On the likelihood of singlepeaked preferences
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DOI: 10.1007/s0035501710330
 Cite this article as:
 Lackner, ML. & Lackner, M. Soc Choice Welf (2017) 48: 717. doi:10.1007/s0035501710330
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Abstract
This paper contains an extensive combinatorial analysis of the singlepeaked domain restriction and investigates the likelihood that an election is singlepeaked. We provide a very general upper bound result for domain restrictions that can be defined by certain forbidden configurations. This upper bound implies that many domain restrictions (including the singlepeaked restriction) are very unlikely to appear in a random election chosen according to the Impartial Culture assumption. For singlepeaked elections, this upper bound can be refined and complemented by a lower bound that is asymptotically tight. In addition, we provide exact results for elections with few voters or candidates. Moreover, we consider the Pólya urn model and the Mallows model and obtain lower bounds showing that singlepeakedness is considerably more likely to appear for certain parameterizations.
1 Introduction
Singlepeaked preferences have several nice properties. First, they guarantee that a Condorcet winner exists and further that the pairwise majority relation is transitive (Inada 1969). Thus singlepeaked preferences are a way to escape Arrow’s paradox (Arrow 1950). Second, nonmanipulable voting rules exist for singlepeaked preferences (Moulin 1980) and hence the singlepeaked restriction also offers a way to circumvent the GibbardSatterthwaite paradox (Gibbard 1973; Satterthwaite 1975). By adopting an algorithmic viewpoint, a third advantage becomes apparent. Restricting the input to singlepeaked preferences often allows for faster algorithms for computationally hard voting problems (Brandt et al. 2015; Betzler et al. 2013; Walsh 2007; Faliszewski et al. 2011b).
In this paper we perform an extensive combinatorial analysis of the singlepeaked domain. Our aim is to establish results on the likelihood that an election is singlepeaked for some axis. To be more precise, we allow the axis to be chosen depending on the preferences and do not assume that it is given together with the election. We consider three probability distributions for elections: the Impartial Culture (IC) assumption in which all total orders are equally likely and are chosen independently, the Pólya urn model which assumes a certain homogeneity among voters and Mallows model in which the probability of a vote depends on the closeness to a given reference vote (with respect to the Kendalltau distance).
Configuration definable restrictions Many domain restrictions can be characterized by forbidden configurations, in particular the singlepeaked domain. We prove a close connection between configurations and permutations patterns. This novel connection allows us to obtain a very general result (Theorem 8), showing that many domain restrictions characterized by forbidden configurations are very unlikely to appear in a random election chosen according to the Impartial Culture assumption. More precisely, while the total number of elections with n votes and m candidates is equal to \((m!)^n\), the number of elections belonging to such a domain restriction can be bounded by \(m! \cdot c^{n m}\) for some constant c.
Counting singlepeaked elections We perform a detailed combinatorial analysis of the singlepeaked domain by counting the number of singlepeaked elections. The number of singlepeaked elections immediately yields the corresponding probability with respect to the Impartial Culture (IC) assumption, which is the number of singlepeaked elections divided by the total number of elections. We establish an upper bound for the number of singlepeaked elections which asymptotically matches our lower bound result (Theorem 11). In addition, we show exact enumeration results for elections with two voters or up to four candidates (Theorem 12). Our results rigorously show that the singlepeaked restriction is highly unlikely to appear in random elections chosen according to the IC assumption. This holds even for elections with few votes and candidates (cf. Sect. 8). Most of our results can easily be translated to the Impartial Anonymous Culture (IAC) assumption (Proposition 13).
Pólya urn model In contrast to the IC assumption, singlepeaked elections are considerably more likely if elections are chosen according to the Pólya urn model. We provide a lower bound on the corresponding likelihood (Theorem 14) and show that, if a sufficiently strong homogeneity is assumed, the probability of an election with n votes being singlepeaked is larger than 1 / n (Corollary 15).
Mallows model We encounter the most likely occurrence of singlepeaked elections under Mallows model. As for the Pólya urn model we establish a lower bound result on the likelihood (Theorem 16). If the dispersion parameter \(\phi \) is sufficiently small , we are able to show that singlepeaked elections are likely to appear (Corollary 17 and Table 4).
An election is manipulable if a voter or a coalition of voters is better off by not voting sincerely but by misrepresenting their true preferences. The GibbardSatterthwaite paradox (Gibbard 1973; Satterthwaite 1975) states that every reasonable voting rule for more than two candidates is susceptible to manipulation. However, the GibbardSatterthwaite paradox does not offer insight into how likely it is that manipulation is possible. Determining this likelihood both for single manipulators and coalitions of manipulators has been the focus of intensive research. Results have been obtained under a variety of probability distributions: for example under the Impartial Culture assumption (Slinko 2002a, b; Friedgut et al. 2008; Isaksson et al. 2012), the Pólya urn model (Lepelley and Valognes 2003), the Impartial Anonymous Culture (Favardin et al. 2002; Slinko 2005).
The likelihood that an election has a Condorcet winner or, its converse, the likelihood of the Condorcet paradox has been the focus of many publications (see the survey of Gehrlein (2006) as well as more recent work of Gehrlein et al. (2013, 2015) for more recent research). In particular, we would like to mention that the likelihood of an election with three candidates having a Condorcet winner under the Impartial Anonymous Culture assumption is \(\frac{15(n+3)^2}{16(n+2)(n+4)}\) for odd n and \(\frac{15(n+2)(n^2+8n+8)}{16(n+1)(n+3)(n+5)}\) for even n (Gehrlein 2002). We will comment on the relation between these result and our results in Sect. 9.
Organization Preliminaries are established in Sect. 2. The results on configuration definable restrictions can be found in Sect. 3, results on counting singlepeaked elections in Sect. 4, results on the IAC assumption in Sect. 5, results on the Pólya urn model in Sect. 6 and results on the Mallows model in Sect. 7. In Sect. 8 we provide numerical evaluations of our results and discuss their implications. We conclude the paper in Sect. 9 with directions for future research.
2 Preliminaries
Sets and orders Let S be a finite set. A relation on S is total if for every \(a,b\in S\), either the pair (a, b) or (b, a) is contained in the relation. A total order on S is a reflexive, antisymmetric, transitive and total relation. Let T be a total order of S. Instead of writing \((a,b)\in T\), we write \(a\le _T b\) or \(b\ge _T a\). We write \(a <_T b\) or \(b >_T a\) to state that \(a\le _T b\) and \(a\ne b\). As a short form, we write \(T:s_1 s_2 s_3 \ldots s_i\) instead of \(s_1>_T s_2>_T s_3>_T \cdots >_T s_i\) for \(s_1, s_2, \ldots , s_i\) in S. We write T(i) to denote the ith largest element with respect to T.
Permutations A permutation \(\pi \) of a finite set S is a bijective function from S to S. We write \(\pi ^{1}\) for the inverse function of \(\pi \). A permutation of the set \(\{1,\ldots ,m\}\) is called an mpermutation. We shall write an mpermutation \(\pi \) as the sequence of values \(\pi (1) \pi (2) \ldots \pi (m)\). For example \(\pi =321\) is the permutation with \(\pi (1)=3, \pi (2)=2\) and \(\pi (3)=1\). Every pair \((T_1, T_2)\) of total orders on a set with m elements yields an mpermutation \(p(T_1, T_2)\), which is defined as follows: i maps to j if the ith largest element in \(T_1\) equals the jth largest element in \(T_2\). For \(T_1:bac\) and \(T_2:cab\) we have \(p(T_1, T_2)=321\). Note that \(p(T_1,T_2)=p(T_2,T_1)^{1}\).
