How to Be Helpful to Multiple People at Once

Vael Gates, Thomas L. Griffiths, Anca D. Dragan

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


When someone hosts a party, when governments choose an aid program, or when assistive robots decide what meal to serve to a family, decision-makers must determine how to help even when their recipients have very different preferences. Which combination of people’s desires should a decision-maker serve? To provide a potential answer, we turned to psychology: What do people think is best when multiple people have different utilities over options? We developed a quantitative model of what people consider desirable behavior, characterizing participants’ preferences by inferring which combination of “metrics” (maximax, maxsum, maximin, or inequality aversion [IA]) best explained participants’ decisions in a drink-choosing task. We found that participants’ behavior was best described by the maximin metric, describing the desire to maximize the happiness of the worst-off person, though participant behavior was also consistent with maximizing group utility (the maxsum metric) and the IA metric to a lesser extent. Participant behavior was consistent across variation in the agents involved and tended to become more maxsum-oriented when participants were told they were players in the task (Experiment 1). In later experiments, participants maintained maximin behavior across multi-step tasks rather than shortsightedly focusing on the individual steps therein (Experiment 2, Experiment 3). By repeatedly asking participants what choices they would hope for in an optimal, just decision-maker, and carefully disambiguating which quantitative metrics describe these nuanced choices, we help constrain the space of what behavior we desire in leaders, artificial intelligence systems helping decision-makers, and the assistive robots and decision-makers of the future.

Original languageEnglish (US)
Article numbere12841
JournalCognitive science
Issue number6
StatePublished - Jun 1 2020

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence


  • Assistive artificial intelligence
  • Fairness
  • Maximin
  • Modeling
  • Preferences


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