Testing strategy-proofness in matching markets: an experimental study

该项目旨在通过调查建议对策略正确性的影响来测试和改进匹配算法。匹配算法用于解决设计市场中的分配问题（如学校或房屋分配问题）。所采用的许多算法都是不受策略影响的：参与者永远不会从制定策略中获益，也就是说，不会从谎报自己的偏好中获益。实验研究似乎证实了策略验证性，但新的证据表明，参与者可能倾向于遵循错误的建议，因此撒谎。为了提高设计市场的表现，该项目建议通过调查建议如何影响策略证明算法中的真相，以及学习是否可以抵消或补充建议的效果，进一步测试策略证明性。

This project seeks to test and improve matching algorithms by investigating the effect of advice on strategy-proofness. Matching algorithms are used to solve allocation problems in designed markets (eg school or house allocation problems). Many of the algorithms employed are strategy-proof: participants never gain from strategising, that is, from lying about their preferences. Strategy-proofness had been seemingly validated by experimental research, but new evidence suggests that participants could be prone to follow wrong advice and therefore lie. In order to improve the performance of designed markets, the project proposes to further test strategy-proofness by investigating how advice can affect truth-telling in strategy-proof algorithms and whether learning can counteract or complement the effect of advice.