arXiv subjects

Nuno C. Martins

Publications and source records attributed to Nuno C. Martins.

At least 19 recordsLinked to original sources

arXiv2026

Learning in Large Populations With Anticipatory Payoffs: Bregman Meets Willems and Lyapunov

We study evolutionary Nash equilibrium learning in large populations whose payoffs are generated by dynamic mechanisms. Counterclockwise dissipativity (CCW) provides a passivity-like framework for establishing learning/convergence under a broad convex cone of so-called well-behaved learning rules satisfying a natural payoff-alignment condition according to which, away from equilibria, the population's state (strategic profile) moves in a direction positively correlated with the payoffs. The cone is closed under conic combinations and encompasses a broad range of noise-free rules, including discontinuous rules such as best response, imitative rules such as the replicator rule that is not $δ$-passive, and all noise-free $δ$-passive rules. Existing CCW results require no knowledge of the particular rule, but establish convergence without providing a Lyapunov function or Lyapunov stability. We develop a methodology that closes this gap by constructing rule-independent Lyapunov functions from CCW storage functions of the payoff mechanism. For a nonlinear class of anticipatory mechanisms, in which agents compare current payoffs with those associated with a smoothed population state, we show that a storage can be derived as a Bregman divergence. The resulting Lyapunov function combines a potential gap with this divergence and establishes global asymptotic stability. A congestion game with a nonlinear congestion-growth toll illustrates the results.

math.OC↗

arXiv2025

Nash Equilibrium Learning In Large Populations With First-Order Payoff Modifications

We establish Nash equilibrium learning in large populations of noncooperative, strategic agents. Our analysis considers the broadest class to date of payoff mechanisms with first-order modifications, capable of modeling bounded rationality and anticipatory effects, averaging, or Padé delay approximations. We propose a framework that, for the first time, combines two nonstandard system-theoretic passivity notions. Our results hold for discontinuous best response dynamics alongside continuous learning rules, significantly extending prior work.

eess.SY↗

arXiv2025

Congestion Reduction in EV Charger Placement Using Traffic Equilibrium Models

Growing EV adoption can worsen traffic conditions if chargers are sited without regard to their impact on congestion. We study how to strategically place EV chargers to reduce congestion using two equilibrium models: one based on congestion games and one based on an atomic queueing simulation. We apply both models within a scalable greedy station-placement algorithm. Experiments show that this greedy scheme yields optimal or near-optimal congestion outcomes in realistic networks, even though global optimality is not guaranteed as we show with a counterexample. We also show that the queueing-based approach yields more realistic results than the congestion-game model, and we present a unified methodology that calibrates congestion delays from queue simulation and solves equilibrium in link-space.

eess.SY↗

arXiv2025

Hierarchical Decision-Making in Population Games

This paper introduces a hierarchical framework for population games, where individuals delegate decision-making to proxies that act within their own strategic interests. This framework extends classical population games, where individuals are assumed to make decisions directly, to capture various real-world scenarios involving multiple decision layers. We establish equilibrium properties and provide convergence results for the proposed hierarchical structure. Additionally, based on these results, we develop a systematic approach to analyze population games with general convex constraints, without requiring individuals to have full knowledge of the constraints as in existing methods. We present a navigation application with capacity constraints as a case study.

eess.SY↗

arXiv2024

Epidemic Population Games And Perturbed Best Response Dynamics

This paper proposes an approach to mitigate epidemic spread in a population of strategic agents by encouraging safer behaviors through carefully designed rewards. These rewards, which adapt to the evolving state of the epidemic, are ascribed by a dynamic payoff mechanism we seek to design. We use a modified SIRS model to track how the epidemic progresses in response to the agents' strategic choices. By employing perturbed best response evolutionary dynamics to model the population's strategic behavior, we extend previous related work so as to allow for noise in the agents' perceptions of the rewards and intrinsic costs of the available strategies. Central to our approach is the use of system-theoretic methods and passivity concepts to obtain a Lyapunov function, ensuring the global asymptotic stability of an endemic equilibrium with minimized infection prevalence under budget constraints. We leverage the Lyapunov function to analyze how the epidemic's spread rate is influenced by the time scale of the payoff mechanism's dynamics. Additionally, we derive anytime upper bounds on both the infectious fraction of the population and the instantaneous cost a social planner must incur to control the spread, allowing us to quantify the trade-off between peak infection prevalence and the corresponding cost. For a class of one-parameter perturbed best response models, we propose a method to learn the model's parameter from data.

