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COMPUTING SCIENCE

Machine Politics

Brian Hayes

(This column appeared originally in the November-December 1996 issue of American Scientist.)

Reapportionment and redistricting are vexing problems of meta-politics. They are "meta" issues because they concern the rules of the political process itself—the way the teams are chosen. In the American political system, a decision about reapportionment and redistricting helps determine who will make all other decisions for the next 10 years. It's no surprise, then, that disputes over these matters have often been rancorous. The first Presidential veto in American history (handed down by Washington in 1792) rejected a Congressional reapportionment plan. By the 1920s the reapportionment issue had become so contentious that the decade ended before Congress could agree on a new formula. More recently, hundreds of redistricting plans have been challenged in court; two Supreme Court decisions last summer invalidated Congressional districts in North Carolina and Texas.

This history of bitter conflict prompts speculation on the meta-meta-political question of how best to resolve meta- political questions. In particular: Would it be feasible to take the process out of politics—indeed to take it out of human hands altogether? The answer is surely yes. Computer programs could readily draw legislative districts. Drawing good districts, however, is a more challenging assignment. And harder still would be persuading the legal and political establishment to give up control of the process and accept an algorithmic solution.

Whether or not computerized redistricting would make for good government, it offers some interesting exercises in mathematics and computer science. Algorithms for redistricting exploit techniques from computational geometry, graph theory, combinatorics and optimization methods. Even if such algorithms are never embodied in law, perhaps they can suggest some ideas that would be useful in a more conventional approach to redistricting.








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