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We investigate the problem of preselecting a subset of buyers (also called agents) participating in a market so as to optimize the performance of stable outcomes. We consider four scenarios arising from the combination of two stability notions, namely market envy-freeness and agent envy-freeness, with the two state-of-the-art objective functions of social welfare and seller’s revenue. When insisting on market envy-freeness, we prove that the problem cannot be approximated within n 1−ε (with n being the number of buyers) for any ε > 0, under both objective functions; we also provide approximation algorithms with an approximation ratio tight up to subpolynomial multiplicative factors for social welfare and the seller’s revenue. The negative result, in particular, holds even for markets with single-minded buyers. We also prove that maximizing the seller’s revenue is NP-hard even for a single buyer, thus closing a previous open question. Under agent envy-freeness and for both objective functions, instead, we design a polynomial time algorithm transforming any stable outcome for a market involving any subset of buyers into a stable outcome for the whole market without worsening its performance. This result creates an interesting middle-ground situation where, if on the one hand buyer preselection cannot improve the performance of agent envy-free outcomes, on the other one it can be used as a tool for simplifying the combinatorial structure of the buyers’ valuation functions in a given market. Finally, we consider the restricted case of multi-unit markets, where all items are of the same type and are assigned the same price. For these markets, we show that preselection may improve the performance of stable outcomes in all of the four considered scenarios, and design corresponding approximation algorithms.

We study digraph k-coloring games where agents are vertices of a directed unweighted graph and arcs represent agents' mutual unidirectional idiosyncrasies or conflicts. Each agent can select one of k different colors, and her payoff in a given state is given by the number of outgoing neighbors with a different color. Such games model lots of strategic real-world scenarios and are related to several fundamental classes of anti-coordination games. Unfortunately, the problem of understanding whether an instance of the game admits a pure Nash equilibrium is NP-complete [Jeremy Kun et al., 2013]. Therefore, in the last few years a relevant research focus has been that of designing polynomial time algorithms able to compute approximate Nash equilibria, i.e., states in which no agent, changing her strategy, can improve her payoff by some bounded multiplicative factor. The only two known algorithms in this respect are those in [Raffaello Carosi et al., 2017]. While they provide theoretical guarantees, their practical performance over real-world instances so far has not been investigated. In this paper, under the further motivation of the lack of practical approximation algorithms for the problem, we experimentally evaluate the above algorithms with the conclusion that, while they were suitably designed for achieving a bounded worst case behavior, they generally have a poor performance. Therefore, we next focus on classical best-response dynamics, and show that, despite of the fact that they might not always converge, they are very effective in practice. In particular, we provide a strong empirical evidence that they outperform existing methods, since surprisingly they quickly converge to exact Nash equilibria in almost all instances arising in practice. This also shows that, while this class of games is known to not always possess pure Nash equilibria, in almost all cases such equilibria exist and can be efficiently computed, even in a distributed uncoordinated way by a decentralized interaction of the agents., LIPIcs, Vol. 233, 20th International Symposium on Experimental Algorithms (SEA 2022), pages 20:1-20:18

International audience; We revisit fundamental problems in undirected and directed graphs, such as the problems of computing spanning trees, shortest paths, steiner trees, and spanning arborescences of minimum cost. We assume that there are d different cost functions associated with the edges of the inputgraph and seek for solutions to the resulting multidimensional graph problems so that the p - norm of the different costs of the solution is minimized. We present combinatorial algorithms that achieve very good approximations for this objective. The main advantage of our algorithmsis their simplicity: they are as simple as classical combinatorial graph algorithms of Dijkstra and Kruskal, or the greedy algorithm for matroids.

