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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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275480107 · Jun 202019922001200920182026
48 results for population game

Study multiple-population games using McKean-Vlasov equations.

problem Mean field games and control problems with multiple populations.
method Coupled forward-backward SDEs and Pontryagin's principle.
result Existence of mean field equilibria under various cooperation scenarios.

NeuPL learns diverse policies in strategy games efficiently.

problem Iterative training of policies in strategy games leads to under-trained good-responses and wasteful repetition.
method NeuPL uses a single conditional model to represent a population of policies, offering convergence guarantees and transfer learning.
result NeuPL achieves better performance and efficiency across various domains, enabling access to novel strategies.

Modeling resource accumulation in a population game to explain wealth distribution.

problem Explaining the distribution of wealth in a population game.
method Modeling resource accumulation as a population game with Hawk-Dove interactions, analyzing fitness/wealth distribution and evolution over time.
result Long-run average fitness/wealth is non-monotonic with resource value, explaining the 'curse of riches'.

Policy mirror ascent achieves Nash equilibrium in mean field games without a population generative model.

problem Achieving Nash equilibrium in mean field games without a population generative model.
method Policy mirror ascent, contractive operator, single-path TD learning.
result Policy mirror ascent converges to Nash equilibrium within O~(ε2)\widetilde{\mathcal{O}}(\varepsilon^{-2}) samples.

A new method simulates large, diverse populations of learning agents evolving in games.

problem Limited scalability and efficiency of Multi-Agent Reinforcement Learning.
method Parallelizable implementation of Policy Gradient and Opponent-Learning Awareness for evolutionary simulations.
result Simulated large, diverse populations of learning agents evolve under various strategies.

Study equilibrium consumption habits in a large population using mean field games.

problem Equilibrium consumption under external habit formation in a large population.
method Formulated and solved mean field games for linear and multiplicative habit formation preferences, constructed approximate Nash equilibria for large n-player games.
result Characterized mean field equilibrium strategies and derived financial implications.

The \$-Game was recently introduced as an extension of the Minority Game. In this paper we compare this model with the well know Minority Game and the Majority Game models. Due to the inter-temporal nature of the market payoff, we introduce a two step transaction with single and mixed group of interacting traders. When…

2003-11-12abs ↗pdf ↗

Study uses MFG approach to model equilibrium pricing with market clearing condition.

problem Continuous asset pricing with market clearing condition.
method Mean field game approach to solve forward-backward SDEs of McKean-Vlasov type.
result Net order flow converges to zero in large N-limit with specified conditions.

Paper studies optimal tracking portfolio in mean field game of large fund competition.

problem Optimal tracking portfolio in large fund competition with relative performance benchmark.
method Formulated mean field game problem, established existence of mean field equilibrium using PDE approach, constructed approximate Nash equilibrium.
result Existence of mean field equilibrium and consistency condition verified.

A game environment simulates competition among many agents for resources.

problem Understanding large-scale multiagent interactions and resource competition.
method Developed a persistent, massively multiplayer AI environment.
result Population size affects the development of skillful behaviors and niche differentiation.

Study on optimal trading in a finite population with market frictions and asymmetric information.

problem Optimal trading in a finite population with market frictions and asymmetric information.
method Investigates stochastic differential games with asymmetric information and market frictions, proving existence and uniqueness of Nash and Stackelberg-Nash equilibria.
result Existence and uniqueness of Nash and Stackelberg-Nash equilibria in both unconstrained and constrained trading scenarios.

Expands MFGs to handle real-world asymmetric multi-agent games efficiently.

problem Applying mean-field games to real-world, heterogeneous multi-agent systems.
method Develops a method to symmetrize and extend finite-player games to infinite-player MFGs, proving approximation bounds and convergence guarantees.
result TD learning converges to approximate Nash equilibria in finite-sample settings, enabling symmetrized learning without explicit MFG models.

Agents learn to play a first-person multiplayer game at human level performance.

problem Training AI agents for complex, multi-agent, real-time environments.
method Population-based deep reinforcement learning with concurrent training of multiple agents.
result Achieved human-level performance in a first-person multiplayer game.

Social learning can make financial markets inefficient, but individual learning can fix this.

problem Inefficiencies in financial markets due to social learning.
method Study of the Minority Game model with social and individual learning mechanisms.
result Individual learning can rescue a population from the inefficiencies caused by social learning.

Unified q-learning for mean-field jump-diffusion models with unobservable population distribution.

problem Continuous-time q-learning in mean-field jump-diffusion models with unobservable population distribution.
method Proposed decoupled Iq-function for unified policy evaluation in MFG and MFC problems; unified q-learning algorithm based on test policies and averaged martingale orthogonality condition.
result Unified policy evaluation rule for MFG and MFC problems based on decoupled Iq-function.

Game-theoretic models predict asset prices in financial markets.

problem Understanding price formation in financial markets with limited liquidity.
method Developed game-theoretic models for many-person and mean-field games, derived analytical formulas, and numerically assessed results.
result The derived price converges to the mean-field counterpart under specific conditions.

We study ranking quantilized mean-field games to select top-performing agents.

problem Selecting top-performing agents in competitive scenarios.
method Developed two formulations: target-based and threshold-based, and provided analytic and semi-explicit solutions.
result Analytic and semi-explicit solutions for quantilized mean-field consistency conditions.

