A Data-Driven Approach to Predicting the 2024 Election: The Delphi Model and Accompanying Statistical Techniques

As the 2024 presidential election approaches, analysts are employing advanced statistical models to predict the likely outcomes. This explores the use of Ordinary Least Squares (OLS) regression, Bayesian logistic regression, and the Delphi game theory model in forecasting the results. These methodologies not only provide insights into each candidate's likelihood of victory but also help in understanding the factors that significantly influence voter behavior.

Phase 1: Ordinary Least Squares (OLS) Regression

Objective: Determine which factors most significantly influence election outcomes using historical data.

Method: OLS regression is a fundamental statistical technique used to estimate the relationships between variables. In this context, OLS regression was applied to historical election data from 2000 to 2020 to identify the most influential factors.

Variables Considered:

1. Approval Rating

2. General Election Polling

3. Battleground State Polling

4. Campaign Finance

5. Economic Indicators (GDP, Unemployment, Inflation, etc.)

6. Campaign Activities (Speeches, Fundraisers, Interviews, Rallies)

7. Social Media Presence

8. Favorability

9. Voter Turnout

10. Regional Voting Patterns

11. Voter Demographics

Results: The OLS regression analysis revealed that the following variables are statistically significant in predicting election outcomes:

• Approval Rating

• General Election Polling

• Social Media Presence

• Favorability

• Voter Turnout

These variables were then used to adjust the Bayesian logistic regression model.

Phase 2: Bayesian Logistic Regression

Objective: Calculate the probability of each candidate winning the 2024 election using significant variables identified by the OLS regression.

Method: Bayesian logistic regression combines prior information with new data to update the probability of an event occurring. In this case, the significant variables identified by the OLS regression were used as inputs.

Significant Variables Used:

• Approval Rating

• General Election Polling

• Social Media Presence

• Favorability

• Voter Turnout

Results: The Bayesian model calculated the following probabilities for the 2024 election:

• Probability of Democratic Victory (Joe Biden): 40.1%

• Probability of Republican Victory (Donald Trump): 59.9%

Bayesian Logistic Regression Probabilities

Phase 3: Delphi Game Theory Model

Objective: Analyze the strategic interactions between candidates and identify optimal strategies using game theory.

Method: The Delphi game theory model (Named after the Oracle of Delphi) considers the strategic interactions between the candidates and uses the concept of Nash equilibrium to determine optimal strategies integrated with the Bayesian Logistic Regression results.

Strategies Considered:

• High Effort (HE)

• Medium Effort (ME)

• Low Effort (LE)

• National Focus (NF)

• Battleground State Focus (BG)

Results: The payoff matrix, based on the updated probabilities and significant variables, revealed the following Nash equilibrium:

• Nash Equilibrium: Biden (Medium Effort, National Focus) vs. Trump (Medium Effort, National Focus)

• Likelihood of Victory:

  • Biden: 36%

  • Trump: 64%

Delphi Game Theory Model Payoff Matrix

Implications for the 2024 Election

The combined analysis of OLS regression, Bayesian logistic regression, and the Delphi game theory model offers a comprehensive forecast for the 2024 election. While the probabilities favor Trump, the race remains competitive, with both candidates having significant paths to victory.

These models underscore the importance of approval ratings, polling data, social media presence, favorability, and voter turnout in shaping election outcomes. As the campaigns progress, real-time data and strategic decisions will continue to influence the dynamics of the race.

In conclusion, while statistical models provide valuable insights, they are not definitive predictors. The 2024 election, like any other, will ultimately be decided by the voters. Analysts and campaign strategists alike will continue to monitor these variables closely, adapting their strategies to the evolving political landscape.