The Design and Composition of Structural Causal Decision Processes

TL;DR

The paper introduces Structural Causal Decision Processes (SCDPs), a new framework for modeling decision-making agents with causal, resource, and discounting considerations, extending existing causal models and surpassing POMDP capabilities.

Contribution

It develops SCDMs and SCDPs, enabling composable, expressive models of decision-making that incorporate endogenous resource limits and discounting, useful for digital economy and cyberinfrastructure applications.

Findings

SCDMs are composable and explicitly represent causal relationships.

SCDPs are more expressive than POMDPs, modeling memory and resource rationality.

SCDPs can model variable discounting and endogenous memory formation.

Abstract

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.