Elections An (n, m)election \((C,\mathcal {P})\) consists of a sizem set C and an ntuple \({\mathcal {P}}=(V_1,\ldots ,V_n)\) of total orders on C. The total orders \(V_1,\ldots ,V_n\) represent votes or preferences. We write \(V\in {\mathcal {P}}\) to denote that there exists an index \(i\in [n]\) such that \(V=V_i\). Given a vote \(V_i\in {\mathcal {P}}\), the intuitive meaning of \(V_i: c_j c_k\) is that the ith voter prefers candidate \(c_j\) to candidate \(c_k\).
We assume that candidate sets are chosen from a fixed, infinite set. When counting elections we do not care about the specific names these candidates have. That means when we count elections we fix the candidate set to \(\{c_1, c_2, \ldots , c_m\}\). Note that the number of (n, m)elections is \((m!)^n\). Throughout the paper we only consider (n, m)elections with \(n\ge 2\) and \(m\ge 2\).
Probability distributions over elections We consider four probability distributions in this paper. The first and simplest is the Impartial Culture (IC) which assumes that in an election all votes, i.e., total orders of candidates, are equally likely and are chosen independently. Thus, the IC assumption can be seen as the uniform distribution over total orders on candidates. The results in our paper concerning IC do not state probabilities but rather count the number of elections. If, e.g., the number of singlepeaked (n, m)elections is a(n, m, SP), then the probability under the IC assumption that an (n, m)election is singlepeaked is \(\frac{a(n,m,SP)}{(m!)^n}\). It is important to note that in our paper elections contain an ordered list of votes. Thus, we distinguish elections that consist of the same votes but these votes appear in a different order. This is in contrast to the Impartial Anonymous Culture (IAC) assumption, in which elections contain a multiset of votes and thus elections are not ordered. The IAC assumption is briefly considered in Sect. 5.
In addition to the IC assumption, we consider the Pólya urn model and the Mallows model. Both distributions are generalizations of the IC assumption and generate more structured elections. We are going to define the Pólya urn and the Mallows model in Sects. 6 and 7, respectively.
Singlepeaked preferences The singlepeaked restriction assumes that the candidates can be ordered linearly on a socalled axis and voters prefer candidates close to their ideal point to candidates that are further away.
Definition 1
Let \((C,\mathcal {P})\) be an election and A a total order of C. A vote V on Ccontains a valley with respect to A on the candidates \(c_1,c_2,c_3\in C\) if \(A: c_1\, c_2\, c_3\) and V ranks \(c_2\) below \(c_1\) and \(c_3\). The election \((C,\mathcal {P})\) is singlepeaked with respect to A if for every \(V\in \mathcal {P}\) and for all candidates \(c_1,c_2,c_3\in C\), V does not contain a valley with respect to A on \(c_1,c_2,c_3\). We then call the total order A the axis. The election \((C,\mathcal {P})\) is singlepeaked if there exists a total order A of C such that \((C,\mathcal {P})\) is singlepeaked with respect to A.
Remark 1
Given an axis on m candidates, there are \(2^{m1}\) votes that are singlepeaked with respect to this axis (Escoffier et al. 2008). This can be seen as follows: The last ranked candidate has to be one of the two outermost candidates on the axis and hence there are two possibilities. Once we have picked this last candidate, we can iterate the argument for the next lowest ranked candidate, where we again have two possibilities. Thus, for all positions in the total order (except for the top ranked candidate), there are two candidates to choose from—which yields \(2^{m1}\) possibilities in total.
3 A general result based on permutation patterns
Before we study the singlepeaked domain in detail, we prove a general result that is applicable to a large class of domain restrictions including the singlepeaked domain. To precisely define this class of domain restrictions, we require the notion of configuration definability.
3.1 Configuration definable domain restrictions
Singlepeaked elections may also be defined in the following way:
Theorem 2
\(V_i: abc\), \(V_i: db\),
\(V_j: cba\) and \(V_j: db\) holds
\(V_i: ba, ca\) (i.e., a is ranked below b and c),
\(V_j: ab, cb\) and
\(V_k: ac, bc\) holds.
Note that this theorem defines singlepeakedness without referring to an axis. Indeed, singlepeakedness is now defined as a local property in the sense that certain configurations must not be contained in the election. Similar definitions have also been found for the singlecrossing (Bredereck et al. 2013b) and groupseparable (Ballester and Haeringer 2011) domain. For other domains such as value, worst, medium and bestrestricted preferences, a characterization via configurations follows immediately from the original definitions (Sen 1966; Sen and Pattanaik 1970). Let us now exactly define what it means for a domain restriction to be configuration definable.
Definition 2
An (l, k)configuration \((S,\mathcal {T})\) consists of a finite set S of cardinality k and a tuple \(\mathcal {T}=(T_1,\ldots ,T_l)\), where \(T_1,\ldots ,T_l\) are total orders on S. An election \((C,\mathcal {P})\)contains configuration \(\mathcal {C}\) if there exist an injective function f from [l] into [n] and an injective function g from S into C such that, for any \(x,y\in S\) and \(i\in [l]\), it holds that \(T_i: xy\) implies \(V_{f(i)}: g(x)g(y)\).
We use \((S,\mathcal {T})\sqsubseteq (C,\mathcal {P})\) as a shorthand notation to denote that the election \((C,\mathcal {P})\) contains the configuration \((S,\mathcal {T})\). An election \((C,\mathcal {P})\)avoids a configuration \((S,\mathcal {T})\) if \((C,\mathcal {P})\) does not contain \((S,\mathcal {T})\). In such a case we say that \((C,\mathcal {P})\) is \((S,\mathcal {T})\)restricted. If the set S is clear from the context, we omit it and just use \(\mathcal {T}\) to describe a configuration.
Example 3
Let us consider an election \((C,\mathcal {P})\) with \(C=\{u,v,w,x,y\}\) and \({\mathcal {P}}=(uvwxy,wyvux,yuxwv)\) and a configuration \((S,\mathcal {T})\) with \(S=\{a,b,c,d\}\) and \({\mathcal {T}}=(dabc,cdba)\). Election \((C,\mathcal {P})\) contains the configuration \((S,\mathcal {T})\) as witnessed by the functions \(f:\{1\mapsto 1, 2\mapsto 3\}\) and \(g:\{a\mapsto v, b\mapsto x, c\mapsto y, d\mapsto u\}\). In Fig. 2, the functions f and g are depicted graphically.
By considering all linearizations of the partial orders appearing in Theorem 2 we can now restate it as follows.
Theorem 4
the following (2, 4)configurations:
(dabc, dcba), (adbc, dcba), (dabc, cdba) and (adbc, cdba)
as well as the following (3, 3)configurations:
(bca, acb, abc), (cba, acb, abc), (bca, cab, abc), (cba, cab, abc), \((bca,acb,bac)\), (cba, acb, bac), (bca, cab, bac), (cba, cab, bac).
The first four configurations correspond to the first condition in Theorem 2, the remaining eight correspond to the second condition.
Definition 3
Let \(\varGamma \) be a set of configurations. A set of elections \(\varPi \) is defined by \(\varGamma \) if \(\varPi \) consists exactly of those elections that avoid all configurations in \(\varGamma \). We call \(\varPi \)configuration definable if there exists a set of configurations \(\varGamma \) which defines \(\varPi \). If \(\varPi \) is definable by a finite set of configurations, it is called finitely configuration definable.
By Theorem 4 we know that the set of all singlepeaked elections is finitely configuration definable. This is also true for the set of groupseparable elections (Ballester and Haeringer 2011) and for the set of singlecrossing elections (Bredereck et al. 2013b).
