The scenario approach meets uncertain game theory and variational inequalities

Variational inequalities are modeling tools used to capture a variety of decision-making problems arising in mathematical optimization, operations research, game theory. The scenario approach is a set of techniques developed to tackle stochastic optimization problems, take decisions based on historical data, and quantify their risk. The overarching goal of this manuscript is to bridge these two areas of research, and thus broaden the class of problems amenable to be studied under the lens of the scenario approach. First and foremost, we provide out-of-samples feasibility guarantees for the solution of variational and quasi variational inequality problems. Second, we apply these results to two classes of uncertain games. In the first class, the uncertainty enters in the constraint sets, while in the second class the uncertainty enters in the cost functions. Finally, we exemplify the quality and relevance of our bounds through numerical simulations on a demand-response model

Urgency-aware optimal routing in repeated games through artificial currencies

When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested users. How can we reconcile the two? In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish users in a system-optimal fashion, while accounting for their temporal preferences. We instantiate the framework in a parallel-network whereby users commute repeatedly (e.g., daily) from a common start node to the end node. Thereafter, we focus on the specific two-arcs case whereby, based on an artificial currency, the users are charged when traveling on the first, fast arc, whilst they are rewarded when traveling on the second, slower arc. We assume the users to be rational and model their choices through a game where each user aims at minimizing a combination of today's discomfort, weighted by their urgency, and the average discomfort encountered for the rest of the period (e.g., a week). We show that, if prices of artificial currencies are judiciously chosen, the routing pattern converges to a system-optimal solution, while accommodating the users’ urgency. We complement our study through numerical simulations. Our results show that it is possible to achieve a system-optimal solution whilst significantly reducing the users’ perceived discomfort when compared to a centralized optimal but urgency-unaware policy

The risks and rewards of conditioning noncooperative designs to additional information

A fundamental challenge in multiagent systems is to design local control algorithms to ensure a desirable collective behaviour. The information available to the agents, gathered either through communication or sensing, defines the structure of the admissible control laws and naturally restricts the achievable performance. Hence, it is fundamental to identify what piece of information can be used to produce a significant performance enhancement. This paper studies, within a class of resource allocation problems, the case when such information is uncertain or inaccessible and pinpoints a fundamental risk-reward tradeoff faced by the system designer

The importance of system-level information in multiagent systems design: cardinality and covering problems

A fundamental challenge in multiagent systems is to design local control algorithms to ensure a desirable collective behavior. The information available to the agents, gathered either through communication or sensing, naturally restricts the achievable performance. Hence, it is fundamental to identify what piece of information is valuable and can be exploited to design control laws with enhanced performance guarantees. This paper studies the case when such information is uncertain or inaccessible for a class of submodular resource allocation problems termed covering problems. In the first part of this paper, we pinpoint a fundamental risk-reward tradeoff faced by the system operator when conditioning the control design on a valuable but uncertain piece of information, which we refer to as the cardinality, that represents the maximum number of agents that can simultaneously select any given resource. Building on this analysis, we propose a distributed algorithm that allows agents to learn the cardinality while adjusting their behavior over time. This algorithm is proved to perform on par or better to the optimal design obtained when the exact cardinality is known a priori

On the range of feasible power trajectories for a population of thermostatically controlled loads

We study the potential of a population of thermostatically controlled loads to track desired power signals with provable guarantees. Based on connecting the temperature state of an individual device with its internal energy, we derive necessary conditions that a given power signal needs to satisfy in order for the aggregation of devices to track it using non-disruptive probabilistic switching control. Our derivation takes into account hybrid individual dynamics, an accurate continuous-time Markov chain model for the population dynamics and bounds on switching rates of individual devices. We illustrate the approach with case studies

On the efficiency of nash equilibria in aggregative charging games

Several works have recently suggested to model the problem of coordinating the charging needs of a fleet of electric vehicles as a game, and have proposed distributed algorithms to coordinate the vehicles towards a Nash equilibrium of such game. However, Nash equilibria have been shown to posses desirable system-level properties only in simplified cases. In this letter, we use the concept of price of anarchy (PoA) to analyze the inefficiency of Nash equilibria when compared to the social optimum solution. More precisely, we show that: 1) for linear price functions depending on all the charging instants, the PoA converges to one as the population of vehicles grows; 2) for price functions that depend only on the instantaneous demand, the PoA converges to one if the price function takes the form of a positive pure monomial; and 3) for general classes of price functions, the asymptotic PoA can be bounded. For finite populations, we additionally provide a bound on the PoA as a function of the number vehicles in the system. We support the theoretical findings by means of numerical simulations

When smoothness is not enough: toward exact quantification and optimization of the price-of-anarchy

Today's multiagent systems have grown too complex to rely on centralized controllers, prompting increasing interest in the design of distributed algorithms. In this respect, game theory has emerged as a valuable tool to complement more traditional techniques. The fundamental idea behind this approach is the assignment of agents' local cost functions, such that their selfish minimization attains, or is provably close to, the global objective. Any algorithm capable of computing an equilibrium of the corresponding game inherits an approximation ratio that is, in the worst case, equal to its price-of-anarchy. Therefore, a successful application of the game design approach hinges on the possibility to quantify and optimize the equilibrium performance.Toward this end, we introduce the notion of generalized smoothness, and show that the resulting efficiency bounds are significantly tighter compared to those obtained using the traditional smoothness approach. Leveraging this newly-introduced notion, we quantify the equilibrium performance for the class of local resource allocation games. Finally, we show how the agents' local decision rules can be designed in order to optimize the efficiency of the corresponding equilibria, by means of a tractable linear program

Optimal price of anarchy in cost-sharing games

The design of distributed algorithms is central to the study of multiagent systems control. In this paper, we consider a class of combinatorial cost-minimization problems and propose a framework for designing distributed algorithms with a priori performance guarantees that are near-optimal. We approach this problem from a game-theoretic perspective, assigning agents cost functions such that the equilibrium efficiency (price of anarchy) is optimized. Once agents' cost functions have been specified, any algorithm capable of computing a Nash equilibrium of the system inherits a performance guarantee matching the price of anarchy. Towards this goal, we formulate the problem of computing the price of anarchy as a tractable linear program. We then present a framework for designing agents' local cost functions in order to optimize for the worst-case equilibrium efficiency. Finally, we investigate the implications of our findings when this framework is applied to systems with convex, nondecreasing costs

Plane algebraic curves with many cusps, with an appendix by Eugenii Shustin

The maximum number of cusps on a plane algebraic curve of degree d is an open classical problem that dates back to the nineteenth century. A related open problem is the asymptotic value of the maximum number of cusps on plane curves of degree d, divided by d^2, when d tends to infinity. In this paper, we improve the best known lower bound for the asymptotic value by constructing curves with the largest known number of cusps for infinitely many degrees. Some particular curves of relatively low degree with many cusps are constructed too. The Appendix to this paper is devoted to the case of degree 11 and it is due to E. Shustin