The Child as Econometrician: A Rational Model of Preference Understanding in Children

By Christopher G. Lucas, T. Griffiths, Fei Xu, Christine Fawcett, Alison Gopnik, Tamar Kushnir, L. Markson, Jane C. Hu. 2014

101 citations recorded by the source corpus.

Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children's ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding.

Subfields: Developmental and Educational Psychology, Economics and Econometrics, General Decision Sciences

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Citations in the corpus

Machine Theory of Mind (2018, methodology): “…relied on hand-crafted models of agents as noisy-rational planners – e.g. using inverse RL (Ng et al., 2000; Abbeel & Ng, 2004), Bayesian inference (Lucas et al., 2014; Evans et al., 2016), Bayesian Theory of Mind (Baker et al., 2011; Jara-Ettinger et al., 2016; Baker et al., 2017) or game…”
A decision network account of reasoning about other people's choices. (2015, methodology): “The logit model, also known as the multinomial logit model, has been used to explain how people infer other people’s preferences after observing their choices (Bergen, Evans, & Tenenbaum, 2010; Jern, Lucas, & Kemp, 2011; Lucas et al., 2014).”
Theory of mind as inverse reinforcement learning (2019, background): “Thus, the principles of RL planning resemble the assumptions that we make about other people’s behavior [5,3,4].”
The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology. (2016, background): “Related ideas have been explored [45,66], although not with the same focus on costreward tradeoffs in childhood as we emphasize here.”
Rational quantitative attribution of beliefs, desires and percepts in human mentalizing (2017, background): “Previous work has proposed both model-based [37, 2, 31, 38, 21, 20] and cue-based [4, 55] models of how both children and adults infer one class of mental states: desires, and associated notions such as goals, intentions, and preferences.”