Difference between revisions of "Background Readings CSSS15"
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* Dynamics in Action (Alicia Juarrero). Recommended by Jelle Bruineberg | * Dynamics in Action (Alicia Juarrero). Recommended by Jelle Bruineberg | ||
* Metabolic Ecology: A Scaling Approach (Sibley, Brown & Kondrik-Brown). Recommonded by Cobain | * Metabolic Ecology: A Scaling Approach (Sibley, Brown & Kondrik-Brown). Recommonded by Cobain | ||
==Brains== | |||
* Principles of Neural Science (Eric Kandel and others). Recommended by Kleber Neves | |||
* Principles of Neural Design (Peter Sterlin and Simon Laughlin). Recommended by Kleber Neves |
Revision as of 18:27, 28 June 2015
Complex Systems Summer School 2015 |
The following is a collection of papers regarded as "classic" literature in Complex Systems Science. This list has been growing over the past few years and was formalized by Dan Rockmore for the 2010 Complex Systems Summer School.
For more readings, please explore the wiki content of previous summer schools.
Science and complexity by Warren Weaver
Last Updated for SFI CSSS13 March 25 2013
Bibles (Awesome Reference Books)
Statistics
* All of Statistics (Wasserman). Recommended by Cosma Shalizi. * All of Nonparametric Statistics (Wasserman). Recommended by Cosma Shalizi. * Advanced Data Analysis from an Elementary Point of View (Shalizi) * Elements of Statistical Learning: Data Mining, Inference, and Prediction (Hastie, Tibshirani, Friedman). Recommended by Glenn Magerman * Pattern Recognition and Machine Learning (Christopher Bishop)
Computer Science
* Nature of Computation (Moore). Recommended by Christopher Moore. * The Algorithm Design Manual (Skiena). Recommended by Richard Barnes.
Data Analysis
* Nonlinear Time Series Analysis (Kantz & Schreiber). Recommended by Liz Bradley. * Tidy Data (Hadley Wickham). Recommended by Brent Schneeman.
Information Theory
* Elements of Information Theory (Thomas M. Cover, Joy A. Thomas). Recommended by Simon DeDeo and others * The Mathematical Theory of Communication (Claude Shannon) * Information Theory, Inference and Learning Algorithms (David MacKay)
Probability Theory
* Probability Theory: The Logic of Science (E.T. Jaynes). Recommended by Simon DeDeo and others
Game Theory
* Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations (Shoham & Leyton-Brown) * Networks, Crowds, and Markets: Reasoning About a Highly Connected World (David Easley & Jon Kleinberg)
Available for free here: http://www.cs.cornell.edu/home/kleinber/networks-book/ Recommended by Jae B. Cho (and Daniel Citron)
Network stuff
* Networks: an Introduction (Mark Newman). Recommended by Daniel Citron (and everyone, really. It's actually that good) * Dynamical Processes on Complex Networks (Barrat, Barthélemy, Vespignani). Recommended by Daniel Citron * Graph Theory (Reinhard Diestel). Great introduction to mathematics/proofs. Recommend by Glenn Magerman * Modern Graph Theory (Bollobas). Great & more intense introduction to math/proofs. Recommended by Glenn Magerman * Social and Economic Networks (Matt Jackson). Recommended by Glenn Magerman * Connections: An Introduction to the Economics of Networks (Sanjeev Goyal). More about game theory on networks. Recommended by Glenn Magerman
Consciousness
* Phi: A Voyage from the Brain to the Soul (Giulio Tononi). Recommended by Stefano Gurciullo * Consciousness as integrated information: a provisional manifesto (Giulio Tononi). Recommended by Stefano Gurciullo
Cognition and Biology
* Mind in Life (Evan Thompson). Recommended by Jelle Bruineberg * Dynamics in Action (Alicia Juarrero). Recommended by Jelle Bruineberg * Metabolic Ecology: A Scaling Approach (Sibley, Brown & Kondrik-Brown). Recommonded by Cobain
Brains
* Principles of Neural Science (Eric Kandel and others). Recommended by Kleber Neves * Principles of Neural Design (Peter Sterlin and Simon Laughlin). Recommended by Kleber Neves