eess.SY↗

arXiv2024

Passivity Tools for Hybrid Learning Rules in Large Populations

Recent work has pioneered the use of system-theoretic passivity to study equilibrium stability for the dynamics of noncooperative strategic interactions in large populations of learning agents. In this and related works, the stability analysis leverages knowledge that certain ``canonical'' classes of learning rules used to model the agents' strategic behaviors satisfy a passivity condition known as $δ$-passivity. In this paper, we consider that agents exhibit learning behaviors that do not align with a canonical class. Specifically, we focus on characterizing $δ$-passivity for hybrid learning rules that combine elements from canonical classes. Our analysis also introduces and uses a more general version of $δ$-passivity, which, for the first time, can handle discontinuous learning rules, including those showing best-response behaviors. We state and prove theorems establishing $δ$-passivity for two broad convex cones of hybrid learning rules. These cones can merge into a larger one preserving $δ$-passivity in scenarios limited to two strategies. In our proofs, we establish intermediate facts that are significant on their own and could potentially be used to further generalize our work. We illustrate the applicability of our results through numerical examples.

math.DS↗

arXiv2024

Incentive Designs for Learning Agents to Stabilize Coupled Exogenous Systems

We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approach is to design a dynamic payoff mechanism capable of shaping the population's strategy profile, thus affecting the ES's state, by offering incentives for specific strategies within budget limits. Employing system-theoretic passivity concepts, we establish conditions under which a payoff mechanism can be systematically constructed to ensure the global asymptotic stability of the ES's equilibrium. In comparison to previous approaches originally studied in the context of the so-called epidemic population games, the method proposed here allows for more realistic epidemic models and other types of ESs, such as predator-prey dynamics. The stability of the equilibrium is established with the support of a Lyapunov function, which provides useful bounds on the transient states.

eess.SY↗

arXiv2024

Counterclockwise Dissipativity, Potential Games and Evolutionary Nash Equilibrium Learning

We use system-theoretic passivity methods to study evolutionary Nash equilibria learning in large populations of agents engaged in strategic, non-cooperative interactions. The agents follow learning rules (rules for short) that capture their strategic preferences and a payoff mechanism ascribes payoffs to the available strategies. The population's aggregate strategic profile is the state of an associated evolutionary dynamical system. Evolutionary Nash equilibrium learning refers to the convergence of this state to the Nash equilibria set of the payoff mechanism. Most approaches consider memoryless payoff mechanisms, such as potential games. Recently, methods using $δ$-passivity and equilibrium independent passivity (EIP) have introduced dynamic payoff mechanisms. However, $δ$-passivity does not hold when agents follow rules exhibiting ``imitation" behavior, such as in replicator dynamics. Conversely, EIP applies to the replicator dynamics but not to $δ$-passive rules. We address this gap using counterclockwise dissipativity (CCW). First, we prove that continuous memoryless payoff mechanisms are CCW if and only if they are potential games. Subsequently, under (possibly dynamic) CCW payoff mechanisms, we establish evolutionary Nash equilibrium learning for any rule within a convex cone spanned by imitation rules and continuous $δ$-passive rules.

cs.GT↗

arXiv2023

Differential Equation Approximations for Population Games using Elementary Probability

Population games model the evolution of strategic interactions among a large number of uniform agents. Due to the agents' uniformity and quantity, their aggregate strategic choices can be approximated by the solutions of a class of ordinary differential equations. This mean-field approach has found to be an effective tool of analysis. However its current proofs rely on advanced mathematical techniques, making them less accessible. In this article, we present a simpler derivation, using only undergraduate-level probability.

cs.GT↗

arXiv2022

Epidemic Population Games With Nonnegligible Disease Death Rate

A recent article that combines normalized epidemic compartmental models and population games put forth a system theoretic approach to capture the coupling between a population's strategic behavior and the course of an epidemic. It introduced a payoff mechanism that governs the population's strategic choices via incentives, leading to the lowest endemic proportion of infectious individuals subject to cost constraints. Under the assumption that the disease death rate is approximately zero, it uses a Lyapunov function to prove convergence and formulate a quasi-convex program to compute an upper bound for the peak size of the population's infectious fraction. In this article, we generalize these results to the case in which the disease death rate is nonnegligible. This generalization brings on additional coupling terms in the normalized compartmental model, leading to a more intricate Lyapunov function and payoff mechanism. Moreover, the associated upper bound can no longer be determined exactly, but it can be computed with arbitrary accuracy by solving a set of convex programs.

