Policy Flow Graphs With Behavior Selection Branches
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Solution Overview
Problem
Existing flow graphs of policy struggle to incorporate behavior selection of individuals into conditional branches, limiting the ability to predict the effects of policies that influence behavior change.
Innovation Solution
An information output program and device that utilize a behavior selection model to predict and output the flow of individuals through policy routes, incorporating behavior selection into conditional branches, allowing for the simulation and evaluation of policy effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conditional determination is executed only with quantitative data or categorical data, then the conditional branch can be clearly defined and processed, but it becomes difficult to incorporate behavior selection of persons that may change with policy prompting
Solution Approach 1:
The patent segments the conditional branch into multiple types: quantitative conditional branches (using examination values), categorical conditional branches (using attributes like gender), and behavior selection conditional branches (using behavior selection models). This segmentation allows each type to be processed appropriately while incorporating behavior selection capability without overwhelming complexity.
Solution Approach 2:
The patent introduces a behavior selection model as an intermediary component that bridges the gap between traditional data-driven conditional determination and human behavior prediction. This model serves as a mediator that processes policy prompting information and generates behavior selection outcomes, which are then integrated into the conditional branch options.
2Measurement precision
If behavior selection model is integrated into policy flow graph, then prediction accuracy of policy effects improves, but processing complexity and computational requirements increase
Solution Approach 1:
The patent divides the policy flow graph processing into distinct segments based on conditional branch types. Quantitative branches use straightforward numerical comparisons, categorical branches use attribute matching, and behavior selection branches use the behavior selection model. This segmentation allows the system to apply appropriate processing complexity only where needed, improving prediction accuracy without uniformly increasing complexity across the entire system.
Solution Approach 2:
The patent applies behavior selection models selectively only to conditional branches where behavior prediction is relevant, rather than applying them universally to all branches. This partial action approach incorporates behavior selection capability where it adds value while avoiding unnecessary computational overhead in branches where traditional quantitative or categorical determination suffices.
3Adaptability or versatility
If multiple types of conditional branches are supported, then the policy flow graph becomes more versatile and realistic, but the difficulty of detecting and measuring flows increases
Solution Approach 1:
The patent segments conditional branches into clearly defined types with distinct processing rules. Each segment (quantitative, categorical, behavior selection) has its own detection and measurement approach, making it easier to track and analyze flows within each segment while maintaining overall system versatility.
Solution Approach 2:
The patent visually distinguishes different types of conditional branches through color coding or visual markers in the flow graph representation. This visual differentiation helps users easily identify and track the type of each conditional branch, simplifying the detection and measurement of flows through complex multi-type policy flow graphs.
Data Source
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AI summary
An information output program for causing a computer to execute a process includes: identifying a flow of persons in a route that includes a plurality of options by using a behavior selection model that indicates which behavior a person selects for a policy; generating information that indicates a prediction result of the policy based on an identified flow of persons; and outputting generated information of a prediction result of policy to a display screen.