Development Economics X Paper Model Thirty-Six
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This paper develops a novel framework that integrates reinforcement learning with the widely recognized quasi-hyperbolic discounting model to enhance the effectiveness of commitment devices. We address the challenge of time-inconsistent preferences, building on the foundational insights of Laibson (1997). Our dynamic optimization model posits that agents can learn optimal strategies to reconcile immediate impulses with their longer-term objectives. Through extensive simulations involving a large cohort of synthetic agents, we demonstrate the robustness of our reinforcement learning-powered commitment devices, particularly those employing Q-Learning and Deep Q-Networks. These adaptive mechanisms exhibit strong adherence to savings goals across a spectrum of present-bias levels. While a simpler, static commitment device can achieve high rates of adherence, our dynamic reinforcement learning approaches offer a significant advantage by adapting incentives over time. For instance, Q-Learning consistently achieves very high adherence rates, while Deep Q-Networks also maintain substantial effectiveness. This adaptive capacity suggests considerable relevance for practical applications, such as a simulated smartphone application designed to promote financial inclusion in developing countries. Our findings offer important policy implications for narrowing the intention-action gap in various domains, from financial behavior to health outcomes, and the adaptability observed in simulations encourages future empirical validation in diverse economic settings.
Opoku-Agyemang, Kweku (2025). "Optimizing Commitment Devices." Development Economics X Paper Model Thirty-Six.
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