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2 Arrow’s Impossibility Theorem

2.1 Overview

This chapter proves Arrow’s Impossibility Theorem via Kalai’s Fourier-analytic approach. For 3 alternatives \(\{ a,b,c\} \) and \(n\) voters, a social welfare function is modelled by an odd Boolean function \(f:\{ 0,1\} ^n\to \mathbb {R}\) applied to each pairwise comparison. The main result is:

Any odd, \(\pm 1\)-valued, unanimous, acyclic social welfare function must be a dictator.

The proof proceeds in three steps:

  1. Acyclicity forces the Fourier correlation function \(\mathrm{corr}(f) = -1/3\).

  2. Since \(\mathrm{corr}(f)\ge -1/3\) always holds (with equality iff all Fourier weight is on level 1), this forces \(W_1[f] = 1\).

  3. A \(\pm 1\)-valued degree-1 unanimous function must be a dictator.

References:

  • Gil Kalai, A Fourier-Theoretic Perspective on the Condorcet Paradox and Arrow’s Theorem, Advances in Applied Mathematics, 2002.

  • Ryan O’Donnell, Analysis of Boolean Functions, Chapter 2.

2.2 Social choice definitions

Definition 2.1 Unanimity
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A social welfare function \(f:\{ 0,1\} ^n\to \mathbb {R}\) is unanimous if \(f(\texttt{false},\dots ,\texttt{false}) = 1\): when all voters prefer alternative \(a\) over \(b\), so does society.

Definition 2.2 Dictatorship
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\(f\) is a dictatorship if there exists a voter \(i\) such that \(f = \mathrm{dict}_i\), i.e. society’s preference always equals voter \(i\)’s.

2.3 Voter ordering model

Each voter holds one of the 6 strict transitive orderings of \(\{ a,b,c\} \). For each ordering we record three pairwise preferences using the sign convention \(\sigma (\texttt{false}) = 1\) (“pro first alternative”) and \(\sigma (\texttt{true}) = -1\) (“pro second”).

The six orderings and their signs \((s_{ab}, s_{bc}, s_{ca})\) are:

Index

Ordering

\(s_{ab}\)

\(s_{bc}\)

\(s_{ca}\)

0

\(a\gt b\gt c\)

\(1\)

\(1\)

\(-1\)

1

\(a\gt c\gt b\)

\(1\)

\(-1\)

\(-1\)

2

\(b\gt a\gt c\)

\(-1\)

\(1\)

\(-1\)

3

\(b\gt c\gt a\)

\(-1\)

\(1\)

\(1\)

4

\(c\gt a\gt b\)

\(1\)

\(-1\)

\(1\)

5

\(c\gt b\gt a\)

\(-1\)

\(-1\)

\(1\)

Definition 2.3 Pairwise preference functions
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\(\mathrm{abPref}(k)\), \(\mathrm{bcPref}(k)\), \(\mathrm{caPref}(k)\) give the Boolean preference of ordering \(k\in \{ 0,\dots ,5\} \) in the \(a\)-vs-\(b\), \(b\)-vs-\(c\), and \(c\)-vs-\(a\) comparisons, respectively.

Definition 2.4 Profile
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A profile assigns each of the \(n\) voters one of the 6 orderings: \(p : \mathrm{Fin}\, n \to \mathrm{Fin}\, 6\).

Definition 2.5 Vote vectors
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Given a profile \(p\), the vote vectors \(\mathrm{abVotes}(p)\), \(\mathrm{bcVotes}(p)\), \(\mathrm{caVotes}(p) \in \{ 0,1\} ^n\) record each voter’s pairwise preference.

Definition 2.6 Acyclicity
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A social welfare function \(f\) is acyclic if no profile of transitive voter orderings produces a Condorcet cycle in society’s preferences. A cycle occurs when \(f(\mathrm{abVotes}(p)) = f(\mathrm{bcVotes}(p)) = f(\mathrm{caVotes}(p)) = 1\) (the cycle \(a\gt b\gt c\gt a\)) or all equal \(-1\) (the reverse cycle).

2.4 Key correlation lemmas

The following lemma encodes the core computation: for a single voter drawn uniformly from the 6 orderings, the expected product of any two distinct pairwise preference signs is \(-1/3\).

