Whats up with the weird vegetable? SPM 9550 Introduction to complexity

Similar documents
Support Vector Machine. Industrial AI Lab.

The phenomenon: complex motion, unusual geometry

Support Vector Machine. Industrial AI Lab. Prof. Seungchul Lee

Organizing Diversity Taxonomy is the discipline of biology that identifies, names, and classifies organisms according to certain rules.

CHAPTER 3 : A SYSTEMATIC APPROACH TO DECISION MAKING

Module 6 : Preventive, Emergency and Restorative Control. Lecture 27 : Normal and Alert State in a Power System. Objectives

Lecture 3: The Reinforcement Learning Problem

A SYSTEM VIEW TO URBAN PLANNING: AN INTRODUCTION

Polarization and Bipolar Probabilistic Argumentation Frameworks

What makes cities complex?

Epistemological and Computational Constraints of Simulation Support for OR Questions

Copyrighted Material. 1.1 Large-Scale Interconnected Dynamical Systems

Introduction. Prediction MATH February 2017

Toward the Analysis and Synthesis of Varied Behavior of Complex Systems

Chapter 1. Introduction: Biology Today. Lectures by Chris C. Romero, updated by Edward J. Zalisko

Decoherence and the Classical Limit

Non-deteriorating Choice Without Full Transitivity

Capital, Institutions and Urban Growth Systems

Chapter 3: The Reinforcement Learning Problem

Stieff Research Group - COPYRIGHT 2009

Title of Activity: Let there be Light! Introduction to Ohm's Law and basic series circuits.

Higher Dimensions. Introduction. Differential Forms

Point Vortex Dynamics in Two Dimensions

Introduction. Spatial Multi-Agent Systems. The Need for a Theory

A Note on the Existence of Ratifiable Acts

Dynamical Systems and Chaos Part I: Theoretical Techniques. Lecture 4: Discrete systems + Chaos. Ilya Potapov Mathematics Department, TUT Room TD325

Industrial Organization Lecture 3: Game Theory

Year 9 plan Victorian Curriculum: Humanities Semester Two (Geography/Economics and Business)

Towards international cooperation in the development of Marine Spatial Plans for the North Pacific: economic, social, and environmental dimensions

networks in molecular biology Wolfgang Huber

Chapter 3: The Reinforcement Learning Problem

CHAPTER 6 A STUDY ON DISC BRAKE SQUEAL USING DESIGN OF EXPERIMENTS

Unusual Frequency Distribution Function Shape Generated by Particles Making Brownian Walk Along Line With Monotone Increasing Friction

Common Knowledge and Sequential Team Problems

Mathematical Biology - Lecture 1 - general formulation

Designing Information Devices and Systems I Spring 2018 Lecture Notes Note 3

The World According to Wolfram

Evaluation Module 5 - Class B11 (September 2012) Responsible for evaluation: Dorte Nielsen / Cristina Lerche Data processing and preparation of

via Topos Theory Olivia Caramello University of Cambridge The unification of Mathematics via Topos Theory Olivia Caramello

Principle of Chemical Process I

On Objectivity and Models for Measuring. G. Rasch. Lecture notes edited by Jon Stene.

Firefly Synchronization

Investigation 1: Separating Mixtures

Reconstruction Deconstruction:

Information Structures, the Witsenhausen Counterexample, and Communicating Using Actions

Atomic Structure. Niels Bohr Nature, March 24, 1921

Chaos and Liapunov exponents

Designing and Evaluating Generic Ontologies

biologically-inspired computing lecture 12 Informatics luis rocha 2015 INDIANA UNIVERSITY biologically Inspired computing

Alp Simsek (MIT) Recitation Notes: 1. Gorman s Aggregation Th eorem2. Normative Representative November 9, Household Theorem / 16

Course of Study For Math

ESSENTIAL CONCEPTS AND SKILL SETS OF THE IOWA CORE CURRICULUM

Lecture 6: Communication Complexity of Auctions

Solving Quadratic Equations Using Multiple Methods and Solving Systems of Linear and Quadratic Equations

The Future of Tourism in Antarctica: Challenges for Sustainability

Approach to Field Research Data Generation and Field Logistics Part 1. Road Map 8/26/2016

correlated to the Nevada Grades 9 12 Science Standards

The Most Important Thing for Your Child to Learn about Arithmetic. Roger Howe, Yale University

RE-THINKING COVARIATION FROM A QUANTITATIVE PERSPECTIVE: SIMULTANEOUS CONTINUOUS VARIATION. Vanderbilt University

Mechanics. In the Science Program, Mechanics contributes to the following program goals described in the Exit Profile:

Chaos and Order in the Integers Primes

Boundary and interior equilibria: what drives convergence in a beauty contest?

Section Objectives: Recognize some possible benefits from studying biology. Summarize the characteristics of living things.

Valentin Goranko Stockholm University. ESSLLI 2018 August 6-10, of 29

B1-1. Closed-loop control. Chapter 1. Fundamentals of closed-loop control technology. Festo Didactic Process Control System

The Fundamental Laws of Chemistry

Designing Information Devices and Systems I Spring 2019 Lecture Notes Note 3

Are Objects Ontologically Dependent on Processes?

Systems of Linear Equations

SPIRITUAL GIFTS. ( ) ( ) 1. Would you describe yourself as an effective public speaker?

