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We consider a stylized model for investment into renewable power plants under long-term uncertainty. We model risk-averse agents facing heterogeneous weather conditions and a common noise including uncertainty on demand trends, future fuel prices and the average national weather conditions. The objective of each agent is to maximize multistage profit by controlling investment in discrete time steps. We analyze this model in a noncooperative game setting with N players, where the interaction among agents occurs through the spot price mechanism. Our model extends to a mean field game with common noise when the number of agents is infinite. We prove that the N-player game admits a Nash equilibrium. Moreover, we prove that under appropriate assumptions, any sequence of Nash equilibria to the N-player game converges to the unique solution of the MFG game. Our numerical experiments highlight the impact of the risk aversion parameter and the importance of correctly specifying the distribution of the heterogeneity among agents. Moreover, we demonstrate that the results obtained by our model cannot be replicated by a model based on a representative agent with a unique parameter that would represent homogenized weather conditions. This emphasizes the importance of including explicit modeling of heterogeneity in prospective models when a heterogeneous parameter is expected to have a significant influence on the outcomes. [ABSTRACT FROM AUTHOR]

As the evolution of smart grids accelerates, distributed energy resources (DERs) emerge as key elements in the transformation of global energy systems. However, the integration of these technologies introduces significant cybersecurity vulnerabilities, notably false data injection (FDI) and a direct load-altering attack (DLAA). Traditional load-altering attacks require a huge attack load and, thus, are not practical to implement. In contrast, in modern DER environments where households become "prosumers" with high-power energy generation, the implications of such attacks are substantially amplified. This paper considers a hybrid cyberattack that includes both FDI and a DLAA, and presents a hierarchical, optimal load adjustment framework that addresses these security concerns. A centralized optimizer first calculates the ideal load-shedding strategies for each substation, which are then securely broadcast to households. To address the complexities at the individual household level, we introduce a novel reinforcement learning algorithm termed Mean Field Deep Deterministic Policy Gradients (MF-DDPG). This algorithm employs mean-field game theory to enable decentrally coordinated decision-making among each household, making it particularly effective in zero-trust scenarios. Through this multifaceted approach, we offer a robust countermeasure against load-altering attacks, thereby enhancing the resilience and stability of advanced smart grids. [ABSTRACT FROM AUTHOR]

We present an approach to describe the group behavior of the high-frequency traders in the stock market based on Mean Field Games and optimal control theory. The problem is formalized as a system of coupled PDEs: Kolmogorov–Fokker–Planck, evolving forward in time with initial condition, and Hamilton–Jacobi–Bellman, evolving backwards in time with terminal condition. Under certain assumptions, the system of PDEs can be reduced to Riccati-type ODEs. We solve the inverse problem to describe the behavior of the high-frequency traders during the Chinese stock market crisis in 2015. [ABSTRACT FROM AUTHOR]

In this paper, the existence of non-trivial time periodic solutions of first order mean field games is proved. It is assumed that there is a non-trivial periodic orbit contained in the Mather set. The whole system is autonomous with a monotonic coupling term. Moreover, the large time convergence of solutions of first order mean field games to time periodic solutions is also considered. [ABSTRACT FROM AUTHOR]

Dynamic programming equations for mean field control problems with a separable structure are Eikonal type equations on the Wasserstein space. Standard differentiation using linear derivatives yield a direct extension of the classical viscosity theory. We use Fourier representation of the Sobolev norms on the space of measures, together with the standard techniques from the finite dimensional theory to prove a comparison result among semi-continuous sub and super solutions, obtaining a unique characterization of the value function. [ABSTRACT FROM AUTHOR]

Motivated by optimal allocation models with relative performance criteria, we introduce a mean field game in which the terminal expected utility of the representative agent depends on her own state as well as the average of her peers. We derive the master equation, which, in view of the presence of controls in the volatility, needs to be coupled with a compatibility condition for the mean field optimal feedback control. We concentrate on the class of separable payoffs under both general utilities and couplings. We derive a solution to the master equation and find the associated optimal feedback control expressed via the value function in the absence of competition and a dynamic coupling function solving a non-local quasilinear equation. In turn, we construct the related optimal state and control processes, and give representative examples. Projecting the mean field solutions on finite dimensions, we recover the solution of the N-game for linear couplings and arbitrary utilities, and we study the proximity of these approximations to their N-player game counterparts. [ABSTRACT FROM AUTHOR]

