Partial Deductive Closure: Logical Simulation and Management Science
提出一种算法，用于对理论进行部分演绎闭包（即发现隐含推论），并过滤掉琐碎结果。该算法应用于组织生态学，生成了有意义的新定理，旨在辅助科学理论的机器化构建。
This research is part of a larger effort to build machine-based tools for developing scientific theories. In analogy with the research process in empirical research, we describe a logical cycle of theory development: (1) starting with an informal version of a theory, (2) then moving to its formal representation, (3) applying formal logic to investigate this representation, and (4) using the results as feedback for the update/revision of the original theory. A central aspect of the logical cycle is the detection of the (hidden) implications of a theory (called “partial deductive closure”). In this paper, we present an algorithm that performs the partial deductive closure for a relevant class of theorems, while filtering out trivial results. The algorithm is applied to an important organization theory, Organizational Ecology, and is shown to generate new theorems of interest.