Mathematical Foundations

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

Linear Systems, estimation and control (Stanford)
https://stanford.edu/class/ee363/lectures.html

Non-linear systems:
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-243j-dynamics-of-nonlinear-systems-fall-2003/

Networked Systems
http://web.mit.edu/~jadbabai/www/ESE680/ese635.html

Hybrid Systems (Berkeley)
https://t.co/wrpiqwyhle
https://inst.eecs.berkeley.edu/~ee291e/sp18/

Convex Optimization (Stanford text book and course material)
https://t.co/qRfsNRN9yM
http://web.stanford.edu/class/ee364a/lectures.html

Stochastic Optimal Control (MIT book table of contents and course material)
https://t.co/W8t568ehBM
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-231-dynamic-programming-and-stochastic-control-fall-2015/

Control w/ Examples for games (Berkeley)
https://t.co/hm8OTyXEkV

stochastic process
https://www.stat.cmu.edu/~cshalizi/754/

mathematics primer for data scientists (more links to materials)
https://medium.com/s/story/essential-math-for-data-science-why-and-how-e88271367fbd

machine learning
http://www.cs.cornell.edu/courses/cs6787/2018fa/
(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
https://bair.berkeley.edu/blog/2017/09/12/learning-to-optimize-with-rl/
https://ml.berkeley.edu/blog/2018/01/10/adversarial-examples/

Ergodic and Non-Ergodic models of stochastic processes (in economic)
https://aip.scitation.org/doi/10.1063/1.4940236 gambling with dynamics
https://ergodicityeconomics.com/lecture-notes/ lecture notes on ergocity economics

Differential Games (root reference textbook from 1965)
https://www.amazon.com/Differential-Games-Mathematical-Applications-Optimization/dp/0486406822

Control applied to iterative games (games and controls)
https://www.ece.ucsb.edu/~jrmarden/teaching.html

Market Design
https://web.stanford.edu/~alroth/alroth.html
http://www.scottkom.com/courses/Market-Design_2018-2019/index.html

Network Science and Markets
https://www.cs.cornell.edu/home/kleinber/networks-book/

Games and Social Networks
https://web.stanford.edu/~jacksonm/netbook.pdf

Algorithmic Game Theory
https://www.cs.cmu.edu/~sandholm/cs15-892F15/algorithmic-game-theory.pdf

Network Optimization
http://web.mit.edu/dimitrib/www/netbook_Full_Book.pdf

Design of Evolutionary Algorithms - Engineering framing
Goldberg - https://www.springer.com/gp/book/9781475736458
Michalewicz - https://www.amazon.com/How-Solve-Heuristics-Zbigniew-Michalewicz/dp/3540224947

Systematic Hierarchical Design
Gielen - http://trent.st/content/2005_DAC_hierarchy.pdf
Chang - https://www.springer.com/kr/book/9780792397946

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:
https://ieeexplore.ieee.org/document/4435009
https://ieeexplore.ieee.org/document/4118472
https://ieeexplore.ieee.org/document/1582620
https://ieeexplore.ieee.org/document/7068965
https://fling.seas.upenn.edu/~afosr/wiki/uploads/Chaserepository/Repository/Learning_GEB_2012.pdf

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