This paper investigates mean field games in continuous time, state and action spaces with an infinite number of agents, where each agent aims to maximize its expected cumulative reward. Using the technique of randomized policies, we show policy evaluation and policy gradient are equivalent to the martingale conditions of a process by focusing on a representative agent. Then combined with fictitious game, we propose online and offline actor-critic algorithms for solving continuous mean field games that update the value function and policy alternatively under the given population state and action distributions. We demonstrate through two numerical experiments that our proposed algorithms can converge to the mean field equilibrium quickly and stably. [ABSTRACT FROM AUTHOR]

This paper focuses on exploring the convergence properties of a generic player's trajectory and empirical measures in an N-player Linear-Quadratic-Gaussian Nash game, where Brownian motion serves as the common noise. The study establishes three distinct convergence rates concerning the representative player and empirical measure. To investigate the convergence, the methodology relies on a specific decomposition of the equilibrium path in the N-player game and utilizes the associated mean field games framework. [ABSTRACT FROM AUTHOR]

The concept of swarm comes from the biological world. Drones gather in groups of 100 or even 1000 to fly like a flock of birds, called swarms. Swarm systems satisfy several assumptions such as decentralized controls, local information and simple platforms. Swarm systems have attractive properties such as resilience, scalability, and ease of development and implementation. Swarm techniques can perform simple tasks such as moving in a coordinated direction. The flocking behavior of a group of animals that converge by local interactions toward the same heading is an example of simple consensus for decentralized dynamic systems. However, the notion of decentralized control based on local information suffers from taking into account the overall behavior of the group. For example, in a complex environment, a swarm will adapt to the presence of obstacles and congestion reactively, whereas we would like more anticipatory control. The objective of this paper is to propose a solution based on Mean Field Game (MFG) concepts to integrate macro-level knowledge at the micro-level in decentralized flocking. [ABSTRACT FROM AUTHOR]

In this paper, we provide a generalization of the forward-backward splitting algorithm for minimizing the sum of a proper convex lower semicontinuous function and a differentiable convex function whose gradient satisfies a locally Lipschitz-type condition. We prove the convergence of our method and derive a linear convergence rate when the differentiable function is locally strongly convex. We recover classical results in the case when the gradient of the differentiable function is globally Lipschitz continuous and an already known linear convergence rate when the function is globally strongly convex. We apply the algorithm to approximate equilibria of variational mean field game systems with local couplings. Compared with some benchmark algorithms to solve these problems, our numerical tests show similar performances in terms of the number of iterations but an important gain in the required computational time. [ABSTRACT FROM AUTHOR]

We investigate a stochastic differential game in which a major player has private information (the knowledge of a random variable), which she discloses through her control to a population of small players playing in a Nash mean field game equilibrium. The major player's cost depends on the distribution of the population, while the cost of the population depends on the random variable known by the major player. We show that the game has a relaxed solution and that the optimal control of the major player is approximatively optimal in games with a large but finite number of small players. [ABSTRACT FROM AUTHOR]

We study an ergodic mean field game problem with state constraints. In our model the agents are affected by idiosyncratic noise and use a (singular) feedback control to prevent the Brownian motion from exiting the domain. We characterize the equilibrium as the (possibly unique) solution to a second-order MFG system, where the value function blows up at the boundary while the density of the players is smooth and flattens near the boundary as a consequence of the singularity of the drift induced by the feedback strategy of the agents. [ABSTRACT FROM AUTHOR]

