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Choice context influences decision processes and is one of the primary determinants of what people choose. This insight has been used by academics and practitioners to study decision biases and to design behavioral interventions to influence and improve choices. We analyzed the effects of context-based behavioral interventions on the computational mechanisms underlying decision-making. We collected data from two large laboratory studies involving 19 prominent behavioral interventions, and we modeled the influence of each intervention using a leading computational model of choice in psychology and neuroscience. This allowed us to parametrize the biases induced by each intervention, to interpret these biases in terms of underlying decision mechanisms and their properties, to quantify similarities between interventions, and to predict how different interventions alter key choice outcomes. In doing so, we offer researchers and practitioners a theoretically principled approach to understanding and manipulating choice context in decision-making.
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Assessing the impact of context inference error and partial observability on RL methods for just-in-time adaptive interventions
Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies. We study the effect of context inference error and partial observability on the ability to learn effective policies. Our results show that the propagation of uncertainty from context inferences is critical to improving intervention efficacy as context uncertainty increases, while policy gradient algorithms can provide remarkable robustness to partially observed behavioral state information.
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- Award ID(s):
- 1722792
- PAR ID:
- 10488020
- Editor(s):
- Evans, Robin J.; Shpitser, Ilya
- Publisher / Repository:
- Proceedings of Machine Learning Research
- Date Published:
- Journal Name:
- UAI '23: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence
- Volume:
- 216
- Page Range / eLocation ID:
- 1047-1057
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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