The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance

We study human performance in two classical NP‐hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem‐solving and in pursuing multiple goals: The relationship between goals and means i...

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Bibliographic Details
Published in:Cognitive science Vol. 47; no. 8; pp. e13330 - n/a
Main Authors: Reichman, Daniel, Lieder, Falk, Bourgin, David D., Talmon, Nimrod, Griffiths, Thomas L.
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc 01.08.2023
Subjects:
Algorithms
Bounded rationality
Computational complexity
Goal systems
Graph theory
ISSN:0364-0213, 1551-6709, 1551-6709
Online Access:Get full text
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Summary:We study human performance in two classical NP‐hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem‐solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more tractable when the structure of the network resembles a tree. Thus, our main prediction is that people should perform better with goal systems that are more tree‐like. We report three behavioral experiments which confirm this prediction. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to choose between multiple means to achieve multiple goals.
Bibliography:Equal contribution.
A preliminary version of our findings about the effect of tree‐width on means‐selection appeared in the Proceedings of the 39th Annual Meeting of the Cognitive Science Society under the title: The Structure of Goal Systems Predicts Human Performance.
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ISSN:0364-0213
1551-6709
1551-6709
DOI:10.1111/cogs.13330