Improving Human Decision-making by Discovering Efficient Strategies for Hierarchical Planning

Consul, Saksham; Heindrich, Lovis; Stojcheski, Jugoslav; Lieder, Falk

doi:10.1007/s42113-022-00128-3

Cited by 8 publications

(10 citation statements)

References 24 publications

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“…Recent work proposed that artificial intelligence can provide a more scalable solution. The basic idea is to combine machine learning and educational software to automatically derive useful decision strategies from descriptions of the decisions people have to make in the real world, and then teach them at scale (Callaway et al, 2022a;Consul et al, 2022;Skirzyński et al, 2021). The method introduced in this article addresses a crucial challenge for this approach, namely the standard methods' sensitivity to the inevitable inaccuracies of our models of the real world.…”

Section: Discussionmentioning

confidence: 99%

“…These findings suggest that automatic strategy discovery can be used to improve human decision-making if the discovered strategies are well-adapted to the real-world situations where people might use them. Finally, Consul et al (2022) found Fig. 3 Illustration of the Mouselab-MDP paradigm.…”

Section: Intelligent Cognitive Tutorsmentioning

confidence: 98%

“…In some of our prior work, we have built on automatic strategy discovery methods to develop intelligent tutors that teach people the optimal planning strategies for a given environment (Lieder et al, 2019;Callaway et al, 2022a;Consul et al, 2022). Most of the tutors let people practice planning in the Mouselab-MDP paradigm and gave them immediate feedback on each planning operation that they chose to perform.…”

Section: Intelligent Cognitive Tutorsmentioning

confidence: 99%

“…Recent work has built on the definition of optimal heuristics for human decisionmaking by (Lieder and Griffiths, 2020) to develop machine learning methods for discovering clever heuristics for human decision-making (Callaway et al, 2018a; Aashay Mehta and Yash Raj Jain contributed equally to this work. Gul et al, 2018;Krueger et al, 2022;Callaway et al, 2022b;Skirzyński et al, 2021;Consul et al, 2022) as well as intelligent cognitive tutors that teach them to people (Callaway et al, 2022a;Consul et al, 2022) and AI-generated decision aids that guide people through the application of the discovered strategies (Becker et al, 2022). Here, we use the term "heuristic" in the broad sense of "any decision strategy that uses only a subset of all potentially relevant information and is not guaranteed to always yield the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic strategy discovery methods compute strategies that achieve the best possible trade-off between the quality of the resulting decisions and the amount of effort that people have to expend to reach them (Callaway et al, 2018a;Consul et al, 2022;Krueger et al, 2022). This optimization is performed on a model of the environment in which the decision strategies are to be used.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Intelligent Cognitive Tutorsmentioning

confidence: 98%

Section: Intelligent Cognitive Tutorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World

et al. 2022

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Towards machines that understand people

Howes,

Jokinen,

Oulasvirta

2023

AI Magazine

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The ability to estimate the state of a human partner is an insufficient basis on which to build cooperative agents. Also needed is an ability to predict how people adapt their behavior in response to an agent's actions. We propose a new approach based on computational rationality, which models humans based on the idea that predictions can be derived by calculating policies that are approximately optimal given human‐like bounds. Computational rationality brings together reinforcement learning and cognitive modeling in pursuit of this goal, facilitating machine understanding of humans.

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Boosting Human Decision-making with AI-Generated Decision Aids

et al. 2022

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Human decision-making is plagued by many systematic errors. Many of these errors can be avoided by providing decision aids that guide decision-makers to attend to the important information and integrate it according to a rational decision strategy. Designing such decision aids used to be a tedious manual process. Advances in cognitive science might make it possible to automate this process in the future. We recently introduced machine learning methods for discovering optimal strategies for human decision-making automatically and an automatic method for explaining those strategies to people. Decision aids constructed by this method were able to improve human decision-making. However, following the descriptions generated by this method is very tedious. We hypothesized that this problem can be overcome by conveying the automatically discovered decision strategy as a series of natural language instructions for how to reach a decision. Experiment 1 showed that people do indeed understand such procedural instructions more easily than the decision aids generated by our previous method. Encouraged by this finding, we developed an algorithm for translating the output of our previous method into procedural instructions. We applied the improved method to automatically generate decision aids for a naturalistic planning task (i.e., planning a road trip) and a naturalistic decision task (i.e., choosing a mortgage). Experiment 2 showed that these automatically generated decision aids significantly improved people’s performance in planning a road trip and choosing a mortgage. These findings suggest that AI-powered boosting might have potential for improving human decision-making in the real world.

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Improving Human Decision-making by Discovering Efficient Strategies for Hierarchical Planning

Cited by 8 publications

References 24 publications

Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World

Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World

Towards machines that understand people

Boosting Human Decision-making with AI-Generated Decision Aids

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