Game of Coins

05/23/2018
by   Alexander Spiegelman, et al.
0

We formalize the current practice of strategic mining in multi-cryptocurrency markets as a game, and prove that any better-response learning in such games converges to equilibrium. We then offer a reward design scheme that moves the system configuration from any initial equilibrium to a desired one for any better-response learning of the miners. Our work introduces the first multi-coin strategic attack for adaptive and learning miners, as well as the study of reward design in a multi-agent system of learning agents.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/16/2013

Game Networks

We introduce Game networks (G nets), a novel representation for multi-ag...
research
02/17/2020

Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning

A large portion of the passenger requests is reportedly unserviced, part...
research
06/11/2022

Bounded strategic reasoning explains crisis emergence in multi-agent market games

The efficient market hypothesis (EMH), based on rational expectations an...
research
08/12/2021

On Liquidity Mining for Uniswap v3

The recently proposed Uniswap v3 replaces the fungible liquidity provide...
research
08/12/2022

Non-strategic Structural Inference (for Initial Play)

We adapt behavioral models developed for predicting human behavior to th...
research
10/04/2022

Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality

Optimizing strategic decisions (a.k.a. computing equilibrium) is key to ...
research
06/24/2016

Human-Agent Decision-making: Combining Theory and Practice

Extensive work has been conducted both in game theory and logic to model...

Please sign up or login with your details

Forgot password? Click here to reset