Lemma 2.7 Sum of paired preference signs
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Label the six strict orderings of three alternatives \(a,b,c\) by \(k\in \{ 0,\dots ,5\} \). For each ordering let \(x_k\in \{ -1,+1\} \) record the \(a\)-versus-\(b\) comparison, taking the value \(+1\) when \(a\) is preferred to \(b\) and \(-1\) when \(b\) is preferred to \(a\), and let \(y_k\in \{ -1,+1\} \) record the \(b\)-versus-\(c\) comparison in the same way, with \(+1\) when \(b\) is preferred to \(c\) and \(-1\) otherwise. Then

\[ \sum _{k=0}^{5} x_k\, y_k \; =\; -2. \]

For each of the six strict orderings \(k \in \{ 0,\dots ,5\} \) of three alternatives \(a,b,c\), let \(\sigma \) be the sign encoding, sending \(\mathrm{false}\) to \(+1\) and \(\mathrm{true}\) to \(-1\). Write \(y_k = \sigma (p_k)\), where \(p_k\) is the Boolean preference of ordering \(k\) in the \(b\)-vs-\(c\) comparison, and \(z_k = \sigma (q_k)\), where \(q_k\) is the preference in the \(c\)-vs-\(a\) comparison. Then

\[ \sum _{k=0}^{5} y_k\, z_k \; =\; -2. \]

For each of the six strict orderings \(k \in \{ 0,\dots ,5\} \) of three alternatives \(a,b,c\), write \(p_{ab}(k)\) and \(p_{ca}(k)\) for the Boolean preferences of ordering \(k\) in the \(a\)-vs-\(b\) and \(c\)-vs-\(a\) comparisons, and let \(\sigma \) be the sign encoding sending \(\mathrm{false}\) to \(1\) and \(\mathrm{true}\) to \(-1\). Then the sum over all six orderings of the products of the encoded signs is

\[ \sum _{k=0}^{5} \sigma \big(p_{ab}(k)\big)\, \sigma \big(p_{ca}(k)\big) \; =\; -2. \]

2.5 Fourier formula for the cycle correlation

Definition 2.8 Correlation function
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The Fourier correlation function of \(f\) is

\[ \mathrm{corr}(f) \; =\; \sum _{S\subseteq [n]} \hat f(S)^2\, (-1/3)^{|S|}. \]

For odd \(f\), only odd-level terms contribute.

Lemma 2.9 Expected pairwise vote product equals the Fourier correlation function
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Let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a Boolean function. Suppose each of the \(n\) voters chooses, independently and uniformly, one of the six strict orderings of three candidates \(a,b,c\); for such a profile \(p\) write \(x_p\in \{ 0,1\} ^n\) for the vector whose \(i\)-th entry records voter \(i\)’s preference in the \(a\)-versus-\(b\) comparison, and \(y_p\in \{ 0,1\} ^n\) for the vector recording each voter’s preference in the \(b\)-versus-\(c\) comparison. Then, averaging the product of the values of \(f\) on these two vectors over all \(6^n\) profiles,

\[ \frac{1}{6^{\, n}}\sum _{p}\, f(x_p)\, f(y_p) \; =\; \sum _{S\subseteq [n]} \hat f(S)^2\, (-1/3)^{|S|}, \]

where \(\hat f(S)\) denotes the Fourier–Walsh coefficient of \(f\) at frequency \(S\).

Lemma 2.10 Lower bound for the Fourier correlation function
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Let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a Boolean function that is odd, so that \(f(\lnot x)=-f(x)\) for all \(x\), and \(\pm 1\)-valued, so that \(f(x)\in \{ -1,1\} \) for all \(x\). Then its Fourier correlation function satisfies

\[ \mathrm{corr}(f) \; =\; \sum _{S\subseteq [n]} \hat f(S)^2\, (-1/3)^{|S|} \; \ge \; -\tfrac 13, \]

where \(\hat f(S)\) denotes the Fourier–Walsh coefficient of \(f\) at frequency \(S\).

