Personalized Incentive Policy Using Success-Stock Behavior Modeling
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Solution Overview
Problem
Existing incentive systems fail to account for individual response differences, leading to inefficient use of incentives and increased costs, as the effect of incentives varies daily with internal states, and a simple, constant incentive method may not effectively manage behavior.
Innovation Solution
An information processing apparatus that acquires behavior history data, estimates user-specific parameter values using a behavior model with psychological accumulated success experiences, and calculates an optimal incentive policy to maintain target behavior cost-effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a constant or monotonously changing incentive amount is given to all users, then the incentive system is simple to implement, but the effectiveness of incentives decreases due to individual response differences and varying internal states
Solution Approach 1:
The incentive amount is transformed from a static constant value to a dynamic value that changes based on user-specific parameters and internal states. The system calculates optimal incentive amounts by considering individual response differences and temporal variations in user motivation, making the incentive system adaptive rather than fixed.
Solution Approach 2:
The incentive system transitions from a uniform approach applied to all users to a personalized approach where each user receives incentives tailored to their specific characteristics, behavior patterns, and internal states. This local customization optimizes effectiveness for each individual user.
2Reliability
If larger incentive amounts are given to account for individual response differences, then the effectiveness of incentives improves, but the cost of implementing the incentive system increases
Solution Approach 1:
The system optimizes incentive parameters by considering user-specific parameters (individual response characteristics) and temporal parameters (internal states varying over time). By dynamically adjusting these parameters, the system achieves high effectiveness without necessarily increasing incentive amounts, as the optimization accounts for when and how users respond best to incentives.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor user responses to incentives and internal states over time. This feedback is used to refine and optimize future incentive decisions, ensuring that incentives are given at optimal times and in optimal amounts, thereby avoiding waste and reducing overall cost while maintaining effectiveness.
3Reliability
If incentive amounts are increased to maintain target behavior, then the behavior maintenance effectiveness improves, but the cost-effectiveness of the incentive policy deteriorates
Solution Approach 1:
The system performs preliminary analysis of user parameters and internal states before determining incentive amounts. By predicting when users are most likely to respond to incentives and what amounts will be most effective, the system can maintain behavior with optimized incentive spending, avoiding both under-incentivization and wasteful over-incentivization.
Data Source
AI summary
According to one embodiment, an information processing apparatus includes: an acquisition unit configured to acquire behavior history data and a condition for optimizing an incentive policy for each of users; a parameter estimation unit configured to estimate a parameter value of a behavior model for each user based on the behavior history data, the behavior model having a success stock indicating a psychological accumulated amount of past success experiences as an internal variable; an optimization unit configured to calculate an optimal incentive policy for each user based on the estimated parameter value and the condition; and an output unit configured to output the optimal incentive policy.