TY - GEN
T1 - Carpooling in social networks
AU - Fiat, Amos
AU - Karlin, Anna R.
AU - Koutsoupias, Elias
AU - Mathieu, Claire
AU - Zach, Rotem
N1 - Funding Information:
The second author acknowledges the support of NSF grant CCF 1420381. The third author acknowledges the support of ERC Advanced Grant 321171 (ALGAME)
PY - 2016/8/1
Y1 - 2016/8/1
N2 - We consider the online carpool fairness problem of [Fagin and Williams, 1983] in which an online algorithm is presented with a sequence of pairs drawn from a group of n potential drivers. The online algorithm must select one driver from each pair, with the objective of partitioning the driving burden as fairly as possible for all drivers. The unfairness of an online algorithm is a measure of the worst-case deviation between the number of times a person has driven and the number of times they would have driven if life was completely fair. We introduce a version of the problem in which drivers only carpool with their neighbors in a given social network graph; this is a generalization of the original problem, which corresponds to the social network of the complete graph. We show that for graphs of degree d, the unfairness of deterministic algorithms against adversarial sequences is exactly d/2. For random sequences of edges from planar graph social networks we give a [deterministic] algorithm with logarithmic unfairness (holds more generally for any bounded-genus graph). This does not follow from previous random sequence results in the original model, as we show that restricting the random sequences to sparse social network graphs may increase the unfairness. A very natural class of randomized online algorithms are so-called static algorithms that preserve the same state distribution over time. Surprisingly, we show that any such algorithm has unfairness θ(√d) against oblivious adversaries. This shows that the local random greedy algorithm of [Ajtai et al, 1996] is close to optimal amongst the class of static algorithms. A natural (non-static) algorithm is global random greedy (which acts greedily and breaks ties at random). We improve the lower bound on the competitive ratio from Ω(log1/3(d)) to Ω(log d). We also show that the competitive ratio of global random greedy against adaptive adversaries is Ω(d).
AB - We consider the online carpool fairness problem of [Fagin and Williams, 1983] in which an online algorithm is presented with a sequence of pairs drawn from a group of n potential drivers. The online algorithm must select one driver from each pair, with the objective of partitioning the driving burden as fairly as possible for all drivers. The unfairness of an online algorithm is a measure of the worst-case deviation between the number of times a person has driven and the number of times they would have driven if life was completely fair. We introduce a version of the problem in which drivers only carpool with their neighbors in a given social network graph; this is a generalization of the original problem, which corresponds to the social network of the complete graph. We show that for graphs of degree d, the unfairness of deterministic algorithms against adversarial sequences is exactly d/2. For random sequences of edges from planar graph social networks we give a [deterministic] algorithm with logarithmic unfairness (holds more generally for any bounded-genus graph). This does not follow from previous random sequence results in the original model, as we show that restricting the random sequences to sparse social network graphs may increase the unfairness. A very natural class of randomized online algorithms are so-called static algorithms that preserve the same state distribution over time. Surprisingly, we show that any such algorithm has unfairness θ(√d) against oblivious adversaries. This shows that the local random greedy algorithm of [Ajtai et al, 1996] is close to optimal amongst the class of static algorithms. A natural (non-static) algorithm is global random greedy (which acts greedily and breaks ties at random). We improve the lower bound on the competitive ratio from Ω(log1/3(d)) to Ω(log d). We also show that the competitive ratio of global random greedy against adaptive adversaries is Ω(d).
KW - Carpool problem
KW - Competitive ratio
KW - Fairness
KW - Online algorithms
KW - Randomized algorithms
UR - http://www.scopus.com/inward/record.url?scp=85012892740&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.ICALP.2016.43
DO - 10.4230/LIPIcs.ICALP.2016.43
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AN - SCOPUS:85012892740
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016
A2 - Rabani, Yuval
A2 - Chatzigiannakis, Ioannis
A2 - Sangiorgi, Davide
A2 - Mitzenmacher, Michael
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016
Y2 - 12 July 2016 through 15 July 2016
ER -