Strategyproof Exchange with Multiple Private Endowments
Authors: Taiki Todo, Haixin Sun, Makoto Yokoo
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study a mechanism design problem for exchange economies where each agent is initially endowed with a set of indivisible goods and side payments are not allowed. We assume each agent can withhold some endowments, as well as misreport her preference. Under this assumption, strategyproofness requires that for each agent, reporting her true preference with revealing all her endowments is a dominant strategy, and thus implies individual rationality. Our objective in this paper is to analyze the effect of such private ownership in exchange economies with multiple endowments. As fundamental results, we first show that the revelation principle holds under a natural assumption and that strategyproofness and Pareto efficiency are incompatible even under the lexicographic preference domain. We then propose a class of exchange rules, each of which has a corresponding directed graph to prescribe possible trades, and provide necessary and sufficient conditions on the graph structure so that they satisfy strategyproofness. |
| Researcher Affiliation | Academia | Taiki Todo, Haixin Sun, Makoto Yokoo Dept. of Informatics Kyushu University Motooka 744, Fukuoka, JAPAN {todo, sunhaixin, yokoo}@agent.inf.kyushu-u.ac.jp |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. Therefore, it does not mention publicly available datasets or provide access information for them. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data or training processes, thus it does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve software implementation details or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments with specific setup details such as hyperparameters or training configurations. |