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Logical Model Predicts When Minority Candidates Run — And When They Win

🔍 What the Model Does

A quantitatively predictive logical model is presented that links deductive logic to electoral outcomes, producing an explicit mathematical formula that estimates the probability a minority candidate will run in a district and the probability that the candidate will win.

📊 Unique Election Evidence Behind the Claim

  • Mayoral elections in Louisiana, 1986–2016
  • State legislative general elections in 36 states, 2012 and 2014

🔧 How the Formula Works

  • Uses a logically derived mathematical expression—rather than purely statistical fitting—to map district conditions onto precise probabilities of candidate emergence and victory.
  • Outputs are probabilistic predictions for both running and winning in individual districts.

✅ Key Findings

  • The logical model predicts about 90% of minority candidate emergence events in the datasets used.
  • The model predicts about 95% of minority electoral success outcomes in the same data.
  • The model’s structure allows direct answers to applied questions about representation under different districting scenarios.

🧩 Applications for Redistricting and Voting Rights

  • Can be used to evaluate the likelihood that minority candidates will emerge and win under proposed maps.
  • Offers a transparent, deductive tool for Voting Rights Act and redistricting cases where probability-based assessments of representation are needed.
  • All model applications are implemented and available via the open-source software "logical."

📌 Why It Matters

Provides a transparent, mathematically rooted predictive tool that connects district conditions to minority representation outcomes, offering courts, policymakers, and advocates a clear way to assess how redistricting decisions and legal standards affect the chances that minority candidates will run and win.

American Political Science Review
A Logical Model for Predicting Minority Representation: Application to Redistricting and Voting Rights Cases was authored by Yuki Atsusaka. It was published in APSR in 2021. Find on Google Scholar10.1017/S000305542100054X
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