A Machine Learning-Based Heuristic to Explain Game-Theoretic Models

This paper introduces a novel methodology that integrates Machine Learning (ML), Operations Research (OR), and Game Theory (GT) to develop an interpretable heuristic for principal-agent models (PAM). We extract solution patterns from ensemble tree models trained on solved instances of a PAM. Using these patterns, we develop a hierarchical tree-based approach that forms an interpretable ML-based heuristic to solve the PAM. This method ensures the interpretability, feasibility, and generalizability of ML predictions for game-theoretic models. The predicted solutions from this ensemble model-based heuristic are consistently high quality and feasible, significantly reducing computational time compared to traditional optimization methods to solve PAM. Specifically, the computational results demonstrate the generalizability of the ensemble heuristic in varying problem sizes, achieving high prediction accuracy with optimality gaps between 1--2% and significant improvements in solution times. Our ensemble model-based heuristic, on average, requires only 4.5 out of the 9 input features to explain its predictions effectively for a particular application. Therefore, our ensemble heuristic enhances the interpretability of game-theoretic optimization solutions, simplifying explanations and making them accessible to those without expertise in ML or OR. Our methodology adds to the approaches for interpreting ML predictions while also improving numerical tractability of PAMs. Consequently, enhancing policy design and operational decisions, and advancing real-time decision support where understanding and justifying decisions is crucial.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Baswapuram, Avinashh Kumar

Chairs dc:contributor.committeechair

• Büyüktahtakın, İ. Esra
• Cai, Wenbo

Committee member dc:contributor.committeemember

• Zhong, Huaiyang

• principal-agent game theoretic model
• machine learning
• ensemble models
• interpretability
• optimization

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41124

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/120679