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.

Aim: This study aimed at developing a composite structure heat pipe. Background: Conventionally, the heat pipe enclosure is made out of a single continuous conductive material corresponding to minor heat losses to the surrounding through the middle section, which ideally needs to be adiabatic in nature. The insulating nature of the carbon fiber reduces the axial heat losses and improves the latent heat of vaporization. Objective: The objective of this study is to develop a carbon fiber-reinforced composite heat pipe and a test rig to check the performance of the heat pipe. Method: The hand lay-up technique is used to develop a composite structure heat pipe with a carbon fiber adiabatic section. A test rig is developed to check the performance of the heat pipe. Moreover, the weight comparison is made for a conventional and composite structure heat pipe. Result: The test results reveal that the composite structure heat pipe gives weight reduction in the range of 25 to 30 percent than the conventional heat pipe for identical dimensions and also shows a faster heat absorption rate. Conclusion: Conventional heat pipe may be replaced with the lightweight composite structure heat pipe.