ABSTRACT

We study the inclusion of loops in automated theory development based on causal logic. As an area of application, we formalize a model of learning, adaptation, and selection in supply chain management. Our methodological contribution is to analyze a causal network with propositional logic, explaining the difference between material and intentional causality and considering cumulative causality. In the application domain, we prove that the ability of a supply chain to attract resources in turbulent environments depends on its governance structures, the degree of decentralization, and learning incentives, while in stable environments, a supply chain fails to attract resources if a dominant firm appropriates the rents created by others or if it lacks the ability to replicate its own structure. Furthermore, in turbulent times, adequate resources and dynamic routines allow the supply chain to survive.

摘要

我们研究基于因果逻辑的自动化理论开发中包含循环。作为一个应用领域，我们形式化了供应链管理中的学习、适应和选择模型。我们的方法论贡献是用命题逻辑分析因果网络，解释物质因果关系和故意因果关系之间的区别，并考虑累积因果关系。在应用领域，我们证明了供应链在动荡环境中吸引资源的能力取决于其治理结构、去中心化程度和学习激励，而在稳定环境中，供应链如果占主导地位，则无法吸引资源。公司挪用他人创造的租金，或者如果它缺乏复制自己结构的能力。此外，在动荡时期，