Modeling active learning decisions during causal learning

Bibtex entry:

@inproceedings{coenen2013modeling,
	abstract = {An important type of decision making concerns how people choose to gather information which reduces their uncertainty about the world. For example, when learning about a novel piece of technology, like a smartphone, people often actively intervene on various aspects in order to better understand the function of the system. Interventions allow us to tell apart causal structures that are indistinguishable through observation, but only if the right variables are intervened on. Normative models of decision making developed in the machine learning literature specify a process of comparing hypotheses to identify those interventions that will allow a learner to distinguish between them. An experiment that asked subjects to decide between two causal hypotheses found that while they often chose useful interventions, they frequently perform interventions whose expected effects were typical of one causal structure but that did not always allow the two structures to be distinguished. We interpret this tendency as a type of positive-test-strategy with a preference for outcomes that are representative of a single causal structure.},
	address = {Princeton, NJ},
	author = {Coenen, Anna and Rehder, Bob and Gureckis, T.M.},
	booktitle = {Proceedings of the 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making},
	pages = {173},
	title = {Modeling active learning decisions during causal learning},
	year = {2013}}

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