Prediction of Voting Patterns Based on Census and Demographic Data Analysis Performed by: Mike He ECE 539, Fall 2005

Abstract  Prediction of Voting Patterns in 2004 Presidential Election  Multi-Layer Perceptron, Back-Propagation  Based on Demographic Data  Population Size  Gender Composition  Racial Composition  Age Composition

Voting Representations Area-Based Winner- Takes-All Map Strict Red/Blue binary color coding Can misrepresent actual popular opinion Population-Based Winner- Takes-All Cartogram Counties resized to reflect actual population More accurately reflects popular opinion Illustrates high density of urban areas and tendency to vote Democratic Linearly Shaded Vote- Percentage Map Colors shaded according to vote percentages Accurately portrays closeness of most races and political homogeneity throughout country

Experimental Procedures  Data Pre-Processing  Network Structure Determination  # of Hidden Layers, Neurons in Layers  Coefficients Determination  Training, Training Error Testing  Error from vote percentages, calling for candidate  Testing on Testing Data Set

Experimental Parameters  14 Features, 3 Outputs  Hyperbolic Tangent Activation Function for Hidden Layers  Sigmoid Activation Function for Output Layer  Learning coefficient α=0.2  Momentum coefficient μ=0.5

Experiment 1 – Network Structure  Many different structures tested according to total square error  Best performers isolated for further testing  Comparison of error across multiple trials between tested structures  Winner: 15 neurons in hidden layer, 4 hidden layers

Experiment 2 - Coefficients  To determine optimum α and μ  Different sets of coefficients tested based on total square error as well as maximum square error  Chosen configuration:  α = 0.2, and μ = 0.5

Classification Results  Application of MLP to attempt to predict which candidate will win each county  100 training and prediction trials  For Wisconsin (training data), 77% classification rate  For Minnesota (testing data), 75% classification rate  Less than 3% standard deviation in classification rate between trials

Concluding Remarks  Impressive overall predictive power  Retains predictive power for different states:  Wisconsin and Minnesota similar demographically, different politically  Predictions based only on demographics – innocuous data leads to powerful results  Demonstrates effectiveness of MLP’s as well as element of truth in common generalizations of demographic voting tendencies