In an extended mean field game the vector field governing the flow of the population can be different from that of the individual player at some mean field equilibrium. This new class strictly includes the standard mean field games. It is well known that, without any monotonicity conditions, mean field games typically contain multiple mean field equilibria and the wellposedness of their corresponding master equations fails. In this paper, a partial order for the set of probability measure flows is proposed to compare different mean field equilibria. The minimal and maximal mean field equilibria under this partial order are constructed and satisfy the flow property. The corresponding value functions, however, are in general discontinuous. We thus introduce a notion of weak-viscosity solutions for the master equation and verify that the value functions are indeed weak-viscosity solutions. Moreover, a comparison principle for weak-viscosity semi-solutions is established and thus these two value functions serve as the minimal and maximal weak-viscosity solutions in appropriate sense. In particular, when these two value functions coincide, the value function becomes the unique weak-viscosity solution to the master equation. The novelties of the work persist even when restricted to the standard mean field games. [ABSTRACT FROM AUTHOR]

We propose a mean field game model to study the question of how centralization of reward and computational power occur in Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we solve explicitly for miners maximizing exponential utility and handle numerically in the case of miners with power utilities. We show that the heterogeneity of their initial wealth distribution leads to greater imbalance of the reward distribution, and increased wealth heterogeneity over time, or a "rich get richer" effect. This concentration phenomenon is aggravated by a higher Bitcoin mining reward and reduced by competition. Additionally, an advantaged miner with cost advantages such as access to cheaper electricity, contributes a significant amount of computational power in equilibrium, unaffected by competition from less efficient miners. Hence, cost efficiency can also result in the type of centralization seen among miners of cryptocurrencies. This paper was accepted by Kay Giesecke, finance. Funding: A. M. Reppen is partly supported by the Swiss National Science Foundation [Grant SNF 181815]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4798. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient and flexible algorithm to solve dynamic mean-field planning problems based on an accelerated proximal gradient method. Besides an easy-to-implement gradient descent step in this algorithm, a crucial projection step becomes solving an elliptic equation whose solution can be obtained by conventional methods efficiently. By induction on iterations used in the algorithm, we theoretically show that the proposed discrete solution converges to the underlying continuous solution as the grid becomes finer. Furthermore, we generalize our algorithm to mean-field game problems and accelerate it using multilevel and multigrid strategies. We conduct comprehensive numerical experiments to confirm the convergence analysis of the proposed algorithm, to show its efficiency and mass preservation property by comparing it with state-of-the-art methods, and to illustrate its flexibility for handling various mean-field variational problems. [ABSTRACT FROM AUTHOR]

This paper studies the convergence of mean field games (MFGs) with finite state space to MFGs with a continuous state space. We examine a space discretization of a diffusive dynamics, which is reminiscent of the Markov chain approximation method in stochastic control, but also of finite difference numerical schemes; time remains continuous in the discretization, and the time horizon is arbitrarily long. We are mainly interested in the convergence of the solution of the associated master equations as the number of states tends to infinity. We present two approaches, to treat the case without or with common noise, both under monotonicity assumptions. The first one uses the system of characteristics of the master equation, which is the MFG system, to establish a convergence rate for the master equations without common noise and the associated optimal trajectories, both when there is a smooth solution to the limit master equation and when there is not. The second approach relies on the notion of monotone solutions introduced by [8, 9]. In the presence of common noise, we show convergence of the master equations, with a convergence rate if the limit master equation is smooth, and by compactness arguments otherwise. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a natural learning rule for mean field games with finite state and action space, the so-called myopic adjustment process. The main motivation for these considerations is the complexity of the computations necessary to determine dynamic mean field equilibria, which makes it seem questionable whether agents are indeed able to play these equilibria. We prove that the myopic adjustment process converges locally towards strict stationary equilibria under rather broad conditions. Moreover, we also obtain a global convergence result under stronger, yet intuitive conditions. [ABSTRACT FROM AUTHOR]

