# 4. Sequential Phragmén's method.¶

This note outlines a multiwinner election method introduced by Edvard Phragmén in the 1890's and specified as a sequential greedy algorithm by Brill et al. (2017), adapted to the problem of electing validators in Polkadot. In particular, we have adapted Brill et al.'s algorithm and proofs to the weighted case.

**Remark.** No objective function is declared in this note. In particular, this algorithm offers no constant-factor approximation guarantee for the maximin support objective.

We give needed notations in Section 1. In Section 2, we show that this algorithm runs in time

if each lookup and floating arithmetic operation is considered constant time, where $m$ is the number of elected validators, and $|E|$ is the number of edges in the nominator-validator relation graph.

In Section 3, we also show that the elected commitee observes the property of Proportional Justified Representation (PJR), a popular axiom in the area of election theory establishing that an election is "fair".

## 1. Notation¶

An instance of NPoS is given by a bipartite graph $(N\cup V, E)$, where $nv\in E$ represents the approval by nominator $n\in E$ of candidate validator $v$, a vector of nominator budgets $b\in \mathbb{R}_{\geq 0}^N$, and the number $m$ of candidate validators to be elected. We also denote by $V_n\subseteq V$ the set of candidates supported by nominator $n$, and by $N_v\subseteq N$ the set of nominators that support validator $v$.

An election is given by the pair where is a committee of elected validators, and is a vector of edge weights where represents the precise amount of stake that nominator assigns to validator . Besides non-negativity constraints, vector must observe the budget constraints: . Finallly, we say that vector is maximally affordable if we have the equality for each nominator that has at least one neighbor in .

## 2. Algorithm¶

The following algorithm finds a committee $S\subseteq V$ of size $m$, together with a maximally affordable edge weight vector $w$ for it. We remark that a better weight vector $w'$ for $S$ can be obtained by finding its min-norm max flow vector as a post-computation - see our note on the MNMF problem.

**Algorithm: Sequential Phragmén Method.**

- Set $S \leftarrow \emptyset, \ l_n \leftarrow 0 \ \forall n\in N, \ l_v \leftarrow 0 \ \forall v\in V$.
- For $i=1,\cdots,m$:
- Update $l_v \leftarrow \frac{1+\sum_{n\in N_v} l_n\cdot b_n}{\sum_{n\in N_v} b_n}$ for each $v\in V\setminus S$ ($l_v$ unchanged for $v\in S$),
- Let $v_i\in argmin_{v\in V\setminus S} l_v$ and update $S\leftarrow S\cup {v_i}$,
- For each $n\in N_{v_i}$, store $w_{nv_i}\leftarrow (l_{v_i} - l_n)b_n$, and update $l_n \leftarrow l_{v_i}$ ($l_n$ unchanged for $n\in N\setminus N_{v_i}$),

- For each edge $nv\in E$ having a non-zero weight, update its weight $w_{nv}\leftarrow w_{nv}/l_{n}$.
- Return $(S,w)$.

**Running time**: We assume that each candidate validator has at least one supporter. Each one of the $m$ rounds performs $O(|E|)$ arithmetic operations, because each relation $nv\in E$ is inspected at most twice per round. Hence, assuming that floating operations and table lookups take constant time, the running time of the algorithm is $O(m|E|)$.

**General idea**: The algorithm elects validators sequentially. It executes $m$ rounds, electing a new validator $v_i$ in the $i$-th round, and adding it to set $S$. The algorithm also progressively builds an edge weight vector, defining all weights of edges incident to $v_i$ as soon as $v_i$ is elected. Finally, in step 3. the weight vector $w$ is updated to ensure that that it is maximally affordable.

The algorithm keeps track of *scores* over nominators and validators. For each nominator $n\in N$, $n$'s score is the fraction of its budget $b_n$ that has been used up so far; i.e., $l_n:=\frac{1}{b_n}\sum_{v\in V_n} w_{nv}$. The guiding principle of this heuristic is to *try to minimize the maximum score $l_n$ over all nominators in each round*. Consider round $i$: if a new validator $v_i$ is elected, we assign one unit of support to it, i.e. we define edge weights so that $\sum_{n\in N_{v_i} }w_{nv_i}=1$ (this choice of constant is irrevelant, and will change when vector $w$ is updated in step 3.). These edge weights are chosen so that all supporters of $v_i$ end up with the same score at the end of round $i$, i.e. for all $n'\in N_{v_i}$:

This common nominator score is precisely our definition of validator $v_i$'s score $l_{v_i}$, and the algorithm greedily chooses the validator with smallest score in each round (breaking ties arbitrarily).

**Proof of correctness**: It remains to show that the chosen edge weights are always non-negative, and that $w$ is maximally affordable after step 3. For this, we need the following lemma, which states that scores never decrease. Let $l_n^{(i)}$ and $l_v^{(i)}$ represent respectively that scores of nominator $n$ and validator $v$ at the end of the $i$-th round.

