In the present work we propose an original analytical model of coopetitive game. We try to apply this analytical model of coopetition - based on game theory and conceived at a macro level - to the Greek crisis, suggesting feasible solutions in a cooperative perspective for the divergent interests which drive the econom…
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48 results for “game analytics”
Survival analysis improves playtime measurement in games.
problem Measuring and improving player retention in games.
method Survival analysis for playtime data without covariates.
result Survival and hazard estimates provide a visual and analytic interpretation of playtime.
Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start settingcs.IR
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.
Paper presents a new method for Bayesian deep learning that scales to Atari games.
problem Training neural networks on complex environments like Atari games is challenging.
method Adapted temporal difference Q-learning to work with Bayesian inference.
result TAGI allows for analytical inference of neural network parameters, achieving performance comparable to gradient-based methods.
The Minority Game: a statistical physics perspectivecond-mat.dis-nn
A brief review is given of the minority game, an idealized model stimulated by a market of speculative agents, and its complex many-body behaviour. Particular consideration is given to analytic results for the model rather than discussions of its relevance in real-world situations.
The Interactive Minority Game: Instructions for Expertscond-mat.dis-nn
The Interactive Minority Game (IMG) is an online version of the traditional Minority Game in which human players can enter into competition with the traditional computer-controlled agents. Through the rich (and, importantly, analytically understood) behaviour of the MG, we can explore humans' behaviour in different kin…
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.
Solves specific mean-field game equations with ODEs.
problem Mean-field game equations in economic applications.
method Reduces coupled PDEs to a quadratically nonlinear system of ODEs.
result Shows specific data leads to solvable ODE system.
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.
The Reality Gameq-fin.GN
We introduce an evolutionary game with feedback between perception and reality, which we call the reality game. It is a game of chance in which the probabilities for different objective outcomes (e.g., heads or tails in a coin toss) depend on the amount wagered on those outcomes. By varying the `reality map', which rel…
Paper tackles learning unknown game parameters from observations.
problem Learning unknown game parameters in games where parameters are not known to all agents.
method Proposes a differentiable, end-to-end learning framework for normal and extensive form games.
result Demonstrates effective learning of game parameters in poker and security game tasks.
The paper analyzes a class of stochastic games involving moving free boundaries and Nash equilibria.
problem Analyzing interactions among players in stochastic games with moving free boundaries.
method Deriving sufficient conditions for Nash equilibrium through verification theorems, solving multi-dimensional free boundary problems, and Skorokhod problems.
result An intriguing connection between NE strategies and controlled rank-dependent stochastic differential equations.
Multi-asset minority gamesphysics.soc-ph
We study analytically and numerically Minority Games in which agents may invest in different assets (or markets), considering both the canonical and the grand-canonical versions. We find that the likelihood of agents trading in a given asset depends on the relative amount of information available in that market. More s…
Deep Q-Learning method for Nash equilibria in stochastic games.
problem Model-free learning for multi-agent stochastic games, especially for general-sum games.
method Data-efficient Deep-Q-learning using local linear-quadratic expansion parametrized by deep neural networks.
result The algorithm learns optimal actions for stochastic games without experiencing all state-action pairs.
Two-layer model studies reinsurance contracts and competition between insurer and reinsurers.
problem Modeling and analyzing reinsurance contracts and competition between insurer and reinsurers.
method Two-layer stochastic game model with insurer negotiating with reinsurers, and reinsurers competing for business.
result Existence and uniqueness of equilibrium strategies for the insurer and reinsurers, characterized in semiclosed form.
Bayesian model improves win probability estimates in soccer.
problem Technical challenges in estimating win probability for soccer.
method Bayesian statistical framework using contextual game state features.
result Well-calibrated win, tie, and loss probabilities demonstrated.
Study on games with degenerate diffusion matrices, proving value existence and convergence.
problem Zero-sum games between singular controller and stopper with degenerate diffusion.
method Probabilistic approach using parameterized approximations, convergence analysis.
result Existence of value and optimal stopping times for the game with degenerate dynamics.
The paper analyzes optimal investment strategies in a game with jump risk, deriving mean field equilibria.
problem Optimal investment strategies in a game with jump risk and peer competition.
method Formulated mean field game and n-player game models, characterized equilibrium states, and derived approximation errors.
result Explicit mean field equilibrium and approximate Nash equilibrium for large n-player games.
Statistical Gamesmath.ST
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.
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'.
Paper optimizes insurer's investment strategy in a fluctuating market with memory effects.
problem Optimizing insurer's investment in a market with regime switching and noisy memory.
method Formulated as a stochastic differential delay game, solved using BSDE approach.
result Derives analytical solutions for a specific case of a quadratic penalty function.
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.
Deep learning enhances solving complex mean field games in finance.
problem Solving large-scale mean field games with financial applications.
method Combining mean field games theory with deep learning techniques.
result Improved solutions for large-scale financial games.
Study proves value of non-Markovian games with partial, asymmetric info.
problem Value of non-Markovian Dynkin games with partial and asymmetric information.
method Probabilistic and functional analytic approach based on Sion's min-max theorem.
result Existence of optimal strategies for both players in randomised stopping times.
Analyzes gaming in federated learning systems and provides design principles.
problem Gaming and cooperation in federated learning systems under partial observability.
method Developed an analytical framework to separate welfare-improving behavior from metric gaming.
result Derived threshold conditions for deterring harmful gaming and preserving cooperation.
Framework for real-time win probability and player ability in sports.
problem Real-time evaluation and player assessment in team sports.
method Continuous dominance indicator (T-score) and T-process formulation.
result Decomposes competitive advantage into interpretable statistical components.
