arXiv research

New framework values football players based on in-game interactions.

Study predicts soccer player market values using machine learning and SHAP for interpretability.

Researchers create a framework to value player actions in CSGO.

A game-theoretic approach selects features by testing their marginal contributions.

The paper analyzes a game where players must balance short-term and long-term interests, leading to cooperative or competitive outcomes.

Professional baseball players are increasingly guaranteed expensive long-term contracts, with over 70 deals signed in excess of \$90 million, mostly in the last decade. These are substantial sums compared to a typical franchise valuation of \$1-2 billion. Hence, the players to whom a team chooses to give such a contrac…

In illiquid markets, option traders may have an incentive to increase their portfolio value by using their impact on the dynamics of the underlying. We provide a mathematical framework within which to value derivatives under market impact in a multi-player framework by introducing strategic interactions into the Almgre…

Option contracts are a type of financial derivative that allow investors to hedge risk and speculate on the variation of an asset's future market price. In short, an option has a particular payout that is based on the market price for an asset on a given date in the future. In 1973, Black and Scholes proposed a valuati…

This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.

Bayesian approach improves Shapley value estimation efficiency.

What would you do if you were invited to play a game where you were given \$25 and allowed to place bets for 30 minutes on a coin that you were told was biased to come up heads 60% of the time? This is exactly what we did, gathering 61 young, quantitatively trained men and women to play this game. The results, in a nut…

We study stochastic multi-armed bandits with many players. The players do not know the number of players, cannot communicate with each other and if multiple players select a common arm they collide and none of them receive any reward. We consider the static scenario, where the number of players remains fixed, and the d…

Paper introduces new actuarial-consistent valuations for insurance liabilities.

We consider a symmetric multi-players zero-sum game with two strategic variables. There are $n$ players, $n\geq 3$. Each player is denoted by $i$. Two strategic variables are $t_i$ and $s_i$, $i\in \{1, \dots, n\}$. They are related by invertible functions. Using the minimax theorem by \cite{sion} we will show that Nas…

A multi-player bandit system resists adversarial attacks with near-optimal regret.

Game contingent claims (GCCs) generalize American contingent claims by allowing the writer to recall the option as long as it is not exercised, at the price of paying some penalty. In incomplete markets, an appealing approach is to analyze GCCs like their European and American counterparts by solving option holder's an…

We consider two-player non-zero-sum stopping games in discrete time. Unlike Dynkin games, in our games the payoff of each player is revealed after both players stop. Moreover, each player can adjust her own stopping strategy according to the other player's action. In the first part of the paper, we consider the game wh…

We consider a fully decentralized multi-player stochastic multi-armed bandit setting where the players cannot communicate with each other and can observe only their own actions and rewards. The environment may appear differently to different players, $\textit{i.e.}$, the reward distributions for a given arm are heterog…

New algorithms for n-player games using a player-centered approach.

We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. T…

Paper recovers uncertainty from dynamic valuation rules.

New algorithm tackles multi-player bandit problems with limited access to arms.

Paper presents Transfer Portal model for accurate player performance predictions.

A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.

Algorithm optimizes multi-player learning with noisy rewards without direct communication.

Study convolution of invariant valuations on Lie groups.

New strategy achieves optimal regret without communication or collisions in multi-player bandit.

New algorithm for multi-player bandits in decentralized, asynchronous systems.

SL(n) covariant valuations on Orlicz spaces are represented and characterized.

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first $\sqrt{T}$-type regret guarantee for th…

The possibility of using player engagement predictions to profile high spending video game users is explored. In particular, individual-player survival curves in terms of days after first login, game level reached and accumulated playtime are used to classify players into different groups. Lifetime value predictions fo…

In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narrativ…

Market valuation duration is 175 years, but drops to 46 years during crises.

Paper simplifies default process modeling and credit valuation.

Business cycles affect startup valuations, both directly and indirectly.

Method predicts NBA players' multi-modal movement trajectories.

New learning dynamics adapt to corrupted games, improving performance in real-world scenarios.

Paper presents content-based models for game recommendation in cold start scenarios.

Classification of SL(n) covariant valuations on Orlicz spaces.

We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider the challenging heterogeneous setting, in which different arms may have different means for differe…

Algorithm reduces regret in multi-player bandits with unknown collision rewards.

Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in …

We prove new kinematic formulas for tensor valuations and simplify previously known Crofton formulas by using the recently developed algebraic theory of translation invariant valuations. The heart of the paper is the computation of the Alesker-Fourier transform on the large class of spherical valuations, which is achie…

Classifies contravariant matrix-valued valuations on polytopes without continuity assumptions.

Let $\mathrm{SO}^+(p,q)$ denote the identity connected component of the real orthogonal group with signature $(p,q)$. We give a complete description of the spaces of continuous and generalized translation- and $\mathrm{SO}^+(p,q)$-invariant valuations, generalizing Hadwiger's classification of Euclidean isometry-invari…

We consider a setting where multiple players sequentially choose among a common set of actions (arms). Motivated by a cognitive radio networks application, we assume that players incur a loss upon colliding, and that communication between players is not possible. Existing approaches assume that the system is stationary…

Study evaluates valuation models for UK companies using case studies.

Equity risk premium is a central component of every risk and return model in finance and a key input to estimate costs of equity and capital in both corporate finance and valuation. An article by Damodaran examines three broad approaches for estimating the equity risk premium. The first is survey based, it consists in …