Should Social Network Structure Be Taken intoAccount in Elections?
Vincent ConitzerDuke University
April 2, 2011
If the social network structure among the voters in an election is known, howshould this be taken into account by the voting rule? In this brief article, I argue,via the maximum likelihood approach to voting, that it is optimal to ignore thesocial network structure altogetherone person, one vote.
1 IntroductionThe maximum likelihood framework provides a natural approach to determining anoptimal voting rule. Under this approach, we suppose that there exists a correctoutcome, which cannot be directly observed; however, voters votes are noisy estimatesof this correct outcome. For example, in an election between two alternatives, we maysuppose that one of the alternatives is better in some objective sense, and each voter ismore likely to vote for the correct (better) alternative than for the incorrect alternative.To make this concrete, suppose that there is a fixed p > 1/2 such that each voter,independently, votes for the correct alternative with probability p, and for the incorrectone with probability 1 p.
Under such a model, it makes sense to choose the maximum likelihood estimate ofthe correct outcome. For example, suppose that we have a specific profile in whichthere are na votes for alternative a, and nb votes for alternative b. The likelihood ofthis particular profile happening given that a is the correct outcome is pna(1 p)nb ,and given that b is the correct outcome it is (1p)napnb . If na > nb, then the former islarger, so a is the maximum likelihood estimate; and vice versa. That is, the maximumlikelihood estimate here is obtained by applying the majority rule: the alternative withmore votes wins.
The study of this maximum likelihood approach to voting dates back to Con-dorcet , who proposed a noise model that generalizes the one above to more than
The technically rather simple observation presented in this short article was presented in full in a shortrump session talk at the 2010 Dagstuhl seminar on the Computational Foundations of Social Choice. Thiswork has been supported by an Alfred P. Sloan Fellowship and by NSF under award numbers IIS-0812113and CAREER-0953756.
two alternatives. Two centuries later, Young [11, 12] showed that the maximum like-lihood solution for this model coincides with the Kemeny rule . A recent bodyof work has been devoted to the investigation of how alternative noise models leadto different rules [5, 3, 10, 2]. There is also a literature on strategic voting in suchcontextse.g., [1, 7]but we will not consider such issues here. Another recent pa-per  explores the relationship between the maximum likelihood framework and thedistance rationalizability framework, where we find the closest consensus electionthat has a clear winner, for some definition of consensus and some distance function,and choose its winner.
A common assumption in the existing work on the maximum likelihood approachis that votes are drawn independently (conditional on the correct outcome). It is easy todispute this assumption. Voters do not make up their minds in a vacuum: they generallydiscuss the matter with others, and this is likely to affect their votes. Specifically, votersthat talk to each other are more likely to reach the same conclusion, whether right orwrong.
So, given that we know the social network structure (or some estimate thereof), howshould this affect the voting rule? We can state various suggestive intuitive arguments,such as the following.
1. Well-connected voters benefit from the insight of others so they are more likely toget the answer right. They should be weighed more heavily.
2. Well-connected voters do not give the issue much independent thought; the rea-sons for their votes are already reflected in their neighbors votes. They shouldbe weighed less heavily.
3. It is oversimplifying matters to discuss the matter merely in terms of votersweights. We can do better by considering richer options, such as an electoralcollege.
In this brief article, I will argue that all of these three suggestions are wrongor,arguably, 1 and 2 are both correct, but cancel each other out. That is, optimal rulesignore the social network structure altogether, affirming the familiar principle of oneperson, one vote. The mathematical result is quite trivial once the model has been setup.
2 ModelIn this section, I give a concrete probabilistic model that takes the social network struc-ture into account; I will discuss the benefits and drawbacks of this model in the finalsection. Let v denote a vertex in the social network graph, corresponding to a voter.Let N(v) denote vs neighbors. Let Vv denote voter vs vote, and VN(v) the subprofileconsisting of the votes of all the neighbors of v. Let c denote the correct outcome.For ease of reading, the reader is encouraged to keep the simple example of an elec-tion between two alternatives in mind, so that there are two outcomes and two possiblevotes. However, this is not at all necessary for the main result: there can be arbitrarilymany alternatives, and in fact we will not require any restriction on the outcome space,on the space of possible votes, or on the relationship between the two. For example,an outcome could consist of an individual alternative, a subset of the alternatives, ora ranking of the alternatives; and so could a vote, independently of what an outcomeconsists of.
For the maximum likelihood approach, we are interested in the probability of thefull profile of votes, given c. I will assume that this probability can be factored as
v fv(Vv, VN(v)|c), where fv is a function associated with vertex v. (The dependenceof f on VN(v) captures that voter vs vote can be affected by her neighbors opinions(votes)as opposed to more standard models that take the form
v fv(Vv|c).) This
type of factorization assumption is commonly made in undirected graphical models ofprobability distributions (Markov random fields).1 It is worth noting that, in the specialcase where the social network graph is a clique, this assumption is not restrictive at all.I now make the following assumption which does introduce a restriction that is crucialfor the result.
Assumption 1 For every v, there exist functions gv and hv so that fv can be factoredas fv(Vv, VN(v)|c) = gv(Vv|c) hv(Vv, VN(v)).
Here, gv is intended to capture the effect that a voter is more likely to vote for thecorrect outcome, and hv to capture the effect that voters are likely to vote similarly totheir neighbors. The key assumption is that gv does not depend on VN(v), and hv doesnot depend on c. That is, the tendency to vote for the correct outcome is in a senseindependent from the tendency to agree with ones neighbors.
For example, in the two-alternative case, Assumption 1 precludes models where,given that all of a vertexs neighbors vote for the correct outcome, this vertex also does
1In this literature, factors can only be associated with cliques of the graph. In contrast, here, a factor canbe associated with the neighborhood of any vertex. Since any clique is contained in the neighborhood of anyone of its vertices, the model studied here is in a sense less restrictivethough this is in any case not themain assumption of this article, which follows next.
so with probability 1; but given that all of the neighbors vote for the incorrect outcome,this vertex still has a positive probability of choosing the correct outcome. If this werethe noise model, then if we found a vertex voting differently from all its neighbors,we should conclude that that vertex has identified the correct outcome, no matter whateveryone else votes. So, in that case, the social network structure would matter.
3 Detailed exampleFor concreteness, let us consider a simple example with two vertices (voters) connectedby an edge, voting over two alternatives (so a vote corresponds simply to one of thealternatives). Let c denote the alternative other than c and let v denote the voterother than v. For each of the two vertices v, we let gv(Vv = c|c) = .7, gv(Vv =c|c) = .3, indicating that votes for the correct outcome are more likely. We also lethv(Vv = c, Vv = c) = 1.142, hv(Vv = c, Vv = c) = .762, indicating that votersare more likely to agree with each other. We can now calculate that P (Vv = c|c) =P (Vv = c, Vv = c|c) + P (Vv = c, Vv = c|c) = .7 1.142 .7 1.