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Adversarial vulnerabilities of human decision-making.

Amir Dezfouli1, Richard Nock2,3, Peter Dayan4,5

  • 1Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Eveleigh, NSW 2015, Australia; amir.dezfouli@data61.csiro.au.

Proceedings of the National Academy of Sciences of the United States of America
|November 5, 2020
PubMed
Summary
This summary is machine-generated.

Researchers explored adversarial vulnerabilities in human learning and choice, developing a framework to manipulate decision-making. This research offers insights into human cognitive processes and potential applications in behavioral science.

Keywords:
decision-makingrecurrent neural networksreinforcement learning

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Adversarial examples are inputs designed to fool artificial and natural neural networks.
  • Human learning and choice processes may exhibit similar vulnerabilities to adversarial manipulation.

Purpose of the Study:

  • To examine adversarial vulnerabilities in human learning and choice.
  • To propose a framework for generating adversarial opponents to shape human choices.
  • To investigate the strategies adversaries use to exploit human decision-making vulnerabilities.

Main Methods:

  • Utilized recurrent neural network models of choice processes.
  • Developed a general framework for creating adversarial opponents.
  • Conducted three experiments on action selection, response inhibition, and social decision-making.

Main Results:

  • Demonstrated the efficacy of the adversarial framework in influencing human choices across different tasks.
  • Identified specific strategies employed by adversaries to exploit human decision-making.
  • Provided empirical evidence for adversarial vulnerabilities in human cognitive processes.

Conclusions:

  • Human learning and choice are susceptible to adversarial manipulation.
  • The proposed framework offers a method to study and potentially mitigate flawed decision-making.
  • Findings have implications for understanding and intervening in behavioral patterns across the behavioral sciences.