Neural Utility Function Learning for Interpretable Choice Models
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Solution Overview
Problem
Existing methods for designing utility functions in discrete choice models either introduce bias due to human intervention or compromise interpretability when using neural networks, leading to a loss of transparency.
Innovation Solution
A computer-implemented method trains a neural network with parameters corresponding to the structure and coefficients of explanatory variables in a discrete choice model, allowing for the specification of an interpretable utility function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a utility function is designed manually using expert knowledge, then the utility function has high interpretability, but bias may be introduced in the explanatory variable or parameter
Solution Approach 1:
The patent introduces an intermediary process between manual design and final utility function by using machine learning models to learn from data while maintaining interpretability constraints. This intermediary approach allows the system to learn from training data without complete manual specification, reducing bias while preserving interpretability through structured parameter learning.
2Reliability
If the entire part or part of a utility function is replaced with a Neural Network, then the possibility of bias is reduced, but the interpretability is degraded
Solution Approach 1:
The patent applies local quality by selectively replacing only specific components of the utility function with neural network elements while maintaining the interpretability of other parts. Instead of complete replacement, the approach uses localized neural network integration that preserves the explanatory structure where needed while leveraging machine learning where pattern recognition is most beneficial.
Solution Approach 2:
The patent creates a composite approach by combining traditional discrete choice model elements with neural network components. This hybrid structure integrates the interpretability of parametric models with the predictive power of neural networks, forming a composite utility function that leverages the strengths of both approaches while mitigating their respective weaknesses.
3Ease of operation
If a discrete choice model is used to model human behavior, then the logic is understandable and the utility function has high interpretability, but manual design may introduce human bias
Solution Approach 1:
The patent enables the system to self-correct bias by using data-driven learning instead of relying solely on human expert judgment. The machine learning models learn patterns directly from training data, allowing the system to serve itself in identifying relationships without human intervention, thereby reducing the introduction of human bias while maintaining the structured framework of discrete choice models.
Data Source
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AI summary
A method for machine learning includes training a neural network including parameters, at least some of the parameters having a structure corresponding to an order and a coefficient of an explanatory variable of a utility function of a discrete choice model, using training data including a value of the explanatory variable and a choice result; and specifying the utility function in the neural network after being subjected to the training.