Introduction to Complex Systems: How to think like nature A bit presumptuous? Unintended consequences: mechanism, function, and purpose Besides, does nature really think? Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org • 1998-2007. The Aerospace Corporation. All Rights Reserved.
A fable • Once upon a time, a state in India had too many snakes. • To solve this problem the government instituted an incentive-based program to encourage its citizens to kill snakes. • It created the No Snake Left Alive program. • Anyone who brings a dead snake into a field office of the Dead Snake Control Authority (DSCA) will be paid a generous Dead Snake Bounty (DSB). • A year later the DSB budget was exhausted. DSCA had paid for a significant number of dead snakes. • But there was no noticeable reduction in the number of snakes plaguing the good citizens of the state. • What went wrong?
DSCA Receive dead snake certificate. Submit certificate to DSCA. Receive money. Catch, kill, and submit a dead snake. Dead snake verifier The DSCA mechanism What would you do if this mechanism were available in your world? Start a snake farm.
Moral: unintended consequences • The preceding is an example of what is sometimes called an unintended consequence. • It represents an entire category of (unintended and unexpected) phenomena in which • a mechanism is installed in an environment, but then • the mechanism is used/exploited in unanticipated ways. • Once a mechanism is installed in the environment, it will be used for whatever purposes “users” can think to make of it … • which may not be that for which it was originally intended. The first lesson of complex systems thinking is that one must always be aware of the relationship between systems and their environments. That’s how nature works.
Parasites that control their hosts • Dicrocoelium dendriticum causes host ants to climb grass blades where they are eaten by grazing animals, which is where D. dendriticum lives out its adult life. • Toxoplasma gondii cause mice not to fear cats, which is where T. gondii reproduces. • Spinochordodes tellinii causes host insects to jump into the water and drown, where S. tellinii grows to adulthood.
Follow the energy/money • Energy (and its proxy money) is fundamental. • Any mechanism that provides access to energy/money/resources is a potential target of unintended consequences. • A niche: • Energy (and its proxy money) is fundamental. • Any mechanism that provides access to energy/money/resources is a potential target of unintended consequences. • A niche: a way of extracting energy/money/ resources from an environment • Example: power is supplied to computer USB ports • Presumably to provide power for USB devices. • The wifi bridge uses the Internet (not USB) Port to transfer data. • But it gets its power from the USB port.
Locomotion in E. coli • [E. coli] movements consist of short straight runs, each lasting a second or less, punctuated by briefer episodes of random tumbling: each tumble reorients the cell and sets it off in a new direction. • Cells of E. coli are propelled by their flagella, four to ten slender filaments that project from random sites on the cell’s surface. … Despite their appearance and name (from the Greek for whip), flagella do not lash; they rotate quite rigidly, not unlike a ship’s propeller. … • A cell … can rotate [its] flagellum either clockwise or counter-clockwise. Runs and tumbles correspond to opposite senses of rotation. • When the flagella turn counter-clockwise [as seen from behind] the individual filaments coalesce into a helical bundle that rotates as a unit and thrusts the cell forward in a smooth straight run. … • Frequently and randomly the sense of the rotation is abruptly reversed, the flagellar bundle flies apart and the cell tumbles until the motor reverses once again. Harold, Franklyn M. (2001) The Way of the Cell: Molecules, Organisms, and the Order of Life, Oxford University Press.
Locomotion in E. coli • Cells that are moving up the gradient of an attractant … tumble less frequently than cells wandering in a homogeneous medium: while cells moving away from the source are more likely to tumble. In consequence, cells take longer runs toward the source and shorter ones away. • How can a cell “know” whether it is traveling up the gradient or down? It measures the attractant concentration at the present instant and “compares” it with that a few milliseconds ago. • E. coli can respond within a millisecond to local changes in concentration, and under optimal conditions readily detects a gradient as shallow as one part in a thousand over the length of a cell. Franklin Harold, The Way of the Cell
Philosophical interlude Mechanism, function, and purpose* • Mechanism: The results of physical processes within an entity. • The chemical reactions built into E.coli that result in its flagella movements. • The DSCA mechanism. • Function: The effect of a mechanism on the environment and on the relationship between an entity and its environment. • E. coli moves about. In particular, it moves up nutrient gradients. • Snakes are killed and delivered; money is exchanged. • Purpose: The consequence for the entity of the change in its environment or its relationship with its environment. • E. coli is better able to feed, which is necessary for self-persistence. • Snake farming is encouraged? Wikipedia Commons Socrates *Compare to Measures of Performance, Effectiveness, and Utility
NetLogo (http://ccl.northwestern.edu/netlogo/) describes itself as “a cross-platform multi-agent programmable modeling environment … for simulating natural and social phenomena.” • It is produced by the Center for Connected Learning and Computer-Based Modeling at Northwestern University. (Uri Wilensky) • It is intended primarily for education (high school, middle school and even earlier) and for qualitative modeling. • It is not a detailed modeling or analysis tool. • It is implemented in Java. • Version 4.0 was released September 2007. • It’s free to download, but it’s not open source. • It produces models that run both as applications and as applets. • It has a large library of models, which also run as both applications and applets, and which can be run directly from the website.