We are now going to characterize which sets of elections are configuration definable. In the following definition, for two elections \((C,\mathcal {P})\) and \((C',\mathcal {P}')\), we write \((C',\mathcal {P}') \mathbin {\sqsubseteq } (C,\mathcal {P})\) if \((C',\mathcal {P}')\), considered as a configuration, is contained in \((C,\mathcal {P})\). Since every election can be seen as a configuration, the configuration containment relation immediately translates to election containment.
Definition 4
A set of elections \(\varPi \) is hereditary if for every election \((C',\mathcal {P}')\) it holds that if there exists an election \((C,\mathcal {P})\in \varPi \) with \((C',\mathcal {P}')\sqsubseteq (C,\mathcal {P})\), then \((C',\mathcal {P}')\in \varPi \).
Proposition 5
A set of elections is configuration definable if and only if it is hereditary.
Proof
Let a set of elections \(\varPi \) be defined by a set of configurations \(\varGamma \) and \((C,\mathcal {P})\in \varPi \). Let \((C',\mathcal {P}')\sqsubseteq (C,\mathcal {P})\). Since \((C,\mathcal {P})\in \varPi \), \((C,\mathcal {P})\) avoids all configurations in \(\varGamma \). Due to \((C',\mathcal {P}')\sqsubseteq (C,\mathcal {P})\), also \((C',\mathcal {P}')\) avoids all configurations in \(\varGamma \) and is therefore contained in \(\varPi \).
For the other direction, let \(\varPi \) be a hereditary set of elections. We define \(\varPi ^c\) to be the complement of \(\varPi \), i.e., an election is contained in \(\varPi ^c\) if it is not contained in \(\varPi \).
We claim that \(\varPi ^c\), considered as a set of configurations, defines \(\varPi \). If \((C,\mathcal {P})\in \varPi \), it avoids all \((C',\mathcal {P}')\in \varPi ^c\) since if there existed a \((C',\mathcal {P}')\in \varPi ^c\) with \((C',\mathcal {P}')\sqsubseteq (C,\mathcal {P})\), this would imply that \((C',\mathcal {P}')\in \varPi \). It remains to show that if an election \((C'',\mathcal {P}'')\) avoids all \((C',\mathcal {P}')\in \varPi ^c\), then \((C'',\mathcal {P}'')\in \varPi \). This follows from noting that \((C'',\mathcal {P}'')\) avoiding all \((C',\mathcal {P}')\in \varPi ^c\) implies \((C'',\mathcal {P}'')\notin \varPi ^c\), which in turn implies \((C'',\mathcal {P}'')\in \varPi \). \(\square \)
As a consequence of Proposition 5, we know that 2D singlepeaked (Barberà et al. 1993) and 1D Euclidean elections (Coombs 1964; Knoblauch 2010) are configuration definable. However, Proposition 5 does not help to answer whether these restrictions are finitely configuration definable. For the 1D Euclidean domain it is even known that it is not finitely configuration definable (Chen et al. 2015). Finite configuration definability has been crucial for establishing algorithmic results (Bredereck et al. 2013a; Elkind and Lackner 2014).
A natural example of a meaningful restriction that is not configuration definable is the set of all elections that have a Condorcet winner. The property of having a Condorcet winner is not hereditary and thus cannot be defined by configurations. Another example is the “singlepeaked on a tree” restriction (Demange 1982).
3.2 The connection to permutation patterns
In this section, we establish a strong link between the concept of configuration containment and the concept of pattern containment in permutations. Pattern containment in permutations is defined as follows.
Definition 5
A kpermutation \(\pi \) is contained as a pattern in an mpermutation \(\tau \) if there is a subsequence of \(\tau \) that is orderisomorphic to \(\pi \). In other words, \(\pi \) is contained in \(\tau \), if there is a strictly increasing map \(\mu : \{1,\ldots ,k\} \rightarrow \{1,\ldots ,m\}\) so that the sequence \(\mu (\pi )=\big (\mu (\pi (1)),\mu (\pi (2)),\ldots ,\mu (\pi (k))\big )\) is a subsequence of \(\tau \). This map \(\mu \) is called a matching of \(\pi \) into \(\tau \). If there is no such matching, \(\tau \) avoids the pattern \(\pi \).
For example, the pattern \(\pi =132\) is contained in \(\tau =32514\) since the subsequence 254 of \(\tau \) is orderisomorphic to \(\pi \). However, the pattern 123 is avoided by \(\tau \). Note that \(\tau \) contains \(\pi \) if and only if \(\tau ^{1}\) contains \(\pi ^{1}\).
We are going to prove two lemmas. The first lemma (Lemma 6) states that every permutation pattern matching query can naturally be translated into a configuration containment query. The second lemma (Lemma 7) states that for (2, k)configurations, a configuration containment query can naturally be translated in a permutation pattern query.
Lemma 6
Proof
Assume that we have a matching \(\mu \) from \(\pi \) into \(\tau \). We have to find an injective function f from \(\{1,2,3\}\) into \(\{1,2,3\}\) and an injective function g from S into C such that, for any \(x,y\in S\) and \(i\in \{1,2,3\}\), it holds that \(T_i: xy\) implies \(V_{f(i)}: g(x)g(y)\). Let f be the function \(\{1\mapsto 1, 2\mapsto 2, 3\mapsto 3\}\) and \(g=\mu \). It holds for \(x_i,x_j \in S\) that \(T_1: x_i\,x_j\) if and only if \(V_1: c_{\mu (i)}\, c_{\mu (j)}\) since \(\mu \) is monotone. The same holds for \(T_2\) and \(V_2\). For \(T_3\) and \(V_3\) observe that \(T_3: x_i\,x_j\) implies \(V_3: c_{\mu (i)}\, c_{\mu (j)}\) since \(\mu \) is a matching. Thus, the election fulfils \((S,{\mathcal {T}})\sqsubseteq (C,\mathcal {P})\).
For the other direction, assume that \((C,\mathcal {P})\) contains \((S,{\mathcal {T}})\). Consequently, there exists an injective function f from \(\{1,2,3\}\) into \(\{1,2,3\}\) and an injective function g from S into C such that, for any \(x,y\in S\) and \(i\in \{1,2,3\}\), it holds that \(T_i: xy\) implies \(V_{f(i)}: g(x)g(y)\). First, we claim that \(f(3)=3\). Observe that f has to map \(T_1\) and \(T_2\) to identical total orders. Thus, unless \(V_1=V_2=V_3\), \(f(3)=3\). In the case that \(V_1=V_2=V_3\), we can assume without loss of generality that \(f(3)=3\). We will construct a function \(\mu \) and show that \(\mu \) is a matching from \(\pi \) into \(\tau \). Let us define \(\mu (i)=j\) if \(g(x_i)=c_j\). Observe that \(\mu \) is strictly increasing since for \(i<j\), \(V_1: c_{g(i)}\,c_{g(j)}\) and \(V_1:c_1 c_2 \cdots c_m\). In addition, \(\mu (\pi )=\big (\mu (\pi (1)),\mu (\pi (2)),\ldots ,\mu (\pi (k))\big )\) is a subsequence of \(\tau \) since, by definition of \(T_3\) and \(V_3\) and the fact that \(f(3)=3\), \(\big (g(x_{\pi (1)}),g(x_{\pi (2)}),\ldots ,g(x_{\pi (k)})\big )\) is a subsequence of \(\big (c_{\tau (1)},c_{\tau (2)},\ldots ,c_{\tau (m)}\big )\). \(\square \)
Next, we will prove the second lemma, which is essential for the main theorem of this section (Theorem 8). As of now, we shall denote by \(S_m(\pi _1,\ldots ,\pi _l)\) the cardinality of the set of mpermutations that avoid the patterns \(\pi _1,\ldots ,\pi _l\).
