Complexity, Agent-Based Modeling, and NetLogo

1. Complexity, Agent-Based Modeling, and NetLogo 2005 Summer Workshop on Agent-Based Modeling with NetLogo

2. Welcome to the 2005 Summer Workshop • Introductions • Overview of the Week • Intro to Agent-Based Modeling, Complexity, and NetLogo

3. Overview • Each Day of the workshop has been designed to provide you with maximum opportunity to immerse yourself learning NetLogo • We split the day between formal group sessions and ample “Learning Lab” time for you to learn NetLogo, either through one of seven “learning labs” or tutorials or by creating your own model

4. The Staff • Michael Gizzi, Mesa State CollegeDirector, Advanced Learning Center • Boyce Baker, Mesa State College, School District 51 and Advanced Learning Center • Richard Vail, Mesa State CollegeDirector, MBA Program • James Steiner Computer Support Specialist, Philadelphia, PA

5. The Schedule • Monday • Morning • Intro Session • Learning Lab • Afternoon • The Modeling Process • Learning Lab • Evening • Excursion to Colorado National Monument, Pizza Picnic

6. The Schedule • Tuesday • Morning • Flowcharting NetLogo Models • NetLogo Grammar • Learning Lab • Afternoon • Testing and Debugging Models • Learning Lab • Evening • On your own

7. The Schedule • Wednesday • Morning • Importing Data into NetLogo: GIS case-study • Learning Lab • Agent-sets and Lists • Lunch Discussion: Teaching Tips and Techniques • Afternoon • Optional: Exporting Data from NetLogo • Learning Lab • Evening - Poolside Dinner, Grand Junction Athletic Club

8. The Schedule • Thursday • Morning • Research Issues: Is there meaning? • Learning Lab • Afternoon • Optional: Extending a Model: Lessons from New Wealth Distribution • Learning Lab • Evening - Carlson Winery Tour and Dinner, Dos Hombres

9. The Schedule • Friday • Morning • Learning Lab • Presentations • Afternoon • Presentations • Evening - Farewell Happy Hour, The Alehouse Restaurant

10. What is this Agent-Based Modeling? • NetLogo is a computer simulation environment that creates “agent-based models” • Agent-Based Modeling is the primary technique used to study complexity theory or what we’ll call Complex Adaptive Systems (CAS)

11. How do you attack a large complex problem to find a solution? • We reduce it to its parts • Disassemble it • Get to its inner-workings • Examples: • Automobiles • Global Terrorism: Al Qaeda • Human Genome Project

12. Reductionism • The history of science can be characterized by what we’ll call reductionism • Through a process of specialization, scientists have worked hard to understand the world by breaking it down into its smallest pieces • Studying the component parts to figure out how the system works as a whole • Example: human biology • Much of what we know about nature is the result of this approach

13. Limits of Reductionism • Breaking systems down into component pieces does not always ensure that we know everything about the system when we “put it back together again” • The product is often larger than the sum of its parts

14. Complexity Science as a new paradigm • The science of complex systems offers an alternative to reductionism • It is based on the understanding that much of the world is not machine-like and comprehensible through a cataloging of its parts • But rather, consists of systems that are difficult to understand by traditional scientific analysis

15. Lets look at a few questions… • Why did individual cells form alliances some 600 million years ago, giving rise to multi-cellular organisms such as seaweeds, jellyfish, insects, and eventually humans? • What is a mind? How does a three-pound lump of ordinary matter (the brain) give rise to such qualities as feeling, thought, purpose, and awareness?

16. Why do humans spend so much time organizing themselves into families, tribes, communities, nations, and societies? • If evolution is just survival of the fittest, then why should it ever produce anything but ruthless competition between individuals? Why should there be any such thing as trust or cooperation?

17. What do these questions have in common? • Each question refers to a system that is complex in the sense that a great many independent agents are interacting with one another in a great many ways. • Think of the billions of chemically reacting proteins, lipids, and nucleic acids making up a living cell • Think of the millions of mutually interdependent individuals making up a human society

18. Self-organizing systems • The richness of these interactions allow the system as a whole to undergo spontaneous self-organization • People trying to satisfy their material needs unconsciously organize themselves into an economy through numerous individual acts of buying and selling • But there is no leader – no one consciously planning it. • The genes in a developing embryo organize themselves in one way to make a liver cell and in another to make a muscle cell. • Birds adapt to the actions of their neighbors, creating a flock

19. That are adaptive • Not only are these systems self-organizing, but they are adaptive • in that they don’t just passively respond to events like the way a rock might roll around in an earthquake • They try to turn whatever happens to their advantage • The brain organizes and reorganizes its billions of neuron connections to learn from experience • Species evolve for better survival in an changing environment – as do corporations and industries

20. Dynamic… • These complex, self-organizing, adaptive systems are dynamic in a way that makes them different from static objects like computer chips or snowflakes – which are just COMPLICATED • Complex systems are more spontaneous, more disorderly, more alive

21. and emergent… • Complex Systems are often considered to have “emergent” properties • Consider the graphic on the next slide: • At the bottom are the components of the system interacting locally • Above is a cloud, with 3 arrows shooting up from the cluster below • Finally two arrows emerge from the cloud towards the cluster

23. Emergence • From the interaction of the individual components emerges some kind of global property • Something you could not have predicted from what you know of the component parts • The global property (the emergent behavior) feeds back to influence the behavior of the individuals that produced it

24. Examples of Emergence • Ecosystem • The interaction of species within the community might provide it with a degree of stability – say resistance to the ravages of a hurricane or invasion by an alien species • Stability is an emergent property • Economics • The aggregate behavior of companies, consumers, and the financial markets produces the modern economy “as if guided by an invisible hand”

26. Lets do a little experiment.. • Its time for a brief “icebreaker” to get to know each other better … and explore an emergent behavior up close

27. Agents: the key to CAS • At their root, CAS consist of agents interacting on a local level resulting in global behavior • Agents take many forms • Protons, electrons, atoms, molecules • Cells, organs, organisms, species • Individuals, families, organizations, nations • Computer software programs

28. Characteristics of Agents • Agents can interact with their environment and with other agents • An agent can respond to what happens around it and can do things more or less purposefully • An agent can follow instructions

29. Strategy • The way an agent responds to its surroundings and pursues its goals • Example: an employee might help another co-worker in the hope that the co-worker will reciprocate • Strategies might change based on how successful they are • Typically human agents have some awareness of their own strategies

30. Agents and Rules • CAS is about local agents interacting with one another and with their environment • They do so through following simple – or complicated – rules of behavior • As we examine CAS, we want to know what rules the agents are using • Rules are simply a convenient way to describe agent strategies

31. Stimulus-Response Rules • Stimulus-response rules are typical and simple to understand: IF stimulus S occurs, THEN give response R.

32. Other Examples of Stimulus-Response Rules • For agents in the central nervous system (neurons):- STIMULI: Pulses arriving at each neuron’s surfaces- RESPONSE: outgoing pulses • For agents in the economy (firms):- STIMULI: Raw Materials and Money- RESPONSE: Goods produced

33. Its all about the rules • A major part of modeling CAS goes into selecting STIMULI-RESPONSE rules because they determine the behavior and strategy of the agents • Once we specify the range of possible stimuli and the set of allowed responses for as given agent, we have determined the kind of rules an agent can have.

34. NetLogo and Agent-Based Modeling • The study of large number of agents with changing patterns of interactions often gets too difficult for traditional mathematical solutions • But ideal for computer simulations in programs like NetLogo • Simulation models can be created to explore agent interactions and to see what occurs as a result of those interactions • This is what we will explore throughout the week