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Robust Aggregation of Correlated Information

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Listed:
  • Henrique de Oliveira
  • Yuhta Ishii
  • Xiao Lin

Abstract

An agent makes decisions based on multiple sources of information. In isolation, each source is well understood, but their correlation is unknown. We study the agent's robustly optimal strategies -- those that give the best possible guaranteed payoff, even under the worst possible correlation. With two states and two actions, we show that a robustly optimal strategy uses a single information source, ignoring all others. In general decision problems, robustly optimal strategies combine multiple sources of information, but the number of information sources that are needed has a bound that only depends on the decision problem. These findings provide a new rationale for why information is ignored.

Suggested Citation

  • Henrique de Oliveira & Yuhta Ishii & Xiao Lin, 2021. "Robust Aggregation of Correlated Information," Papers 2106.00088, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2106.00088
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    References listed on IDEAS

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    4. Itai Areili & Yakov Babichenko & Rann Smorodinsky, 2017. "Robust Forecast Aggregation," Papers 1710.02838, arXiv.org, revised Feb 2018.
    5. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    6. de Oliveira, Henrique, 2018. "Blackwell's informativeness theorem using diagrams," Games and Economic Behavior, Elsevier, vol. 109(C), pages 126-131.
    7. Giuseppe Moscarini & Lones Smith, 2002. "The Law of Large Demand for Information," Econometrica, Econometric Society, vol. 70(6), pages 2351-2366, November.
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