Abstract
We try to calculate the position of the six largest German political parties to each other in 2013 and 2017, based on data of Wahl-O-Mat, a German Voting Advice Application. Different to other existing approaches, we do not try to locate these parties in an Euclidean space, but rather on topological trees (with the straight line, the usual left-right model, being the simplest one). This approach has the advantage that – different to two- or higher dimensional spaces – our model allows betweenness information, keeping the parties linearly ordered at least at parts of the tree, with possible conclusions about center or periphery of the political landscape, and possible coalitions. We do not focus primarily on distance but after the topological model is found, we attempt to approximate these distances, in a second step.
Acknowledgment
I would like to thank the two anonymous reviewers for very detailed and very helpful comments.
Appendix A: Proof of the Lemma and Proposition
Lemma
Assume we have a party tree with labels of 0,
Proof
All we need to show is that the labeling is monotonously extendable for any unlabeled dummy vertex x of degree 3 with all three neighbors y1, y2, y3 party vertices. Let ℓ(y1), ℓ(y2), and ℓ(y3) be the labeling of these vertices.
Let us first note that if we have a path over yi and yj, i≠j∈{1, 2, 3}, that was monotonous before labeling x and if we label x with the label of yi or yj, then this path remains monotonous.
Therefore if at least two of the labelings of y1, y2, y3 are the same, say ℓ(y1)=ℓ(y2), then we use this repeatedly occurring label as the label of x and are done by the previous remark.
If ℓ(y1), ℓ(y2), and ℓ(y3) are all different, say ℓ(y1)=0,
Proposition
Let T≺dH for two n-party trees T and H. We assume we have corresponding partial labelings of all the party vertices in both trees, and all dummy vertices are unlabeled. Note that if x is a party vertex, x and the contraction vertex c are considered corresponding vertices, and have therefore the same labels.
If this partial labeling is monotonously extendable in T, then it is also monotonously extendable in H.
Proof
Let xy be an edge of H, with y a dummy vertex, and let T be obtained from H by contracting this edge into a vertex c. We take a monotonous extension (ℓ) of the labeling of T, labeling all vertices of T, and label all corresponding dummy vertices of H in the same way. If x and c are dummy vertices, we label x the same as c, ℓ(x)=ℓ(c). By our construction this is also the case if x and c are both party vertices. The only unlabeled vertex in H so far is then y. Every path in H not containing y corresponds to a path in T with exactly the same labels, and is therefore already monotonuously labeled.
Now we label y in the same way c was labeled in T, ℓ(y)=ℓ(c). We take any path in H containing y and have two cases. Either this path does not contain x, then it corresponds 1-1 to a path in T with c instead of x. Therefore the labeling extension in H is monotonuous then. The other case is that this path does contain x, then it contains the edge xy, with both labels equal, ℓ(x)=ℓ(y)=ℓ(c). Contracting this edge xy, we get a corresponding path in T, which is by our assumption monotonuos. Since replacing a label by two identical consecutive copies of that label does not destroy monotony, this path is monotonuosly labeled then too.
Appendix B: Additional Figures and Tables

All 6-party trees considered, ordered by the number of dummy vertices (in the rows) and the number of end vertices (in the columns).
Contraction (≻d) of the 6-party trees.
| T0: | |
| T1: | |
| T2: | |
| T3: | |
| T4: | |
| T5: | |
| T6: | T0, T1 |
| T7: | T0, T1, T2 |
| T8: | T2 |
| T9: | T1, T3 |
| T10: | T2, T3 |
| T11: | T2, T3 |
| T12: | T1, T3, T4 |
| T13: | T1, T4 |
| T14: | T3, T5 |
| T15: | T4, T5 |
| T16: | T3, T5 |
| T17: | T5 |
| T18: | T6, T7, T12, T13 |
| T19: | T6, T13 |
| T20: | T7, T10, T12 |
| T21: | T8, T10, T11 |
| T22: | T6, T7, T9, T11 |
| T23: | T9, T13, T15, T16 |
| T24: | T10, T11, T14, T16 |
| T25: | T9, T12, T14, T15 |
| T26: | T12, T13, T15, T16 |
| T27: | T11, T16 |
| T28: | T14, T16, T17 |
| T29: | T15, T17 |
| T30: | T21, T24, T27 |
| T31: | T18, T20, T22, T24, T25, T26 |
| T32: | T18, T19, T22, T23, T26, T27 |
| T33: | T23, T25, T26, T28, T29 |
| T34: | T24, T27, T28 |
| T35: | T30, T34 |
The tree to the left can be contracted into the trees to the right by contracting an edge with at least one dummy vertex.
