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  Adversarial manipulation of human decision-making

Dezfouli, A., Nock, R., & Dayan, P. (submitted). Adversarial manipulation of human decision-making.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-6B82-F Version Permalink: http://hdl.handle.net/21.11116/0000-0006-6B83-E
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 Creators:
Dezfouli, A, Author
Nock, R, Author
Dayan, P1, 2, Author              
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Adversarial examples are carefully crafted input patterns that are surprisingly poorly classified by artificial and/or natural neural networks. Here we examine adversarial vulnerabilities in the processes responsible for learning and choice in humans. Building upon recent recurrent neural network models of choice processes, we propose a general framework for generating adversarial opponents that can shape the choices of individuals in particular decision-making tasks towards the behavioural patterns desired by the adversary. We show the efficacy of the framework through two experiments involving action selection and response inhibition. We further investigate the strategy used by the adversary in order to gain insights into the vulnerabilities of human choice. The framework may find applications across behavioural sciences in helping detect and avoid flawed choice.

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 Dates: 2020-03
 Publication Status: Submitted
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 Identifiers: DOI: 10.1101/2020.03.15.992875
 Degree: -

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