Nash Flow Research Articles

Bicriteria Nash flows over time

Flows over time are a natural way to incorporate flow dynamics that arise in various applications such as traffic networks. In this paper we introduce a natural variant of the deterministic fluid queuing model in which users aim to minimize their costs subject to arrival at their destination before a pre-specified deadline. We determine the existence and the structure of Nash flows over time and fully characterize the price of anarchy for this model. The price of anarchy measures the ratio of the quality of the equilibrium and the quality of the optimum flow, where we evaluate the quality using two different natural performance measures: the throughput for a given deadline and the makespan for a given amount of flow. While it turns out that both prices of anarchy can be unbounded in general, we provide tight bounds for the important subclass of parallel path graphs.

Flows Over Time as Continuous Limits of Packet-Based Network Simulations

This study examines the connection between an agent-based transport simulation and Nash flows over time. While the former is able to represent many details of traffic and model large-scale, real-world traffic situations with a co-evolutionary approach, the latter provides an environment for provable mathematical statements and results on exact user equilibria. The flow dynamics of both models are very similar with the main difference that the simulation is discrete in terms of vehicles and time while the flows over time model considers continuous flows and continuous time. This raises the question whether Nash flows over time are the limit of the convergence process when decreasing the vehicle and time step size in the simulation coherently. The experiments presented in this study indicate this strong connection which provides a justification for the analytical model and a theoretical foundation for the simulation.

Improving Selfish Routing for Risk-Averse Players

We investigate how and to which extent one can exploit risk-aversion and modify the perceived cost of the players in selfish routing so that the Price of Anarchy (PoA) wrt. the total latency is improved. The starting point is to introduce some small random perturbations to the edge latencies so that the expected latency does not change, but the perceived cost of the players increases, due to risk-aversion. We adopt the simple model of γ-modifiable routing games, a variant of selfish routing games with restricted tolls. We prove that computing the best γ-enforceable flow is NP-hard for parallel-link networks with affine latencies and two classes of heterogeneous risk-averse players. On the positive side, we show that for parallel-link networks with heterogeneous players and for series-parallel networks with homogeneous players, there exists a nicely structured γ-enforceable flow whose PoA improves fast as γ increases. We show that the complexity of computing such a γ-enforceable flow is determined by the complexity of computing a Nash flow for the original game. Moreover, we prove that the PoA of this flow is best possible in the worst-case, in the sense that there are instances where (i) the best γ-enforceable flow has the same PoA, and (ii) considering more flexible modifications does not lead to any improvement on the PoA.

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

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.

Analysis of Price of Anarchy in Traffic Networks With Heterogeneous Price-Sensitivity Populations

In this paper, we investigate how the scaled marginal-cost road pricing improves the price of anarchy (POA) in a traffic network with one origin–destination pair, where each edge in the network is associated with a latency function. The POA is defined as the worst possible ratio between the total latency of Nash flow and that of the socially optimal flow. All players in the noncooperative congestion game are divided into groups based on their price sensitivities. First, we consider the case where all players are partitioned into two groups in a network with two routes. In this case, it is shown that the total latency of the Nash flow can always reach the total latency of the socially optimal flow if the designed road price is charged on each link. We then analyze the POA for general case. For a distribution of price sensitivities satisfying certain conditions, a road pricing scheme is designed such that the unique Nash flow can achieve the social optimal flow, i.e., $\text {POA}=1$ . An algorithm is proposed to find the price scheme that optimizes the POA for any distribution of price sensitivities and any traffic network with one origin–destination pair. Finally, the results are applied to a traffic routing problem.