Proposes a method for kernel learning using feature maps.
problem Improving SVM margin through iterative refinement.
method Fourier-analytic characterization and iterative feature maps.
result Optimal and generalization guarantees for SVM margin improvement.
A Game of Competition for Riskq-fin.CP
Models analyze strategic risk-taking in continuous action games.
problem Strategic risk-taking dynamics in continuous action games.
method Normal form game, multi-player scenarios, regret minimization algorithms, numerical algorithm for calculation.
result Nash equilibrium also serves as a correlated equilibrium in continuous games.
Proposes a deep learning method for solving complex financial games with delays.
problem Financial modeling with multi-agent interactions and delayed effects.
method Parameterizes controls using recurrent neural networks and trains them with modified fictitious play.
result Demonstrates effectiveness on finance problems with known solutions and new problems with derived Nash equilibria.
AlphaZero assesses new chess variants for balance and dynamics.
problem Designing engaging and balanced game rules, especially for chess variants.
method Used AlphaZero to learn near-optimal strategies for nine chess variants.
result AlphaZero reveals novel strategic and tactical patterns in chess variants.
DRLViz interprets deep RL agent memory for better understanding.
problem Understanding complex deep RL agent memory.
method Visual analytics interface to reduce and interpret memory vectors.
result Experts can better understand and investigate agent decisions.
Paper develops a distributed power control method for large energy harvesting networks using deep reinforcement learning.
problem Optimal power control for large energy harvesting networks with limited causal information.
method Multi-agent reinforcement learning framework to solve a mean-field game problem.
result Proposed method converges to optimal power control policies in a distributed fashion.
Develops an adversarial clustering algorithm for detecting cyber attacks.
problem Dealing with active adversaries in cyber security data analytics.
method Grid-based adversarial clustering algorithm using game theoretic ideas.
result Identifies normal and attack objects, sub-clusters, overlapping areas, and outliers.
This paper proposes an efficient method for calculating Shapley values in Naive Bayes classifiers.
problem The need for explaining machine learning model decisions.
method An exact analytic expression of Shapley values for Naive Bayes classifiers.
result The proposed Shapley values provide informative results with low complexity and low computation time.
We discuss a simple model based on the Minority Game which reproduces the main stylized facts of anomalous fluctuations in finance. We present the analytic solution of the model in the thermodynamic limit and show that stylized facts arise only close to a line of critical points with non-trivial properties. By a simple…
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.
We solve the dynamics of large spherical Minority Games (MG) in the presence of non-negligible time dependent external contributions to the overall market bid. The latter represent the actions of market regulators, or other major natural or political events that impact on the market. In contrast to non-spherical MGs, t…
We propose an analytically tractable variation of the minority game in which rational agents use probabilistic strategies. In our model, agents choose between two alternatives repeatedly, and those who are in the minority get a pay-off 1, others zero. The agents optimize the expectation value of their discounted fu…
New model considers wealth and time affecting risk aversion in portfolio selection.
problem Optimal investment strategy and consumption process depend on wealth and future income balance.
method Proposed a new mean-variance-utility framework with time and state-dependent risk aversion, solved using game theory.
result Equilibrium investment and consumption policies derived, aligning with investor behavior.
New sampling method estimates Shapley values more accurately.
problem Exponential time complexity of computing Shapley values.
method Multilinear sampling algorithm based on game theory.
result Our method reduces variance and provides more accurate Shapley value estimations.
Broadens Jourdain and Martini's method to non-linear stochastic processes.
problem Applying pricing methods to non-linear stochastic processes.
method Analyzes from probabilistic and analytic viewpoints, extending Jourdain and Martini's method.
result Broadens applicability of pricing methods to non-linear frameworks.
Paper analyzes virtual economies, reducing volatility and inflation.
problem Reduces player satisfaction and developer revenue in virtual economies.
method Analytical methods from financial literature applied to virtual economies.
result High level overview of virtual economic activity with conservative trading volume estimates.
Paper resolves ambiguity in non-convex bilevel optimization problems.
problem Ambiguity in bilevel optimization with non-convex lower-level objectives.
method Introduces selection maps to define critical points and resolves ambiguity.
result Validates new analytical tools in Morse theory for implicit differentiation.
The paper models asset pricing in a partially observed market using mean field game theory and exponential quadratic Gaussian framework.
problem Asset pricing in a market with partial observation and heterogeneous agents.
method Mean field game theory, exponential quadratic Gaussian framework, Kalman-Bucy filtering theory.
result Characterization of equilibrium risk premium through mean field BSDE and construction of unobservable risk premium process.
CausalGame benchmarks LLM agents' causal thinking in games.
problem Evaluating causal thinking in AI Scientists with LLMs.
method Interactive games with 14 scenarios incorporating selection bias, measurement error, and hidden confounders.
result None of the 30 LLM agents demonstrated reliable causal thinking, with the best model achieving only 68.0% survival.
Model asset pricing with habit formation in a large market.
problem Understanding asset pricing in large heterogeneous markets with habit formation.
method Mean field game theory and quadratic-growth mean field BSDEs.
result Derives a semi-analytic solution for asset pricing model.
Develops asset pricing models with mean field game theory for heterogeneous agents.
problem Tackles equilibrium asset pricing in incomplete markets with heterogeneous agents.
method Uses mean field game theory and mean field backward stochastic differential equations (BSDEs).
result Derives equilibrium risk premium and shows market clearing in the large population limit.
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.