TY - JOUR
T1 - A complexity approach for core-selecting exchange under conditionally lexicographic preferences
AU - Fujita, Etsushi
AU - Lesca, Julien
AU - Sonoda, Akihisa
AU - Todo, Taiki
AU - Yokoo, Makoto
N1 - Funding Information:
A preliminary version of this paper was appeared in the proceeding of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15) (Fujita et al., 2015). ‘is work was partially supported by JSPS KAKENHI Grant Number JP24220003, JP26730005, JP17H00761, JP17H04695, JSPS
Publisher Copyright:
© 2018 AI Access Foundation. All rights reserved.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - Core-selection is a crucial property of rules in the literature of resource allocation. It is also desirable, from the perspective of mechanism design, to address the incentive of agents to cheat by misreporting their preferences. this paper investigates the exchange problem where (i) each agent is initially endowed with (possibly multiple) indivisible goods, (ii) agents' preferences are assumed to be conditionally lexicographic, and (iii) side payments are prohibited. We propose an exchange rule called augmented top-trading-cycles (ATTC), based on the original TTC procedure. We first show that ATTC is core-selecting and runs in polynomial time with respect to the number of goods. We then show that finding a beneficial misreport under ATTC is NP-hard. We finally clarify relationship of misreporting with splitting and hiding, two different types of manipulations, under ATTC.
AB - Core-selection is a crucial property of rules in the literature of resource allocation. It is also desirable, from the perspective of mechanism design, to address the incentive of agents to cheat by misreporting their preferences. this paper investigates the exchange problem where (i) each agent is initially endowed with (possibly multiple) indivisible goods, (ii) agents' preferences are assumed to be conditionally lexicographic, and (iii) side payments are prohibited. We propose an exchange rule called augmented top-trading-cycles (ATTC), based on the original TTC procedure. We first show that ATTC is core-selecting and runs in polynomial time with respect to the number of goods. We then show that finding a beneficial misreport under ATTC is NP-hard. We finally clarify relationship of misreporting with splitting and hiding, two different types of manipulations, under ATTC.
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U2 - 10.1613/jair.1.11254
DO - 10.1613/jair.1.11254
M3 - Article
AN - SCOPUS:85057123696
VL - 63
SP - 515
EP - 555
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
SN - 1076-9757
ER -