Towards Improving Electoral Forecasting by Including Undecided Voters and Interval-valued Prior Knowledge

Dominik Kreiss, Georg Schollmeyer, Thomas Augustin
Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, PMLR 147:201-209, 2021.

Abstract

Increasing numbers of undecided voters constitute a severe challenge for conventional pre-election polls in multi-party systems. While these polls only provide the still pondering individuals with the options to either state a precise party or to drop out, we suggest to regard their valuable information in a set-valued way. The resulting consideration set, listing all the options the individual is still pondering between, can be interpreted under epistemic imprecision. Within this paper we extend the already existing approaches including this valuable information, by making first steps to utilize interval-valued prior information. Including background information is common in election forecasting while we focus on realistically obtainable and credible interval-valued prior information about transition probabilities from the undecided to the eventual choice. We introduce two approaches utilizing this interval-valued information, weighting the credibility against the precision of the results. For the first approach, we narrow the most cautious and wide so-called Dempster bounds by deploying the prior information on the transition probabilities as new worst and best case scenarios for each party. The second approach applies if these interval-valued results are still too wide for useful application. We hereby narrow them towards a good guess of the eventual choice, estimated by a further model-based source of information making use of the covariates. These single-valued estimates on the individual level are regarded as realizations of an underlying probability distribution, which we combine with the prior knowledge in a Bayesian way. The approach can thus be seen as an attempt to combine two, for the needed outcome by themselves inadequate, sources of information to obtain more concise results. We conduct a simulation study showing the applicability and virtues of the new approaches and compare them to conventional ones.

Cite this Paper

BibTeX
@InProceedings{pmlr-v147-kreiss21a, title = {Towards Improving Electoral Forecasting by Including Undecided Voters and Interval-valued Prior Knowledge}, author = {Kreiss, Dominik and Schollmeyer, Georg and Augustin, Thomas}, booktitle = {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications}, pages = {201--209}, year = {2021}, editor = {Cano, Andrés and De Bock, Jasper and Miranda, Enrique and Moral, Serafı́n}, volume = {147}, series = {Proceedings of Machine Learning Research}, month = {06--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v147/kreiss21a/kreiss21a.pdf}, url = {https://proceedings.mlr.press/v147/kreiss21a.html}, abstract = {Increasing numbers of undecided voters constitute a severe challenge for conventional pre-election polls in multi-party systems. While these polls only provide the still pondering individuals with the options to either state a precise party or to drop out, we suggest to regard their valuable information in a set-valued way. The resulting consideration set, listing all the options the individual is still pondering between, can be interpreted under epistemic imprecision. Within this paper we extend the already existing approaches including this valuable information, by making first steps to utilize interval-valued prior information. Including background information is common in election forecasting while we focus on realistically obtainable and credible interval-valued prior information about transition probabilities from the undecided to the eventual choice. We introduce two approaches utilizing this interval-valued information, weighting the credibility against the precision of the results. For the first approach, we narrow the most cautious and wide so-called Dempster bounds by deploying the prior information on the transition probabilities as new worst and best case scenarios for each party. The second approach applies if these interval-valued results are still too wide for useful application. We hereby narrow them towards a good guess of the eventual choice, estimated by a further model-based source of information making use of the covariates. These single-valued estimates on the individual level are regarded as realizations of an underlying probability distribution, which we combine with the prior knowledge in a Bayesian way. The approach can thus be seen as an attempt to combine two, for the needed outcome by themselves inadequate, sources of information to obtain more concise results. We conduct a simulation study showing the applicability and virtues of the new approaches and compare them to conventional ones.} }
Endnote
%0 Conference Paper %T Towards Improving Electoral Forecasting by Including Undecided Voters and Interval-valued Prior Knowledge %A Dominik Kreiss %A Georg Schollmeyer %A Thomas Augustin %B Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2021 %E Andrés Cano %E Jasper De Bock %E Enrique Miranda %E Serafı́n Moral %F pmlr-v147-kreiss21a %I PMLR %P 201--209 %U https://proceedings.mlr.press/v147/kreiss21a.html %V 147 %X Increasing numbers of undecided voters constitute a severe challenge for conventional pre-election polls in multi-party systems. While these polls only provide the still pondering individuals with the options to either state a precise party or to drop out, we suggest to regard their valuable information in a set-valued way. The resulting consideration set, listing all the options the individual is still pondering between, can be interpreted under epistemic imprecision. Within this paper we extend the already existing approaches including this valuable information, by making first steps to utilize interval-valued prior information. Including background information is common in election forecasting while we focus on realistically obtainable and credible interval-valued prior information about transition probabilities from the undecided to the eventual choice. We introduce two approaches utilizing this interval-valued information, weighting the credibility against the precision of the results. For the first approach, we narrow the most cautious and wide so-called Dempster bounds by deploying the prior information on the transition probabilities as new worst and best case scenarios for each party. The second approach applies if these interval-valued results are still too wide for useful application. We hereby narrow them towards a good guess of the eventual choice, estimated by a further model-based source of information making use of the covariates. These single-valued estimates on the individual level are regarded as realizations of an underlying probability distribution, which we combine with the prior knowledge in a Bayesian way. The approach can thus be seen as an attempt to combine two, for the needed outcome by themselves inadequate, sources of information to obtain more concise results. We conduct a simulation study showing the applicability and virtues of the new approaches and compare them to conventional ones.
APA
Kreiss, D., Schollmeyer, G. & Augustin, T.. (2021). Towards Improving Electoral Forecasting by Including Undecided Voters and Interval-valued Prior Knowledge. Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 147:201-209 Available from https://proceedings.mlr.press/v147/kreiss21a.html.

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