International audience; We consider k-envy-free assignments for scheduling problems in which the completion time of each machine is not k times larger than the one she could achieve by getting the jobs of another machine, for a given factor k ≥ 1. We introduce and investigate the notion of price of k-envy-freeness, defined as the ratio between the makespan of the best k-envy-free assignment and that of an optimal allocation achievable without envy-freeness constraints. We provide exact or asymptotically tight bounds on the price of k-envy-freeness for all the basic scheduling models, that is unrelated, related and identical machines. Moreover, we show how to efficiently compute such allocations with a worsening multiplicative factor being at most the best approximation ratio for the minimum makespan problem guaranteed by a polynomial time algorithm for each specific model. Finally, we extend our results to the case of restricted assignments and to the objective of minimizing the sum of the completion times of all the machines.

In an optical network using the wavelength division multiplexing (WDM) technology, routing a request consists in assigning it a route in the physical network and a wavelength. If each request uses $1/g$ of the bandwidth of the wavelength, we will say that the grooming factor is $g$. That means that on a given edge of the network we can groom (group) at most $g$ requests on the same wavelength. With this constraint the objective can be either to minimize the number of wavelengths (related to the transmission cost) or minimize the number of Add Drop Multiplexers (shortly ADM) used in the network (related to the cost of the nodes). Here, we first survey the main theoretical results obtained for different grooming factors on various topologies: complexity, (in)approximability, optimal constructions, approximation algorithms, heuristics, etc. Then, we give an ILP formulation for multilayer traffic grooming and present some experimental results.

We study the existence of allocations of indivisible goods that are envy-free up to one good (EF1), under the additional constraint that each bundle needs to be connected in an underlying item graph. If the graph is a path and the utility functions are monotonic over bundles, we show the existence of EF1 allocations for at most four agents, and the existence of EF2 allocations for any number of agents; our proofs involve discrete analogues of the Stromquist's moving-knife protocol and the Su--Simmons argument based on Sperner's lemma. For identical utilities, we provide a polynomial-time algorithm that computes an EF1 allocation for any number of agents. For the case of two agents, we characterize the class of graphs that guarantee the existence of EF1 allocations as those whose biconnected components are arranged in a path; this property can be checked in linear time., Accepted journal version

In load-balancing problems there is a set of clients, each wishing to select a resource from a set of permissible ones to execute a certain task. Each resource has a latency function, which depends on its workload, and a client’s cost is the completion time of her chosen resource. Two fundamental variants of load-balancing problems are selfish load balancing (a.k.a. load-balancing games ), where clients are non-cooperative selfish players aimed at minimizing their own cost solely, and online load balancing , where clients appear online and have to be irrevocably assigned to a resource without any knowledge about future requests. We revisit both problems under the objective of minimizing the Nash Social Welfare , i.e., the geometric mean of the clients’ costs. To the best of our knowledge, despite being a celebrated welfare estimator in many social contexts, the Nash Social Welfare has not been considered so far as a benchmarking quality measure in load-balancing problems. We provide tight bounds on the price of anarchy of pure Nash equilibria and on the competitive ratio of the greedy algorithm under very general latency functions, including polynomial ones. For this particular class, we also prove that the greedy strategy is optimal, as it matches the performance of any possible online algorithm.

We consider the problem of computing approximate Nash equilibria in monotone congestion games (a class of games generalizing network congestion games) with polynomially decreasing cost functions. In particular, we consider the case in which each resource j has a cost $$c_j$$ and the cost that each player incurs when using j is given by $$c_j/x^{\beta }$$ for some constant $${\beta }>0$$ , where x is the number of players using j. Observe that, for $${\beta }=1$$ , we obtain the fundamental Shapley cost sharing method. We design a parametric distributed algorithm for computing $${\alpha }$$ -approximate Nash equilibria. The complexity of this algorithm depends on $${\alpha }$$ , being polynomial for $${\alpha }=\varOmega (p^{\beta })$$ , for every fixed $${\beta }>0$$ , with p being the number of players. Our algorithm provides the first non-trivial approximability results for this class of games and achieves an almost tight performance for network games in directed graphs. For the case of ring networks, we show that an approximate equilibrium can be polynomially computed for every fixed $${\alpha } >1$$ . On the negative side, we prove that the problem of computing a Nash equilibrium in Shapley network cost sharing games is PLS-complete even in undirected graphs, where previous hardness results where known only in the directed case.