Lemma 2.11 Extremal correlation concentrates weight on level one
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Let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a \(\pm 1\)-valued Boolean function that is odd, meaning \(f(\lnot x)=-f(x)\) for every \(x\), and suppose its Fourier correlation function attains the value

\[ \mathrm{corr}(f)\; =\; \sum _{S\subseteq [n]}\hat f(S)^2\, (-1/3)^{|S|}\; =\; -\tfrac 13. \]

Then \(\hat f(S)=0\) for every subset \(S\subseteq [n]\) with \(|S|\neq 1\); that is, all Fourier–Walsh weight of \(f\) is carried by the singleton frequencies.

2.6 Acyclicity implies degree-1 structure

Lemma 2.12 Acyclicity forces correlation \(-1/3\)
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Let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a Boolean function that is odd, \(\pm 1\)-valued, and acyclic. Then its Fourier correlation function satisfies

\[ \mathrm{corr}(f)\; =\; \sum _{S\subseteq [n]}\hat f(S)^2\, (-1/3)^{|S|}\; =\; -\tfrac {1}{3}. \]

2.7 Degree-1 implies dictatorship

Lemma 2.13 Degree-one unanimous voting rules are dictatorships
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Let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a Boolean function that is odd, is \(\pm 1\)-valued, and is unanimous. Suppose further that \(f\) has degree one, in the sense that its Fourier–Walsh coefficient \(\hat f(S)\) vanishes for every set \(S\subseteq [n]\) with \(\left\lvert S\right\rvert \neq 1\). Then \(f\) is a dictatorship: there is a coordinate \(i\) with \(f=\mathrm{dict}_i\).

2.8 Arrow’s Impossibility Theorem

Theorem 2.14 Arrow’s impossibility theorem
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Let \(n\) be a natural number and let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a social welfare function that aggregates the pairwise opinions of \(n\) voters. Suppose \(f\) is odd, meaning \(f(\bar x) = -f(x)\) for every \(x\), where \(\bar x\) is the bitwise complement of \(x\); is \(\pm 1\)-valued, meaning \(f(x)\in \{ -1,1\} \) for every \(x\); is unanimous, meaning \(f\) takes the value \(1\) at the all-zeros input; and is acyclic, meaning that no profile of voter orderings produces a Condorcet cycle. Here a profile assigns to each of the \(n\) voters one of the six linear orderings of three alternatives \(a,b,c\), and from it one forms three vote vectors in \(\{ 0,1\} ^n\) whose \(i\)-th entries record voter \(i\)’s preference in the \(a\)-vs-\(b\), \(b\)-vs-\(c\), and \(c\)-vs-\(a\) comparisons; acyclicity requires that \(f\) never sends all three of these vectors to \(1\), and never sends all three to \(-1\). Then \(f\) is a dictatorship: there is a voter \(i\) such that \(f(x) = (-1)^{x_i}\) for all \(x\).

2.9 Additional declarations

Lemma 2.15 Balance of the a-vs-b preference signs
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There are six strict orderings \(k \in \{ 0,1,\dots ,5\} \) of the three alternatives \(a,b,c\). For each such ordering, let \(p_{ab}(k)\) be its \(a\)-vs-\(b\) preference, the Boolean that records whether \(b\) is ranked above \(a\), and apply the sign encoding \(\sigma \), so that \(\sigma (p_{ab}(k))\) equals \(+1\) when ordering \(k\) ranks \(a\) above \(b\) and \(-1\) when it ranks \(b\) above \(a\). Then these six signed preferences sum to zero:

\[ \sum _{k=0}^{5} \sigma \bigl(p_{ab}(k)\bigr) = 0. \]
Lemma 2.16 Balanced b-versus-c preferences over the six orderings
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Consider the six strict orderings of three alternatives \(a\), \(b\), and \(c\), indexed by \(k \in \{ 0,\dots ,5\} \), and let \(p(k)\) denote the \(b\)-versus-\(c\) pairwise preference of ordering \(k\). Under the sign encoding \(\sigma \), these preferences sum to zero across all six orderings:

\[ \sum _{k=0}^{5} \sigma \bigl(p(k)\bigr) = 0. \]
Lemma 2.17 Vanishing sum of signed \(c\)-vs-\(a\) preferences
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For each of the six strict orderings \(k\in \{ 0,1,\dots ,5\} \) of three alternatives, let \(b_k\in \mathrm{Bool}\) record the \(c\)-vs-\(a\) preference of ordering \(k\), and encode it as a sign by \(\sigma (b_k)=1\) when \(b_k\) is false and \(\sigma (b_k)=-1\) when \(b_k\) is true. Then these six signed preferences sum to zero:

