Dynamically Aggregating Diverse Information

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=MYlXAxdo7rE


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Duration: 54:21
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Annie Liang (Northwestern University)
https://simons.berkeley.edu/talks/tbd-464
Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

An agent has access to multiple information sources, each modeled as a Brownian motion whose drift provides information about a different component of an unknown Gaussian state. Information is acquired continuously—where the agent chooses both which sources to sample from, and also how to allocate attention across them—until an endogenously chosen time, at which point a decision is taken. We demonstrate conditions on the agent’s prior belief under which it is possible to exactly characterize the optimal information acquisition strategy. We then apply this characterization to derive new results regarding: (1) endogenous information acquisition for binary choice, (2) the dynamic consequences of attention manipulation, and (3) strategic information provision by biased news sources.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Quantifying Uncertainty: Stochastic Adversarial and Beyond
Annie Liang