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Activity Number: 319 - SLDS CSpeed 6
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319171
Title: Testing Hypotheses in Agent-Based Models
Author(s): Georgiy Bobashev* and Hang Xiong
Companies: RTI International and Huazhong Agricultural University, China
Keywords: agent-based models ; hypothesis testing; learning; microsimulation; uncertainty; computational hypothesis

Computer simulations have potential to provide a new approach to testing empirical hypotheses for social sciences studies. We present a method for conducting computational hypotheses using agent-based and microsimulation models. Computational hypothesis could be expressed in the form of structural parameters being equal to zero. Considering computational uncertainty intervals from simulation models instead of traditional confidence intervals in data-based hypothesis setting we can infer whether certain computational structures are indistinguishable from noise. Occam razor principle become important especially when the number of validation observations is not very big. This approach allows one to develop efficient and simpler simulation models. We discuss the usefulness of this approach in applications to theoretical settings and practical simulation models in social science.

Authors who are presenting talks have a * after their name.

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