Mathematical Foundations

Links to Academic textbooks and coursework which serves to provide foundational mathematics for token engineering.

Linear Systems, estimation and control (Stanford)

Non-linear systems:

Networked Systems

Hybrid Systems (Berkeley)

Convex Optimization (Stanford text book and course material)

Stochastic Optimal Control (MIT book table of contents and course material)

Control w/ Examples for games (Berkeley)

stochastic process

mathematics primer for data scientists (more links to materials)

machine learning
(open systems need to be viewed like machine learning algorithms where the people not the computers are the ones actually performing the stochastic gradient descent and the local optimization objects are local/private to the agents, nonetheless, this is the mathematical justification for our intuition that market behavior leads to equilibria… stochastic process convergence.

Reinforcement Learning

Ergodic and Non-Ergodic models of stochastic processes (in economic) gambling with dynamics lecture notes on ergocity economics

Differential Games (root reference textbook from 1965)

Control applied to iterative games (games and controls)

Market Design

Network Science and Markets

Games and Social Networks

Algorithmic Game Theory

Network Optimization

Design of Evolutionary Algorithms - Engineering framing
Goldberg -
Michalewicz -

Systematic Hierarchical Design
Gielen -
Chang -

There is an even more disparate supply of relevant literature on multi-agent coordination but you’d be digging through IEEE and economic games journals here’s an example:

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