Data for 2013.
| Name | Maximum number of feasible statements | Shuffled data |
Dominated by | |||
|---|---|---|---|---|---|---|
| Sample mean | Sample standard deviation | z-Score point estimate | Number of rounds | |||
| T0 | 24 | 12.17 | 1.12 | 10.58 | 100,000 | – |
| T1 | 19 | 11.60 | 0.97 | 7.66 | 10,000 | – |
| T2 | 14 | 11.20 | 0.98 | 2.85 | 10,000 | – |
| T3 | 12 | 10.23 | 0.71 | 2.49 | 10,000 | – |
| T4 | 12 | 9.73 | 0.67 | 3.39 | 10,000 | – |
| T5 | 8 | 8.00 | 0.00 | – | 1000 | – |
| T6 | 25 | 13.27 | 1.12 | 10.52 | 100,000 | – |
| T7 | 24 | 13.33 | 1.10 | 9.7 | 10,000 | T0 |
| T8 | 15 | 11.77 | 1.00 | 3.22 | 10,000 | – |
| T9 | 19 | 11.89 | 0.89 | 7.98 | 10,000 | T1 |
| T10 | 14 | 11.62 | 0.93 | 2.57 | 10,000 | T2 |
| T11 | 15 | 12.14 | 0.91 | 3.15 | 10,000 | – |
| T12 | 19 | 12.24 | 0.90 | 7.55 | 10,000 | T1 |
| T13 | 19 | 12.25 | 0.93 | 7.23 | 10,000 | T1 |
| T14 | 12 | 10.32 | 0.70 | 2.41 | 10,000 | T3 |
| T15 | 12 | 10.02 | 0.59 | 3.37 | 10,000 | T4 |
| T16 | 12 | 10.73 | 0.71 | 1.78 | 10,000 | T3 |
| T17 | 8 | 8.00 | 0.00 | – | 1000 | T5 |
| T18 | 25 | 14.09 | 1.06 | 10.33 | 40,000 | T6 |
| T19 | 25 | 13.61 | 1.09 | 10.44 | 40,000 | T6 |
| T20 | 24 | 13.75 | 1.06 | 9.64 | 10,000 | T7-T0 |
| T21 | 15 | 12.84 | 0.97 | 2.22 | 10,000 | T8, T11 |
| T22 | 25 | 14.07 | 1.06 | 10.30 | 40,000 | T6 |
| T23 | 19 | 12.46 | 0.88 | 7.44 | 10,000 | T9-T1, T13-T1 |
| T24 | 15 | 12.40 | 0.87 | 3.01 | 10,000 | T11 |
| T25 | 19 | 12.41 | 0.85 | 7.73 | 10,000 | T9-T1, T12-T1 |
| T26 | 19 | 12.80 | 0.89 | 6.95 | 10,000 | T12-T1, T13-T1 |
| T27 | 15 | 12.38 | 0.89 | 2.96 | 10,000 | T11 |
| T28 | 12 | 10.77 | 0.71 | 1.72 | 10,000 | T14-T3, T16-T3 |
| T29 | 12 | 10.01 | 0.59 | 3.36 | 10,000 | T15-T4 |
| T30 | 15 | 13.24 | 0.94 | 1.87 | 10,000 | T21-…, T24-…, T27-… |
| T31 | 25 | 14.56 | 1.03 | 10.10 | 10,000 | T18-…, T21-… |
| T32 | 25 | 14.50 | 1.04 | 10.07 | 10,000 | T18-T6, T19-T6, T22-T6 |
| T33 | 19 | 12.91 | 0.88 | 6.95 | 10,000 | T23-…, T25-…, T26-… |
| T34 | 15 | 12.56 | 0.88 | 2.78 | 10,000 | T24-T11, T27-T11 |
| T35 | 15 | 13.30 | 0.94 | 1.82 | 5000 | T30-…, T34-… |
Data for 2017.
