🤖 AI Summary
This study addresses the tendency of time-inconsistent agents to abandon long-term tasks prematurely by developing a continuous-time dynamic model that characterizes their behavior under deadlines and investigates optimal goal-setting and reward-scheduling mechanisms. It provides, for the first time in a continuous-time framework, analytical trajectories for the generalized hyperbolic discounting class, delineating precise conditions under which agents complete tasks, quit immediately, or partially disengage, while clarifying the fundamental differences between continuous and discrete interventions. Leveraging variational methods and optimal control theory, the work derives optimal goals both when exploitative rewards are permitted and prohibited. It further proves that, for a fixed number of stages, equal-length intervals paired with uniform rewards are optimal, and that progressively finer reward segmentation monotonically enhances final progress until reaching a discounting-independent limit.
📝 Abstract
Designing effective goals and rewards for time-inconsistent agents is a central problem in many long-term tasks, such as learning, exercise, work, and project completion. An agent may initially plan to complete a task, but later abandon it because, under non-exponential discounting, the perceived trade-off between immediate effort and delayed reward changes over time. This paper develops a tractable continuous-time model for analyzing and designing interventions for such agents in deadline-constrained progress-based tasks. In the model, an agent repeatedly chooses a future progress trajectory that minimizes perceived cost and then follows its infinitesimal initial direction. Although this leads to a continuous-time dynamic behavior defined through a variational problem, we show that the resulting trajectory admits a concise analytical representation under generalized hyperbolic discounting, a broad class of discount functions that includes exponential and hyperbolic discounting as special cases. Using this representation, we characterize when the agent completes the task, abandons it immediately, or exhibits time-inconsistent abandonment after making partial progress. We then study two intervention design problems: optimal goal setting and optimal reward scheduling. For goal setting, we derive optimal goals both when exploitative rewards are allowed and when they are prohibited, and we identify conditions under which exploitative rewards are ineffective. For reward scheduling, we show that, for a fixed number of stages, equal-length periods and equal rewards are optimal, and that finer reward splitting monotonically improves final progress up to a discount-independent limit. These results provide a continuous-time framework for intervention design for time-inconsistent agents and clarify how optimal interventions differ from those in existing discrete-time models.