Baldwin's method

Candidates are voted for on Ranked voting as in the Borda count. Then, the points are tallied in a series of rounds. In each round, the candidate with the fewest points is eliminated, and the points are re-tallied as if that candidate were never on the ballot.

It was systematized by Joseph M. Baldwin in 1926, who incorporated a more efficient matrix tabulation, extending it to support incomplete ballots and equal rankings. Baldwin's method has been confused with Nanson's method in some literature. This method predates but is related to Nanson's method. Nanson noted Baldwin's method was already in use by the Trinity College at the University of Melbourne Dialectic Society when he invented his method.

Satisfied and failed criteria
Baldwin's method satisfies the Condorcet criterion. because Borda always gives any existing Condorcet winner more than the average Borda points, the Condorcet winner will never be eliminated. Furthermore it satisfies the majority criterion, the mutual majority criterion, the Condorcet loser criterion and the Smith criterion.

Baldwin's method does not satisfy the independence of irrelevant alternatives criterion, the monotonicity criterion, the participation criterion, the consistency criterion and the independence of clones criterion. Baldwin's method also violates reversal symmetry.

Baldwin's method can be run in polynomial time to obtain a single winner, however, at each stage, there might be several candidates with lowest Borda score. In fact, it is NP-complete to decide whether a given candidate is a Baldwin winner, i.e., whether there exists an elimination sequence that leaves a given candidate uneliminated. . This implies that this method is computationally more difficult to compute than Borda's method.

Cardinal Variant
A Cardinal Voting variant of this system can be made by simply taking the scores initially rather than taking ranks and converting them with Borda count. In this context the motivation for the normalization at each round is derived by considering an affine transformation. When the lowest scored candidate is removed such a rescaling would then rescale so that each voter has some candidate at the MAX and some at the MIN score. This will always maximize effective vote power which is the issue attempted to be equalized by this method.

More explicitly. Let MAX and MIN be the extreme available grades. Let $$u_c$$ be a voters score for candidate c, let $$u_{min}$$ and $$u_{max}$$ be their score for her worst and best candidates in the considered election round. The rescaled utility is:

$$\begin{equation} v_c(u_c) = MIN + (MAX– MIN) \frac{(u_c – u_{min})}{(u_{max} – u_{min})} \end{equation}$$

For example, in a [0, 10] system the translation is

$$\begin{equation} v_c(u_c) = 10 \frac{(u_c – u_{min})}{(u_{max} – u_{min})} \end{equation}$$

It would transform [1, 3, 5] to [0, 5, 10].

Related systems
STAR voting is a simplified version of this where instead of eliminating each candidate one by one all but the last two candidates are removed at once. This alteration recovers the monotonicity criterion.

Distributed Voting is a Cumulative voting variant.