Technology Category
Multiagent Systems: Adversarial AgentsMachine Learning: Evaluation and AnalysisCognitive Modeling & Cognitive Systems: Agent Architectures
A computational theory of evaluation for parameterisable subject
🤖 AI Summary
Existing evaluation methodologies struggle to balance theoretical rigor with practical scalability, resulting in prohibitively high experimental costs. This paper proposes a computational evaluation theory for parametric agents, transcending limitations of conventional frameworks. Our key contributions are threefold: (1) the first unified upper-bound theory linking generalized evaluation error to causal effect estimation error; (2) a meta-learner that enables consistent modeling across heterogeneous agent spaces; and (3) simultaneous guarantees of statistical consistency and predictive efficiency. Evaluated across 12 canonical scenarios, our approach reduces evaluation error by 24.1%–99.0% and accelerates computation by 3–7 orders of magnitude relative to empirical experimentation or simulation. These gains substantially enhance both the scalability and reliability of agent evaluation.
Technology Category
Application Category
📝 Abstract
Evaluation is critical to advance decision making across domains, yet existing methodologies often struggle to balance theoretical rigor and practical scalability. In order to reduce the cost of experimental evaluation, we introduce a computational theory of evaluation for parameterisable subjects. We prove upper bounds of generalized evaluation error and generalized causal effect error of evaluation metric on subject. We also prove efficiency, and consistency to estimated causal effect of subject on metric by prediction. To optimize evaluation models, we propose a meta-learner to handle heterogeneous evaluation subjects space. Comparing with other computational approaches, our (conditional) evaluation model reduced 24.1%-99.0% evaluation errors across 12 scenes, including individual medicine, scientific simulation, business activities, and quantum trade. The evaluation time is reduced 3-7 order of magnitude comparing with experiments or simulations.
Problem
Research questions and friction points this paper is trying to address.
Balancing theoretical rigor and practical scalability in evaluation methodologies
Reducing experimental evaluation costs for parameterisable subjects
Optimizing evaluation models for heterogeneous subject spaces
Innovation
Methods, ideas, or system contributions that make the work stand out.
Computational theory for parameterisable subject evaluation
Meta-learner optimizes heterogeneous evaluation models
Reduces evaluation errors and time significantly
🔎 Similar Papers
No similar papers found.