Lemma 7
Let \((S,{\mathcal {T}})\) be a (2, k)configuration with \({\mathcal {T}}=(T_1,T_2)\). Furthermore, let \(V_1\) be a total order on the candidate set \(C=\{c_1,\ldots ,c_m\}\). Then the number of total orders \(V_2\) such that the election \((C,\mathcal {P})\) with \(\mathcal {P}=( V_1, V_2 )\) avoids \((S,{\mathcal {T}})\) is equal to \(S_m(\pi , \pi ^{1})\), where \(\pi =p(T_1,T_2)\).
Proof
Let us start by proving the following statement: The configuration \((S,{\mathcal {T}})\) is contained in an election \((C,\mathcal {P})\) with \(\mathcal {P}=(V_1,V_2)\) if and only if the permutation \(\pi \) or the permutation \(\pi ^{1}\) is contained in \(p(V_1,V_2)\). In order to alleviate notation, we will assume in the following that \(C=\{1, 2, \ldots m\}\) and \(S=\{1, 2, \ldots k\}\).
Let \(\mu \) be a matching witnessing that \(\pi \) is contained in \(p(V_1,V_2)\). We can assume without loss of generality that \(T_1: 12\ldots k\) and \(V_1:12 \ldots m\). Then the functions \(f=\{1\mapsto 1, 2\mapsto 2\}\) and \(g=\mu \) show that \((S,{\mathcal {T}})\sqsubseteq (C,\mathcal {P})\) (cf. Definition 2). If \(\pi ^{1}\) is contained in \(p(V_1,V_2)\) as witnessed by a matching \(\mu \), then the functions \(f=\{1\mapsto 2, 2\mapsto 1\}\) and \(g=\mu \) show that \((S,{\mathcal {T}})\sqsubseteq (C,\mathcal {P})\).
For the other direction, let \((S,{\mathcal {T}})\sqsubseteq (C,\mathcal {P})\). Without loss of generality we assume that \(T_1:12 \ldots k\). Note that renaming C does not change whether \((S,{\mathcal {T}})\sqsubseteq (C,\mathcal {P})\). Thus, it is safe to rename the candidates according to the f function: If \(f=\{1\mapsto 1, 2\mapsto 2\}\), let \(V_1:12 \ldots n\). Since \(f(1)=1\), g is monotonic. It is easy to verify that g is a matching from \(\pi \) into \(p(V_1,V_2)\). If \(f=\{1\mapsto 2, 2\mapsto 1\}\), let \(V_2:12 \ldots n\). Now, g is a matching from \(\pi \) into \(p(V_2,V_1)=(p(V_1,V_2))^{1}\). This is equivalent to g being a matching from \(\pi ^{1}\) into \(p(V_1,V_2)\).
It follows that \((C,\mathcal {P})\) avoids the configuration \((S,{\mathcal {T}})\) if and only if the permutation \(p(V_1,V_2)\) avoids both the patterns \(\pi \) and \(\pi ^{1}\). Moreover, for the fixed total order \(V_1\) and a fixed mpermutation \(\tau \), there is a single total order \(V_2\) such that \(p(V_1,V_2)=\tau \). Thus the number of votes \(V_2\) such that \(p(V_1,V_2)\) avoids \(\pi \) and \(\pi ^{1}\) (and equivalently the number of votes \(V_2\) such that \((C,\mathcal {P})\) avoids \((S,{\mathcal {T}})\)) is equal to \(S_m(\pi , \pi ^{1})\), the number of mpermutations avoiding \(\pi \) and \(\pi ^{1}\). \(\square \)
From this lemma follows the main theorem of this section that is applicable to any set of configurations that contains at least one configuration of cardinality two.
3.3 Elections that avoid a (2, k)configuration
With the help of Lemma 7, we are able to establish the following result.
Theorem 8
This result shows that forbidding any (2, k)configuration is a very strong restriction. Indeed, \(m!\cdot c_k^{(n1)m}\) is very small compared to the total number of (n, m)elections which is \((m!)^n\). This result allows us to bound the number of singlepeaked and groupseparable elections. However, let us prove this result first before we explore its consequences.
In order to prove this result we make use of the link between configuration avoiding elections and pattern avoiding permutations established in Lemma 7 and profit from a very strong result within the theory of pattern avoidance in permutations, the MarcusTardos theorem (former StanleyWilf conjecture).
Proof
Now we apply the famous MarcusTardos theorem (Marcus and Tardos 2004): For every permutation \(\pi \) of length k there exists a constant \(c_k\) such that for all positive integers m we have \(S_m(\pi ) \le {c_k}^m\). Putting this together with Equation (1) and noting that \(a(n,m,\left\{ (S,{\mathcal {T}}) \right\} )\) is an upper bound for \(a(n,m,\varGamma )\), we obtain the desired upper bound. \(\square \)
Let us discuss the implications of this theorem. It is applicable to all (not necessarily finite) configuration definable domain restrictions that contain a configuration of cardinality two. In particular, we obtain the following upper bounds for singlepeaked and groupseparable elections.
Corollary 9
Let \(a(n,m,\varGamma _{sp})\) denote the number of singlepeaked (n, m)elections. For \(n,m \ge 2\) it holds that \(a(n,m, \varGamma _{sp}) \le m!\cdot 4^{(m1)(n1)}\).
Proof
We know from Theorem 2 that the singlepeaked domain avoids the (2, 4)configurations (dabc, dcba), (adbc, dcba), (dabc, cdba) and (adbc, cdba). We can use Eq. (1) in the proof of Theorem 8 to bound \(a(n,m,\varGamma _{sp})\). For this, we have to compute the permutations and their inverses corresponding to the four configurations. We obtain the permutations \(\pi _1=p(dabc,dcba)=1432\), \(\pi _2=p(adbc,dcba)=4132\), \(\pi _3=p(dabc,cdba)=2431\) and \(\pi _4=p(adbc,cdba)=4231\). Their inverses are \(\pi _1^{1}=\pi _1\), \(\pi _2^{1}=\pi _3\), \(\pi _3^{1}=\pi _2\) and \(\pi _4^{1}=\pi _4\). Hence it holds that the number of (n, m)elections that avoid these four configurations is bounded by \(m! \cdot S_m(\pi _1, \pi _2, \pi _3, \pi _4)^{n1}\). The enumeration problem for this permutation class has been solved by Guibert (1995) in his PhD thesis with the help of the method of generating trees. A more direct and combinatorial approach to this permutation class can be found in the first author’s PhD thesis (Bruner 2015). It holds that \(S_m(\pi _1, \pi _2, \pi _3, \pi _4)=\left( {\begin{array}{c}2m2\\ m1\end{array}}\right) \), which, in turn, is bounded by \(4^{m1}\). \(\square \)
This upper bound also holds for the 1D Euclidean domain (Coombs 1964; Knoblauch 2010), since this domain is a subset of the singlepeaked domain. In the next section, we will see that the growth rate of \(a(n,m, \varGamma _{sp})\) is indeed of the form \(m!\cdot c^{(m1)(n1)}\) for some constant c. However, the constant found in Corollary 9 is not optimal as we will see by providing a better bound for the singlepeaked restriction that is even asymptotically optimal.