As the evolution of smart grids accelerates, distributed energy resources (DERs) emerge as key elements in the transformation of global energy systems. However, the integration of these technologies introduces significant cybersecurity vulnerabilities, notably false data injection (FDI) and a direct load-altering attack (DLAA). Traditional load-altering attacks require a huge attack load and, thus, are not practical to implement. In contrast, in modern DER environments where households become “prosumers” with high-power energy generation, the implications of such attacks are substantially amplified. This paper considers a hybrid cyberattack that includes both FDI and a DLAA, and presents a hierarchical, optimal load adjustment framework that addresses these security concerns. A centralized optimizer first calculates the ideal load-shedding strategies for each substation, which are then securely broadcast to households. To address the complexities at the individual household level, we introduce a novel reinforcement learning algorithm termed Mean Field Deep Deterministic Policy Gradients (MF-DDPG). This algorithm employs mean-field game theory to enable decentrally coordinated decision-making among each household, making it particularly effective in zero-trust scenarios. Through this multifaceted approach, we offer a robust countermeasure against load-altering attacks, thereby enhancing the resilience and stability of advanced smart grids.

In this manuscript, we study a model of human capital accumulation during the spread of disease following an agent-based approach, where agents behave maximising their intertemporal utility. We assume that the agent interaction is of mean field type, yielding a mean field game description of the problem. We discuss how the analysis of a model including both the mechanism of change of species from one epidemiological state to the other and an optimisation problem for each agent leads to an aggregate behaviour that is not easy to describe, and that sometimes exhibits structural issues. Therefore we eventually propose and study numerically a SEIRD model in which the rate of infection depends on the distribution of the population, given exogenously as the solution to the mean field game system arising as the macroscopic description of the discrete multi-agent economic model for the accumulation of human capital. Such a model arises in fact as a simplified but tractable version of the initial one. [ABSTRACT FROM AUTHOR]

We introduce two smoothed policy iteration algorithms (SPIs) as rules for learning policies and methods for computing Nash equilibria in second order potential mean field games (MFGs). Global convergence is proved if the coupling term in the MFG system satisfies the Lasry--Lions monotonicity condition. Local convergence to a stable solution is proved for a system which may have multiple solutions. The convergence analysis shows close connections between SPIs and the fictitious play algorithm, which has been widely studied in the MFG literature. Numerical simulation results based on finite difference schemes are presented to supplement the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this note we prove the uniqueness of solutions to a class of mean field games systems subject to possibly degenerate individual noise. Our results hold true for arbitrary long time horizons and for general nonseparable Hamiltonians that satisfy a so-called displacement monotonicity condition. This monotonicity condition that we propose for nonseparable Hamiltonians is sharper and more general than the one proposed in the work [W. Gangbo et al., Ann. Probab., 50 (2022), pp. 2178-2217]. The displacement monotonicity assumptions imposed on the data actually provide not only uniqueness, but also the existence and regularity of the solutions. Our analysis uses elementary arguments and does not rely on the well-posedness of the corresponding master equations. [ABSTRACT FROM AUTHOR]

Recently, R. Carmona, Q. Cormier, and M. Soner proposed a Mean Field Game (MFG) version of the classical Kuramoto model, which describes synchronization phenomena in a large population of "rational" interacting oscillators. The MFG model exhibits several stationary equilibria, but the characterization of these equilibria and their ability to capture dynamic equilibria in long time remains largely open. In this paper, we demonstrate that, up to a phase translation, there are only two possible stationary equilibria: the incoherent equilibrium and the self-organizing equilibrium, given that the interaction parameter is sufficiently large. Furthermore, we present some local stability properties of the self-organizing equilibrium. [ABSTRACT FROM AUTHOR]

The formulation of mean field games (MFG) typically requires continuous differentiability of the Hamiltonian in order to determine the advective term in the Kolmogorov--Fokker--Planck equation for the density of players. However, in many cases of practical interest, the underlying optimal control problem may exhibit bang-bang controls, which typically lead to nondifferentiable Hamiltonians. We develop the analysis and numerical analysis of stationary MFG for the general case of convex, Lipschitz, but possibly nondifferentiable Hamiltonians. In particular, we propose a generalization of the MFG system as a partial differential inclusion (PDI) based on interpreting the derivative of the Hamiltonian in terms of subdifferentials of convex functions. We establish the existence of a weak solution to the MFG PDI system, and we further prove uniqueness under a similar monotonicity condition to the one considered by Lasry and Lions. We then propose a monotone finite element discretization of the problem, and we prove strong H1-norm convergence of the approximations of the value function and strong Lq-norm convergence of the approximations of the density function. We illustrate the performance of the numerical method in numerical experiments featuring nonsmooth solutions. [ABSTRACT FROM AUTHOR]

