publications

2026

1. in press

   

   Estimating the Recommendation Accuracy in Candidate-based Voting Advice Applications

   Fynn Bachmann, Daan Van Der Weijden, Cristina Sarasua, and Abraham Bernstein

   Politics and Governance, Jan 2026
2. under review

   

   Adaptive Questionnaires for Voting Advice Applications: Three User Experiments on Recommendation Quality, Transparency, and Predictive Influence

   Fynn Bachmann, Cristina Sarasua, and Abraham Bernstein

   Working paper

2025

1. PLOS One

   

   Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactions

   Fynn Bachmann, Daan Van Der Weijden, Lucien Heitz, Cristina Sarasua, and Abraham Bernstein

   PLoS One, May 2025

   Abs DOI

   Adaptive questionnaires dynamically select the next question for a survey participant based on their previous answers. Due to digitalisation, they have become a viable alternative to traditional surveys in application areas such as political science. One limitation, however, is their dependency on data to train the model for question selection. Often, such training data (i.e., user interactions) are unavailable a priori. To address this problem, we (i) test whether Large Language Models (LLM) can accurately generate such interaction data and (ii) explore if these synthetic data can be used to pre-train the statistical model of an adaptive political survey. To evaluate this approach, we utilise existing data from the Swiss Voting Advice Application (VAA) Smartvote in two ways: First, we compare the distribution of LLM-generated synthetic data to the real distribution to assess its similarity. Second, we compare the performance of an adaptive questionnaire that is randomly initialised with one pre-trained on synthetic data to assess their suitability for training. We benchmark these results against an “oracle” questionnaire with perfect prior knowledge. We find that an off-the-shelf LLM (GPT-4) accurately generates answers to the Smartvote questionnaire from the perspective of different Swiss parties. Furthermore, we demonstrate that initialising the statistical model with synthetic data can (i) significantly reduce the error in predicting user responses and (ii) increase the candidate recommendation accuracy of the VAA. Our work emphasises the considerable potential of LLMs to create training data to improve the data collection process in adaptive questionnaires in LLM-affine areas such as political surveys.

2. ACM FAccT

   

   Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee

   Joshua C. Yang and Fynn Bachmann

   In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency

   Presented at FAccT 2025 in Athens, Greece

   Abs DOI

   Democratic processes increasingly integrate large-scale voting with face-to-face deliberation to reconcile individual preferences with collective decision-making. This work introduces algorithmic methods to bridge online voting with face-to-face deliberation, tested in two real-world scenarios: Kultur Komitee 2024 (KK24) and vTaiwan. We present three key contributions: (1) Preference-based Clustering for Deliberation (PCD), enabling both focused and broad discussions by computing balanced homogeneous and heterogeneous groups; (2) Humanin-the-loop MES, enhancing the Method of Equal Shares algorithm with real-time feedback, giving participants control over algorithmic decision-making; and (3) the ReadTheRoom method, using opinion mapping to identify agreement and divergence while tracking opinion shifts during deliberation. These actionable frameworks extend in-person deliberation with scalable digital methods that address the complexities of modern participatory decision-making.

2024

1. ECML/PKDD

   

   Fast and Adaptive Questionnaires for Voting Advice Applications

   Fynn Bachmann, Cristina Sarasua, and Abraham Bernstein

   In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

   Presented at ECML 2024 in Vilnius, Lithuania

   Abs DOI

   The effectiveness of Voting Advice Applications is often compromised by the length of their questionnaires. To address user fatigue and incomplete responses, some applications (such as the Swiss Smartvote) offer a condensed version of their questionnaire. However, these condensed versions cannot ensure the accuracy of recommended parties or candidates, which we show to remain below 40%. To address these limitations, this work introduces an adaptive questionnaire approach that selects subsequent questions based on users’ previous answers, aiming to enhance recommendation accuracy while reducing the number of questions posed to the voters. Our method uses an encoder and decoder module to predict missing values at any completion stage, leveraging a two-dimensional latent space that is reflective of the traditional methods used in political science for visualizing ideology. Additionally, a selector module is proposed to determine the most informative subsequent question based on the voter’s current position in the latent space and the remaining unanswered questions. We validated our approach using the Smartvote dataset from the Swiss Federal elections in 2019, testing various spatial models and selection methods to optimize the system’s predictive accuracy. Our findings indicate that employing the IDEAL model both as encoder and decoder, combined with a PosteriorRMSE method for question selection, significantly improves the accuracy of recommendations, achieving 74% accuracy after asking the same number of questions as in the condensed version.

2023

1. ECML/PKDD

   

   Wasserstein t-SNE

   Fynn Bachmann, Philipp Hennig, and Dmitry Kobak

   In Machine Learning and Knowledge Discovery in Databases

   Presented at ECML 2022 in Grenoble, France

   Abs DOI

   Scientific datasets often have hierarchical structure: for example, in surveys, individual participants (samples) might be grouped at a higher level (units) such as their geographical region. In these settings, the interest is often in exploring the structure on the unit level rather than on the sample level. Units can be compared based on the distance between their means, however this ignores the within-unit distribution of samples. Here we develop an approach for exploratory analysis of hierarchical datasets using the Wasserstein distance metric that takes into account the shapes of within-unit distributions. We use tSNE to construct 2D embeddings of the units, based on the matrix of pairwise Wasserstein distances between them. The distance matrix can be efficiently computed by approximating each unit with a Gaussian distribution, but we also provide a scalable method to compute exact Wasserstein distances. We use synthetic data to demonstrate the effectiveness of our Wasserstein t-SNE, and apply it to data from the 2017 German parliamentary election, considering polling stations as samples and voting districts as units. The resulting embedding uncovers meaningful structure in the data.