Modeling the electoral college as a knapsack problem

In my previous and first post about the electoral college, I tried to show how it is possible to get the necessary 270 votes to win the election in the college and do it winning the popular election in a minority of the states. Now we model the electoral college as a knapsack problem.

My approach was a bit simplistic (heuristic) and now I will show how to model the electoral college as an example of a very well-known problem in mathematical programming: the knapsack problem. In the knapsack problem, you want to fill the knapsack with a few items. Each item has a weight and some value to you, and you want to pack the knapsack with as much value as possible within the weight the knapsack (and your own back!) can hold.

transfer functions neural network
Kind of funny because the Democrats carry a big handicap in the electoral college
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Electoral College Optimization

The US Constitution establishes an indirect system to elect the president and vice president. People elect a college (a group) of electors that then elects the president. This used to be a non-issue and non-event for ages up until in November of 2000 the election was so close in Florida that it required waiting for a few weeks and a Supreme Court decision to decide the winner. Another eventful election happened in 2016 (sans the Supreme Court intervention).

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