As another corollary of Theorem 8, we prove a bound on the number of groupseparable elections. An election is group separable if for every subset of candidates \(C'\) there exists a partition \(C_1,C_2\) of \(C'\) such that in every vote either all candidates in \(C_1\) are preferred to all candidates in \(C_2\) or vice versa. Ballester and Haeringer (2011) showed that the groupseparable domain is finitely configuration definable. In particular, this domain avoids the configuration (abcd, bdac). Therefore, Theorem 8 is applicable.
Corollary 10
Let \(a(n,m,\varGamma _{gs})\) denote the number of groupseparable (n, m)elections. For \(n,m \ge 2\) it holds that \(a(n,m, \varGamma _{gs}) \le m!\cdot (3 +2\sqrt{2})^{m(n1)}\).
Proof
The proof is similar to the one of Corollary 9. We use Equation (1) in the proof of Theorem 8 to bound \(a(n,m,\varGamma _{gs})\), i.e., \(a(n,m,\varGamma _{gs})\le m! \cdot S_m(\pi , \pi ^{1})^{n1}\), where \(\pi = p(abcd,bdac) = 3142\) and \(\pi ^{1} = 2413\). Permutations avoiding these two patterns are known under the name of separable permutations. It is known that separable permutations are counted by the large Schröder numbers (OEIS A006318) and that \(S_m(\pi , \pi ^{1}) \le (3 +2\sqrt{2})^m\) (West 1995). \(\square \)
4 Counting results and the Impartial Culture assumption
As in the previous section, let \(a(n,m, \varGamma _{\text {sp}})\) denote the number of singlepeaked elections. In this section, we prove a lower and upper bound on \(a(n,m, \varGamma _{\text {sp}})\). These two bounds are asymptotically optimal, i.e., the lower bound converges to the upper bound for every fixed m and \(n\rightarrow \infty \). In addition, we prove exact enumeration results for \(a(2,m, \varGamma _{\text {sp}})\), \(a(n,3, \varGamma _{\text {sp}})\) and \(a(n,4, \varGamma _{\text {sp}})\).
Our results immediately imply bounds on the probability that an (n, m)election is singlepeaked assuming that elections are drawn uniformly at random, i.e., according to the Impartial Culture assumption. The probability is simply \(a(n,m, \varGamma _{\text {sp}})/(m!)^n\).
Theorem 11
Proof
First observe that an election is singlepeaked with respect to an axis if and only if it is singlepeaked with respect to its reverse, i.e., the axis read from right to left. Thus the total number of axes on m candidates that need to be considered is m! / 2. Second, recall that the number of votes that are singlepeaked with respect to a given axis is \(2^{m1}\) (cf. Remark 1).
Now we have gathered all facts necessary for the upper bound. For every one of the m! / 2 axes considered, select an ordered set of votes from the \(2^{m1}\) votes that are singlepeaked with respect to this axis. There are exactly \(2^{(m1)\cdot n}\) such possibilities, which yields the upper bound. Since an election may be singlepeaked with respect to more than two axes, this number is only an upper bound for \(a(n,m, \varGamma _{\text {sp}})\).
In the next theorem we prove exact enumeration formulæ for \(a(n,m, \varGamma _{\text {sp}})\) for \(n=2\), \(m=3\) and \(m=4\). Note that for \(m\le 2\) and for \(n=1\) all (n, m)elections are singlepeaked. For \(n>2\) and for \(m>4\) we have not been able to find exact enumeration formulas.
Theorem 12
 (i.)
\(a(2,m, \varGamma _{\text {sp}})=m!\cdot \left( {\begin{array}{c}2m2\\ m1\end{array}}\right) \) for \(m \ge 1\),
 (ii.)
\(a(n,3, \varGamma _{\text {sp}})=6\cdot 2^{n1}\left( 2^n1\right) \) and
 (iii.)
\(a(n,4, \varGamma _{\text {sp}})=24\cdot 4^{n1}\cdot \left( 2^{n+1}3\right) \).
Proof
 (i.)
\(a(2,m, \varGamma _{\text {sp}})=m!\cdot \left( {\begin{array}{c}2m2\\ m1\end{array}}\right) \): This follows from Lemma 7. We choose the first vote arbitrarily (m! possibilities). The second vote has to be chosen in such a way that all configurations that characterize singlepeakedness are avoided. Since we consider only elections with two votes, the relevant configurations are (dabc, dcba), (adbc, dcba), (dabc, cdba) and (adbc, cdba) (Theorem 4). We obtain the permutations \(\pi _1=p(dabc,dcba)=1432\), \(\pi _2=p(adbc,dcba)=4132\), \(\pi _3=p(dabc,cdba)=2431\) and \(\pi _4=p(adbc,cdba)=4231\). Their inverses are \(\pi _1^{1}=\pi _1\), \(\pi _2^{1}=\pi _3\), \(\pi _3^{1}=\pi _2\) and \(\pi _4^{1}=\pi _4\). Thus, the number of \(a(2,m, \varGamma _{\text {sp}})=S_m(\pi _1, \pi _2, \pi _3, \pi _4)\), which is equal to \(\left( {\begin{array}{c}2m2\\ m1\end{array}}\right) \), as shown by Guibert (1995) and Bruner (2015).
 (ii.)
\(a(n,3, \varGamma _{\text {sp}})=m!\cdot 2^{n1}\left( 2^n1\right) \): We consider all elections with three candidates. There are m! many possibilities for the first vote \(V_1\). Without loss of generality, let us consider only the vote \(V_1: abc\). Since we have only three candidates, singlepeakedness boils down to having at most two last ranked candidates (cf. Theorem 2). Due to our assumption that \(V_1: abc\), we distinguish three cases: elections in which the votes rank either a or c last, elections in which the votes rank either b or c last and elections in which all votes rank c last. The number of elections in which the votes rank either a or c last can be determined as follows: every vote can either be abc, bac, cba or bca. Hence, there are \(4^{n1}\) possibilities for elections in which the votes rank either a or c last and where \(V_1:abc\) holds. By the same argument, the number of elections in which the votes rank either b or c last is \(4^{n1}\) as well. The number of elections where c is always ranked last is \(2^{n1}\). We obtain a total number of singlepeaked elections with a fixed first vote of \(4^{n1}+4^{n1}2^{n1}=2^{n1}\cdot (2\cdot 2^{n1}1)\). Given that 6 options for the first vote exist, we obtain the stated enumeration result.
