Time Discount Rate Estimation via Behavior Transition Analysis
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Solution Overview
Problem
Existing methods for measuring time discount rates, such as questionnaires, suffer from dispersion of answers, heavy respondent burden, and difficulty in accurately capturing changes over time, making it challenging to measure and compare individual time discount rates effectively.
Innovation Solution
A time discount rate estimation device that utilizes deep learning to analyze behavior data from wearable devices, calculating transition times between behaviors and reducing errors in a machine learning model to accurately estimate time discount rates without questionnaires.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-completed questionnaire method is used, then burden on respondents is reduced, but measurement precision deteriorates due to dispersed answers
Solution Approach 1:
The patent replaces the mechanical questionnaire-based measurement system with an automated machine learning system that processes behavioral data. The time discount rate estimation apparatus uses trained models to automatically estimate time discount rates from behavioral records, eliminating the need for respondents to manually answer questionnaire questions while achieving higher measurement precision through algorithmic analysis of actual behavior patterns.
2Measurement precision
If choice questionnaire method is used, then measurement precision is improved, but burden on respondents increases and measurement frequency decreases
Solution Approach 1:
The system enables self-service measurement by automatically collecting and analyzing behavioral data without requiring active respondent participation. The time discount rate estimation apparatus processes behavioral records that are automatically generated from users' daily activities, eliminating the burden of answering multiple questionnaire questions while maintaining high measurement precision through continuous behavioral monitoring and analysis.
3Measurement precision
If questionnaire methods are used, then time discount rate can be measured, but difficulty in detecting changes over time increases due to medium to long measurement intervals
Solution Approach 1:
The patent implements continuous measurement by automatically collecting behavioral data from users' daily lives without interruption. The time discount rate estimation apparatus continuously processes behavioral records as they are generated, enabling real-time or near-real-time estimation of time discount rates and immediate detection of changes over time, eliminating the time intervals inherent in periodic questionnaire administration.
Data Source
AI summary
An object of the present disclosure is to accurately estimate a time discount rate of a user without depending on a measurement method using questionnaires.Therefore, the present disclosure provides a time discount rate estimation device that estimates a time discount rate in a learning phase, the time discount rate estimation device including: a behavior transition time calculation unit that calculates a transition time from a behavior of a predetermined user recorded at each date and time to all types of behaviors of the predetermined user and outputs behavior transition time feature data for each behavior recorded at each date and time; and a time discount rate estimation model learning unit that calculates an error between a value of a time discount rate obtained by inputting the behavior transition time feature data to a time discount rate estimation model obtained by deep learning and a time discount rate serving as correct answer data based on an answer by the predetermined user and performs machine learning on the time discount rate estimation model so as to reduce the error.