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.

我们研究了在未知计算限制下追求未知目标的代理人群建模问题。通过引入潜在推断预算模型（L-IBM），我们能够明确地建模代理人的计算限制，并通过一种迭代推断算法的运行时间来控制。我们通过三个建模任务展示了L-IBM在不确定决策时的能力优于Boltzmann模型，并且推断的推断预算本身具有意义，计算高效，并且与玩家技能、合作人技能和任务难度相关。

建模具有潜在推理预算的有界理性代理