Characterizes preferences for decision-making under uncertainty using a leader-follower game model.
problem Decision-making under uncertainty and ambiguity aversion.
method Characterizes niveloidal preferences through a leader-follower game model, satisfying specific axioms.
result The leader's strategy space can serve as an ambiguity aversion index.
We introduce CSE for MLSF games and devise online learning algorithms for achieving no-external Stackelberg-regret.
problem Learning equilibrium in leader-follower games with noisy bandit feedback.
method Proposed Correlated Stackelberg Equilibrium (CSE) and online learning algorithms balancing exploration and exploitation.
result Achieves no-external Stackelberg-regret, converging to approximate CSE.
We analyze linear McKean-Vlasov forward-backward SDEs arising in leader-follower games with mean-field type control and terminal state constraints on the state process. We establish an existence and uniqueness of solutions result for such systems in time-weighted spaces as well as a {convergence} result of the solution…
New algorithm reduces individual regret and communication costs in cooperative bandits.
problem Optimal individual and group regret in cooperative multi-agent bandits.
method Integrates a new communication policy into a learning algorithm.
result Achieves optimal individual regret and constant communication costs.
AMM finds optimal contract for LPs to maximize order flow.
problem Maximizing order flow in AMMs with LPs.
method Leader-follower stochastic game, closed-form equilibrium solutions.
result LPs incentivized to add liquidity when external price attracts more noise trading.
The paper analyzes reinsurance strategies in peer-to-peer insurance schemes.
problem Strategic interaction between plan managers and reinsurers in P2P insurance.
method Develops two game-theoretic contract designs: Pareto and Bowley designs, deriving optimal contracts and analyzing their welfare effects.
result The Bowley design yields a unique optimal contract, while the Pareto design allows for multiple Pareto-optimal contracts.
A new algorithm learns policies from batch data in hierarchical RL.
problem Learning policies from fixed batches of data without full exploration.
method Modeling RL as a two-player game with a leader-follower structure, proposing StackelbergLearner.
result StackelbergLearner achieves competitive performance in batch RL and real-world datasets.
New mechanism designs regulate herding in financial markets.
problem Herding causes irrational market decisions and volatility.
method A trilateral game framework based on optimal control theory.
result Effective mechanisms improve social welfare.
Communities in social networks or graphs are sets of well-connected, overlapping vertices. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities and ability to scale with the size of the data. In this work, we provide three contributions. First, we show t…
Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed deep learning systems and aim to achieve differential privacy with good convergence rate and low communication cost. To achieve this goal, w…
New RL algorithms learn QSE from strategic feedbacks with sample efficiency.
problem Learning QSE in Markov games with strategic feedbacks.
method Proposes sample-efficient algorithms for online and offline settings, combining quantal response model learning and RL.
result Achieves sublinear regret bounds and quantifies model uncertainty.
Using a two-point correlation technique, we study emergence of market efficiency in the emergent Russian futures market by focusing on lagged correlations. The correlation strength of leader-follower effects in the lagged inter-market correlations on the hourly time frame is seen to be significant initially (2009-2011)…
Black-Scholes (BS) is the standard mathematical model for option pricing in financial markets. Option prices are calculated using an analytical formula whose main inputs are strike (at which price to exercise) and volatility. The BS framework assumes that volatility remains constant across all strikes, however, in prac…
The online problem of computing the top eigenvector is fundamental to machine learning. In both adversarial and stochastic settings, previous results (such as matrix multiplicative weight update, follow the regularized leader, follow the compressed leader, block power method) either achieve optimal regret but run slow,…
Paper presents content-based models for game recommendation in cold start scenarios.
problem Cold start problem in game recommendation where new games and players have no historical data.
method Uses survey data to develop content-based interaction models that generalize to new games, players, and both.
result Content models outperform collaborative filtering in predicting new interactions.
In this work, we ask the following question: Can visual analogies, learned in an unsupervised way, be used in order to transfer knowledge between pairs of games and even play one game using an agent trained for another game? We attempt to answer this research question by creating visual analogies between a pair of game…
Potential games, originally introduced in the early 1990's by Lloyd Shapley, the 2012 Nobel Laureate in Economics, and his colleague Dov Monderer, are a very important class of models in game theory. They have special properties such as the existence of Nash equilibria in pure strategies. This note introduces graphical…
IGGP learns game rules from varying quality game play, finding no overall trend.
problem Learn game rules from varying quality game play.
method Used Sancho's intelligent game traces and ILP systems (Metagol, Aleph, ILASP) to induce game rules from traces of varying quality and volume.
result No overall trend in accuracy of learned game rules from varying quality and volume of training data.
A game on diagrams switches crossing directions to achieve connectedness.
problem Achieving connectedness in diagrams through crossing switches.
method Players switch crossing directions on regions of a diagram to achieve connectedness.
result Connectedness can be achieved through strategic crossing switches.
We present a new general board game (GBG) playing and learning framework. GBG defines the common interfaces for board games, game states and their AI agents. It allows one to run competitions of different agents on different games. It standardizes those parts of board game playing and learning that otherwise would be t…
Introduces SM-games to analyze machine learning interactions.
problem Lack of understanding and control in n-player games.
method Introduces SM-games with pairwise zero-sum interactions.
result SM-games are amenable to first-order optimization methods.
