##What artificial agents can teach us about social decisions John Pearson Biostatistics and Bioinformatics [pearsonlab.github.io/artificial-agents-social-decisions](https://pearsonlab.github.io/artificial-agents-social-decisions)
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Kelsey McDonald

Scott Huettel

Penalty Shot

McDonald, Broderick, Huettel, Pearson

How it works

  • Human, computer opponents
  • Incentive compatible
  • Coevolving strategies

Highly variable strategies


How do we model this?

$$p(\text{change}) = \Phi(f(s(t), \omega(t)))$$ $$f(s(t), \omega(t)) \sim \mathrm{GP}(0, k)$$

Informally:

  • strategy depends on game state $s$, opponent $\omega$
  • $\mathrm{GP}$ is a distribution over functions
  • Bayesian model: we can quantify uncertainty
  • Joint model: can do "counterfactual" experiments

How likely are you to win?

How likely are you to change course?

Real and predicted change points

How opponent-sensitive are you?

Does identity matter?

  • Of course!
  • But context also matters.
  • Can we separate identity from context?
  • Major challenge in social neuro/pscyh

Does identity matter?

Summary

  • Flexible models let us study more realistic decisions
  • Efficient models let us make individualized predictions
  • Accurate models (artificial agents) let us ask "what if" questions, perform synthetic experiments

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