 (iii.)\(a(n,4, \varGamma _{\text {sp}})=m!\cdot 4^{n1}\cdot \left( 2^{n+1}3\right) \): As in the previous proof, we fix \(V_1:abcd\). This vote \(V_1\) already rules out some possible axes. Indeed, only eight axes are singlepeaked axes for \(V_1\), namely \(A_1:abcd\), \(A_2:bacd\), \(A_3:cabd\), \(A_4:cbad\), and their reverses. Since the reverse of an axis permits the same singlepeaked votes, we have to consider only \(A_1, A_2, A_3, A_4\). For \(1 \le i \le 4\), let \(W_i\) denote the set of fourcandidate votes that are singlepeaked with respect to axis \(A_i\). We count the number of singlepeaked elections with four candidates by using the inclusionexclusion principle, i.e.,It is easy to verify that \(W_1\cap W_2=W_1\cap W_4 = W_2\cap W_3=\{abcd, bacd\}\). Consequently, all intersections of three or four sets consist also of these two votes. The remaining intersections look as follows:$$\begin{aligned} a(n,4, \varGamma _{\text {sp}}) =&\; m!\cdot (W_1+W_2+W_3+W_4\\&W_1\cap W_2W_1\cap W_3W_1\cap W_4\\&W_2\cap W_3W_2\cap W_4W_3\cap W_4\\&+W_1\cap W_2\cap W_3+W_1\cap W_2\cap W_4\\&+W_1\cap W_3\cap W_4+W_2\cap W_3\cap W_4\\&W_1\cap W_2\cap W_3\cap W_4) \end{aligned}$$The number of votes singlepeaked with respect to one axis is \(2^{m1}\) (see Remark 1), i.e., in our case 8. We obtain$$\begin{aligned} W_1\cap W_3&=\{abcd, bacd,cbad, bcad\},\\ W_2\cap W_4&=\{abcd, bacd,cabd, acbd\},\\ W_3\cap W_4&=\{abcd, bacd,badc, abdc\}. \end{aligned}$$$$\begin{aligned} a(n,4, \varGamma _{\text {sp}})&= 4!\cdot \left( 4\cdot 8^{n1}  3\cdot 2^{n1}  3\cdot 4^{n1} + 4\cdot 2^{n1}  2^{n1}\right) \\&= 24\cdot \left( 4\cdot 8^{n1}  3\cdot 4^{n1}\right) . \end{aligned}$$
5 The Impartial Anonymous Culture assumption
The counting results from the previous section on the Impartial Culture (IC) assumption can easily be adapted to the Impartial Anonymous Culture (IAC) assumption as we will see in the following. For the proofs of these results, it is important to keep in mind that an election sampled according to the IAC model is a multiset of votes, i.e., the order of the votes is of no relevance. Thus, the total number of (n, m)elections is equal to \(\left( \left( \genfrac{}{}{0.0pt}{}{m!}{n}\right) \right) =\left( {\begin{array}{c}m!+n1\\ n\end{array}}\right) \).
In the following, let \(p_{A}(n,m)\) denote the probability that an (n, m)election created according to the IAC assumption is singlepeaked.
Proposition 13
 (i.)where \(\epsilon (n,m) \rightarrow 0\) as \(n \rightarrow \infty \) for \(n,m \ge 2,\)$$\begin{aligned} \frac{m!}{2} \frac{\left( \left( \genfrac{}{}{0.0pt}{}{2^{m1}}{n}\right) \right) }{\left( \left( \genfrac{}{}{0.0pt}{}{m!}{n}\right) \right) } \cdot \left( 1 + \epsilon (n,m)\right) \le p_{A}(n,m) \le \frac{m!}{2} \frac{\left( \left( \genfrac{}{}{0.0pt}{}{2^{m1}}{n}\right) \right) }{\left( \left( \genfrac{}{}{0.0pt}{}{m!}{n}\right) \right) }, \end{aligned}$$
 (ii.)
\(p_{A}(2,m)=\frac{1}{m!+1}\left( \left( {\begin{array}{c}2m2\\ m1\end{array}}\right) + 1\right) \) for \(m \ge 1\) and
 (iii.)
\(p_{A}(n,3)=\frac{60n}{(n+2)(n+3)(n+4)}\) for \(n\ge 1\).
Proof
 (i.)
Given a fixed axis, there are \(\left( \left( \genfrac{}{}{0.0pt}{}{2^{m1}}{n}\right) \right) \) (n, m)elections that are singlepeaked with respect to this axis. Multiplying with the number of axes that need to be considered and dividing by the total number of elections leads to the upper bound on the probability.
For the lower bound, we fix a vote V and determine the number of elections that are singlepeaked and contain both V and \({\bar{V}}\):where it can be checked easily that \(\epsilon (n,m) \rightarrow 0\) as n tends to infinity. This gives the lower bound.$$\begin{aligned}&\left( \left( \genfrac{}{}{0.0pt}{}{2^{m1}}{n}\right) \right) 2\cdot \left( \left( \genfrac{}{}{0.0pt}{}{2^{m1}1}{n}\right) \right) + \left( \left( \genfrac{}{}{0.0pt}{}{2^{m1}2}{n}\right) \right) \\&\quad = \left( \left( \genfrac{}{}{0.0pt}{}{2^{m1}}{n}\right) \right) \cdot \left( 1  2 \cdot \frac{2^{m1} 1}{2^{m1}+n1}+ \frac{(2^{m1}1)(2^{m1}2)}{(2^{m1}+n1)(2^{m1}+n2)}\right) \\&\quad = \left( \left( \genfrac{}{}{0.0pt}{}{2^{m1}}{n}\right) \right) \cdot \left( 1 + \epsilon (n,m)\right) , \end{aligned}$$  (ii.)We pick one vote at random, the second vote can be one of \(\left( {\begin{array}{c}2m2\\ m1\end{array}}\right) \) possibilities (cf. Theorem 12). However, the order of votes does not matter and we are thus doublecounting profiles that consist of two distinct votes–there are \(m!(\left( {\begin{array}{c}2m2\\ m1\end{array}}\right) 1)/2\) such profiles. Adding the m! profiles in which the same vote occurs twice, we obtain the following number of possibilities:Dividing by the total number of (2, m)elections leads to the probability.$$\begin{aligned} \frac{m!}{2}\left( \left( {\begin{array}{c}2m2\\ m1\end{array}}\right) + 1\right) \end{aligned}$$
 (iii.)An election with three candidates is singlepeaked if and only if it has at most two lastranked candidates. Using inclusionexclusion one obtains that the total number of possible elections is:Again, dividing by the total number of elections gives the probability. \(\square \)$$\begin{aligned} 3\cdot \left( \left( \left( \genfrac{}{}{0.0pt}{}{4}{n}\right) \right) \left( \left( \genfrac{}{}{0.0pt}{}{2}{n}\right) \right) \right) = \frac{n+1}{2} \left( (n+2)(n+3)6\right) . \end{aligned}$$
The case with 4 candidates that corresponds to case (iv) in Theorem 12 can not be directly derived from the IC case and would need a far more involved inclusionexclusion argument. It is thus omitted here.
6 The Pólya urn model
The Pólya urn model (also refereed to as the PólyaEggenberger urn model) (Johnson and Kotz 1977; Berg 1985; Mahmoud 2008) is an approach to sample elections with a variable degree of social homogeneity, i.e., where preferences are not independent but voters tend to have the same preferences as other voters. In the following the parameter a, a nonnegative integer, describes the degree of social homogeneity. As we will see in a moment, the case \(a=0\) corresponds to the Impartial Culture assumption, i.e., a population with no homogeneity.
The setting of the Pólya urn model for an election with n votes and m candidates can be described as follows. Consider a large urn containing m! balls. Every ball represents one of the m! possible votes on the candidate set and has a different color. An election is then created by subsequently pulling n balls out of the urn according to the following rule. The first ball is pulled at random and constitutes the first vote of the election. Then the pulled ball is returned to the urn and a other balls of the same color are added to the urn. This procedure is repeated n times until an election consisting of n votes is created.
At a first glance, it might seem that the probability assigned to a certain election within the Pólya urn model depends on the order of the votes. However this is not the case: Any election that can be obtained by rearranging a given election \((C,\mathcal {P})\), i.e, by changing the order of the votes, has exactly the same probability of occurring as the election \((C,\mathcal {P})\) itself. First, when the ith ball is drawn from the urn, i.e., when the ith vote is chosen, there are always \(m! + (i1)\cdot a\) balls present in the urn. Second, for any vote V the number of balls corresponding to V, i.e., the number of favourable cases, only depends on how often the vote V has already been pulled out of the urn and is equal to \((1+k\cdot a)\) where k is the number of times V has already been pulled.
It is now easy to give a concise characterization of this discrete distribution. In order to alleviate notation, let us use the so called Pochhammer ksymbol as introduced by Díaz and Pariguan (2007).