Time-dependent mean field games are a coupled system of a forward parabolic and backward parabolic partial differential equation. Stationary solutions are of interest, and then naturally the forward-backward structure in time becomes irrelevant. Forward-forward mean field games have been introduced with the rationale that they may be used to straightforwardly compute such stationary solutions. We perform some numerical simulations to find that typically stationary solutions of mean field games are unstable to the forward-forward evolution, i.e. frequently only trivial solutions can be found in this way. We then ask whether there are situations in which one would have reason to believe that the stationary solutions would be stable, and we use the exchange-of-stability phenomenon in bifurcation theory to give a class of examples for which the forward-forward solutions do converge to nontrivial stationary solutions as time increases. [ABSTRACT FROM AUTHOR]

We present an approach to describe the Chinese stock market crisis in 2015, where the shock impacts took place. Based on the Pareto demand theory, we use a generalized nonparametric method for constructing economic indices of trading volumes of Chinese investment banking companies whose stock dynamics caused the crisis. The behavior of a large amount of economic agents in the stock market is described by the concept of mean field games (MFGs). We apply this concept to describe the behavior of the economic agents and reproduce the dynamic of the aggregated price index of the main investment banks of China during the crisis period. [ABSTRACT FROM AUTHOR]

We investigate the resolution of second-order, potential, and monotone mean field games with the generalized conditional gradient algorithm, an extension of the Frank-Wolfe algorithm. We show that the method is equivalent to the fictitious play method. We establish rates of convergence for the optimality gap, the exploitability, and the distances of the variables to the unique solution of the mean field game, for various choices of stepsizes. In particular, we show that linear convergence can be achieved when the stepsizes are computed by linesearch. [ABSTRACT FROM AUTHOR]

The modeling and control of epidemics, such as the novel Coronavirus, have become crucial on a global scale, for effective management of epidemic situations. This paper is focused on using the mean field approach for modeling and control of epidemics. The mean field approach is an effective alternative to the classical approach of using continuous time Markov decision process (CTMDP) models, which suffer from the curse of dimensionality and entail knowledge of global state information. The mean field approach captures the collective behavior of a dynamic system consisting of numerous interacting nodes representing individuals in the population. The objectives of this paper are twofold: (a) to provide an overview of the mean field approach to epidemic modeling and control, and (b) to present recent advances in this area. Emphasizing the importance of containing and suppressing epidemic spread through non-pharmaceutical interventions, the paper highlights the need to minimize loss of lives, reduce suffering, and alleviate the burden on the public healthcare system. A potential challenge here is the presence of a section of the population who act on their free will and deviate from recommended best practices, which could lead to a potential public health crisis. Motivated by this, the paper explores two specific threads to modeling and control. The first thread assumes that individual nodes comply with a socially optimal control policy mandated by a regulatory authority. The second thread allows for independent and strategic behavior by the individual nodes, modeled as a mean field game, where the strategies of rational agents are based on mean field Nash equilibria. The paper begins by discussing the modeling of epidemics using an extended SIVR (Susceptible-Infected-Vaccinated-Recovered) compartmental model, accompanied by an illustrative example. Next, a literature review is provided, focusing on the mean field approach for socially optimal control of epidemics and how a regulatory authority can effectively contain the spread. Subsequently, the paper presents an update on the use of mean field game-based approaches in studying epidemic spread and control. Finally, future research directions in this important area are discussed. [ABSTRACT FROM AUTHOR]

We propose and investigate a general class of discrete time and finite state space mean field game (MFG) problems with potential structure. Our model incorporates interactions through a congestion term and a price variable. It also allows hard constraints on the distribution of the agents. We analyze the connection between the MFG problem and two optimal control problems in duality. We present two families of numerical methods and detail their implementation: (i) primal-dual proximal methods (and their extension with nonlinear proximity operators), (ii) the alternating direction method of multipliers (ADMM) and a variant called ADM-G. We give some convergence results. Numerical results are provided for two examples with hard constraints. [ABSTRACT FROM AUTHOR]

