Digital Companions for Goal-Setting, Goal-Achievement, and Self-Improvement
Computing Optimal Sub-Goals
A Gamified App that Helps People Overcome Self-Limiting Beliefs by Promoting Metacognition
Helping People Choose Their Values
A Digital Companion That Helps People Achieve Their Goals
Development of Measures for Goal Setting and Pursuit
Value-Driven Hierarchical Goal-Setting
Effective Goal-Setting
AI for Productivity
Solve Education
Intelligent Cognitive Prostheses
Computing Optimal Incentive Structures
Executive Functions Training
Measuring the Cost of Planning with Bayesian Inverse Reinforcement Learning
In this project, we investigate to which extent seemingly irrational planning decisions are a consequence of how people individually experience the costs and benefits of deliberate decision-making. We start from the empirically-grounded assumption that many sub-optimal decisions arise from being short-sighted when balancing the costs and benefits of a particular decision. To achieve this, we leverage Bayesian Inverse Reinforcement Learning [Ramachandran and Amir, IJCAI 2007] to infer experienced reward functions. In future work, we will investigate personalized interventions based on differences in these experienced costs. This work may result in insights about human decision-making, applicable to a wide range of domains such as public policy, psychiatric treatment, and the field of education.
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