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2018


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Discovering and Teaching Optimal Planning Strategies

Lieder, F., Callaway, F., Krueger, P. M., Das, P., Griffiths, T. L., Gul, S.

In The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (inproceedings)

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Project Page [BibTex]

2018


Project Page [BibTex]


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Discovering Rational Heuristics for Risky Choice

Gul, S., Krueger, P. M., Callaway, F., Griffiths, T. L., Lieder, F.

The 14th biannual conference of the German Society for Cognitive Science, GK, The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (conference)

Abstract
How should we think and decide to make the best possible use of our precious time and limited cognitive resources? And how do people’s cognitive strategies compare to this ideal? We study these questions in the domain of multi-alternative risky choice using the methodology of resource-rational analysis. To answer the first question, we leverage a new meta-level reinforcement learning algorithm to derive optimal heuristics for four different risky choice environments. We find that our method rediscovers two fast-and-frugal heuristics that people are known to use, namely Take-The-Best and choosing randomly, as resource-rational strategies for specific environments. Our method also discovered a novel heuristic that combines elements of Take-The-Best and Satisficing. To answer the second question, we use the Mouselab paradigm to measure how people’s decision strategies compare to the predictions of our resource-rational analysis. We found that our resource-rational analysis correctly predicted which strategies people use and under which conditions they use them. While people generally tend to make rational use of their limited resources overall, their strategy choices do not always fully exploit the structure of each decision problem. Overall, people’s decision operations were about 88% as resource-rational as they could possibly be. A formal model comparison confirmed that our resource-rational model explained people’s decision strategies significantly better than the Directed Cognition model of Gabaix et al. (2006). Our study is a proof-of-concept that optimal cognitive strategies can be automatically derived from the principle of resource-rationality. Our results suggest that resource-rational analysis is a promising approach for uncovering people’s cognitive strategies and revisiting the debate about human rationality with a more realistic normative standard.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning to select computations

Callaway, F., Gul, S., Krueger, P. M., Griffiths, T. L., Lieder, F.

In Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference, August 2018, Frederick Callaway and Sayan Gul and Falk Lieder contributed equally to this publication. (inproceedings)

Abstract
The efficient use of limited computational resources is an essential ingredient of intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete and domain-general learning algorithm for approximating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search algorithm for a computation-selecting metalevel policy based on the insight that the value of information lies between the myopic value of information and the value of perfect information. We evaluate BMPS on three increasingly difficult metareasoning problems: when to terminate computation, how to allocate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and compared favorably to previously proposed metareasoning heuristics. Finally, we demonstrate the practical utility of BMPS in an emergency management scenario, even accounting for the overhead of metareasoning.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


A resource-rational analysis of human planning
A resource-rational analysis of human planning

Callaway, F., Lieder, F., Das, P., Gul, S., Krueger, P. M., Griffiths, T. L.

In Proceedings of the 40th Annual Conference of the Cognitive Science Society, May 2018, Frederick Callaway and Falk Lieder contributed equally to this publication. (inproceedings)

Abstract
People's cognitive strategies are jointly shaped by function and computational constraints. Resource-rational analysis leverages these constraints to derive rational models of people's cognitive strategies from the assumption that people make rational use of limited cognitive resources. We present a resource-rational analysis of planning and evaluate its predictions in a newly developed process tracing paradigm. In Experiment 1, we find that a resource-rational planning strategy predicts the process by which people plan more accurately than previous models of planning. Furthermore, in Experiment 2, we find that it also captures how people's planning strategies adapt to the structure of the environment. In addition, our approach allows us to quantify for the first time how close people's planning strategies are to being resource-rational and to characterize in which ways they conform to and deviate from optimal planning.

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DOI [BibTex]

DOI [BibTex]


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Beyond bounded rationality: Reverse-engineering and enhancing human intelligence

Lieder, F.

University of California, Berkeley, 2018 (phdthesis)

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[BibTex]