The complexity of forecast testing

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The complexity of forecast testing: abstract

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Electronic Commerce archive
Proceedings of the 9th ACM conference on Electronic commerce table of contents

Chicago, Il, USA

SESSION: Eliciting the truth and worrying about lying table of contents

Pages 139-139

Year of Publication: 2008

ISBN:978-1-60558-169-9

Authors

Lance Fortnow	Northwestern University, Evanston, IL, USA
Rakesh Vohra	Northwestern University, Evanston, IL, USA

Sponsors

ACM: Association for Computing Machinery
SIGEcom: ACM Special Interest Group on Electronic Commerce

Publisher

ACM New York, NY, USA

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ABSTRACT

Consider a weather forecaster predicting a probability of rain for the next day. We consider tests that given a finite sequence of forecast predictions and outcomes will either pass or fail the forecaster. Sandroni shows that any test which passes a forecaster who knows the distribution of nature can also be probabilistically passed by a forecaster with no knowledge of future events.

We look at the computational complexity of such forecasters and exhibit a linear-time test and distribution of nature such that any forecaster without knowledge of the future that can fool the test must be able to solve computationally difficult problems. Thus, unlike Sandroni, a computationally efficient forecaster cannot always fool this test independent of nature.

INDEX TERMS

Primary Classification:
J. Computer Applications
J.4 SOCIAL AND BEHAVIORAL SCIENCES
Subjects: Economics