Learning the Preferences of Bounded Agents
نویسندگان
چکیده
A range of work in applied machine learning, psychology, and social science involves inferring a person’s preferences and beliefs from their choices or decisions. This includes work in economics on Structural Estimation, which has been used to infer beliefs about the rewards of education from observed work and education choices [1] and preferences for health outcomes from smoking behavior [2]. In machine learning, Inverse Reinforcement Learning has been applied to diverse planning and decision tasks to learn preferences and task-specific strategies [3, 4]. Large-scale systems in industry also learn preferences from behavior: for example, people’s behavior on social networking sites is used to infer what movies, articles, and photos they will like [5].
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