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.
New foundation for Shapley value immune to coalitional manipulations.
Federated learning models are analyzed through game theory to determine optimal model sharing.
Fair insurance contracts are designed to handle default risk using cooperative game theory.
New research on Shapley values for feature attribution in machine learning, considering model vs. data fidelity.
A game-theoretic approach selects features by testing their marginal contributions.
A new sampling scheme based on DOE improves Shapley value estimation accuracy and speed.
The paper analyzes reinsurance strategies in peer-to-peer insurance schemes.
RKHS-SHAP uses Shapley values for kernel methods to provide feature attributions.
NDDV estimates data point value from a single stochastic trajectory.
This paper analyzes voter coalitions in MakerDAO's decentralized governance.
Paper uses Shapley values to explain how clusters of training data affect predictions.
The paper introduces Absolute Shapley Value to handle negative contributions in machine learning model training.
Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architectures that guarantee preservation of data privacy, trustworthy sequence of iterative learning and equ…
The paper extends game theory using Hodge theory on graphs.
Paper proposes Coalitional BAE to improve explainability of unsupervised deep learning models.
We study the problem of interpreting trained classification models in the setting of linguistic data sets. Leveraging a parse tree, we propose to assign least-squares based importance scores to each word of an instance by exploiting syntactic constituency structure. We establish an axiomatic characterization of these i…
This paper introduces a novel framework for designing fair and sustainable unemployment benefits, grounded in cooperative game theory and real-time fiscal policy. The labor market is modeled as a coalitional game, where a random subset of participants is employed, generating stochastic economic output. To ensure fairne…
Improved Shapley Value method for better model interpretation.
The paper uses Black-Scholes model to analyze political support and coalition agreements.
We describe a new model to simulate the dynamic interactions between market price and the decisions of two different kind of traders. They possess spatial mobility allowing to group together to form coalitions. Each coalition follows a strategy chosen from a proportional voting ``dominated'' by a leader's decision. The…
Bayesian approach improves Shapley value estimation efficiency.
Understanding the influence of features in machine learning is crucial to interpreting models and selecting the best features for classification. In this work we propose the use of principles from coalitional game theory to reason about importance of features. In particular, we propose the use of the Banzhaf power inde…
MRC improves credit assignment in multi-agent LLM systems, achieving high returns and transparency.
Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees. However, the theory of random forests has long been outpaced by their application. In this paper, we propose a novel random forests algorithm based on cooperative game theory. Banzhaf power index …
Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifiers have recently been used for identifying ASD from functional magnetic resonance imaging (fMRI) with higher accuracy than traditional lear…
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have…
Generative Policy-based Models aim to enable a coalition of systems, be they devices or services to adapt according to contextual changes such as environmental factors, user preferences and different tasks whilst adhering to various constraints and regulations as directed by a managing party or the collective vision of…
Representing distributions over permutations can be a daunting task due to the fact that the number of permutations of objects scales factorially in . One recent way that has been used to reduce storage complexity has been to exploit probabilistic independence, but as we argue, full independence assumptions impo…
The paper explains DNNs by quantifying interactions among input variables.
Paper presents content-based models for game recommendation in cold start scenarios.
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.
We introduce a topological combinatorial game called the Region Smoothing Swap Game. The game is played on a game board derived from the connected shadow of a link diagram on a (possibly non-orientable) surface by smoothing at crossings. Moves in the game are performed on regions of the diagram and can switch the direc…
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…
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…
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 …
Educational game on crypto investment helps students grasp macroeconomics.
Simplified NFT games discussed with methods for extracting value.
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.
AEC Games model represents software MARL environments better than POSGs.
Gradient Descent Ascent converges to von-Neumann solution in hidden zero-sum games.
Generalizes region select game to -colored knot diagrams.
We introduce CSE for MLSF games and devise online learning algorithms for achieving no-external Stackelberg-regret.
Deep Reinforcement Learning automates match-3 game testing.