\[ \sum _{k=0}^{5} \sigma (b_k) = 0. \]
Lemma 2.18 Product over a finset as an indicator product
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Let \(A \subseteq \mathrm{Fin}\, n\) be a finite set of indices and let \(g : \mathrm{Fin}\, n \to \mathbb {R}\). Then

\[ \prod _{j \in A} g(j) \; =\; \prod _{j : \mathrm{Fin}\, n} \begin{cases} g(j) & \text{if } j \in A, \\ 1 & \text{if } j \notin A, \end{cases} \]

so that a product taken only over the members of \(A\) may equivalently be written as a product over all of \(\mathrm{Fin}\, n\) in which each factor outside \(A\) is replaced by \(1\).

Lemma 2.19 General profile kernel for paired preference assignments
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Let \(x, y : \mathrm{Fin}\, 6 \to \mathrm{Bool}\) be two preference assignments whose sign encodings have balanced marginals, \(\sum _{k=1}^{6}\sigma (x_k) = \sum _{k=1}^{6}\sigma (y_k) = 0\), and whose signed correlation is \(\sum _{k=1}^{6}\sigma (x_k)\, \sigma (y_k) = -2\). For a profile \(p : \mathrm{Fin}\, n \to \mathrm{Fin}\, 6\), write \(x\circ p\) and \(y\circ p\) for the points of the Boolean hypercube whose \(i\)-th coordinates are \(x_{p(i)}\) and \(y_{p(i)}\). Then for all subsets \(S, T \subseteq [n]\), the average of the product of Walsh characters over all \(6^n\) profiles satisfies

\[ \Bigl(\tfrac 16\Bigr)^{n}\sum _{p:\mathrm{Fin}\, n \to \mathrm{Fin}\, 6} \chi _S(x\circ p)\, \chi _T(y\circ p) \; =\; \begin{cases} \bigl(-\tfrac 13\bigr)^{\left\lvert S\right\rvert } & S = T,\\[2pt] 0 & S \ne T. \end{cases} \]
Lemma 2.20 Orthogonality kernel for the \(ab\)–\(bc\) vote pair
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Fix \(n\), and for each profile \(p \in \{ 0,\dots ,5\} ^n\) let \(v_{ab}(p), v_{bc}(p) \in \{ 0,1\} ^n\) be the vote vectors recording, voter by voter, each voter’s pairwise preference in the \(a\)-vs-\(b\) and \(b\)-vs-\(c\) comparisons, and for \(S \subseteq [n]\) let \(\chi _S\) be the associated Walsh–Fourier character. Then for all subsets \(S, T \subseteq [n]\), the uniform average of the product \(\chi _S(v_{ab}(p))\, \chi _T(v_{bc}(p))\) over the \(6^n\) profiles satisfies

\[ \Bigl(\tfrac {1}{6}\Bigr)^{\! n}\sum _{p} \chi _S\bigl(v_{ab}(p)\bigr)\, \chi _T\bigl(v_{bc}(p)\bigr) \; =\; \begin{cases} \left(-\tfrac {1}{3}\right)^{\left\lvert S\right\rvert } & S = T,\\[2pt] 0 & S \ne T. \end{cases} \]
Lemma 2.21 The \(bc\)–\(ca\) profile kernel
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Let \(S, T \subseteq [n]\), and for a profile \(p \colon \mathrm{Fin}\, n \to \mathrm{Fin}\, 6\) write \(x(p)\) for its \(bc\)-vote vector and \(y(p)\) for its \(ca\)-vote vector, each lying in \(\{ 0,1\} ^n\). Averaging the product of Walsh characters over all \(6^n\) profiles gives

