The human ability to reason about the causes behind other people' behavior is critical for navigating the social world. Recent empirical research with both children and adults suggests that this ability is structured around an assumption that other agents act to maximize some notion of subjective utility. In this paper, we present a formal theory of this Naïve Utility Calculus as a probabilistic generative model, which highlights the role of cost and reward tradeoffs in a Bayesian framework for action-understanding. Our model predicts with quantitative accuracy how people infer agents' subjective costs and rewards based on their observable actions. By distinguishing between desires, goals, and intentions, the model extends to complex action scenarios unfolding over space and time in scenes with multiple objects and multiple action episodes. We contrast our account with simpler model variants and a set of special-case heuristics across a wide range of action-understanding tasks: inferring costs and rewards, making confidence judgments about relative costs and rewards, combining inferences from multiple events, predicting future behavior, inferring knowledge or ignorance, and reasoning about social goals. Our work sheds light on the basic representations and computations that structure our everyday ability to make sense of and navigate the social world.

中文翻译：

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朴素效用演算作为统一的、量化的行动理解框架

人类推理他人行为背后原因的能力对于驾驭社交世界至关重要。最近对儿童和成人的实证研究表明，这种能力是围绕这样一个假设构建的，即其他代理的行为是为了最大化某些主观效用的概念。在本文中，我们将这种朴素效用演算的正式理论作为概率生成模型提出，该模型强调了成本和奖励权衡在行动理解的贝叶斯框架中的作用。我们的模型定量准确地预测人们如何根据他们的可观察行为推断代理的主观成本和奖励。通过区分欲望、目标和意图，该模型扩展到在具有多个对象和多个动作情节的场景中在空间和时间上展开的复杂动作场景。我们将我们的帐户与更简单的模型变体和一组针对各种动作理解任务的特例启发式进行对比：推断成本和奖励，对相对成本和奖励做出置信判断，结合来自多个事件的推断，预测未来行为，推断知识或无知，以及对社会目标的推理。我们的工作阐明了基本的表示和计算，这些表示和计算构成了我们理解和驾驭社交世界的日常能力。以及对社会目标的推理。我们的工作阐明了基本的表示和计算，这些表示和计算构成了我们理解和驾驭社交世界的日常能力。以及对社会目标的推理。我们的工作阐明了基本的表示和计算，这些表示和计算构成了我们理解和驾驭社交世界的日常能力。