Fractal neural networks play SimCity and Conway's Game of Life on varying scales.

problem Generalizing agents' performance to larger gameboards than during training.
method Reinforcement learning in a custom environment, using fractal neural networks.
result Agents can generalize to larger gameboards, solving a minigame unsolvable with local strategies.

Study on mean field games with singular controls and their applications.

problem Optimal productivity expansion in dynamic oligopolies.
method Existence and uniqueness of mean field equilibria through nonlinear equations, Abelian limit for discounted and ergodic games.
result Valid connection between discounted and ergodic games, approximation of Nash equilibria.

A new method uses Mean Field Games to optimize mixture models of Bernoulli and categorical distributions.

problem Optimizing parameters of finite mixture models of Bernoulli and categorical distributions.
method Mean Field Games theory applied to multi-population systems.
result The Mean Field Games approach provides a method to compute mixture model parameters.

Study on stock price formation on trees with multi-population and non-rational agents.

problem Equilibrium price formation for risky stock with multi-population and non-rational agents.
method Combining mean-field game theory with binomial tree framework, proving existence of unique equilibrium, deriving explicit formula for transition probabilities.
result Existence of unique mean-field market-clearing equilibrium with explicit analytic formula for stock price transition probabilities.

New methods learn correlated equilibria in large games without structural assumptions.

problem Learning correlated equilibria in large, anonymous games with exponential player count.
method Developed Mean-Field correlated and coarse-correlated equilibria, and used classical algorithms to learn them efficiently.
result Efficiently learned correlated equilibria in all games without structural assumptions.

Algorithmic traders optimize execution and arbitrage in markets with hidden information.

problem Optimal execution and statistical arbitrage in markets with latent factors.
method Solve a large stochastic game with mean-field game limit, using convex analysis and FBSDE.
result Prove the MFG equilibrium is an ε-Nash equilibrium for finite player games.

Deep fictitious play converges to Nash equilibrium in stochastic differential games.

problem Finding Nash equilibrium in large stochastic differential games.
method Decouples the game into sub-optimization problems and solves each player's optimal strategy with deep BSDE method.
result Deep fictitious play converges to the true Nash equilibrium.

This paper analyzes convergence of model-free learning in Mean Field Games.

problem Designing scalable algorithms for large populations of interacting agents.
method Analyze convergence of iterative fictitious scheme using any single agent learning algorithm.
result First time convergence of model-free learning algorithms towards non-stationary MFG equilibria.

The existence of a phase transition with diverging susceptibility in batch Minority Games (MGs) is the mark of informationally efficient regimes and is linked to the specifics of the agents' learning rules. Here we study how the standard scenario is affected in a mixed population game in which agents with the `optimal'…

2007-12-03abs ↗pdf ↗

Generalization of the minority game to more than one market is considered. At each time step every agent chooses one of its strategies and acts on the market related to this strategy. If the payoff function allows for strong fluctuation of utility then market occupancies become inhomogeneous with preference given to th…

2008-09-23abs ↗pdf ↗

New framework for portfolio management using binomial markets and game theory.

problem Investment behavior in competitive and incomplete markets.
method Introduces PRFPP framework, constructs and analyzes for both finite and mean field games.
result Relative performance concerns do not always lead to more risky asset investment.

We formulate a stochastic game of mean field type where the agents solve optimal stopping problems and interact through the proportion of players that have already stopped. Working with a continuum of agents, typical equilibria become functions of the common noise that all agents are exposed to, whereas idiosyncratic r…

2016-05-30abs ↗pdf ↗

A new MFG framework for evolving clusters from Gaussian mixtures.

problem Evolutionary clustering of time-dependent Gaussian mixtures.
method Control-theoretic framework based on Mean Field Games (MFG) with coupled HJB and Fokker-Planck systems.
result MFG dynamics recover classical EM algorithm trajectories with mass conservation.

Study on HFTs' interactions with a large trader using mean field game theory.

problem Interactions between high-frequency traders and a large trader executing assets at discrete times.
method Modeling HFTs' behavior using a jump process and solving the equilibrium through mean field game approach.
result Inventory-averse HFTs lower LT's costs when market impact is large.

Study designs steering rewards for MFGs with unknown dynamics and model uncertainty.

problem Designing incentives for large populations of agents in MFGs with uncertain model details.
method Developed optimistic exploration algorithms for agents with no-adaptive regret behaviors.
result Sub-linear regret guarantees for cumulative gaps between agent behaviors and desired outcomes.

The widening inequality in income distribution in recent years, and the associated excessive pay packages of CEOs in the U.S. and elsewhere, is of growing concern among policy makers as well as the common person. However, there seems to be no satisfactory answer, in conventional economic theories and models, to the fun…

2014-06-25abs ↗pdf ↗

The study examines how brokers' identity affects their trading strategies on the Toronto Stock Exchange.

problem Impact of anonymous trading on brokers' optimal execution strategies.
method Formulated a stochastic differential game and mean-field game to analyze the optimal execution problem of anonymous and identity-revealed trading.
result Obtained a closed-form solution for the optimal strategy under Almgren-Chris price impact framework.