Simplicity, Scientific Inference and Econometric Modelling
探讨简洁性在科学推断中的定义与最优条件，结合贝叶斯推断与算法信息论，并讨论其在计量经济建模中的作用，如“从一般到特殊”的建模方法。
Two issues are discussed in this paper. The first is whether a formal definition and justification of simplicity (parsimony) in scientific inference can be found, and whether an optimal level of simplicity is obtainable. A definition of simplicity is possible, as are the optimum conditions for the desired degree of simplicity. The model of inference used here relates Bayesian inference to algorithmic information theory. Simplicity is examined in the light of induction, the Duhem-Quine thesis, and bounded rationality. The second issue relates to the role that simplicity might play in econometric modelling. This is elucidated with some remarks on the `general to specific' approach to modelling and discussions on the purpose of a model.