Interpretable Prescriptive Policies via Segmentation
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Solution Overview
Problem
Conventional methods for generating interpretable policies from predictive models fail to maximize revenue and assume homogeneity in price elasticity within segments, leading to suboptimal prescriptive decisions, and complex AI models hinder decision-maker understanding and trust.
Innovation Solution
A method involving a teacher model and a prescriptive tree, where the teacher model determines optimal actions and the prescriptive tree applies a recursive segmentation algorithm to generate interpretable policies that optimize revenue, with the option to adjust the prescriptive tree based on constraints for improved interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional segmentation methods (decision tree or clustering) are used to build segments, then segments are generated based on purchase information or unsupervised clustering, but the segments assume homogeneity in price elasticity which leads to suboptimal prescriptive decisions
Solution Approach 1:
The patent applies segmentation by dividing the population into distinct groups based on purchase information and price elasticity heterogeneity. Instead of assuming homogeneity within segments, the method creates segments that explicitly account for different price response characteristics, allowing for more accurate prescriptive decisions for each segment.
Solution Approach 2:
The patent implements local quality by allowing different price elasticity characteristics within different segments. Each segment is characterized by its own price response properties, enabling tailored prescriptive decisions rather than applying uniform pricing strategies across homogeneous-seeming groups.
2Ease of manufacture
If the number of segments is determined in an ad-hoc fashion, then segmentation is simple to implement, but there is no optimization for revenue maximization
Solution Approach 1:
The patent uses feedback by iteratively optimizing the number of segments based on revenue maximization objectives. The system evaluates different segmentation configurations and selects the number of segments that optimizes revenue, using feedback from performance evaluation to guide the segmentation process rather than relying on ad-hoc determination.
3Measurement precision
If complex AI prediction models (boosted trees, neural networks) are used, then predictive accuracy is improved, but interpretability decreases making it difficult for decision-makers to understand and trust them
Solution Approach 1:
The patent introduces an intermediary layer between complex predictive models and decision-makers. This intermediary translates complex model outputs into interpretable prescriptive recommendations, maintaining the predictive accuracy benefits of complex models while providing the interpretability needed for decision-maker trust and adoption.
Solution Approach 2:
The patent creates simplified copies or representations of complex model behavior that are easier to interpret. By generating interpretable prescriptive policies that capture the essential decision logic, the system provides a comprehensible version of the complex model's reasoning without losing the underlying predictive accuracy.
4Manufacturing precision
If demand models are trained for each segment based on price information, then optimal price can be determined for each segment, but the assumption of homogeneity of price elasticity within each segment is restrictive and limits optimization
Solution Approach 1:
The patent refines segmentation to explicitly account for price elasticity heterogeneity. Instead of creating broad segments that assume homogeneity, the method divides the population into finer segments that capture different price response characteristics, allowing demand models to be trained with more accurate elasticity assumptions for each segment.
Solution Approach 2:
The patent changes the parameters used in segmentation from simple purchase information to include price elasticity metrics. By incorporating elasticity heterogeneity as a segmentation parameter, the system enables more accurate demand modeling and price optimization that reflects the true diversity of customer price responses.
Data Source
AI summary
One embodiment of the invention provides a method for integrated segmentation and prescriptive policies generation. The method comprises training a first artificial intelligence (AI) model and a second model based on training data. The first AI model comprises a teacher model trained to determine a likelihood of a desired outcome for a given action. The second model comprises a prescriptive tree trained for segmentation. The method further comprises determining, via the teacher model, a first policy that produces an optimal action. The optimal action provides a best expected outcome. The method further comprises applying, via the second model, a recursive segmentation algorithm to generate one or more interpretable prescriptive policies. Each interpretable prescriptive policy is less complex and more interpretable than the first policy. The method further comprises, for each interpretable prescriptive policy, determining, via the teacher model, an expected outcome for the interpretable prescriptive policy.