Let’s try it File > Models Library > Biology > Ants Click Open
Three tabs Interface tab:control the model. To run most models, press setup and then go. Press goagain to stop the run. Informationtab:documentation about the model Procedurestab:the model in NetLogo code Online guide: http://ccl.northwestern.edu/netlogo/docs/
Simple ant foraging model • Ant rules • If youare notcarrying food, • Move up the chemical-scent gradient, if any. • Pick up food, if any. • Otherwise move randomly. • If you are carrying food, move up the nest-scent gradient. When you reach the nest, deposit the food. • population: number of ants • diffusion-rate: rate at which the chemical (pheromone) spreads • evaporation-rate: rate at which chemical evaporates Turns plotting on/off. In “tolook-for-food” procedure, change “orange” to “blue”. Implemented chemically in real ants, by software in NetLogo. After running once, play around with the population, diffusion-rate, and evaporation-rate. Can you get this picture, with paths to all food sources simultaneously?
Applications, e.g., email, IM, Wikipedia WWW (HTML) — browsers + servers Presentation Session Transport Network Physical Two levels of emergence • No individual chemical reaction inside the ants is responsible for making them follow the rules that describe their behavior. • That the internal chemical reactions together do is an example of emergence. • No individual rule and no individual ant is responsible for the ant colony gathering food. • That the ants together bring about that result is a second level of emergence. Colony results Ant behaviors Ant chemistry As we’ll see later, each layer is called a level of abstraction Notice the similarity to layered communication protocols
Complex systems terms • Emergence. A level of abstraction that can be described independently of its implementation. • Examples include the movement E. coli and ants through space toward a food source, which can be described independently of how it is brought about. • Multi-scalar. Applicable to systems that are understood on multiple levels simultaneously, especially when a lower level implements the emergence of some functionality at a higher level. • E. coli motion and ant foraging are both examples of multi-scalar systems. Isn’t that true of all systems? System: a construct or collection of different elements that together produce results not obtainable by the elements alone. System: a construct or collection of different elements that together produce results not obtainable by the elements alone. — Eberhardt Rechtin Systems Architecting of Organizations: Why Eagles Can't Swim, CRC, 1999. We are in the business of producing emergence
Parasites that control their hosts Details follow
One more—because it’s so famous File > Models Library > Social Science > Segregation Click Open
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.
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?
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.
D. dendriticum spends its adult life inside the liver of its host. After mating, the eggs are excreted in the feces. The first intermediate host, the terrestrial snail (Cionella lubrica in the United States), eats the feces, and becomes infected by the larval parasites. … The snail tries to defend itself by walling the parasites off in cysts, which it then excretes and leaves behind in the grass. The second intermediate host, an ant (Formica fusca in the United States) swallows a cyst loaded with hundreds of juvenile lancet flukes. The parasites enter the gut and then drift through its body. Some move to a cluster of nerve cells where they take control of the ant's actions. Every evening the infested ant climbs to the top of a blade of grass until a grazing animal comes along and eats the grass—and the ant and the fluke. The fluke grows to adulthood and lives out its life inside the animal—where it reproduces, and the cycle continues. * Text and image from Wikipedia.org. Dicrocoelium dendriticum * See also, Shelby Martin, “The Petri Dish: The journeys of the brainwashing parasite,” The Stanford Daily, April 20, 2007. http://daily.stanford.edu/article/2007/4/20/thePetriDishTheJourneysOfTheBrainwashingParasite
The life cycle of T. gondii has two phases. The sexual part of the life cycle (coccidia like) takes place only in members of the Felidae family (domestic and wild cats). The asexual part of the life cycle can take place in any warm-blooded animal. T. gondii infections have the ability to change the behavior of rats and mice, making them drawn to rather than fearful of the scent of cats. This effect is advantageous to the parasite, which will be able to sexually reproduce if its host is eaten by a cat. The infection is almost surgical in its precision, as it does not impact a rat's other fears such as the fear of open spaces or of unfamiliar smelling food. * Text and image from Wikipedia.org. Toxoplasma gondii * See also, Charles Q. Choi, “Bizarre Human Brain Parasite Precisely Alters Fear,” Live Science, April 2, 2007. http://www.livescience.com/animals/070402_cat_urine.html
* Text and image from Wikipedia.org. Spinochordodes tellinii * • The nematomorph hairworm Spinochordodes tellinii is a parasitic worm whose larvae develop in Orthopteran insects. • When it is ready to leave the host, the parasite causes the host to jump into water, where it drowns, but which returns the parasite to the medium where it grows to adulthood. See also, James Owen, “Suicide Grasshoppers Brainwashed by Parasite Worms,” National Geographic News, September 1, 2005. http://news.nationalgeographic.com/news/2005/09/0901_050901_wormparasite.html