arXiv research

A study on how a principal can incentivize an agent to make better decisions in a repeated game.

Deep learning solves complex PA mean field games with market-clearing conditions.

Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.

Study of repeated games with unobserved agent rewards using MAB framework.

Optimal penalties for RECs balance environmental and revenue impacts.

Study optimizes scoring rules for incentivizing agent's information gathering in online settings.

Algorithm learns optimal coordination for strategic agents in uncertain settings.

We analyze conditional optimization problems arising in discrete time Principal-Agent problems of delegated portfolio optimization with linear contracts. Applying tools from Conditional Analysis we show that some results known in the literature for very specific instances of the problem carry over to translation invari…

The main purpose of this paper is to formalize the modelling process, analysis and mathematical definition of corruption when entering into a contract between principal agent and producers. The formulation of the problem and the definition of concepts for the general case are considered. For definiteness, all calculati…

Study a continuous-time PA problem with private effort and consumption decisions.

A model for collaborative learning with principal-agent interaction.

Study proposes new framework for Board-CEO relationship.

Optimal contracts are found for agents with quadratic effort costs.

A note on learning with agents having global perspectives and a principal optimizing their performance.

In this paper we provide an alternative framework to tackle the first-best Principal-Agent problem under CARA utilities. This framework leads to both a proof of existence and uniqueness of the solution to the Risk-Sharing problem under very general assumptions on the underlying contract space. Our analysis relies on an…

Study uses machine learning to estimate effective policies in settings with hidden individual actions.

We use a principal-agent model to analyze the structure of a book-driven dealer market when the dealer faces competition from a crossing network or dark pool. The agents are privately informed about their types (e.g. their portfolios), which is something that the dealer must take into account when engaging his counterp…

Study shows fiduciary duty reduces municipal bond yields by 9% after SEC rule.

Optimal reinsurance contracts designed for a continuum of risk types.

The paper proposes incentivizing human annotators with 'golden questions' to improve data quality.

We consider the problem of designing a derivatives exchange aiming at addressing clients needs in terms of listed options and providing suitable liquidity. We proceed into two steps. First we use a quantization method to select the options that should be displayed by the exchange. Then, using a principal-agent approach…

This paper addresses reward estimation and incentive design for agents with hidden rewards.

We consider an exchange who wishes to set suitable make-take fees to attract liquidity on its platform. Using a principal-agent approach, we are able to describe in quasi-explicit form the optimal contract to propose to a market maker. This contract depends essentially on the market maker inventory trajectory and on th…

The independence of Central Banks is shown to be a myth.

We consider a continuous time Principal-Agent model on a finite time horizon, where we look for the existence of an optimal contract both parties agreed on. Contrary to the main stream, where the principal is modelled as risk-neutral, we assume that both the principal and the agent have exponential utility, and are ris…

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.

The paper develops an economic foundation for multi-agent learning in markets.

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 $k$-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.

Unified framework for Bayesian and Frequentist statistics.

EBIs/ESOs substantially change the traditional production/service function because ESOs/EBIs can have different psychological effects(motivation or de-motivation), and can create intangible capital and different economic payoffs. Although Game Theory is flawed, it can be helpful in describing interactions in ESO/EBIs t…

New game approximates mean curvature flow evolution.