| Name | Maximum number of feasible statements | Shuffled data |
Dominated by | |||
|---|---|---|---|---|---|---|
| Sample mean | Sample standard deviation | z-Score point estimate | Number of rounds | |||
| T0 | 20 | 11.72 | 1.09 | 7.58 | 100,000 | – |
| T1 | 16 | 10.87 | 0.91 | 5.61 | 10,000 | – |
| T2 | 15 | 10.86 | 0.95 | 4.38 | 10,000 | – |
| T3 | 11 | 9.48 | 0.70 | 2.18 | 10,000 | – |
| T4 | 10 | 8.88 | 0.63 | 1.77 | 10,000 | – |
| T5 | 7 | 7.00 | 0.00 | – | 1000 | – |
| T6 | 20 | 12.88 | 1.10 | 6.45 | 10,000 | T0 |
| T7 | 21 | 12.70 | 1.07 | 7.74 | 40,000 | – |
| T8 | 16 | 11.65 | 1.03 | 4.23 | 10,000 | – |
| T9 | 16 | 11.06 | 0.88 | 5.60 | 10,000 | T1 |
| T10 | 15 | 11.12 | 0.93 | 4.18 | 10,000 | T2 |
| T11 | 15 | 11.92 | 0.94 | 3.30 | 10,000 | T2 |
| T12 | 16 | 11.34 | 0.86 | 5.41 | 10,000 | T1 |
| T13 | 16 | 11.62 | 0.91 | 4.80 | 10,000 | T1 |
| T14 | 11 | 9.51 | 0.70 | 2.13 | 10,000 | T3 |
| T15 | 10 | 9.01 | 0.60 | 1.65 | 10,000 | T4 |
| T16 | 11 | 10.15 | 0.75 | 1.13 | 10,000 | T3 |
| T17 | 7 | 7.00 | 0.00 | – | 1000 | T5 |
| T18 | 21 | 13.57 | 1.05 | 7.06 | 40,000 | T7 |
| T19 | 20 | 13.29 | 1.12 | 5.99 | 10,000 | T6-T0 |
| T20 | 21 | 12.98 | 1.04 | 7.68 | 40,000 | T7 |
| T21 | 16 | 12.80 | 1.03 | 3.11 | 10,000 | T8 |
| T22 | 21 | 13.55 | 1.06 | 7.00 | 40,000 | T7 |
| T23 | 16 | 11.74 | 0.91 | 4.69 | 10,000 | T9-T1, T13-T1 |
| T24 | 15 | 12.08 | 0.91 | 3.19 | 10,000 | T10-T2, T11-T2 |
| T25 | 16 | 11.46 | 0.86 | 5.27 | 10,000 | T9-T1, T12-T1 |
| T26 | 16 | 12.06 | 0.90 | 4.38 | 10,000 | T12-T1, T13-T1 |
| T27 | 15 | 12.22 | 0.96 | 2.90 | 10,000 | T11-T2 |
| T28 | 11 | 10.16 | 0.76 | 1.10 | 10,000 | T14-T3, T16-T3 |
| T29 | 10 | 9.02 | 0.58 | 1.69 | 10,000 | T15-T4 |
| T30 | 16 | 13.29 | 1.02 | 2.65 | 10,000 | T21-T8 |
| T31 | 21 | 13.93 | 1.04 | 6.79 | 10,000 | T18-T7, T20-T7, T22-T7 |
| T32 | 21 | 14.05 | 1.07 | 6.51 | 10,000 | T18-T7, T22-T7 |
| T33 | 16 | 12.11 | 0.92 | 4.24 | 10,000 | T23-…, T25-…, T26-… |
| T34 | 15 | 12.35 | 0.96 | 2.77 | 10,000 | T24-…, T27-… |
| T35 | 16 | 13.39 | 1.02 | 2.54 | 5000 | T30-T21-T8 |
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©2018 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Articles
- The Financial Crisis, Fiscal Federalism, and the Creditworthiness of US State Governments
- An Attempt to Position the German Political Parties on a Tree for 2013 and 2017
- Estimating the Conflict Dimensionality in the German Länder from Vote Advice Applications, 2014–2017
Articles in the same Issue
- Frontmatter
- Articles
- The Financial Crisis, Fiscal Federalism, and the Creditworthiness of US State Governments
- An Attempt to Position the German Political Parties on a Tree for 2013 and 2017
- Estimating the Conflict Dimensionality in the German Länder from Vote Advice Applications, 2014–2017