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**New Mexico Computer Science for All**

Exploring Complex Systems through Computer Models By Irene Lee December 27, 2012 Hi, my name is Irene Lee. I am the principal investigator on NM CSforAll, In this video I’m going to introduce the program.

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Outline Introduction to complex systems What are they Why do we study them How do we study them

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**What is a complex system?**

Complex (adj.) difficult-to-understand or difficult to predict System (noun) A group of interacting, interrelated, or interdependent parts forming a whole. A “Complex System” is collections of simple units or agents interacting in a system. Large- scale behaviors of the system are difficult to understand or difficult to predict and may change, evolve, or adapt. There’s a joke here… a complex system is greater than the sum of its parts. As we will see, a complex system, like its definition, is greater than the sum of its parts.

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**Characteristics of Complex Adaptive Systems**

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**Characteristics of Complex Adaptive Systems**

Leaderless (a.k.a. decentralized)

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**A classic example Birds Flocking**

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**A classic example flocking - Craig Reynolds**

Separation: steer to avoid crowding local flockmates Alignment: steer towards the average heading of local flockmates Cohesion: steer to move toward the average position of local flockmates

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**A classic example Boids - Craig Reynolds**

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**Characteristics of Complex Adaptive Systems**

Emergent patterns develop from the simple interactions of agents

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**A classic example Termites**

Termites model

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**A classic example Mound building in StarLogo TNG**

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**Characteristics of Complex Adaptive Systems**

Non-linear The sum of the parts is not equal to the whole.

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**Non-linear means: f(a+b) f(a) + f(b)**

In Mathematics Non-linear means: f(a+b) f(a) + f(b) Ex.) the exponential function is non-linear. f(2 + 3) f(2) + f(3) f(5) f(2) + f(3) *Non-linear systems are systems that cannot be mathematically described as the sum of their components.

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**Characteristics of Complex Adaptive Systems**

Self-organization The system organizes itself.

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**A classic example Schelling Segregation Model**

Developed by Thomas C. Schelling (Micromotives and Macrobehavior, 1978).

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**A classic example Schelling Segregation Model**

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**4 Characteristics of Complex Adaptive Systems**

1. Leaderless there is no leader (boids) 2. Emergent patterns develop from the simple interactions of agents. (termites) 3. Non-linear The sum of the parts does not equal the whole. 4. Self-organization The system organizes itself

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**Why is it important to learn about complex systems and approaches to understanding complex systems?**

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**Many of the daunting problems of the 21st Century can be studied as complex systems problems.**

Climate change Loss of biodiversity Pollution Civil violence Spread of disease Emergency Egress Traffic jams Forest fire

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**Epidemics Epidemics are studied as complex systems.**

Humans travel and have social networks through which they transmit disease etc. Non-linear growth / feedback Simple rules Randomness Emergent patterns - pockets of resistance, outbreaks Hufnagel, L. et al PNAS 101: Forecast and control of epidemics in a globalized world Copyright ©2004 by the National Academy of Sciences

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**Epidemics NATURE|Vol 460|6 August 2009 Science in the 21st Century**

Computer models are used by scientists to understand complex systems and possibly prevent (or study interventions for) daunting problems. Such as epidemics. Josh Epstein in a recent issue of Nature states: “As the world braces for an autumn wave of swine flu (H1N1), the relatively new technique of agent-based computational modelling is playing a central part in mapping the disease’s possible spread, and designing policies for its mitigation. … Classical epidemic modelling, which began in the 1920s, was built on differential equations. These models assume that the population is perfectly mixed, with people moving from the susceptible pool, to the infected one, to the recovered (or dead) one. Within these pools, everyone is identical, and no one adapts their behaviour. But such models are ill-suited to capturing complex social networks and the direct contacts between individuals, who adapt their behaviours — perhaps irrationally — based on disease prevalence. Agent-based models (ABMs) embrace this complexity. ABMs are artificial societies: every single person (or ‘agent’) is represented as a distinct software individual., “ Deputy LANL director, MacBranch, said models were useful to gain an “intuition” about large complex systems NATURE|Vol 460|6 August 2009

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**Networks upload.wikimedia.org/.../Internet_map_4096.png**

Visualization of the various routes through a portion of the Internet The internet, Non-linear growth Adaptive Local interactions / global structure Feedback, the more get more, distribution of a few very big sites and lots of smaller sites, Self-organized leaderless upload.wikimedia.org/.../Internet_map_4096.png

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**Ocean Circulation - Ecosystems**

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**Transportation Systems**

Southwest Airlines Cargo Bottleneck

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Workflow Simulation Eli Lilly R&D Workflow Simulation and Portfolio Scheduling

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Crowd Dynamics

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Crowd Dynamics

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Crowd Dynamics

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We will learn about agent-based modeling and simulation as an approach to understanding complex systems

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**The Computational Science Process**

Begin here NetLogo is a tool used to create a Computational Model

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