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Description

Technology has managed to seamlessly grow into every industry fathomable without much resistance. This could be due to the fact that the majority of industries that have integrated technology have lacked insurmountable barriers which could hold back strategic innovations. Even

Technology has managed to seamlessly grow into every industry fathomable without much resistance. This could be due to the fact that the majority of industries that have integrated technology have lacked insurmountable barriers which could hold back strategic innovations. Even with a wide array of industries applying technology to their framework, some haven’t managed to reach the true capability of technological advances. One industry that has both taken wide advantage of technology while also barely scraping the surface of the depth behind its potential has been politics. Electronic voting booths, targeted online marketing campaigns, and live streamed debates have been integral parts of our modern-day political environment, however, approval rating-based forecasting for elections has been an area that isn’t commonly referenced by both large political players.

In an age of information where data can be extracted just about anywhere and interpolated using extensive statistical processing, the fact that systems modeling isn’t a pillar of campaign efforts seems ludicrous. A field that is heavily dependent on pivoting concern based on lack of support would make sense to heavily depend on a modeling system that can accurately predict future points of interest.
This report aims to lay the foundation that can be built upon through providing pitfalls in potential modeling, importance of a modeling system, and a barebones skeleton model in AnyLogic with a scheme of how the model would work. I hope this report can serve political interests by providing context on which modeling can accurately provide insight.

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Details

Title
  • PRESIDENTIAL APPROVAL RATINGS MODEL: A collection of information which pertains to the construction of an Approval Rating Model.
Contributors
Date Created
2020-05
Resource Type
  • Text
  • Machine-readable links