Just as war is sometimes fallaciously represented as a zero sum game -- when in fact war is a negative sum game - stock market trading, a positive sum game over time, is often erroneously represented as a zero sum game. This is called the "zero sum fallacy" -- the erroneous belief that one trader in a stock market exch…
The existence of stationary Markov perfect equilibria in stochastic games is shown under a general condition called "(decomposable) coarser transition kernels". This result covers various earlier existence results on correlated equilibria, noisy stochastic games, stochastic games with finite actions and state-independe…
Game theory helps analyze ESOs/EBIs in production and service sectors.
problem Economic incentives affect traditional production/service functions and create intangible capital.
method Uses game theory to analyze interactions in ESO/EBI transactions.
result No perfect Nash Equilibria for two-stage games involving many participants.
Combinatorial two-player games have recently been applied to knot theory. Examples of this include the Knotting-Unknotting Game and the Region Unknotting Game, both of which are played on knot shadows. These are turn-based games played by two players, where each player has a separate goal to achieve in order to win the…
We start briefly surveying research on optimal stopping games since their introduction by E.B.Dynkin more than 40 years ago. Recent renewed interest to dynkin's games is due, in particular, to the study of Israeli (game) options introduced in 2000. We discuss the work on these options and related derivative securities …
The paper explores how regularization can lead to convergence in imperfect information games.
problem Finding equilibrium in imperfect information games with imperfect information.
method Investigates Follow the Regularized Leader dynamics and how adding a regularization term can lead to strong convergence guarantees.
result The approach leads to algorithms that converge exactly to the Nash equilibrium in imperfect information games.
Educational game on crypto investment helps students grasp macroeconomics.
problem Weak connections between microeconomic decision-making and macroeconomic concepts in classroom games.
method Design and study of an educational game on cryptocurrency investment.
result Engages students in understanding macroeconomics through incentivized individual investment decisions.
Simplified NFT games discussed with methods for extracting value.
problem Issues influencing NFT games' structure and stability.
method Three methods for extracting value from NFT games.
result Various design constraints and mutual beneficial games.
The paper proposes a method to learn continuous-action graphical games from perturbed equilibria.
problem Learning the exact structure of continuous-action graphical games from limited data.
method A ℓ12− block regularized method to recover the graphical game structure. result The method recovers the exact structure of the graphical game under certain conditions.
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose …
Paper tackles hidden game problem in AI alignment and language games.
problem Hidden game problem in AI alignment and language games.
method Developed a composition of regret minimization techniques to discover and exploit hidden structures.
result Achieved optimal external and swap regret bounds for rapid convergence to correlated equilibria.
AEC Games model represents software MARL environments better than POSGs.
problem POSGs are conceptually unsuitable for software MARL environments.
method Introduced AEC Games model as an equivalent to POSGs.
result AEC Games model is more representative of software MARL environments.
Gradient Descent Ascent converges to von-Neumann solution in hidden zero-sum games.
problem Understanding dynamics of zero-sum games with hidden structure.
method Gradient Descent Ascent applied to hidden zero-sum games with specific convex-concave structure.
result Gradient Descent Ascent converges to von-Neumann solution in strictly convex-concave hidden games.
Generalizes region select game to k-colored knot diagrams.
problem Play game on knot diagrams with multiple colors.
method Generalize region select game to k-colored knot diagrams. result Generalization of the region select game to k-colored knot diagrams. Deep Reinforcement Learning automates match-3 game testing.
problem Reducing human effort in testing match-3 video games.
method Dueling Deep Q-Network paradigm applied to Jelly Juice game.
result The network outperforms random player and adapts to game difficulty.
Unified framework for Bayesian and Frequentist statistics.
problem Embedding Bayesian statistics within a broader decision-making framework.
method Game theory and statistical analysis.
result Statistical games unify Bayesian and Frequentist statistics.
Estimates network structure and interaction rules from multiple agent trajectories.
problem Modeling multi-agent systems on networks from data.
method Jointly infers network topology and interaction kernels using non-convex optimization.
result ORALS estimator is consistent and asymptotically normal under coercivity conditions.
New game approximates mean curvature flow evolution.
problem Approximating geometric mean curvature flow evolution.
method Two-player zero-sum game with probabilistic elements.
result Value function approximates mean curvature flow.
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.
Gradient methods converge exponentially in concave network games.
problem Finding Nash equilibria in concave network zero-sum games.
method Gradient Ascent and Optimistic Gradient Ascent analyses.
result Exponential convergence rates in various game settings.
Federated learning linked to mean-field games for large-scale learning.
problem Large-scale distributed and privacy-preserving learning algorithms.
method Established a connection between federated learning and mean-field games, presenting federated learning as a differential game.
result Properties of the equilibrium of the federated learning game were discussed.
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.
This paper gives a critical account of the minority game literature. The minority game is a simple congestion game: players need to choose between two options, and those who have selected the option chosen by the minority win. The learning model proposed in this literature seems to differ markedly from the learning mod…
Develops a deep metric learning approach for detecting bugs in video games.
problem Automated detection of bugs in video games.
method State-State Siamese Networks (S3N) for deep metric learning.
result S3N learns meaningful embeddings to identify various types of bugs.
Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents. This paper deals with the relatively under-explored but equally important "inverse" setting, where the param…
Neural operators approximate Stackelberg game solutions.
problem Intractability of follower's best-response operator in dynamic Stackelberg games.
method Used attention-based neural operators to approximate the best-response operator.
result Approximate best-response operator yields close game value.
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.