Definition 6
The Pochhammer ksymbol is defined as \( (x)_{n,k} = \prod _{i=1}^{n} (x+ (i1)\cdot k)\) where in our context \(x\in {\mathbb {R}}\) and n, k are nonnegative integers. Note that \( (x)_{n,1}=x(x+1)(x+2)\ldots (x+ n1)\) is the ordinary Pochhammer symbol (also known as rising factorial) and \( (1)_{n,1}=n!\).
Since we have consider IC and IAC already in Sects. 4 and 5 and have obtained asymptotically optimal results, the following lower bound theorem is interesting only for \(a>1\).
Theorem 14
Proof
Finally, for singlepeaked elections that have more than two distinct votes, we only consider elections that contain a vote V and also its reverse \({\bar{V}}\). Let us denote the probability of this event by \(p_3\). As in the proof of the bounds under the IC assumption this idea is based on the following fact about singlepeakedness: If a vote V and its reverse vote \({\bar{V}}\) are both present within an election, then there are at most two axes with respect to which this election can be singlepeaked, namely the axes V and \({\bar{V}}\). Thus, if a singlepeaked election contains both the vote V and \({\bar{V}}\), all the other votes must be among the \(2^{m1}2\) votes that are also singlepeaked with respect to the axis V (respectively \({\bar{V}}\)). Let us denote this set of votes that are not equal to V or \({\bar{V}}\) and that are singlepeaked with respect to the axis V by \(S_V\).
To illustrate the rather involved lower bound of Theorem 14, we consider the special case of \(a=m!\). This special case corresponds to highly homogeneous elections; the probability that the first and the second vote are identical is roughly 50%. It is a typical assumption that a is a multiple of m! (McCabeDansted and Slinko 2006; Walsh 2010, 2011) since otherwise, i.e., for a fixed a, the actual homogeneity of elections drawn according to the Pólya urn model would depend on the number of candidates m.
Corollary 15
We see that for \(a=m!\) and small n, there is a significant probability that the Pólya urn model produces singlepeaked elections.
7 Mallows model
The Mallows model (Mallows 1957) assumes that there is a reference vote and votes are more likely to appear in an election if they are close to this reference vote. Closeness is measured by the Kendall tau rank distance, defined as follows.
Definition 7
Note that \(\kappa (V,W)\) is also the minimum number of transpositions, i.e., swaps, of adjacent elements, needed to transform V into W or vice versa. We can now define the Mallows model.
Definition 8
Note that choosing \(\phi =1\) corresponds the Impartial Culture assumption and as \(\phi \rightarrow 0\) one obtains a distribution that concentrates all mass on V.
Theorem 16
Proof
Second, we need to compute the number of votes W that are singlepeaked with respect to A and that fulfil \(\kappa (V,W)=1\). Votes W with \(\kappa (V,W)=1\) are votes in which the order of exactly one pair of candidates \((c_i, c_{i+1})\) has been changed in V. Since there are \((m1)\) pairs of adjacent candidates in V, there are exactly \((m1)\) votes W with \(\kappa (V,W)=1\).
If \(c_1\) and \(c_2\) are interchanged, the position of the peak on the axis A is changed, but clearly no new peaks arise.
If two other candidates \(c_i\) and \(c_{i+1}\) are interchanged, one of these two candidates lies to the left of the peak on A and the other one to the right of the peak. Thus, interchanging only these two candidates does not create a new peak either.
The lower bound result of Theorem 16 does not give an immediate intuition for the likelihood of singlepeakedness under Mallows model. Hence we consider the special case \(\phi ={1\over m}\). This substitution yields a simpler lower bound, which is considerably larger than, e.g., the lower bound of roughly \((2^m/m!)^n\) obtained for the Impartial Culture Assumption (Theorem 11). For a short discussion on “realistic” parameter values \(\phi \) we refer to Sect. 8.
Corollary 17
Proof
The likelihood that an (n, m)election is singlepeaked when drawn according to the Impartial Culture assumption
(n, m)  Exact probability  (n, m)  Lower bound  Upper bound 

(2, 3)  1  (2, 5)  0.58  0.58 
(5, 3)  0.38  (5, 5)  \(1.6 \times 10^{4}\)  \(2.6 \times 10^{3}\) 
(10, 3)  0.05  (10, 5)  \(2.2 \times 10 ^{8}\)  \(1.1 \times 10^{7}\) 
(25, 3)  \(1.19\times 10^{4}\)  (25, 5)  \(5.0 \times 10 ^{21}\)  \(8.0 \times 10^{21}\) 
(50, 3)  \(4.70\times 10^{9}\)  (50, 5)  \(9.7 \times 10^{43}\)  \(1.1 \times 10^{42}\) 
(2, 4)  0.83  (2, 10)  \(1.3 \times 10^{2}\)  \(1.3 \times 10^{2}\) 
(5, 4)  0.05  (5, 10)  \(7.6 \times 10^{18}\)  \(1.1 \times 10^{13}\) 
(10, 4)  \(2.03\times 10^{4}\)  (10, 10)  \(1.9 \times 10 ^{36}\)  \(5.7 \times 10^{33}\) 
(25, 4)  \(1.42\times 10^{11}\)  (25, 10)  \(2.3 \times 10 ^{93}\)  \(1.0 \times 10^{90}\) 
(50, 4)  \(1.67\times 10^{23}\)  (50, 10)  \(4.6 \times 10 ^{189}\)  \(5.5 \times 10^{187}\) 
8 Numerical evaluations
In this section we provide numerical evaluations of our probability results from the previous sections and make some observations based on these evaluations. In Table 1, we list exact probabilities that an (n, m)election is singlepeaked assuming the Impartial Culture assumptions for small values of m and bounds for these probabilities for a larger number of candidates. Table 2 shows probabilities for elections of the same size assuming the Impartial Anonymous Culture.
The likelihood that an (n, m)election is singlepeaked when sampled according to the Impartial Anonymous Culture assumption
(n, m)  Lower bound  Upper bound  (n, m)  Lower bound  Upper bound 

(2, 3)  1  1  (2, 5)  0.58  0.58 
(5, 3)  0.59  0.59  (5, 5)  \(2.17 \times 10^{4}\)  \(4.13 \times 10^{3}\) 
(10, 3)  0.27  0.27  (10, 5)  \(1.19 \times 10^{7}\)  \(7.98 \times 10^{7}\) 
(25, 3)  0.068  0.068  (25, 5)  \(1.44 \times 10^{16}\)  \(3.76 \times 10^{16}\) 
(50, 3)  0.02  0.02  (50, 5)  \(2.91 \times 10^{28}\)  \(4.94 \times 10^{28}\) 
(2, 4)  0.84  0.84  (2, 10)  \(1.3 \times 10^{2}\)  \(1.3 \times 10^{2}\) 
(5, 4)  \(1.46 \times 10^{2}\)  \(9.67 \times 10^{2}\)  (5, 10)  \(7.78 \times 10^{18}\)  \(1.03 \times 10^{13}\) 
(10, 4)  \(8.34 \times 10^{4}\)  \(2.52 \times 10^{3}\)  (10, 10)  \(2.06 \times 10^{36}\)  \(6.19 \times 10^{33}\) 
(25, 4)  \(7.89 \times 10^{7}\)  \(1.30 \times 10^{6}\)  (25, 10)  \(3.69 \times 10^{93}\)  \(1.76 \times 10^{90}\) 
(50, 4)  \(4.28 \times 10^{10}\)  \(5.58 \times 10^{10}\)  (50, 10)  \(4.29 \times 10^{188}\)  \(5.05 \times 10^{186}\) 
The probabilities shown in Table 1 illustrate how unlikely it is that an election drawn according to IC is singlepeaked. Singlepeakedness is a strong combinatorial property, so it is not surprising that is is not satisfied by elections sampled uniformly at random. However, it is noteworthy that even for very small n and m the probability is small, e.g., for \(m=n=5\) it is less than 0.0026. Conversely, our results indicate that even for very small realworld singlepeaked data sets it is highly unlikely that their singlepeakedness is the product of mere chance.
