Goal-directed behavior is a deeply important part of human psychology. People constantly set goals for themselves and pursue them in many domains of life. In this paper, we develop computational models that characterize how humans pursue goals in a complex dynamic environment and test how well they describe human behavior in an experiment. Our models are motivated by the principle of resource rationality and draw upon psychological insights about people's limited attention and planning capacities. We find that human goal pursuit is qualitatively different and substantially less efficient than optimal goal pursuit in our simulated environment. Models of goal pursuit based on the principle of resource rationality capture human behavior better than both a model of optimal goal pursuit and heuristics that are not resource-rational. We conclude that the way humans pursue goals is shaped by the need to achieve goals effectively as well as cognitive costs and constraints on planning and attention. Our findings are an important step toward understanding humans' goal pursuit as cognitive limitations play a crucial role in shaping people's goal-directed behavior.

Teaching people clever heuristics is a promising approach to improve decision-making under uncertainty. The theory of resource rationality makes it possible to leverage machine learning to discover optimal heuristics automatically. One bottleneck of this approach is that the resulting decision strategies are only as good as the model of the decision problem that the machine learning methods were applied to. This is problematic because even domain experts cannot give complete and fully accurate descriptions of the decisions they face. To address this problem, we develop strategy discovery methods that are robust to potential inaccuracies in the description of the scenarios in which people will use the discovered decision strategies. The basic idea is to derive the strategy that will perform best in expectation across all possible real-world problems that could have given rise to the likely erroneous description that a domain expert provided. To achieve this, our method uses a probabilistic model of how the description of a decision problem might be corrupted by biases in human judgment and memory. Our method uses this model to perform Bayesian inference on which real-world scenarios might have given rise to the provided descriptions. We applied our Bayesian approach to robust strategy discovery in two domains: planning and risky choice. In both applications, we find that our approach is more robust to errors in the description of the decision problem and that teaching the strategies it discovers significantly improves human decision-making in scenarios where approaches ignoring the risk that the description might be incorrect are ineffective or even harmful. The methods developed in this article are an important step towards leveraging machine learning to improve human decision-making in the real world because they tackle the problem that the real world is fundamentally uncertain.

BACKGROUND Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission. OBJECTIVE Here, we introduce a principled mathematical method for determining how many points should be awarded/deducted for the enactment/omission of the desired behavior depending on when and how often the person has succeeded versus failed to enact it in the past. We call this approach optimal gamification of behavior change. METHODS As a proof of concept, we design a chatbot that applies our optimal gamification method to help people build a healthy water drinking habit. We evaluated the effectiveness of this gamified intervention in a 40-day long field experiment with two control groups that used the same chatbot without points and without feedback and reminders, respectively. RESULTS We found that, during the intervention, users who received feedback based on optimal gamification enacted the desired behavior more often than the active control group or the passive control group. After the intervention the experimental group enacted the desired behavior just as often as the two control groups. CONCLUSIONS Our findings suggest that optimal gamification can be used to make digital behavior change interventions more effective.

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