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How to Be Helpful to Multiple People at Once.

Vael Gates1, Thomas L Griffiths2, Anca D Dragan3

  • 1Department of Psychology, University of California, Berkeley.

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Summary
This summary is machine-generated.

People prefer decision-makers to prioritize the worst-off individuals (maximin metric) when faced with diverse preferences. This maximin approach, focusing on maximizing the minimum well-being, guides choices in social and AI contexts.

Keywords:
Assistive artificial intelligenceFairnessMaximinModelingPreferences

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Area of Science:

  • Decision-making psychology
  • Social welfare theory
  • Human-computer interaction

Background:

  • Decision-makers often face recipients with diverse preferences.
  • Determining how to allocate resources or aid equitably is a complex challenge.
  • Understanding human judgment in these scenarios is crucial for designing fair systems.

Purpose of the Study:

  • To quantitatively model what people consider desirable behavior in decision-making scenarios with varied preferences.
  • To identify which social welfare metrics best explain human choices.
  • To inform the design of ethical AI and assistive technologies.

Main Methods:

  • Developed a quantitative model to assess preferences using metrics like maximax, maxsum, maximin, and inequality aversion (IA).
  • Conducted drink-choosing tasks where participants indicated their preferred outcomes.
  • Varied experimental conditions, including participant roles and task complexity (multi-step decisions).

Main Results:

  • Participant behavior was best explained by the maximin metric (maximizing the well-being of the least well-off).
  • Maxsum (maximizing total group utility) and IA metrics also showed some consistency with participant behavior.
  • Maximin behavior remained consistent across multi-step tasks, indicating a non-shortsighted approach.

Conclusions:

  • Human decision-making in scenarios with diverse preferences leans towards ensuring the well-being of the most disadvantaged.
  • Findings provide a basis for developing AI and robotic systems that align with human values of fairness.
  • The study helps define desirable behaviors for future leaders and automated decision-making systems.