1. Dealing with Complexity Robert Love, Venkat Jayaraman July 24, 2008 SSTP Seminar – Lecture 10

2. Overview Presentation – Studying Complexity in a system – Defining Complexity – Representations of Complexity – Optimization and Dealing with Complexity Discussion Activity 5/4/2015UF Flight Controls Lab2

3. Studying Complexity 5/4/2015UF Flight Controls Lab3 System – Group of objects interacting to accomplish a purpose How to study a system? – Measurements on an existing system – What to do, if the system does not exist really? – What to do, if changes are expensive or time consuming? – Mathematical analysis – Good solutions, but only feasible for simple solutions – Real world systems are far complex. Eg Factory, computer – Simulation – Build the behavior of a system within a program – Emulation – Not only is the system reproduced, but the system itself is somehow reproduced

4. What is simulation? A simulation is the imitation of a real world system over time What is the method? Generate an artificial history of a system Draw inferences from the artificial history concerning the characteristics of the system. How is it done? Developing a model of the system Simulation- Introduction

5. What is a model? A representation of a system for the purpose of studying the system Physical model Prototype of a system for the purpose of study Mathematical model Mathematical equations to represent the system Simulation model is a kind of mathematical model Types of simulation models Static – Represents a system at a particular point of time Dynamic – Represents a system over a time interval Deterministic – Model without random variables Stochastic – Model with random variables Discrete – System state changes only at discrete time points Continuous System state changes continuously Types of Model

6. Problem Formulation Setting objectives of overall project plan Model ConfigurationData Collection Model Translation Experimental Design Program runs and analysis Implementation Validated? More Runs? No Yes 1.Setting up the problem 2. Model building and data collection 3. Run the model 4.Implementation Yes Steps in Simulation

7. Complexity Grand Engineering Challenges What is complexity? – Static – Dynamic – Evolving – Self Organizing What is information? How can we organize information into something useful? What information is provided in these examples? What are the (dis)advantages of these approaches? 5/4/2015UF Flight Controls Lab7

8. Ex: Subway Map 5/4/2015UF Flight Controls Lab8

9. Ex: Mind Map 5/4/2015UF Flight Controls Lab9

10. Ex: Friend Maps 5/4/2015UF Flight Controls Lab10

11. Silk Rugs 5/4/2015UF Flight Controls Lab11

12. Ex: Equations 5/4/2015UF Flight Controls Lab12

13. Anthills and Icebergs 5/4/2015UF Flight Controls Lab13

14. The Internet, Blog’s and Wiki’s? 5/4/2015UF Flight Controls Lab14 Meme’s and Teme’s, Tracking EmotionsTracking Emotions

15. Ex: Hardware 5/4/2015UF Flight Controls Lab15

16. Basic Optimization Calculus Review: Local Max/Min How do we know we found a global minimum? 5/4/2015UF Flight Controls Lab16

17. Design and Optimization Some Approaches – Gradient Based Algorithms (just addressed) – Genetic Algorithms – Neural Networks – Structural Optimization (next slide) 5/4/2015UF Flight Controls Lab17

18. Optimization: Example Structural Optimization: put more material in the load path, less away from it, minimize total weight… 5/4/2015UF Flight Controls Lab18

19. Design Centric vs. Optimization Centric 5/4/2015UF Flight Controls Lab19 Design centric Optimization centric

20. Complex Project Scheduling 5/4/2015UF Flight Controls Lab20 Critical Path method (CPM) – Mathematically based algorithm to schedule a set of project activities CPM requires list of all the activities, their time duration and dependencies between the activities Determines the critical activities, shortest time to complete the project and floating time for each activity

21. Attention/Time Economy With complexity and information everywhere, your attention becomes a commodity: where will you put it? What are you looking at here? Why? Time banking Heat Maps 5/4/2015UF Flight Controls Lab21

22. More Uses for Your Computer.. Artificial Intelligence Numerical Methods Fractals, Using Automata Chaos theory, Game theory Genetic Algorithms: Monkey’s typing? Emergence/self-organization Example: Traveling Salesman Problem Example: Adaptive controls and robotics (2:11-2:45)Adaptive controls and robotics 5/4/2015UF Flight Controls Lab22

23. Extensions Old School Science: Quantification – Engineers want to use equations and numbers to describe things and processes. Is this a good way to handle complexity? A New Kind of Science (Wolfram) – Cellular Automata: Are we looking for a small pattern that builds the larger trend? Is this a good way to handle complexity? Complexity Theory – States that critically interacting components self-organize to form potentially evolving structures exhibiting a hierarchy of emergent system properties Adaptation in design suddenly is KEY, not perfect fixed relationships 5/4/2015UF Flight Controls Lab23

24. Activity Think of an application which requires you to deal with a large amount of information Invent a new way of dealing with large amounts of information (ex: tables, mind maps, graphs) – How do you take it in? – How do you present it to a person? – Why did you choose to present the data in that manner? – Can someone use your method to make a decision? If not, how would your method help them? 5/4/2015UF Flight Controls Lab24