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An Attempt to Position the German Political Parties on a Tree for 2013 and 2017

  • Erich Prisner EMAIL logo
Published/Copyright: October 11, 2018
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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, 12, 1 on all party vertices, and on all dummy vertices of degree 4 or more, and on all dummy vertices adjacent to other dummy vertices. If this labeling is monotonous, then it is monotonously extendable.

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 (y1), (y2), and (y3) be the labeling of these vertices.

Let us first note that if we have a path over yi and yj, ij∈{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 (y1)=(y2), then we use this repeatedly occurring label as the label of x and are done by the previous remark.

If (y1), (y2), and (y3) are all different, say (y1)=0, (y2)=12, (y3)=1, then we can choose (x)=12. By the remark above all that has to be checked is whether paths over y1 and y3, that have been monotonous before labeling x are still monotonous after labeling x. But such a path was obviously increasing …≤(y1)<(y3)≤… and remains increasing …≤(y1) <(x) <(y3)≤….

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 () 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, (x)=(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, (y)=(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, (x)=(y)=(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

Figure 10: All 6-party trees considered, ordered by the number of dummy vertices (in the rows) and the number of end vertices (in the columns).
Figure 10:

All 6-party trees considered, ordered by the number of dummy vertices (in the rows) and the number of end vertices (in the columns).

Table 3:

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
  1. The tree to the left can be contracted into the trees to the right by contracting an edge with at least one dummy vertex.

Table 4:

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-…
Table 5:

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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Published Online: 2018-10-11
Published in Print: 2018-06-26

©2018 Walter de Gruyter GmbH, Berlin/Boston

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