Modeling Boundedly Rational Agents with Latent Inference Budgets
This addresses the challenge of modeling diverse populations of suboptimal agents in AI and cognitive science, though it is incremental as it builds on existing bounded rationality frameworks.
The paper tackled the problem of modeling agents with unknown goals and computational constraints by introducing a latent inference budget model (L-IBM) that explicitly simulates constrained inference, showing it matches or outperforms Boltzmann models in tasks like navigation, communication, and chess.
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.