Modeling Boundedly Rational Agents with Latent Inference Budgets
Athul Paul Jacob Abhishek Gupta Jacob Andreas
We study the problem of modeling a population of agents pursuing unknown
goals subject to unknown computational constraints. In standard models of
bounded rationality, sub-optimal decision-making is simulated by adding
homoscedastic noise to optimal decisions rather than explicitly simulating
constrained inference. In this work, we introduce a latent inference budget
model (L-IBM) that models agents' computational constraints explicitly, via a
latent variable (inferred jointly with a model of agents' goals) that controls
the runtime of an iterative inference algorithm. L-IBMs make it possible to
learn agent models using data from diverse populations of suboptimal actors. In
three modeling tasks -- inferring navigation goals from routes, inferring
communicative intents from human utterances, and predicting next moves in human
chess games -- we show that L-IBMs match or outperform Boltzmann models of
decision-making under uncertainty. Inferred inference budgets are themselves
meaningful, efficient to compute, and correlated with measures of player skill,
partner skill and task difficulty.