三个理论猜想奠定基础。首先，意外性可被视为贝叶斯规则的一种泛化形式。其次，意外性的频率主义内核可与追踪世界遍历特性的功能建立联系。第三，意外性可视为世界（环境）熵与观察者（系统）多样性之间多种散度测度的构成要素。由此形成的理论框架指向超越概率论与逻辑学方法分野的研究方向，有望为因果关系抽取以及描述性机制在学习中的作用带来新见解。