Magnetic Leakage Fields as Indicators of Eddy Current Testing

V&V MURI Overview Caltech, October 2008

Chapter 2 The Naïve Bayes Model in the Context of Word Sense Disambiguation

CONTROLLING CHAOS. Sudeshna Sinha. The Institute of Mathematical Sciences Chennai

DECISION TREE BASED QUALITATIVE ANALYSIS OF OPERATING REGIMES IN INDUSTRIAL PRODUCTION PROCESSES *

Chapter 1. Introduction to Nonlinear Space Plasma Physics

CSCI 5582 Artificial Intelligence. Today 9/28. Knowledge Representation. Lecture 9

1 This book is a translation of an amended and enlarged version of Part 1 of the following monograph

Increasingly, economists are asked not just to study or explain or interpret markets, but to design them.

Cellular Automata. and beyond. The World of Simple Programs. Christian Jacob

Global analysis of the nonlinear Duffing-van der Pol type equation by a bifurcation theory and complete bifurcation groups method

Linearize a non-linear system at an appropriately chosen point to derive an LTI system with A, B,C, D matrices

Turning landscape rambles into integrative, interdisciplinary, intergenerational field experiences

Economics 385: Suggested Solutions 2

EE 230 Lecture 21. Nonlinear Op Amp Applications. Nonlinear analysis methods Comparators with Hysteresis

Decryption of Kaons decay modes

Multidimensional Divide and Conquer 1 Skylines

Linear Algebra (Part II) Vector Spaces, Independence, Span and Bases

Synthesis of Azeotropic Separation Systems by Case-Based Reasoning

Q1. (a) Define the term activation energy for a chemical reaction. (2)

r10_summary.qxd :19 Page 245 ABOUT THE BOOK

LINEAR MMSE ESTIMATION

NORMAL MODES, WAVE MOTION AND THE WAVE EQUATION. Professor G.G.Ross. Oxford University Hilary Term 2009

5 LIFE HOW DO WE DEFINE LIFE AND WHAT DO WE KNOW ABOUT IT?

for summative evaluation Definition of the domain Biology Secondary V BLG Ecology

Oct National Academy of Sciences Extreme Attribution

6.207/14.15: Networks Lecture 11: Introduction to Game Theory 3

SOCIAL STUDIES GRADE 6. I Can Checklist REGIONS AND PEOPLE OF THE EASTERN HEMISPHERE. Office of Teaching and Learning Curriculum Division

Boundary and interior equilibria: what drives convergence in a beauty contest?

THEORY OF OLIGOPOLIES: DYNAMICS AND STABILITY OF EQUILIBRIA

Transcription:

Whats up with the weird vegetable? 1

spm 9550: Introduction to Complexity Dr. ir. Igor Nikolic 12-03-10 Delft University of Technology Challenge the future

Lecture goals Have a general understanding of (the history of) systems thinking and Complex Adaptive System properties Know and understand two definitions of complexity Mikulecky Kauffman Understand the difference between complexity and complicatedness Understand the concepts of generative science 3

What is a System? a regularly interacting or interdependent group of items forming a unified whole. an organized set of doctrines, ideas, or principles usually intended to explain the arrangement or working of a systematic whole. manner of classifying, symbolizing, or schematizing harmonious arrangement or pattern or order http://www.merriam-webster.com/dictionary/system 4

Linear system Linear systems are ``simple''. An change in the systems setting results in a system response linearly proportional to the input. (y=ax+b) often used as an approximation of more complicated systems because they are easy to calculate. (matrix inversion) 5

Nonlinear systems Nonlinear systems have a response that is not proportional to the input. can be straightforward and predictable (y = sin x) 6

Nonlinear chaotic systems Some non-linear systems express Chaotic behavior. That is, extreme sensitivity to initial conditions, Z = Z^2 + Ci 7

What is a complex system? 8

Some examples I can think of : 9

What do all of these systems have in common? 10

Some properties of all complex systems: emergence observer-dependency robustness diversity self-similarity order randomness adaptiveness path-dependency non-linearity chaos instability evolution 11

Systems perspective I Taking into account all of the behaviors of a system as a whole in the context of its environment is the systems perspective. While the concept of system itself is a general notion that indicates separation of part of the universe from the rest, the idea of a systems perspective is to use a non-reductionist approach in the task of describing the properties of the system itself. Y. Bar-Yam. Concepts in complex systems: Systems perspective. http://necsi.edu/guide/concepts/system_perspective.html, 2008. 12

Systems perspective II In the systems perspective, once one has identified the system as a separate part of the universe, one is not allowed to progressively decompose the system into isolated parts. Instead, one is obligated to describe the system as a whole. If in describing system properties one separates them into parts, this produces an incomplete description of the behavior of the whole. Any description of the whole must include an explanation of the relationships between these parts and any additional information needed to describe the behavior of the entire system. Y. Bar-Yam. Concepts in complex systems: Systems perspective. http://necsi.edu/guide/concepts/system_perspective.html, 2008. 13

Complexity is......the property of a real world system that is manifest in the inability of any one formalism being adequate to capture all its properties. It requires that we find distinctly different ways of interacting with systems. Distinctly different in the sense that when we make successful models, the formal systems needed to describe each distinct aspect are not derivable from each other. D.C. Mikulecky. The emergence of complexity: science coming of age or science growing old Computers and Chemistry, 25(4):341 348, 2001. 14

Complex Adaptive Systems are :...a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents. M.M. Waldorp. Complexity: Theemerging scienceat theedgeof order and chaos. Simon and Schuster, New York, 1992. 15

Complexity-on-astick 16

Complex vs. Complicated 17

Supply side sustainability, Hoekstra, Allen and Tainter, Systems research and Behavioural Science, 16, 403-427,1999 18

The fact that it is on this screen makes it complex. 19