As can be seen in Table 2, the probability that an election is singlepeaked is slightly higher when it is sampled according to the IAC model than when it is sampled according to the IC model. This can be explained heuristically as follows: Under the IAC model, elections with many coinciding votes have the same likelihood of appearing as elections consisting of many different votes. However, in the IC model, elections consisting of many different votes have a higher chanced of being sampled because of the many ways in which the votes can be rearranged. It is clear that an election where most votes are the same has a higher chance of being singlepeaked than an election in which very many different votes appear. Thus, it is not surprising if the likelihood of singlepeakedness is higher under the IAC than under the IC model.
For the Pólya urn model (Table 3) we observe significantly higher probabilities. This is of course due to our chosen parameter values a—recall that \(a=0\) implies IC and \(a=1\) implies IAC. In particular for \(a=m!\) we see that singlepeaked profiles arise with considerable likelihood. The assumption of \(a=m!\) is common in the literature (McCabeDansted and Slinko 2006; Walsh 2010, 2011) and even values of up to \(a=3m!\) have been considered (Dominique Lepelley 2003). As a consequence, we learn from these probabilities that setting \(a=m!\) generates extremely homogeneous profiles, even to the extent that they become singlepeaked.
Even higher probabilities are shown in Table 3. We see that for \(\phi =0.05\) singlepeakedness is likely to be observed, e.g., with a probability \(>0.49\) for \(n=50\) and \(m=5\). Clearly, \(\phi =0.05\) is a strong assumption and profiles obtained in this way are highly homogeneous; in fact, \(\phi =0.05\) implies that all voters share the same preferences except for minor deviations, which, in turn, enables the singlepeakedness property to hold. For \({\phi =0.1}\) we still see a significant chance of singlepeakedness, for larger values of \(\phi \) the likelihood deteriorates quickly. The question arises: what are typical values for \(\phi \)? Betzler et al. (2014) compute maximumlikelihood estimates of \(\phi \) for different realworld data sets. They find values^{1} ranging from 0.7 to almost 1. They also generate elections using values for \(\phi \) ranging from 0, 37 to almost 1. Other publications generate election with \(\phi \) in the interval [0.3, 1] (Boutilier et al. 2014) and [0.6, 0.9] (Oren et al. 2013). We see that all these parameter values are too large to imply singlepeakedness with nonnegligible probability. On the one hand, this implies that values for \(\phi \) small enough to generate singlepeakedness profiles are generally too restrictive to be found in (published) experiments. On the other hand, our results allow to argue that the parameter values in the aforementioned papers have been chosen sensibly since the accordingly generated elections contain (at least) enough disagreement as to prevent singlepeakedness to arise with significant likelihood.
Lower bounds obtained from Theorem 14 on the likelihood that an (n, m)election is singlepeaked when sampled according to the Pólya urn model with homogeneity a
(n, m)  \(a=10\)  \(a=m!/2\)  \(a=m!\) 

(10, 5)  \(1.6 \times 10^{4}\)  0.13  0.43 
(25, 5)  \(8.4\times 10^{8}\)  \(3.0\times 10^{2}\)  0.21 
(50, 5)  \(1.5\times 10^{10}\)  \(9.1\times 10^{3}\)  0.12 
(10, 10)  \(3.6\times 10^{36}\)  \(2.0\times 10^{2}\)  0.10 
(25, 10)  \(2.3\times 10^{91}\)  \(3.6\times 10^{3}\)  \( 4.4\times 10^{2}\) 
(50, 10)  \(2.6\times 10^{181}\)  \(9.7\times 10^{4}\)  \( 2.2\times 10^{2}\) 
9 Conclusions and directions for future research
We have seen that the likelihood of singlepeaked preferences varies significantly for the Impartial (Anonymous) Culture assumption, the Pólya urn and the Mallows model. For elections chosen according to the IC or the IAC assumption, it is extremely unlikely that singlepeakedness arises (cf. Table 1). With Theorem 8, we have shown that unlikeliness also holds for arbitrary domain restrictions that avoid a (2, k)configuration. In contrast, for the Pólya urn and the Mallows model with parameter a (\(\phi \)) chosen sufficiently large (small) it is rather likely that elections are singlepeaked. Numerical probabilities in Tables 3 and 4 affirm this claim.
Let us conclude with directions for future research. Theorem 8 requires that the domain restriction avoids a (2, k)configuration and thus is not applicable to domain restrictions such as the singlecrossing restriction (Roberts 1977; Bredereck et al. 2013b) or 2D singlepeaked restriction (Barberà et al. 1993). It remains open whether this result can be extended to such domain restrictions as well and how the corresponding bound would look like. It would also be interesting to complement Theorem 8 with a corresponding lower bound result. In general, the likelihood of other domain restrictions such as the singlecrossing (Roberts 1977) or the 2D singlepeaked restriction (Barberà et al. 1993) has yet to be studied.
Lower bounds via Theorem 16 on the likelihood that an (n, m)election is singlepeaked when sampled according to the Mallows model with dispersion parameter \(\phi \)
(n, m)  \(\phi =0.3\)  \(\phi =0.2\)  \(\phi =0.1\)  \(\phi =0.05\)  \(\phi =0.01\) 

(10, 5)  0.02  0.15  0.59  0.86  0.99 
(25, 5)  \(5.7 \times 10^{5}\)  \(8.7\times 10^{3}\)  0.26  0.70  0.98 
(50, 5)  \(3.3 \times 10^{9}\)  \(7.6 \times 10^{5}\)  \(7.2\times 10^{2}\)  0.49  0.97 
(10, 10)  \(3.7 \times 10^{6}\)  \(2.7 \times 10^{3}\)  0.20  0.66  0.98 
(25, 10)  \(2.7 \times 10^{14}\)  \(3.7 \times 10^{7}\)  \(1.9\times 10^{2}\)  0.36  0.96 
(50, 10)  \(7.5 \times 10^{28}\)  \(1.4 \times 10^{13}\)  \(3.7 \times 10^{4}\)  0.13  0.92 
Another direction is to consider other probability distributions such as the PlackettLuce model (Plackett 1975; Luce 1959) or Mallows mixture models where more than one reference vote is considered (Murphy and Martin 2003). One could also analyze the probability distribution that arises when assuming that all elections are singlepeaked and that all elections of the same size are equally likely. This would allow allow to ask questions such as “How likely is it that a singlepeaked election is also singlecrossing?”. Finally, a recent research direction is to consider elections that are nearly singlepeaked, i.e., elections that have a small distance to being singlepeaked according to some notion of distance (Faliszewski et al. 2011a; Elkind et al. 2012; Cornaz et al. 2012, 2013; Erdélyi et al. 2013; Bredereck et al. 2013a). The likelihood that elections are nearly singlepeaked remains a worthwhile direction for future research.
Acknowledgements
The first author was supported by the Austrian Science Foundation FWF, Grant P25337N23, the second author by the FWF, Grant P25518N23 and Y698 and by the European Research Council (ERC) under Grant Number 639945 (ACCORD).
Funding information
Funder Name  Grant Number  Funding Note 

Austrian Science Fund 
 
Austrian Science Fund 

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