This paper studies stochastic games on large graphs and their graphon limits. We propose a new formulation of graphon games based on a single typical player's label-state distribution. In contrast, other recently proposed models of graphon games work directly with a continuum of players, which involves serious measure-theoretic technicalities. In fact, by viewing the label as a component of the state process, we show in our formulation that graphon games are a special case of mean field games, albeit with certain inevitable degeneracies and discontinuities that make most existing results on mean field games inapplicable. Nonetheless, we prove the existence of Markovian graphon equilibria under fairly general assumptions as well as uniqueness under a monotonicity condition. Most importantly, we show how our notion of graphon equilibrium can be used to construct approximate equilibria for large finite games set on any (weighted, directed) graph that converges in cut norm. The lack of players' exchangeability necessitates a careful definition of approximate equilibrium, allowing heterogeneity among the players' approximation errors, and we show how various regularity properties of the model inputs and underlying graphon lead naturally to different strengths of approximation. Funding: D. Lacker was partially supported by the Air Force Office of Scientific Research [Grant FA9550-19-1-0291] and the National Science Foundation [Award DMS-2045328]. [ABSTRACT FROM AUTHOR]

We study the propagation of monetary shocks in a sticky‐price general equilibrium economy where the firms' pricing strategy features a complementarity with the decisions of other firms. In a dynamic equilibrium, the firm's price‐setting decisions depend on aggregates, which in turn depend on the firms' decisions. We cast this fixed‐point problem as a Mean Field Game and prove several analytic results. We establish existence and uniqueness of the equilibrium and characterize the impulse response function (IRF) of output following an aggregate shock. We prove that strategic complementarities make the IRF larger at each horizon. We establish that complementarities may give rise to an IRF with a hump‐shaped profile. As the complementarity becomes large enough, the IRF diverges, and at a critical point there is no equilibrium. Finally, we show that the amplification effect of the strategic interactions is similar across models: the Calvo model and the Golosov–Lucas model display a comparable amplification, in spite of the fact that the non‐neutrality in Calvo is much larger. [ABSTRACT FROM AUTHOR]

We consider a mean field game describing the limit of a stochastic differential game of N -players whose state dynamics are subject to idiosyncratic and common noise and that can be absorbed when they hit a prescribed region of the state space. We provide a general result for the existence of weak mean field equilibria which, due to the absorption and the common noise, are given by random flow of sub-probabilities. We first use a fixed point argument to find solutions to the mean field problem in a reduced setting resulting from a discretization procedure and then we prove convergence of such equilibria to the desired solution. We exploit these ideas also to construct ɛ -Nash equilibria for the N -player game. Since the approximation is two-fold, one given by the mean field limit and one given by the discretization, some suitable convergence results are needed. We also introduce and discuss a novel model of bank run that can be studied within this framework. [ABSTRACT FROM AUTHOR]

We present the notion of monotone solution of mean field games master equations in the case of a continuous state space. We establish the existence, uniqueness and stability of such solutions under standard assumptions. This notion allows us to work with solutions which are merely continuous in the measure argument, in the case of first order master equations. We study several structures of common noises, in particular ones in which common jumps (or aggregate shocks) can happen randomly, and ones in which the correlation of randomness is carried by an additional parameter. [ABSTRACT FROM AUTHOR]

The classical Kuramoto model is studied in the setting of an infinite horizon mean field game. The system is shown to exhibit both synchronization and phase transition. Incoherence below a critical value of the interaction parameter is demonstrated by the stability of the uniform distribution. Above this value, the game bifurcates and develops self-organizing time homogeneous Nash equilibria. As interactions get stronger, these stationary solutions become fully synchronized. Results are proved by an amalgam of techniques from nonlinear partial differential equations, viscosity solutions, stochastic optimal control and stochastic processes. [ABSTRACT FROM AUTHOR]

