1. 1 Complex systems: How to think like nature Unintended consequences. Emergence: what’s right and what’s wrong with reductionism.

2. 2 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.

3. 3 Locomotion in E. coli Shana Topp and Justin P. Gallivan, “Guiding Bacteria with Small Molecules and RNA,” J. Amer. Chem. Soc. May 2007. http://pubs.acs.org/cgi-bin/sample.cgi/jacsat/2007/129/i21/pdf/ja0692480.pdf

4. 4 Mechanism, function, and purpose in E. coli Cells [which happen to be] 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, the cell takes 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

5. 5 Mechanism, function, and purpose* Mechanism: The results of physical processes within an entity. –E.g., the chemical reactions built into E.coli that result in its flagella movements. Function: The effect of a mechanism on the environment and on the relationship between an entity and its environment. –E.g., E. coli moves about. In particular, it moves up nutrient gradients. Purpose: The consequence for the entity of the change in its environment or its relationship with its environment. –E.g., E. coli is better able to feed, which is necessary for self-persistence. *Compare to Measures of Performance, Effectiveness, and Utility Wikipedia Commons Socrates Philosophical interlude

6. 6 How to drive a bacterium Shana Topp and Justin P. Gallivan “Guiding Bacteria with Small Molecules and RNA” J. Amer. Chem. Soc., May 2007. http://pubs.acs.org/cgi-bin/sample.cgi/jacsat/2007/129/i21/pdf/ja0692480.pdf Two ways: Fool its sensing equipment Fool its motor control. The unintended consequence of being a bacterium is the potential for this sort of manipulation.

7. 7 Complex systems terms Emergence. A level of abstraction that can be described independently of its implementation. –Examples include E. coli motion through space (runs and tumbles), 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 is a good example of a multi-scalar system. Two of the scales are the chemistry that result in the operation of its flagella and the motion of the entity as a whole through space. A system is a construct or collection of different elements that together produce results not obtainable by the elements alone. – Eberhard Rechtin. Isn’t that true of all systems? We are in the business of producing emergence

8. 8 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.

9. 9 Try it out File > Models Library > Biology > Termites Click Open

10. 10 Three tabs Interface tab: control the model. To run most models, press setup and then go. Press go again to stop the run. Information tab: documentation about the model Procedures tab: the model in NetLogo code Online guide: http://ccl.northwestern.edu/netlogo/docs/

11. 11 Termite rules Wander about aimlessly (randomly) until you bump into a wood chip. If you are not holding a wood chip –Pick up the new chip. –Move away from your current location. –Go back to wandering about aimlessly. If you are holding a wood chip –Put down your held chip in a nearby empty space. –Move away from your current location. –Go back to wandering about aimlessly. Net effect: wood chips are deposited near other wood chips, eventually forming a single pile. Wikipedia commons Run the program and watch what happens. Exercise: prove that this will always happen

12. 12 Reading the code: the Procedures tab to go search-for-chip find-new-pile put-down-chip end to search-for-chip ifelse pcolor = yellow [ set pcolor black set color orange fd 20 ] [ wiggle search-for-chip ] end to find-new-pile if pcolor != yellow [ wiggle find-new-pile ] end to put-down-chip ifelse pcolor = black [ set pcolor yellow set color white get-away ] [ rt random 360 fd 1 put-down-chip ] end to get-away rt random 360 fd 20 if pcolor != black [ get-away ] end to wiggle fd 1 rt random 50 lt random 50 end

13. 13 Two levels of emergence No individual line of code is responsible for making the termites behave the way they do, i.e., to follow the rules described two slides ago. That the lines of code together do that is an example of emergence. No individual rule is responsible for the gathering of the wood chips into a single pile. That the rules together bring about that result is a second level of emergence. Wood chips in one pile Termite behavior rules Lines of code As we’ll see later, each layer is called a level of abstraction Notice the similarity to layered communication protocols

14. 14 Let’s try another one File > Models Library > Biology > Ants Click Open

15. 15 population: number of ants diffusion-rate: rate at which the chemical (pheromone) spreads evaporation-rate: rate at which chemical evaporates Ant rules If you are not carrying 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. In “to look-for-food”, change “orange” to “blue”. After running once, play around with the population, diffusion-rate, and evaporation-rate. Simple ant foraging model Turns plotting on/off.

16. 16 Group-level emergence Both the termite and ant models illustrate emergence (and multi- scalarity). In both cases, individual, local, low-level rules and interactions produce “emergent” higher level results. –The wood chips were gathered into a single pile. –The food was brought to the nest. Ant and termite colonies may seem different from E. coli because we see them as groups of individual entities, often called agents. But emergence as a phenomenon is the same. In both cases we can explain the design of the system, i.e., how the system works. In Evolution for Everyone, David Sloan Wilson argues that all biological and social elements are best understood as both groups and entities. http://evolution.binghamton.edu/dswilson/

17. 17 Breeding groups/teams Chickens are fiercely competitive for food and water. Commercial birds are beak-trimmed to reduce cannibalization. Breeding individual chickens to yield more eggs compounds the problem. Chickens that produce more eggs are more competitive. Instead Muir bred chickens by groups. At the end of the experiment Muir's birds' mortality rate was 1/20 that of the control group. His chickens produced three percent more eggs per chicken and 45% more eggs per group. Traditional evolutionary theory says there is no such thing as group selection, only individual selection. Bill Muir (Purdue) demonstrated that was wrong. Evolutionary processes are fundamental to complex systems Wikipedia commons http://www.ansc.purdue.edu/faculty/muir_r.htm

18. 18 Emergence demystified As we said earlier, emergence is simply the consequence of a design, i.e., components interacting. The design might be –naturally arising, i.e., created and forged by evolution –man-made. It might relate components of what would normally be considered an entity. –The emergent property is at the entity level. –In might be mechanical, e.g., a clock with lots of gears. Or, it might relate “agents” interacting as part of what would normally be considered a collective. –The emergent property is at the collective level. Emergence doesn't necessarily imply a complicated system. Emergence: the existence of a phenomenon that can be described independently of its implementation.

