
The Price of Anarchy in Routing Games as a Function of the Demand
Most of the literature concerning the price of anarchy has focused on th...
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A Strategic Routing Framework and Algorithms for Computing Alternative Paths
Traditional navigation services find the fastest route for a single driv...
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The Value of Information in Selfish Routing
Path selection by selfish agents has traditionally been studied by compa...
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Traffic Optimization For a Mixture of Selfinterested and Compliant Agents
This paper focuses on two commonly used path assignment policies for age...
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Capacitated Network Design Games on a Generalized Fair Allocation Model
The costsharing connection game is a variant of routing games on a netw...
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Fair Tree Connection Games with TopologyDependent Edge Cost
How do rational agents selforganize when trying to connect to a common ...
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Scalable NodeDisjoint and EdgeDisjoint Multiwavelength Routing
Probabilistic messagepassing algorithms are developed for routing trans...
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DataDriven Models of Selfish Routing: Why Price of Anarchy Does Depend on Network Topology
We investigate traffic routing both from the perspective of real world data as well as theory. First, we reveal through data analytics a natural but previously uncaptured regularity of real world routing behavior. Agents only consider, in their strategy sets, paths whose freeflow costs (informally their lengths) are within a small multiplicative (1+θ) constant of the optimal freeflow cost path connecting their source and destination where θ≥0. In the case of Singapore, θ=1 is a good estimate of agents' route (pre)selection mechanism. In contrast, in Pigou networks the ratio of the freeflow costs of the routes and thus θ is infinite, so although such worst case networks are mathematically simple they correspond to artificial routing scenarios with little resemblance to real world conditions, opening the possibility of proving much stronger Price of Anarchy guarantees by explicitly studying their dependency on θ. We provide an exhaustive analysis of this question by providing provably tight bounds on PoA(θ) for arbitrary classes of cost functions both in the case of general congestion/routing games as well as in the special case of pathdisjoint networks. For example, in the case of the standard Bureau of Public Roads (BPR) cost model, c_e(x)= a_e x^4+b_e and more generally quartic cost functions, the standard PoA bound for θ=∞ is 2.1505 (Roughgarden, 2003) and it is tight both for general networks as well as pathdisjoint and even paralleledge networks. In comparison, in the case of θ=1, the PoA in the case of general networks is only 1.6994, whereas for pathdisjoint/paralleledge networks is even smaller (1.3652), showing that both the route geometries as captured by the parameter θ as well as the network topology have significant effects on PoA (Figure 1).
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