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 algorithm improves performance in nontransitive games.
BOP-Elites uses Bayesian Optimisation for QD search, improving efficiency and insight.
The paper explores game-theoretic alignment of LLMs with human preferences, finding limitations and conditions.
Wavelets model complex interactions in spatial transcriptomics.
This paper makes a small step towards a non-stochastic version of superhedging duality relations in the case of one traded security with a continuous price path. Namely, we prove the coincidence of game-theoretic and measure-theoretic expectation for lower semicontinuous positive functionals. We consider a new broad de…
Game-theoretic models predict asset prices in financial markets.
In this expository paper we illustrate the generality of game theoretic probability protocols of Shafer and Vovk (2001) in finite-horizon discrete games. By restricting ourselves to finite-horizon discrete games, we can explicitly describe how discrete distributions with finite support and the discrete pricing formulas…
Paper compares fairness measures and feature importance measures using SHAP.
Game-theoretic model captures investor interactions for stock price forecasting.
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.
We consider the game-theoretic scenario of testing the performance of Forecaster by Sceptic who gambles against the forecasts. Sceptic's current capital is interpreted as the amount of evidence he has found against Forecaster. Reporting the maximum of Sceptic's capital so far exaggerates the evidence. We characterize t…
We study the dynamics of co-evolution of producers and customers described by bit-strings representing individual traits. Individual ''size-like'' properties are controlled by binary encounters which outcome depends upon a recognition process. Depending upon the parameter set-up, mutual selection of producers and custo…
A game-theoretic framework identifies influential hyperparameters for neural networks.
In data science, determining proximity between observations is critical to many downstream analyses such as clustering, information retrieval and classification. However, when the underlying structure of the data probability space is unclear, the function used to compute similarity between data points is often arbitrar…
In this article we consider a game theoretic approach to the Risk-Sensitive Benchmarked Asset Management problem (RSBAM) of Davis and Lleo \cite{DL}. In particular, we consider a stochastic differential game between two players, namely, the investor who has a power utility while the second player represents the market …
A game-theoretic approach for unsupervised domain adaptation.
Paper proposes a game-theoretic approach to generate unlearnable examples.
GNMC reduces XCSF population size while preserving function approximation and policy accuracy.
The paper sketches a recent progress and formulates several open problems in studying equivariant quasiconformal and quasisymmetric homeomorphisms in negatively curved spaces as well as geometry and topology of noncompact geometrically finite negatively curved manifolds and their boundaries at infinity having Carnot--C…
GT-DDP optimizer trains residual networks using game theory.
Proposes a game-theoretic approach for class-dependent rationalization.
Paper applies NFSP to Mini-RTS, a small RTS game.
We study the origins of the effect in finance and SDE. In particular, we show, in the game-theoretic framework, that market volatility is a consequence of the absence of riskless opportunities for making money and that too high volatility is also incompatible with such opportunities. More precisely, riskles…
Game-theoretic analysis of mining gaps in blockchain systems.
New method for evaluating LLMs reduces bias in open-ended evaluations.
The paper improves dropout's utility by reducing interactions in deep neural networks.
Develops game theory framework for UAS integration into NAS.
This paper establishes a non-stochastic analogue of the celebrated result by Dubins and Schwarz about reduction of continuous martingales to Brownian motion via time change. We consider an idealized financial security with continuous price path, without making any stochastic assumptions. It is shown that typical price …
We solve a continuous-time game-theoretic problem for Kihlstrom-Mirman preferences.
The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.
Motivated by the recent applications of game-theoretical learning techniques to the design of distributed control systems, we study a class of control problems that can be formulated as potential games with continuous action sets, and we propose an actor-critic reinforcement learning algorithm that provably converges t…
End-to-end model predicts multiagent trajectories using game theory and neural nets.
Paper analyzes adversarial attacks and defenses using game theory.
For a monotonically advancing front, the arrival time is the time when the front reaches a given point. We show that it is twice differentiable everywhere with uniformly bounded second derivative. It is smooth away from the critical points where the equation is degenerate. We also show that the critical set has finite …
We present a simple one-parameter model for spatially localised evolving agents competing for spatially localised resources. The model considers selling agents able to evolve their pricing strategy in competition for a fixed market. Despite its simplicity, the model displays extraordinarily rich behavior. In addition t…
Proposes CoPO, a new policy optimization method for competitive games.
Decentralised optimisation tasks are important components of multi-agent systems. These tasks can be interpreted as n-player potential games: therefore game-theoretic learning algorithms can be used to solve decentralised optimisation tasks. Fictitious play is the canonical example of these algorithms. Nevertheless fic…
It is now well known that decentralised optimisation can be formulated as a potential game, and game-theoretical learning algorithms can be used to find an optimum. One of the most common learning techniques in game theory is fictitious play. However fictitious play is founded on an implicit assumption that opponents' …
Study on stochastic mean curvature flow on networks using Ito calculus.
Game-theoretic flow allocation models network dynamics.
This paper gives yet another definition of game-theoretic probability in the context of continuous-time idealized financial markets. Without making any probabilistic assumptions (but assuming positive and continuous price paths), we obtain a simple expression for the equity premium and derive a version of the capital a…
This work presents a game-theoretic method for AVs that handles imperfect communication and individual rewards.
We investigate upper and lower hedging prices of multivariate contingent claims from the viewpoint of game-theoretic probability and submodularity. By considering a game between "Market" and "Investor" in discrete time, the pricing problem is reduced to a backward induction of an optimization over simplexes. For Europe…
Game theory enhances preference learning, improving feature selection and interpretability.
This work develops secure distributed algorithms for machine learning to protect against data poisoning and network attacks.
With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to mi…
Company mergers and acquisitions are often perceived to act as catalysts for corporate growth in free markets systems: it is conventional wisdom that those activities lead to better and more efficient markets. However, the broad adoption of this perception into corporate strategy is prone to result in a less diverse an…