On the hardness of network design for bottleneck routing games

In routing games, the selfish behavior of the players may lead to a degradation of the network performance at equilibrium. In more than a few cases however, the equilibrium performance can be significantly improved if we remove some edges from the network. This counterintuitive fact, widely known as Braessʼs paradox, gives rise to the (selfish) network design problem, where we seek to recognize routing games suffering from the paradox, and to improve their equilibrium performance by edge removal. In this work, we investigate the computational complexity and the approximability of the network design problem for non-atomic bottleneck routing games, where the individual cost of each player is the bottleneck cost of her path, and the social cost is the bottleneck cost of the network, i.e. the maximum latency of a used edge. We first show that bottleneck routing games do not suffer from Braessʼs paradox either if the network is series-parallel, or if we consider only subpath-optimal Nash flows. On the negative side, we prove that even for games with strictly increasing linear latencies, it is NP-hard not only to recognize instances suffering from the paradox, but also to distinguish between instances for which the Price of Anarchy (PoA) can decrease to 1 and instances for which the PoA cannot be improved by edge removal, even if their PoA is as large as Ω(n0.121). This implies that the network design problem for linear bottleneck routing games is NP-hard to approximate within a factor of O(n0.121−ε), for any constant ε>0. The proof is based on a recursive construction of hard instances that carefully exploits the properties of bottleneck routing games, and may be of independent interest. On the positive side, we present an algorithm for finding a subnetwork that is almost optimal with respect to the bottleneck cost of its worst Nash flow, when the worst Nash flow in the best subnetwork routes a non-negligible amount of flow on all used edges. We show that the running time is essentially determined by the total number of paths in the network, and is quasipolynomial when the number of paths is quasipolynomial.

Improving the Price of Anarchy for Selfish Routing via Coordination Mechanisms

We reconsider the well-studied Selfish Routing game with affine latency functions. The Price of Anarchy for this class of games takes maximum value 4/3; this maximum is attained already for a simple network of two parallel links, known as Pigou’s network. We improve upon the value 4/3 by means of Coordination Mechanisms.We increase the latency functions of the edges in the network, i.e., if ℓ e (x) is the latency function of an edge e, we replace it by \(\hat{\ell}_{e}(x)\) with \(\ell_{e}(x) \le \hat{\ell}_{e}(x)\) for all x. Then an adversary fixes a demand rate as input. The engineered Price of Anarchy of the mechanism is defined as the worst-case ratio of the Nash social cost in the modified network over the optimal social cost in the original network. Formally, if \(\hat{C}_{N} (r)\) denotes the cost of the worst Nash flow in the modified network for rate r and C opt (r) denotes the cost of the optimal flow in the original network for the same rate then $$\mathit{ePoA} = \max_{r \ge 0} \frac{\hat{C}_N(r)}{C_{\mathit{opt}}(r)}. $$ We first exhibit a simple coordination mechanism that achieves for any network of parallel links an engineered Price of Anarchy strictly less than 4/3. For the case of two parallel links our basic mechanism gives 5/4=1.25. Then, for the case of two parallel links, we describe an optimal mechanism; its engineered Price of Anarchy lies between 1.191 and 1.192.

Efficient methods for selfish network design

Intuitively, Braess’s paradox states that destroying a part of a network may improve the common latency of selfish flows at Nash equilibrium. Such a paradox is a pervasive phenomenon in real-world networks. Any administrator who wants to improve equilibrium delays in selfish networks, is facing some basic questions:–Is the network paradox-ridden?–How can we delete some edges to optimize equilibrium flow delays?–How can we modify edge latencies to optimize equilibrium flow delays? Unfortunately, such questions lead to NP-hard problems in general. In this work, we impose some natural restrictions on our networks, e.g. we assume strictly increasing linear latencies. Our target is to formulate efficient algorithms for the three questions above. We manage to provide: –A polynomial-time algorithm that decides if a network is paradox-ridden, when latencies are linear and strictly increasing.–A reduction of the problem of deciding if a network with (arbitrary) linear latencies is paradox-ridden to the problem of generating all optimal basic feasible solutions of a Linear Program that describes the optimal traffic allocations to the edges with constant latency.–An algorithm for finding a subnetwork that is almost optimal wrt equilibrium latency. Our algorithm is subexponential when the number of paths is polynomial and each path is of polylogarithmic length.–A polynomial-time algorithm for the problem of finding the best subnetwork which outperforms any known approximation for the case of strictly increasing linear latencies.–A polynomial-time method that turns the optimal flow into a Nash flow by deleting the edges not used by the optimal flow, and performing minimal modifications on the latencies of the remaining ones. Our results provide a deeper understanding of the computational complexity of recognizing the most severe manifestations of Braess’s paradox, and our techniques show novel ways of using the probabilistic method and of exploiting convex separable quadratic programs.

