Evolving Accuracy: A Genetic Algorithm to Improve Election Night Forecasts
This provides a more accurate method for market researchers and political scientists needing rapid election night forecasts, though it is incremental as it applies an existing algorithm to a new domain.
The paper tackles the problem of forecasting election results quickly for live television by applying a genetic algorithm, showing it outperforms existing approaches with real data from a 2010 local election in Styria, Austria.
In this paper, we apply genetic algorithms to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be made within seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provide an argument to tighten the intersection between computer science and social science, especially political science, further. We scrutinize the performance of our algorithm's runtime behavior to evaluate its applicability in the field. Numerical results with real data from a local election in the Austrian province of Styria from 2010 substantiate the applicability of the proposed approach.