19. 19 One more—because it’s so famous File > Models Library > Social Science > Segregation Click Open

20. 20 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.

21. 21 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?

22. 22 More emergence: a satellite in a geostationary orbit The satellite is fixed with respect to the earth as a reference frame. But nothing is tying it down; no cable is holding it in place. Wikipedia commons period of the orbit = period of the earth’s rotation Mechanism: ?Function: ?Purpose: ? This example is typical of complex system mechanisms. Multiple independent or quasi-independent processes — which are not directly connected causally — interact within an environment to produce a result. In some ways the simplest possible complex system.

23. 23 More emergence: E. coli (again) E. coli has genes to produce lactase, which digests lactose. But it should produce lactase only when lactose is present. Imagine that you were asked to design a system that would produce a product only under certain conditions. How would you do it?

24. 24 A (quasi-top-down) functional analysis solution Lactose sensor How does one know one can build these pieces? What enables the interfaces? What holds it all together? The unasked questions Lactase production system Switch Off On Is it really top-down?

25. 25 Let lactose flip its own switch Lactose Repressor Lactose itself binds to the repressor, pulling it out of the way. lacYlacAlacX RNAP Repressor Three lac genes RNA polymerase can’t bind to DNA. Transcription blocked. lacYlacAlacX RNAP lac operon It’s often said that a first step in systems engineering is to agree on the system boundaries. What are the system boundaries in this case? It’s often said that a first step in systems engineering is to agree on the system boundaries. What are the system boundaries in this case? See movie http://www.biologycorner.com/bio4/notes/gene-expression.php.http://www.biologycorner.com/bio4/notes/gene-expression.php How does one know one can build these pieces? What enables the interfaces? What holds it all together? The unasked questions Nature is notoriously bottom-up Lactose Repressor lacYlacAlacX RNA polymerase can now bind to DNA. Transcription enabled. The genes are expressed. RNAP

26. 26 Emergence Naturally occurring and Designed Entities and Groups Naturally occurringHuman designedCombination Entity E. coli locomotion, virtually any biological entity (including ourselves), … Computer, satellite, integrated system, … Nation state, chicken group, market economy, religious unit, … Group Ant/termite colony, human family/tribe, … Government, corporation, military unit (SMC), … (Multi-national) system of systems, … Don’t take these categories too seriously. There is lots of overlap. As Wilson says, everything is both an entity and a group.

27. 27 The Game of Life Click Open File > Models Library > Computer Science > Cellular Automata > Life

28. 28 Try it out ifelse live-neighbors = 3 [ cell-birth ] [ if live-neighbors != 2 [ cell-death ] ] Simple rule at each cell Try a few runs. setup-random go-forever People love the Game of Life because one gets amazing complexity from a very simple rule. Try add-cells while it’s running. Zoom (Ctrl =); stop; setup-blank; add-cells; build a glider. A glider is an emergent epiphenomenon. What is its ontological status? Try add-cells while it’s running. Zoom (Ctrl =); stop; setup-blank; add-cells; build a glider. A glider is an emergent epiphenomenon. What is its ontological status? What about you, me, Theseus’s ship?

29. 29 The Game of Life is a programmable platform Go to http://www.math.com/students/wonders/life/life.html –Alternative: http://www.ibiblio.org/lifepatterns/ Scroll down about 70% and click Run Gun 30. Expand to full screen before clicking Go. Open Glider Guns. Generates gliders with different periods. Zoom: 2. Open Primer. Speed: Don’t skip. Zoom: 0. Apparently implements the Sieve of Eratosthenes. Can program a Turing Machine. Not shown here. Gave rise to “digital physics.” Nature as a cellular automaton. Fredkin, Zeus, and Wolfram. Not just a component that performs a specified function The Game of Life is a programmable platform

30. 30 The reductionist blind spot: epiphenomena and downward entailment Wikipedia Commons Socrates Philosophical interlude Can everything be reduced to physics? –“[L]iving matter, while not eluding the ‘laws of physics’ … is likely to involve ‘other laws,’ [which] will form just as integral a part of [its] science.” Erwin Schrödinger, What is Life?, 1944. –“At each level of complexity entirely new properties appear. … The whole becomes not only more than but very different from the sum of its parts. Philip Anderson, “More is Different,” 1972. Emergence Gliders and Game of Life (GoL) Turing Machines are epiphenomena. (They have no causal power.) –They are causally but not ontologically reducible. But computability theory applies to GoL Turing Machines. –Hence it is undecidable whether a GoL configuration will become stable. (Downward entailment.) –Reducing away epiphenomena produces a reductionist blind spot.

31. 31 Complexity typically involves multiple autonomous components Poincare showed a century ago that the three-body gravitational problem has no general solution. Distributed software is the most difficult to write. Making effective use of multi-core computers will be the computing challenge of the next decade. Recall the ants and termites; the snake control authority and the snake farmers. All are multi-agent. Command and Control (or organization management) is still more art than science. All this contrasts with monolithic top-down control. Wikipedia commons When multiple autonomous entities interact within an environment, one often gets emergent results. Systems of that sort tend to be called complex. } Imagine having to program the Game of Life. Derogatory terms in the complex systems world Power to the edge!