By Gabriella Pigozzi, Marija Slavkovik, Leendert van der Torre (auth.), Francesca Rossi, Alexis Tsoukias (eds.)
This quantity includes the papers offered at ADT 2009, the 1st foreign convention on Algorithmic choice thought. The convention used to be held in San Servolo, a small island of the Venice lagoon, in the course of October 20-23, 2009. this system of the convention incorporated oral shows, posters, invited talks, and tutorials.
The convention got sixty five submissions of which 39 papers have been approved (9 papers have been posters). the subjects of those papers variety from computational social selection choice modeling, from uncertainty to choice studying, from multi-criteria determination making to online game theory.
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Additional info for Algorithmic Decision Theory: First International Conference, ADT 2009, Venice, Italy, October 20-23, 2009. Proceedings
4 Minimizing Manipulations The number of manipulations required is an important factor. It may be advantageous for the coalition to manipulate as few games as possible to avoid detection or to minimize the cost of bribing players. We show that there is a polynomial algorithm to calculate manipulations which throw a minimal number of games. This highlights the vulnerability of the two most common types of competitions in sports to manipulation. 1 Minimal Number of Manipulations for Cup Competitions Computing the minimal number of manipulations simply requires keeping a count within our algorithm for computing a manipulation.
1, for example). We add a weight of 1 to each edge (vi , vj ) where (vi , vj ) ∈ / V and therefore represents a manipulation. All other edges have the weight 0. The feasible ﬂow which uses the fewest of the non-zero edges is the minimal number of tournament manipulations to achieve a constructive manipulation. Since the value of c can be determined in a linear number of steps, we only need to do a single min cost ﬂow computation, which is polynomial, to determine the remainder of the minimum number of manipulations necessary to make vw the team with the highest Copeland score.
Airiau and U. Endriss Table 1. 7560 ⎦ (there is a unique cycle). First, let us explain the computation of the expected payoﬀs; then we will describe the properties of the outcome. State a would lose an election against state c, and win an election against state b. If the current alternative is state a one step before the deadline, the second and third agents should propose state c, the ﬁrst agent should propose the status quo. As the agents are chosen to make a proposal with equi-probability, the probability to stay in a is 13 , the probability to move to c is 23 , and the probability to move to b is zero.
Algorithmic Decision Theory: First International Conference, ADT 2009, Venice, Italy, October 20-23, 2009. Proceedings by Gabriella Pigozzi, Marija Slavkovik, Leendert van der Torre (auth.), Francesca Rossi, Alexis Tsoukias (eds.)