We present two new classes of causal models of decision-making agents. Our approach is motivated by the needs of modeling the economics of computing systems. These systems are composed of subsystems and can exhibit endogenous limits on cognitive resources and value discounting. Structural Causal Decision Models (SCDMs) expand on Structural Causal Influence Models. Like SCIMs, they explicitly represent the causal relationships between model variables and the payoffs of agent decisions. Additionally, agent decisions can be constrained by their causal antecedents, and SCDMs can have open root variables for which no probability distribution or structural equation is given. We show that SCDMs have a well-defined and computationally useful property of composability. Building on SCDMs, we then define a Structural Causal Decision Process (SCDP) as a recurring SCDM with a discount variable. SCDPs benefit from the useful composition properties of SCDMs. Moreover, SCDPs are strictly more expressive than POMDPs because they do not assume rational belief formation. Indeed, an SCDP can endogenously model the memory-formation process, and is thus useful for modeling resource rational agents in dynamic settings. SCDPs are also capable of modeling variable discounting, a tool used widely in social scientific modeling. We pose that SCDPs are a useful framework for policy simulation for the digital economy, mechanism design for information systems, and digital twin modeling of cyberinfrastructure.

翻译：我们提出了两类新的决策主体因果模型。该研究受计算系统经济学建模需求的驱动，这些系统由子系统构成，并可能呈现认知资源与价值贴现的内生限制。结构因果决策模型（SCDMs）是对结构因果影响模型的扩展。与SCIMs类似，SCDMs显式表示模型变量与决策主体收益之间的因果关系。此外，决策主体的选择可受其因果前件约束，且SCDMs可包含未给定概率分布或结构方程的开根变量。我们证明SCDMs具有定义明确且具备计算实用性的可组合性。基于SCDMs，我们将结构因果决策过程（SCDP）定义为带有贴现变量的递归式SCDM。SCDP继承了SCDMs的有用组合性质，且因其不假设理性信念形成，其表达能力严格强于部分可观测马尔可夫决策过程（POMDPs）。具体而言，SCDP能内生建模记忆形成过程，因而适用于动态环境中资源理性主体的建模。SCDP还能对社会科学建模中广泛使用的变贴现进行建模。我们认为，SCDP为数字经济政策模拟、信息系统机制设计及网络基础设施数字孪生建模提供了有效框架。