# §4.2: Tribes

In this section we study the tribes DNF formulas, which serve as an important examples and counterexamples in analysis of boolean functions. Perhaps the most notable feature of the tribes function is that (for a suitable choice of parameters) it is essentially unbiased and yet all of its influences are quite tiny.

Recall from Chapter 2.1 that the function $\mathrm{Tribes}_{w,s} : \{-1,1\}^{sw} \to \{-1,1\}$ is defined by its width-$w$, size-$s$ DNF representation: \begin{multline*} \mathrm{Tribes}_{w,s}(x_1, \dots, x_w, \dots, x_{(s-1)w+1}, \dots, x_{sw}) \\ = (x_1 \wedge \cdots \wedge x_w)\ \vee\ \cdots\ \vee\ (x_{(s-1)w+1} \wedge \cdots \wedge x_{sw}). \end{multline*} (We are using the notation where $-1$ represents logical $\mathsf{True}$ and $1$ represents logical $\mathsf{False}$.)

Fact 10 $\mathop{\bf Pr}_{\boldsymbol{x}}[\mathrm{Tribes}_{w,s}({\boldsymbol{x}}) = -1] = 1 – (1-2^{-w})^s$.

The most interesting setting of parameters makes this probability as close to $1/2$ as possible (a slightly different choice than the one in Exercise 2.11):

Definition 11 For $w \in {\mathbb N}^+$, let $s = s_w$ be the largest integer such that $1 – (1-2^{-w})^s \leq 1/2$. Then for $n = n_w = sw$ we define $\mathrm{Tribes}_n : \{-1,1\}^n \to \{-1,1\}$ to be $\mathrm{Tribes}_{w,s}$. Note this is only defined only for certain $n$: $1$, $4$, $15$, $40$, $\dots$

Here $s \approx \ln(2) 2^w$, hence $n \approx \ln(2) w 2^w$ and therefore $w \approx \log n – \log \ln n$ and $s \approx n/\log n$. A slightly more careful accounting (see the exercises) yields:

Proposition 12 For the $\mathrm{Tribes}_n$ function as in Definition 11:

• $s = \ln(2) 2^w – \Theta_w(1)$;
• $n = \ln(2) w 2^w – \Theta(w)$, thus $n_{w+1} = (2 + o(1)) n_w$;
• $w = \log n – \log \ln n + o_n(1)$, and $2^w = \frac{n}{\ln n} (1+o_n(1))$;
• $\mathop{\bf Pr}[\mathrm{Tribes}_{n}({\boldsymbol{x}}) = -1] = 1/2 – O\left(\frac{\log n}{n}\right)$.

Thus with this setting of parameters $\mathrm{Tribes}_n$ is essentially unbiased. Regarding its influences:

Proposition 13 $\mathbf{Inf}_i[\mathrm{Tribes}_n] = \frac{\ln n}{n}(1\pm o(1))$ for each $i \in [n]$ and hence $\mathbf{I}[\mathrm{Tribes}_n] = (\ln n)(1\pm o(1))$.

Proof: Thinking of $\mathrm{Tribes}_n = \mathrm{Tribes}_{w,s}$ as a voting rule, voter $i$ is pivotal if and only if: a) all other voters in $i$’s “tribe” vote $-1$ ($\mathsf{True}$); b) all other tribes produce the outcome $1$ ($\mathsf{False}$). The probability of this is indeed $2^{-(w-1)} \cdot (1-2^{-w})^{s-1} = \tfrac{2}{2^w - 1} \cdot \mathop{\bf Pr}[\mathrm{Tribes}_n = 1] = \tfrac{\ln n}{n}(1\pm o(1)),$ where we used Fact 10 and then Proposition 12. $\Box$

Thus if we are interested in (essentially) unbiased voting rules in which every voter has small influence, $\mathrm{Tribes}_n$ is a much stronger example than $\mathrm{Maj}_n$ where each voter has influence $\Theta(1/\sqrt{n})$. You may wonder if the maximum influence can be even smaller than $\Theta\bigl(\frac{\ln n}{n}\bigr)$ for unbiased voting rules. Certainly it can’t be smaller than $\frac{1}{n}$, since the Poincaré inequality says that $\mathbf{I}[f] \geq 1$ for unbiased $f$. In fact the famous KKL Theorem shows that the $\mathrm{Tribes}_n$ example is tight up to constants:

Kahn–Kalai–Linial (KKL) Theorem For any $f : \{-1,1\}^n \to \{-1,1\}$, $\max_{i \in [n]} \{\mathbf{Inf}_i[f] \} \geq \mathop{\bf Var}[f] \cdot \Omega\Bigl(\frac{\log n}{n}\Bigr).$

We prove the KKL Theorem in Chapter 10.

We conclude this section by recording a formula for the Fourier coefficients of $\mathrm{Tribes}_{w,s}$. The proof is an exercise.

Proposition 14 Suppose we index the Fourier coefficients of $\mathrm{Tribes}_{w,s} \{-1,1\}^{sw} \to \{-1,1\}$ by sets $T = (T_1, \dots, T_s) \subseteq [sw]$, where $T_i$ is the intersection of $T$ with the $i$th “tribe”. Then $\widehat{\mathrm{Tribes}_{w,s}}(T) = \begin{cases} 2(1-2^{-w})^s - 1 & \text{if T = \emptyset,} \\ 2(-1)^{k + |T|}2^{-kw} (1-2^{-w})^{s-k} & \text{if k = \#\{i : T_i \neq \emptyset\} > 0.} \end{cases}$