The paper investigates a family of robust reinsurance-investment strategies under relative performance criteria for the insurers who have exponential utility and trade in a common reinsurance-investment horizon in log-normal markets. Assume that two correlated Brownian motions used to characterize the insurance surplus risk and stock price risk. By applying stochastic control theories, we derive the closed-form solutions for the n-players game and the corresponding mean field game, furthermore the equilibria strategies are shown to be unique in the class of constant equilibria. Finally, we present the influence of model parameters on reinsurance and investment strategies with economic explanations by some numerical figures. [ABSTRACT FROM AUTHOR]

The singular perturbation of mean field game systems arising from minimization problems with control of acceleration is addressed, that is, we analyze the behavior of solutions as the acceleration costs vanishes. In this setting, the Hamiltonian fails to be strictly convex and coercive w.r.t. the momentum variable and, so, the classical results for Tonelli Hamiltonian systems cannot be applied. However, we show that the limit system is of MFG type in two different cases: we first study the convergence to the classical MFG system and, then, by a finer analysis of the Euler–Lagrange flow associated with the control of acceleration, we prove the convergence to a class of MFG systems, known as, MFG of control. [ABSTRACT FROM AUTHOR]

In this paper, we consider discrete-time partially observed mean-field games with the risk-sensitive optimality criterion. We introduce risk-sensitivity behavior for each agent via an exponential utility function. In the game model, each agent is weakly coupled with the rest of the population through its individual cost and state dynamics via the empirical distribution of states. We establish the mean-field equilibrium in the infinite-population limit using the technique of converting the underlying original partially observed stochastic control problem to a fully observed one on the belief space and the dynamic programming principle. Then, we show that the mean-field equilibrium policy, when adopted by each agent, forms an approximate Nash equilibrium for games with sufficiently many agents. We first consider finite-horizon cost function and then discuss extension of the result to infinite-horizon cost in the next-to-last section of the paper. [ABSTRACT FROM AUTHOR]

An optimal daily scheme is presented to coordinate a large population of heterogeneous battery electric vehicles when charging in daytime work solar-powered parking lots and discharging into homes during evening peak-demand hours. First, we develop a grid-to-vehicle strategy to share the solar energy available in a parking lot between vehicles where the statistics of their arrival states of charge are dictated by an aggregator. Then, we develop a vehicle-to-grid strategy so that vehicle owners with a satisfactory level of energy in their batteries could help to decongest the grid when they return by providing backup power to their homes at an aggregate level per vehicle based on a duration proposed by an aggregator. Both strategies, with concepts from Mean Field Games, would be implemented to reduce the standard deviation in the states of charge of batteries at the end of charging/discharging vehicles while maintaining some fairness and decentralization criteria. Realistic numerical results, based on deterministic data while considering the physical constraints of vehicle batteries, show, first, in the case of charging in a parking lot, a strong to slight decrease in the standard deviation in the states of charge at the end, respectively, for the sunniest day, an average day, and the cloudiest day; then, in the case of discharging into the grid, over three days, we observe at the end the same strong decrease in the standard deviation in the states of charge.

We construct numerical approximations for Mean Field Games with fractional or nonlocal diffusions. The schemes are based on semi-Lagrangian approximations of the underlying control problems/games along with dual approximations of the distributions of agents. The methods are monotone, stable, and consistent, and we prove convergence along subsequences for (i) degenerate equations in one space dimension and (ii) nondegenerate equations in arbitrary dimensions. We also give results on full convergence and convergence to classical solutions. Numerical tests are implemented for a range of different nonlocal diffusions and support our analytical findings. [ABSTRACT FROM AUTHOR]

Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min–max characterization of the optimal control and price variables. Our main theoretical contribution is the development of a posteriori estimates as a tool to evaluate the convergence of the training process. We illustrate our results with numerical experiments for linear dynamics and both quadratic and non-quadratic models. [ABSTRACT FROM AUTHOR]