**Lemma 1**: $l_v^{(i)}\leq l_v^{(i+1)}$ and $l_n^{(i)}\leq l_n^{(i+1)}$ for each $n\in N$, $v\in S$ and $i<m$.

*Proof*. We prove the inequalities by strong induction on $i$, where the base case $i=0$ is trivial if we set $l_v^{(0)}=l_n^{(0)}:=0$ for each $n$ and $v$. Assume now that all the proposed inequalities hold up to $i-1$.

Validator inequalities: Consider a validator $v_j\in S$. If $j\leq i$, then the identity $l_{v_j}^{(i+1)}=l_{v_j}^i$ follows from the fact that a validator's score doesn't change anymore once it has been elected. Else, if $j>i$,

where we used the nominator inequalities $l_n^{(i-1)}\leq l_n^{(i)}$ assumed by induction hypothesis. This shows the validator inequalities up to $i$.

Nominator inequalities: Consider now a nominator $n$, and assume by contradiction that $l_n^{(i+1)}<l_n^{(i)}$. As $n$'s score has changed in round $i+1$, $n$ must support validator $v_{i+1}$, and so $l_n^{(i+1)}=l_{v_{i+1}}^{(i+1)}$. On the other hand, $l_n^{(i)}=l_n^{(j)} = l_{v_{j}}^{(j)}$ for some $j\leq i$. Putting things together,

where the last inequality follows from validator inequalities up to $i$, which we just proved in the previous paragraph. We conclude that in round $j$, validator $v_{i+1}$ had a strictly smaller score than $v_j$, which contradicts the choice of $v_j$. $\square$

It easily follows that all edge weights are non-negative. Moreover, using the definition of the nominator scores, before step 3. we have the equalities $l_n\cdot b_n=\sum_{v\in S\cap V_n}w_{nv}$ for each nominator $n$ with at least one neighbor in $S$, so after step 3. we have $b_n=\sum_{v\in S\cap V_n}w_{nv}$.

## 3. Axiomatic properties¶

In the research literature of approval-based miltiwinner elections, it is common to take an axiomatic approach and define properties of voting methods that are intuitively desirable (see our main reference Brill et al. (2017), as well as Sánchez-Fernández et al. (2018)). These properties apply to the elected committee only, ignoring the edge weights.

For example, a voting method is called *house monotonic* if, for any instance, the elected candidates are all still elected if the number $m$ of winners is increased. As our algorithm elects validators iteratively, it is trivially house monotonic.

We focus on the property of *proportional justified representation* (PJR), which establishes that if a group of nominators has sufficient budget, and their preferences are sufficiently aligned, then they must be well represented in the elected committee. More formally, a voting method satifies PJR if for any instance $(N\cup V, E, b, m)$ electing a committee $S$, and any integer $1\leq t\leq m$, there is no nominator subset $N'\subseteq N$ such that

- ,
- , and
- .

Brill et al (2017) proved that the proposed algorithm, sequential Phragmén, satifies PJR, making it the first known polynomial-time method with this property. We present a proof next.

**Lemma 2:** Sequential Phragmén satisfies PJR.

*Proof:* Assume the opposite, hence there is an instance $(N\cup V, E, b, m)$ with output committe $S$, an integer $1\leq t\leq m$ and a nominator subset $N'\subseteq N$ as in the definition above.

For simplicity, we ignore the update of the weight vector performed in step 3. of the algorithm. Hence, every elected validator in $S$ receives a support of one unit, and the sum of supports over $S$ is $m$. Since we know that each budget constraint is violated by a multiplicative term of at most $l_{v_m}$ (the score of the last added validator), we obtain the bound

As $l_{v_m}$ is an upper bound on the nominator score $l_n$ for each $n\in N$ (by Lemma 1), and $l_n$ is the proportion of $n$'s budget that's used, the previous inequality is tight only if $l_n = m/\sum_{n\in N} b_n$ for each $n\in N$.

Let $S'=S\cap(\cup_{n\in N'} V_n)$, where $|S'|\leq t-1$ by hypothesis. Since nominators in $N'$ only need to provide support to validators in $S'$, the sum over $N'$ of used budgets must be smaller than $|S'|$, i.e. By a (weighted) average argument, this implies that there is a nominator $n'\in N'$ with score where the last inequality is by hypothesis. This implies that the inequality $l_{v_m} > m/\sum_{n\in N} b_n$ is not tight.

Consider now running a new round (round $m+1$) on the algorithm, and fix an unelected validator $v'\in \cap_{n\in N'} V_n$ (which must exist by hypothesis). If we compute the score of $v'$ in this round, we get where we used the fact that $N'\subseteq N_{v'}$, and that reducing the set of nominators over which the unit support for $v'$ is split can only increase the nominator scores. Using the known upper bound on the nominator, and the known lower bound on the denominator, we obtain This implies that $l_{v_m} > l_{v'} \geq l_{v_{m+1}}$, which contradicts Lemma 1. $\square$