math.DS↗

arXiv2022

Population Games With Erlang Clocks: Convergence to Nash Equilibria For Pairwise Comparison Dynamics

The prevailing methodology for analyzing population games and evolutionary dynamics in the large population limit assumes that a Poisson process (or clock) inherent to each agent determines when the agent can revise its strategy. Hence, such an approach presupposes exponentially distributed inter-revision intervals, and is inadequate for cases where each strategy entails a sequence of sub-tasks (sub-strategies) that must be completed before a new revision time occurs. This article proposes a methodology for such cases under the premise that a sub-strategy's duration is exponentially-distributed, leading to Erlang distributed inter-revision intervals. We assume that a so-called pairwise-comparison protocol captures the agents' revision preferences to render our analysis concrete. The presence of sub-strategies brings on additional dynamics that is incompatible with existing models and results. Our main contributions are twofold, both derived for a deterministic approximation valid for large populations. We prove convergence of the population's state to the Nash equilibrium set when a potential game generates a payoff for the strategies. We use system-theoretic passivity to determine conditions under which this convergence is guaranteed for contractive games.

eess.SY↗

arXiv2022

Epidemic Population Games And Evolutionary Dynamics

We propose a system theoretic approach to select and stabilize the endemic equilibrium of an SIRS epidemic model in which the decisions of a population of strategically interacting agents determine the transmission rate. Specifically, the population's agents recurrently revise their choices out of a set of strategies that impact to varying levels the transmission rate. A payoff vector quantifying the incentives provided by a planner for each strategy, after deducting the strategies' intrinsic costs, influences the revision process. An evolutionary dynamics model captures the population's preferences in the revision process by specifying as a function of the payoff vector the rates at which the agents' choices flow toward strategies with higher payoffs. Our main result is a dynamic payoff mechanism that is guaranteed to steer the epidemic variables (via incentives to the population) to the endemic equilibrium with the smallest infectious fraction, subject to cost constraints. We use a Lyapunov function not only to establish convergence but also to obtain an (anytime) upper bound for the peak size of the population's infectious portion.

math.DS↗

arXiv2021

Pairwise Comparison Evolutionary Dynamics with Strategy-Dependent Revision Rates: Stability and Delta-Passivity (Expanded Version)

We report on new stability conditions for evolutionary dynamics in the context of population games. We adhere to the prevailing framework consisting of many agents, grouped into populations, that interact noncooperatively by selecting strategies with a favorable payoff. Each agent is repeatedly allowed to revise its strategy at a rate referred to as revision rate. Previous stability results considered either that the payoff mechanism was a memoryless potential game, or allowed for dynamics (in the payoff mechanism) at the expense of precluding any explicit dependence of the agents' revision rates on their current strategies. Allowing the dependence of revision rates on strategies is relevant because the agents' strategies at any point in time are generally unequal. To allow for strategy-dependent revision rates and payoff mechanisms that are dynamic (or memoryless games that are not potential), we focus on an evolutionary dynamics class obtained from a straightforward modification of one that stems from the so-called impartial pairwise comparison strategy revision protocol. Revision protocols consistent with the modified class retain from those in the original one the advantage that the agents operate in a fully decentralized manner and with minimal information requirements - they need to access only the payoff values (not the mechanism) of the available strategies. Our main results determine conditions under which system-theoretic passivity properties are assured, which we leverage for stability analysis.

q-bio.PE↗

arXiv2021

Stabilizing a Queue Subject to Action-Dependent Server Performance

We consider a discrete-time system comprising a first-come-first-served queue, a non-preemptive server, and a scheduler that governs the assignment of tasks in the queue to the server. The server has an availability state that indicates, at each instant, whether the server is busy working on a task or is available. In the latter case, if the queue is nonempty, then a task-assignment control policy implemented by the scheduler either assigns a new task to the server or allows it to rest. The server also has an integer-valued activity state that is non-increasing during rest periods, and is non-decreasing otherwise. An instantaneous service rate function ascribes to each possible value of the activity state a probability that the server can complete a task in one time step. For a typical instantaneous service rate function, the completion probability decreases (server performance worsens) as the activity state increases. The scheduler policy has access to the queue size and the entire state of the server. In this article, we study the problem of designing scheduler policies that stabilize the queue. We show that stability, whenever viable, can be achieved by a simple policy that bases its decisions on the availability state, a threshold applied to the activity state, and a flag that indicates when the queue is empty. The supremum of the service rates achievable by stabilizing policies can be determined by a finite search. Our results remain valid even when the instantaneous service rate function is not monotonic.