We consider conditional McKean--Vlasov stochastic differential equations (SDEs), as the ones arising in the large-system limit of mean field games and particle systems with mean field interactions when common noise is present. The conditional time-marginals of the solutions to these SDEs are governed by non-linear stochastic partial differential equations (SPDEs) of the second order, whereas their laws satisfy Fokker--Planck equations on the space of probability measures. Our paper establishes two superposition principles: The first asserts that any solution of the SPDE can be lifted to a solution of the conditional McKean--Vlasov SDE, and the second guarantees that any solution of the Fokker--Planck equation on the space of probability measures can be lifted to a solution of the SPDE. We use these results to obtain a mimicking theorem which shows that the conditional time-marginals of an Itô process can be emulated by those of a solution to a conditional McKean--Vlasov SDE with Markovian coefficients. This yields, in particular, a tool for converting open-loop controls into Markovian ones in the context of controlled McKean--Vlasov dynamics. [ABSTRACT FROM AUTHOR]

We provide an abstract framework for submodular mean field games and identify verifiable sufficient conditions that allow us to prove the existence and approximation of strong mean field equilibria in models where data may not be continuous with respect to the measure parameter and common noise is allowed. The setting is general enough to encompass qualitatively different problems, such as mean field games for discrete time finite space Markov chains, singularly controlled and reflected diffusions, and mean field games of optimal timing. Our analysis hinges on Tarski's fixed point theorem, along with technical results on lattices of flows of probability and subprobability measures. Funding: Financial support by the German Research Foundation [Collaborative Research Centre Grant 1283/2 2021–317210226] is acknowledged. [ABSTRACT FROM AUTHOR]

We study a mean field game in continuous time over a finite horizon, T, where the state of each agent is binary and where players base their strategic decisions on two, possibly competing, factors: the willingness to align with the majority (conformism) and the aspiration of sticking with the own type (stubbornness). We also consider a quadratic cost related to the rate at which a change in the state happens: changing opinion may be a costly operation. Depending on the parameters of the model, the game may have more than one Nash equilibrium, even though the corresponding N-player game does not. Moreover, it exhibits a very rich phase diagram, where polarized/unpolarized, coherent/incoherent equilibria may coexist, except for T small, where the equilibrium is always unique. We fully describe such phase diagram in closed form and provide a detailed numerical analysis of the N-player counterpart of the mean field game. In this finite dimensional setting, the equilibrium selected by the population of players is always coherent (favoring the subpopulation whose type is aligned with the initial condition), but it does not necessarily minimize the cost functional. Rather, it seems that, among the coherent ones, the equilibrium prevailing is the one that most benefits the underdog subpopulation forced to change opinion. [ABSTRACT FROM AUTHOR]

We introduce and analyze a new finite-difference scheme, relying on the theta-method, for solving monotone second-order mean field games. These games consist of a coupled system of the Fokker–Planck and the Hamilton–Jacobi–Bellman equation. The theta-method is used for discretizing the diffusion terms: we approximate them with a convex combination of an implicit and an explicit term. On contrast, we use an explicit centered scheme for the first-order terms. Assuming that the running cost is strongly convex and regular, we first prove the monotonicity and the stability of our thetascheme, under a CFL condition. Taking advantage of the regularity of the solution of the continuous problem, we estimate the consistency error of the theta-scheme. Our main result is a convergence rate of order O(hr) O (h r) $ \mathcal{O}({h}^r)$ for the theta-scheme, where ℎ is the step length of the space variable and r ∈ (0, 1) is related to the Hölder continuity of the solution of the continuous problem and some of its derivatives. [ABSTRACT FROM AUTHOR]

An iterative finite difference scheme for mean field games (MFGs) is proposed. The target MFGs are derived from control problems for multidimensional systems with advection terms. For such MFGs, linearization using the Cole-Hopf transformation and iterative computation using fictitious play are introduced. This leads to an implementation-friendly algorithm that iteratively solves explicit schemes. The convergence properties of the proposed scheme are mathematically proved by tracking the error of the variable through iterations. Numerical calculations show that the proposed method works stably for both one- and two-dimensional control problems. [ABSTRACT FROM AUTHOR]