Decision Oriented Technique (DOTechnique): Finding Model Validity Through Decision-Maker Context

2025-10-12

AI Summary

Existing model validity frameworks lack principled, a priori definitions of applicability, particularly when no ground-truth validity criteria are available. Method: This paper proposes a decision-consistency–driven validity verification method: a surrogate model is deemed valid within the input region where its decisions—derived from the same inputs as those of a high-fidelity reference model—remain logically consistent. Departing from conventional output-similarity–based paradigms, our approach establishes the first decision-oriented validity assessment framework, integrating domain-specific constraints, symbolic reasoning, and constraint propagation to drastically reduce the verification search space. Results: Evaluated on a highway lane-changing simulation system, the method successfully identifies and delineates precise validity boundaries for surrogate models without predefined validity standards, demonstrating practicality, robustness against input perturbations, and computational efficiency.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Model validity is as critical as the model itself, especially when guiding decision-making processes. Traditional approaches often rely on predefined validity frames, which may not always be available or sufficient. This paper introduces the Decision Oriented Technique (DOTechnique), a novel method for determining model validity based on decision consistency rather than output similarity. By evaluating whether surrogate models lead to equivalent decisions compared to high-fidelity models, DOTechnique enables efficient identification of validity regions, even in the absence of explicit validity boundaries. The approach integrates domain constraints and symbolic reasoning to narrow the search space, enhancing computational efficiency. A highway lane change system serves as a motivating example, demonstrating how DOTechnique can uncover the validity region of a simulation model. The results highlight the potential of the technique to support finding model validity through decision-maker context.

Problem

• Determining model validity through decision consistency rather than output similarity
• Identifying validity regions without relying on predefined validity boundaries
• Integrating domain constraints to efficiently narrow the search space

Innovation

• Determines model validity via decision consistency
• Evaluates surrogate models using equivalent decisions
• Integrates domain constraints and symbolic reasoning