\[ \left(\tfrac {1}{6}\right)^{n} \sum _{p} \chi _S\bigl(x(p)\bigr)\, \chi _T\bigl(y(p)\bigr) \; =\; \begin{cases} \left(-\tfrac {1}{3}\right)^{\left\lvert S\right\rvert } & \text{if } S = T,\\[2pt] 0 & \text{if } S \ne T. \end{cases} \]
Lemma 2.22 Kernel of the ab–ca vote correlation
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Fix a natural number \(n\) and consider profiles \(p\) of \(n\) voters, each of whom reports one of the six strict orderings of the three alternatives \(a, b, c\); there are \(6^n\) such profiles. For a profile \(p\), let \(x^{ab}(p) \in \{ 0,1\} ^n\) be the vector recording each voter’s preference in the \(a\)-vs-\(b\) comparison, and \(x^{ca}(p) \in \{ 0,1\} ^n\) the vector recording each voter’s preference in the \(c\)-vs-\(a\) comparison. Then for any two subsets \(S, T \subseteq \{ 1,\dots ,n\} \), the averaged product of the corresponding Walsh–Fourier characters satisfies

\[ \left(\tfrac {1}{6}\right)^{\! n} \sum _{p} \chi _S\bigl(x^{ab}(p)\bigr)\, \chi _T\bigl(x^{ca}(p)\bigr) \; =\; \begin{cases} \left(-\tfrac {1}{3}\right)^{|S|} & \text{if } S = T,\\[2pt] 0 & \text{if } S \neq T, \end{cases} \]

where the sum ranges over all \(6^n\) profiles.

Lemma 2.23 Expected product from the character kernel
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Let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a Boolean function, and let \(\mathrm{votes}_1,\mathrm{votes}_2\) each assign to every profile \(p\in (\mathrm{Fin}\, 6)^{\, n}\) a point of the Boolean hypercube \(\{ 0,1\} ^n\). Suppose the pair satisfies the kernel identity

\[ \Bigl(\tfrac 16\Bigr)^{\! n}\sum _{p} \chi _S(\mathrm{votes}_1(p))\, \chi _T(\mathrm{votes}_2(p)) \; =\; \begin{cases} (-1/3)^{|S|} & S = T,\\ 0 & S \ne T,\end{cases} \]

for all \(S,T\subseteq [n]\), where the sum runs over all \(6^n\) profiles. Then

\[ \Bigl(\tfrac 16\Bigr)^{\! n}\sum _{p} f(\mathrm{votes}_1(p))\, f(\mathrm{votes}_2(p)) \; =\; \mathrm{corr}(f) \; =\; \sum _{S\subseteq [n]} \hat f(S)^2\, (-1/3)^{|S|}. \]
Lemma 2.24 Expected product of the \(bc\) and \(ca\) vote vectors
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Let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a Boolean function, and for each profile \(p:\mathrm{Fin}\, n\to \{ 0,\dots ,5\} \) write \(x_{bc}(p),\, x_{ca}(p)\in \{ 0,1\} ^n\) for the \(b\)-vs-\(c\) and \(c\)-vs-\(a\) vote vectors it induces. Then the average of the product \(f\bigl(x_{bc}(p)\bigr)\, f\bigl(x_{ca}(p)\bigr)\) over all \(6^n\) profiles equals the Fourier correlation function of \(f\):

\[ \Bigl(\tfrac 16\Bigr)^{n}\sum _{p} f\bigl(x_{bc}(p)\bigr)\, f\bigl(x_{ca}(p)\bigr) \; =\; \mathrm{corr}(f) \; =\; \sum _{S\subseteq [n]} \hat f(S)^2\, (-1/3)^{|S|}. \]
Lemma 2.25 Expected product of the \(ab\) and \(ca\) votes
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Let \(f:\{ 0,1\} ^n\to \mathbb {R}\) be a Boolean function. For a profile \(p\) that assigns each of the \(n\) voters one of the six strict orderings of the three alternatives \(a,b,c\), let \(x^{ab}(p)\in \{ 0,1\} ^n\) record each voter’s preference in the \(a\)-vs-\(b\) comparison and let \(x^{ca}(p)\in \{ 0,1\} ^n\) record each voter’s preference in the \(c\)-vs-\(a\) comparison. Averaging uniformly over all \(6^n\) profiles, the expected product of \(f\) at these two vote vectors equals the Fourier correlation function of \(f\):

\[ \Bigl(\tfrac {1}{6}\Bigr)^{n}\sum _{p} f\bigl(x^{ab}(p)\bigr)\, f\bigl(x^{ca}(p)\bigr) \; =\; \sum _{S\subseteq [n]} \hat f(S)^{2}\, (-1/3)^{|S|}. \]