2019年度 人工知能学会全国大会（第33回）
2019年6月4日〜6月7日朱鷺メッセ 新潟コンベンションセンター
2019年度 人工知能学会全国大会（第33回）
2019年6月4日〜6月7日朱鷺メッセ 新潟コンベンションセンター
[2F1-E-3-04]Analysis of Incentive Ratio in Top-Trading-Cycles Algorithms
〇Taiki Todo1(1. Kyushu University)
The main objective of this paper is to analyze some variants of the classical top-trading-cycles (TTC) algorithm for slightly modified models of the housing market. Extensions of TTC for such modified models are not necessarily strategy-proof, as pointed out by Fujita et al.\ (2015), and thus some alternative analysis of agents' selfish behavior is needed. In this paper, the incentive ratio, originally proposed by Chen et al.\ (2011), of the variants of TTC algorithm is analyzed in both (i) the multi-item exchange and (ii) an exchange model with a specific form of externalities.