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  Predict choice: a comparison of 21 mathematical model

Schulz, E., Speekenbrink, M., & Shanks, D. (2014). Predict choice: a comparison of 21 mathematical model. In 36th Annual Meeting of the Cognitive Science Society (CogSci 2014): Cognitive Science Meets Artificial Intelligence: Human and Artificial Agents in Interactive Contexts (pp. 2889-2894). Austin, TX, USA: Cognitive Science Society.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0006-B450-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0006-B451-3
Genre: Conference Paper

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https://ericschulz.github.io/publications/Schulz2014predict.pdf (Any fulltext)
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 Creators:
Schulz, E1, Author           
Speekenbrink, M, Author
Shanks, DR, Author
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1External Organizations, ou_persistent22              

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 Abstract: How should we choose a model that predicts human choices? Two important factors in this choice are a model's predictive power and a model's fexibility. In this paper, we compare these aspects of models in a large set of models applied to an experiment in which participants chose between brands of fictitious chocolate bars and a quasi-experiment predicting movies' gross revenue. We show that there is a trade-o ff between flexibility and predictive power, but that this trade-o ff appears to lie more towards the "flexible" side than what was previously thought.

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 Dates: 2014-072014-11
 Publication Status: Issued
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Title: 36th Annual Meeting of the Cognitive Science Society (CogSci 2014)
Place of Event: Quebec City, Canada
Start-/End Date: 2014-07-23 - 2014-07-26

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Title: 36th Annual Meeting of the Cognitive Science Society (CogSci 2014): Cognitive Science Meets Artificial Intelligence: Human and Artificial Agents in Interactive Contexts
Source Genre: Proceedings
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Publ. Info: Austin, TX, USA : Cognitive Science Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2889 - 2894 Identifier: ISBN: 978-1-63439-116-0