  The Colonel Blotto game is a deeply studied theoretical model for competitive
allocation environments including elections, advertising, and ecology. However,
the original formulation of Colonel Blotto has had few practical implications
due to the lack of fast algorithms to compute its optimal strategies and the
limited applicability of its winner-take-all reward distribution. We
demonstrate that the Colonel Blotto game can be a practical model for
competitive allocation environments by implementing the multiplicative weights
update algorithm from Beaglehole et al. (2023). In particular, using that this
algorithm allows for arbitrary winning-rules, we study strategies for a more
realistic model of political campaigning we term Electoral Colonel Blotto.
Contrary to existing theory and the implemented allocation strategies from U.S.
presidential elections, we find that the optimal response to Democratic and
Republican strategies in the 2008 and 2020 presidential elections was to focus
allocations on a subset of states and sacrifice winning probability on others.
We also found that campaigners should compete for undecided voters even in
states where the opponent has significantly many more decided voters.
