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https://arxiv.org/html/2407.06328v2
I Introduction
II Preliminaries and Problem Description
II-A Task Allocation Game Model
II-B Learning Rule, Stochastic Alarm Clock, and Evolutionary Dynamics Model
II-C Payoff Vector Estimation
III Convergence Analysis and Revision Rate
https://arxiv.org/html/2608.02817v1
expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. The authors are grateful for the invaluable feedback they received on an earlier draft from Jón
https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2309.08431v3
Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision
1 Introduction
2 Constant function markets and concentrated liquidity
2.1 Constant function markets
2.2 Concentrated liquidity markets
3 The wealth of
https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release)
Synthetic risk–neutral generator
https://arxiv.org/html/2407.04889v1
behavior of other learners
1 Introduction
Setting.
Zero-sum games.
General-sum games.
1.1 Preliminaries
1.2 Algorithms for the learner
1.3 Our results
1.3.1 Zero sum games
Continuous-time games.
Discrete-time games.
1.3.2 A lower bound
https://arxiv.org/list/math/new
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[1] arXiv:2608