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The HoneyComb Paradigm for Research on Collective Human Behavior
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Exploring behavior patterns in human and machine interactions.

Ping Zhao1,2, Yunjie Wei1,3, Shouyang Wang1,3

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

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Summary
This summary is machine-generated.

This study introduces a framework to analyze human-machine interaction, finding AlphaGo and humans exhibit similar interaction structures but differ in long-term payoff sensitivity.

Keywords:
Frequency responseMachine behaviorMultivariable Granger causalityNarrativesSocial dilemma

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Analyzing human and machine interaction is crucial for understanding behavior patterns.
  • Existing methods may lack objectivity in interpreting complex interactions.
  • Response-dependence theory offers a novel lens for interaction analysis.

Purpose of the Study:

  • To propose a framework for analyzing human and machine interaction patterns using response-dependence theory.
  • To compare the interaction behaviors of AlphaGo and human professional Go players.
  • To identify differences in decision-making and sensitivity to payoffs.

Main Methods:

  • Developed a framework based on response-dependence theory and a multiple-input multiple-output model.
  • Analyzed sequential machine data and frequency response magnitudes.
  • Conducted an empirical study comparing AlphaGo and human players in the game of Go.

Main Results:

  • Identified similar connectivity structures in vertical analysis between AlphaGo and human players.
  • Observed differences in response magnitude intervals for specific stimulus-response pairs.
  • Found AlphaGo to be more sensitive to long-term payoff changes than human players.

Conclusions:

  • The proposed cross-disciplinary framework provides perceptive and objective explanations for human-machine interaction behavior.
  • The findings contribute to AI ethics and machine behavior interpretation using big data.
  • Response-dependence theory offers valuable insights into interactive narrative features and decision-making.