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Concept
Computational social choice
Résumé
Computational social choice is a field at the intersection of social choice theory, theoretical computer science, and the analysis of multi-agent systems. It consists of the analysis of problems arising from the aggregation of preferences of a group of agents from a computational perspective. In particular, computational social choice is concerned with the efficient computation of outcomes of voting rules, with the computational complexity of various forms of manipulation, and issues arising from the problem of representing and eliciting preferences in combinatorial settings.
The usefulness of a particular voting system can be severely limited if it takes a very long time to calculate the winner of an election. Therefore, it is important to design fast algorithms that can evaluate a voting rule when given ballots as input. As is common in computational complexity theory, an algorithm is thought to be efficient if it takes polynomial time. Many popular voting rules can be evaluated in polynomial time in a straightforward way (i.e., counting), such as the Borda count, approval voting, or the plurality rule. For rules such as the Schulze method or ranked pairs, more sophisticated algorithms can be used to show polynomial runtime. Certain voting systems, however, are computationally difficult to evaluate. In particular, winner determination for the Kemeny-Young method, Dodgson's method, and Young's method are all NP-hard problems. This has led to the development of approximation algorithms and fixed-parameter tractable algorithms to improve the theoretical calculation of such problems.
By the Gibbard-Satterthwaite theorem, all non-trivial voting rules can be manipulated in the sense that voters can sometimes achieve a better outcome by misrepresenting their preferences, that is, they submit a non-truthful ballot to the voting system. Social choice theorists have long considered ways to circumvent this issue, such as the proposition by Bartholdi, Tovey, and Trick in 1989 based on computational complexity theory.
Source officielle
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