1. 1 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Modeling, the externalization of thought, and how engineers and computer scientists think Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

2. 2 Schelling’s Segregation Model File > Models Library > Social Science > Segregation Click Open

3. 3 Credited with being the first agent-based model Reasonable micro-level preferences produce macro-level segregation. Each agent wants the percentage of like agents to be as indicated in %-similarity wanted. –Similar agents/total agents. Empty neighbors ignored. Starts out at ~50% similar since scattered at random. But some are unhappy. They move to a random empty spot. Repeat until all agents happy. Easier to see if more agents. Set number to 2500 agents. 30%-similarity-wanted produces 75% similarity. 40%-similarity-wanted produces 80% similarity. Try this. Set %-similarity-wanted to 75%. (Ethnic cleansing!) At about 2% unhappy, set it to 76%. Switch back and forth. An artifact of the model.

4. 4 Lots of artifacts Counts only 8 neighbors. Can mitigate clustering (and produce stripes at 30%-similar-wanted) by adding one line. to update-turtles ask turtles [ ;; in next two lines, we use "neighbors" to test the eight patches ;; surrounding the current patch set similar-nearby count (turtles-on neighbors) with [color = [color] of myself] set other-nearby count (turtles-on neighbors) with [color != [color] of myself] set total-nearby similar-nearby + other-nearby set happy? similar-nearby >= ( %-similar-wanted * total-nearby / 100 ) and other-nearby >= ( %-similar-wanted * total-nearby / 200 ) ] end Sets non-similar requirement to be half as many as similar requirement. Want a separate slider for %-other-wanted?

5. 5 What to conclude from the segregation model? Models can illustrate mechanisms, e.g., for “self- organization” such as clusters and stripes. Models can offer insight but often do not provide complete answers. –What else do the agents want? Good schools, safe neighborhoods? Etc. –What do they really mean by “similar”? Etc. Models can be overly simple. Models can be manipulated.

6. 6 Modeling problems: the difficulty of looking downward Strict reductionism implies that it is impossible to find a non-arbitrary base level for models. –What are we leaving out that might matter? Use Morse code to transmit messages on encrypted lines. No good models of biological arms races. –Combatants exploit and/or disrupt or otherwise foil each other’s epiphenomena. Insects vs. plants: bark, bark boring, toxin, anti-toxin, …. Geckos use the Van der Waals “force” to climb. Models of computer security or terrorism will always be incomplete. Can only model unimaginative enemies. Epiphenomenal Nature is not segmented into a strictly layered hierarchy.

7. 7 Don’t know how to build models that can notice emergent phenomena and characterize their interactions. We don’t know what we aren’t noticing. –We/they can use our commercial airline system to deliver mail/bombs. Model gravity as an agent-based system. –Ask system to find equation of earth’s orbit. –Once told what to look for, system can find ellipse. (GP) –But it won’t notice the yearly cycle of the seasons — even though it is similarly emergent. Modeling problems: the difficulty of looking upward Models of computer security or terrorism will always be incomplete. Can only model unimaginative enemies. Exploit an existing process

8. 8 Intellectual leverage in Computer Science: executable externalized thought Engineers and Computer Scientists both turn ideas into reality. The first step in turning ideas into reality is to externalize them in a form that allows them to be examined and explored. Computer languages enable executable externalized thought— different from all other forms of externalized thought throughout history! –There is nothing comparable in engineering—or any other field. –All other forms of externalized thought require a human being to interpret them. The bit provides a floor that is both symbolic and real. –Bits are: symbolic, physically real, and atomic. –Bits don’t have error bars. –Can build (ontologically real) levels of abstraction above them. But the bit limits realistic modeling. –E.g., no good models of evolutionary arms races and many other multi-scale (biological) phenomena. No justifiable floor. –Challenge: build a computer modeling framework that supports dynamically varying floors.

9. 9 Engineering is both cursed and blessed by its attachment to physicality. –There is no reliable floor. “Engineering systems often fail … because of [unanticipated interactions among well designed components, e.g. acoustic coupling] that could not be identified in isolation from the operation of the full systems.” National Academy of Engineering, Design in the New Millennium, 2000. –But if a problem appears, engineers (like scientists) can dig down to a lower level to solve it. Intellectual leverage in Engineering: mathematical modeling Engineering gains intellectual leverage through mathematical modeling and functional decomposition. –Models approximate an underlying reality (physics). –Models are judged by the width of their error bars. –Models don’t create ontologically independent entities.

10. 10 Engineers and computer scientists are different — almost as different as Venus and Mars Computer scientists live in a world of abstractions. –Physics has very little to do with computer science worlds. –For computer scientists, there is more than physics, i.e., emergence—but may have had a hard time saying what it is. –When designing systems, Computer scientists start with the bit and build it up to the idea—using levels of abstraction. Computer science is (cautiously) applied philosophy. Computer scientists live in a world of abstractions. –Physics has very little to do with computer science worlds. –For computer scientists, there is more than physics, i.e., emergence—but may have had a hard time saying what it is. –When designing systems, Computer scientists start with the bit and build it up to the idea—using levels of abstraction. Computer science is (cautiously) applied philosophy. Engineers are grounded in physics. –Ultimately there is nothing besides physics. –Even though engineers build things that have very different (emergent) properties from their components, engineers tend to think at the level of physics. –When designing systems, engineers start with an idea and build it down to the physics—using functional decomposition. Engineering is (proudly) applied physics. Engineers are grounded in physics. –Ultimately there is nothing besides physics. –Even though engineers build things that have very different (emergent) properties from their components, engineers tend to think at the level of physics. –When designing systems, engineers start with an idea and build it down to the physics—using functional decomposition. Engineering is (proudly) applied physics.