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.

意外性是简化理论的一个核心概念，与卡尔莫格洛夫复杂度的计算相关联，而非概率。本文以三个理论猜想为基础，阐述了意外性的工作原理以及其与贝叶斯规则、示性机制的关联，为超越概率和逻辑方法的研究方向提供了线索。

意外性的三个猜想

Three Conjectures on Unexpectedeness

The paper presents the main characteristics and a preliminary implementation
of a novel computational framework named CompLog. Inspired by probabilistic
programming systems like ProbLog, CompLog builds upon the inferential
mechanisms proposed by Simplicity Theory, relying on the computation of two
Kolmogorov complexities (here implemented as min-path searches via ASP
programs) rather than probabilistic inference. The proposed system enables
users to compute ex-post and ex-ante measures of unexpectedness of a certain
situation, mapping respectively to posterior and prior subjective
probabilities. The computation is based on the specification of world and
mental models by means of causal and descriptive relations between predicates
weighted by complexity. The paper illustrates a few examples of application:
generating relevant descriptions, and providing alternative approaches to
disjunction and to negation.

CompLog 是一种新的计算框架，通过实施 ASP 程序中的最小路径搜索计算两个 Kolmogorov 复杂度来计算特定情况的意外性，从而使用户能够计算事后和事前的主观概率。该论文还列举了几个应用实例，包括生成相关描述以及对析取和否定提供替代方法。