stat.AP↗

arXiv2021

Channels, Remote Estimation and Queueing Systems With A Utilization-Dependent Component: A Unifying Survey Of Recent Results

In this article, we survey the main models, techniques, concepts, and results centered on the design and performance evaluation of engineered systems that rely on a utilization-dependent component (UDC) whose operation may depend on its usage history or assigned workload. Specifically, we report on research themes concentrating on the characterization of the capacity of channels and the design with performance guarantees of remote estimation and queueing systems. Causes for the dependency of a UDC on past utilization include the use of replenishable energy sources to power the transmission of information among the sub-components of a networked system, and the assistance of a human operator for servicing a queue. Our analysis unveils the similarity of the UDC models typically adopted in each of the research themes, and it reveals the differences in the objectives and technical approaches employed. We also identify new challenges and future research directions inspired by the cross-pollination among the central concepts, techniques, and problem formulations of the research themes discussed.

math.OC↗

arXiv2020

An Optimality Gap Test for a Semidefinite Relaxation of a Quadratic Program with Two Quadratic Constraints

We propose a necessary and sufficient test to determine whether a solution for a general quadratic program with two quadratic constraints (QC2QP) can be computed from that of a specific convex semidefinite relaxation, in which case we say that there is no optimality gap. Originally intended to solve a nonconvex optimal control problem, we consider the case in which the cost and both constraints of the QC2QP may be nonconvex. We obtained our test, which also ascertains when strong duality holds, by generalizing a closely-related method by Ai and Zhang. An extension was necessary because, while the method proposed by Ai and Zhang also allows for two quadratic constraints, it requires that at least one is strictly convex. In order to illustrate the usefulness of our test, we applied it to two examples that do not satisfy the assumptions required by prior methods. Our test guarantees that there is no optimality gap for the first example -- a solution is also computed from the relaxation -- and we used it to establish that an optimality gap exists in the second.

math.OC↗

arXiv2020

Dissipativity Tools for Convergence to Nash Equilibria in Population Games

We analyze the stability of a nonlinear dynamical model describing the noncooperative strategic interactions among the agents of a finite collection of populations. Each agent selects one strategy at a time and revises it repeatedly according to a protocol that typically prioritizes strategies whose payoffs are either higher than that of the current strategy or exceed the population average. The model is predicated on well-established research in population and evolutionary games, and has two sub-components. The first is the payoff dynamics model (PDM), which ascribes the payoff to each strategy according to the proportions of every population adopting the available strategies. The second sub-component is the evolutionary dynamics model (EDM) that accounts for the revision process. In our model, the social state at equilibrium is a best response to the payoff, and can be viewed as a Nash-like solution that has predictive value when it is globally asymptotically stable (GAS). We present a systematic methodology that ascertains GAS by checking separately whether the EDM and PDM satisfy appropriately defined system-theoretic dissipativity properties. Our work generalizes pioneering methods based on notions of contractivity applicable to memoryless PDMs, and more general system-theoretic passivity conditions. As demonstrated with examples, the added flexibility afforded by our approach is particularly useful when the contraction properties of the PDM are unequal across populations.

eess.SY↗

arXiv2020

Queueing Subject To Action-Dependent Server Performance: Utilization Rate Reduction

We consider a discrete-time system comprising a first-come-first-served queue, a non-preemptive server, and a stationary non-work-conserving scheduler. New tasks enter the queue according to a Bernoulli process with a pre-specified arrival rate. At each instant, the server is either busy working on a task or is available. When the server is available, the scheduler either assigns a new task to the server or allows it to remain available (to rest). In addition to the aforementioned availability state, we assume that the server has an integer-valued activity state. The activity state is non-decreasing during work periods, and is non-increasing otherwise. In a typical application of our framework, the server performance (understood as task completion probability) worsens as the activity state increases. In this article, we build on and transcend recent stabilizability results obtained for the same framework. Specifically, we establish methods to design scheduling policies that not only stabilize the queue but also reduce the utilization rate - understood as the infinite-horizon time-averaged portion of time the server is working. This article has a main theorem leading to two key results: (i) We put forth a tractable method to determine, using a finite-dimensional linear program (LP), the infimum of all utilization rates that can be achieved by scheduling policies that are stabilizing, for a given arrival rate. (ii) We propose a design method, also based on finite-dimensional LPs, to obtain stabilizing scheduling policies that can attain a utilization rate arbitrarily close to the aforementioned infimum. We also establish structural and distributional convergence properties, which are used throughout the article, and are significant in their own right.

stat.AP↗