Estimating Present Bias Using Behavior History

抄録

Present bias, the cognitive bias that prioritizes immediate rewards over future ones, is considered one of the factors that can hinder goal achievement. Estimating present bias is crucial for developing effective intervention strategies for behavioral change. This paper proposes a novel method for estimating present bias using using behavior history data collected by wearable devices. We utilize the Transformer model due to its proficiency in learning relationships within sequential data, such as behavioral history, which includes continuous data (e.g., heart rate) and event data (e.g., sleep onset). To enable the Transformer to capture behavior patterns potentially affected by present bias, we introduce two novel architectures for effectively processing continuous and event data timestamp information in behavioral history: temporal and event encoders (TE and EE). TE discerns the periodic characteristics of continuous data, while EE examines temporal interdependencies in the event data. These encoders enable our proposed model to capture temporally (ir)regular behavioral patterns that may associated with present bias. Our experiments using the behavior history logs of 257 subjects collected over 28 days demonstrated that our method estimates the subjects’ present bias accurately.