Truthful Mechanisms for Selfish Routing and Two-Parameter Agents

We prove a general monotonicity result about Nash flows in directed networks, which generalizes earlier results and can be used for the design of truthful mechanisms in the setting where each edge of the network is controlled by a different selfish agent, who incurs costs when her edge is used.Moreover, we consider a general mechanism design setting with two-parameter agents, which generalizes the well-known setting of one-parameter agents in two directions by allowing nonlinear cost functions and a fixed cost component as part of each agent’s private data. We give complete characterizations of the sets of output functions that can be turned into truthful mechanisms for two-parameter agents in the case that an agent always incurs her fixed costs and in the case that she only incurs her fixed costs when she is assigned a positive amount of work. Moreover, we give explicit formulas for the payment functions in both cases and show that the truthful payments are uniquely determined by the output function up to a constant as in the one-parameter setting.

Stackelberg thresholds in network routing games or the value of altruism

It is well known that the Nash equilibrium in network routing games can have strictly higher cost than the optimum cost. In Stackelberg routing games, where a fraction of flow is centrally-controlled, a natural problem is to route the centrally-controlled flow such that the overall cost of the resulting equilibrium is minimized. We consider the scenario where the network administrator wants to know the minimum amount of centrally-controlled flow such that the cost of the resulting equilibrium solution is strictly less than the cost of the Nash equilibrium. We call this threshold the Stackelberg threshold and prove that for networks of parallel links with linear latency functions, it is equal to the minimum of the Nash flows on links carrying more optimum flow than Nash flow. Our approach also provides a simpler proof of characterization of the minimum fraction that must be centrally controlled to induce the optimum solution.

AN INTERNATIONAL MARINE POLLUTANT SINK IN AN ASYMMETRIC ENVIRONMENTAL TECHNOLOGY GAME

Abstract A differential game with N countries as players is developed for the analysis of a marine resource with open access used by the countries as a pollutant sink and for production of an ecosystem good. Consumption, investment, abatement, and environmental research sectors may differ across countries. Under the assumption that property rights are well defined within the sovereignty of each country although there is a lack of enforcement in the international area, the game is solved for the feedback Nash equilibrium, using the Isaacs–Bellman dynamic programming technique, identifying the nonlinear feedback Nash flows of consumption, investment, harvest effort, abatement, and environmental research effort in each country, which is then compared to the Pareto optimal global solution.

Linear tolls suffice: New bounds and algorithms for tolls in single source networks

We show that tolls that are linear in the latency of the maximum latency path are necessary and sufficient to induce heterogeneous network users to independently choose routes that lead to traffic with minimum average latency. This improves upon the earlier bound of O ( n 3 l max ) given by Cole, Dodis, and Roughgarden in STOC 03. (Here, n is the number of nodes in the network; and l max is the maximum latency of any edge.) Our proof is also simpler, relating the Nash flow to the optimal flow as flows rather than cuts. We model the set of users as the set [ 0 , 1 ] ordered by their increasing willingness to pay tolls to reduce latency—their valuation of time. Cole et al. give an algorithm that computes optimal tolls for a bounded number of agent valuations, under the very strong assumption that they know which path each user type takes in the Nash flow imposed by these (unknown) tolls. We show that in series parallel graphs, the set of paths traveled by users in any Nash flow with optimal tolls is independent of the distribution of valuations of time of the users. In particular, for any continuum of users (not restricted to a finite number of valuation classes) in series parallel graphs, we show how to compute these paths without knowing α . We give a simple example to demonstrate that if the graph is not series parallel, then the set of paths traveled by users in the Nash flow depends critically on the distribution of users’ valuations of time.