Causality and Decision-making: A Logical Framework for Systems and Security Modelling

2025-08-03

AI Summary

Ensuring safety—encompassing correctness, security, and resilience—in complex system ecosystems requires rigorous causal reasoning and behavioral decision modeling, yet existing approaches lack a unified formal foundation for integrating causality with decision logic. Method: This paper proposes a unified formal framework that integrates causal inference with decision logic. Technically, it innovatively combines the Halpern–Pearl counterfactual causal model with transition systems and modal logic, introducing intervention operators and separation conjunctions to precisely characterize both system-state logic and localized causal dependencies in decision-making. Contribution/Results: Theoretically, it establishes an equivalence foundation between system behavior and causal reasoning, and proves logical consistency via rigorous theorems. Practically, the framework is validated in microservice-based distributed decision scenarios, demonstrating both analytical tractability and effectiveness in modeling, verifying, and reasoning about safety-critical decisions.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Causal reasoning is essential for understanding decision-making about the behaviour of complex `ecosystems' of systems that underpin modern society, with security -- including issues around correctness, safety, resilience, etc. -- typically providing critical examples. We present a theory of strategic reasoning about system modelling based on minimal structural assumptions and employing the methods of transition systems, supported by a modal logic of system states in the tradition of van Benthem, Hennessy, and Milner, and validated through equivalence theorems. Our framework introduces an intervention operator and a separating conjunction to capture actual causal relationships between component systems of the ecosystem, aligning naturally with Halpern and Pearl's counterfactual approach based on Structural Causal Models. We illustrate the applicability through examples of of decision-making about microservices in distributed systems. We discuss localized decision-making through a separating conjunction. This work unifies a formal, minimalistic notion of system behaviour with a Halpern--Pearl-compatible theory of counterfactual reasoning, providing a logical foundation for studying decision making about causality in complex interacting systems.

Problem

• Develop a logical framework for causal reasoning in complex systems
• Integrate intervention operators to model actual causal relationships
• Apply theory to decision-making in distributed systems security

Innovation

• Uses transition systems for strategic reasoning
• Introduces intervention and separating conjunction operators
• Aligns with Halpern-Pearl's counterfactual approach