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Erschienen in: Theory of Computing Systems 1/2019

26.12.2017

The Impact of Worst-Case Deviations in Non-Atomic Network Routing Games

verfasst von: Pieter Kleer, Guido Schäfer

Erschienen in: Theory of Computing Systems | Ausgabe 1/2019

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Abstract

We introduce a unifying model to study the impact of worst-case latency deviations in non-atomic selfish routing games. In our model, latencies are subject to (bounded) deviations which are taken into account by the players. The quality deterioration caused by such deviations is assessed by the Deviation Ratio, i.e., the worst case ratio of the cost of a Nash flow with respect to deviated latencies and the cost of a Nash flow with respect to the unaltered latencies. This notion is inspired by the Price of Risk Aversion recently studied by Nikolova and Stier-Moses (Nikolova and Stier-Moses 2015). Here we generalize their model and results. In particular, we derive tight bounds on the Deviation Ratio for multi-commodity instances with a common source and arbitrary non-negative and non-decreasing latency functions. These bounds exhibit a linear dependency on the size of the network (besides other parameters). In contrast, we show that for general multi-commodity networks an exponential dependency is inevitable. We also improve recent smoothness results to bound the Price of Risk Aversion.

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Fußnoten
1
We remark that for certain types of (0, β)-deviations, e.g., scaled marginal tolls, better bounds can be obtained; see the section “Relations to network toll problems” in Appendix B for relevant literature.
 
2
For example, there are parallel-arc networks for which the Biased Price of Anarchy is unbounded, whereas the Deviation Ratio is a constant.
 
3
Meir and Parkes [13] define a function l to be (1, μ)-smooth if xl(y) ≤ μyl(y) + xl(x) for all x, y ≥ 0 (which is slightly different from Roughgarden’s original smoothness definition [19]). Lineas et al. [11] only require local smoothness where y is taken fixed.
 
4
The existence of a risk-averse Nash flow is proven in [15].
 
5
Note that the values lP(x) + δP(x)are the same for all flow-carrying paths, but this is not necessarily true for the valueslP(x).
 
6
Note that ηi ≤⌈(n − 1)/2⌉.
 
7
Note that the pathsPlcan overlap, use parts of B, or even be subpaths of each other.
 
8
We use the standard notation δ(v) and δ+(v) to refer to the set of outgoing and incoming edges of a node v, respectively.
 
9
Note that the value ⌈(n − 1)/2⌉is the same for n ∈{2m, 2m + 1}with \(m \in \mathbb {N}\).The example shows tightness for n = 2m.The tightness for n = 2m + 1then follows trivially by adding a dummy node.
 
10
For example \(y_{m}(g) = m(m-1)\beta \max \{0,(g - \frac {1}{m})\}\).That is, we define ymto be zero for 0 ≤ g ≤ 1/m and we let it increase with constant rate toβ in 1/(m − 1).
 
11
We assume that the infimum and supremum are attained in the set Δ(𝜃); in particular, this is true for (α, β)-deviations.
 
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Metadaten
Titel
The Impact of Worst-Case Deviations in Non-Atomic Network Routing Games
verfasst von
Pieter Kleer
Guido Schäfer
Publikationsdatum
26.12.2017
Verlag
Springer US
Erschienen in
Theory of Computing Systems / Ausgabe 1/2019
Print ISSN: 1432-4350
Elektronische ISSN: 1433-0490
DOI
https://doi.org/10.1007/s00224-017-9829-y

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