Introduction to Complex Systems: How to think like nature Emergence: what’s right and what’s wrong with reductionism Russ Abbott Sr. Engr. Spec. Rotn to CCAE A bit presumptuous? 310-336-1398 Russ.Abbott@Aero.org 1998-2007. The Aerospace Corporation. All Rights Reserved. 1 Complex systems: How to think like nature • Unintended consequences. • Emergence: what’s right and what’s wrong with reductionism. 2 Try it out File > Models Library > Biology > Termites Click Open 3 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/ 4 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 Wikipedia commons – 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. Run the program and watch what happens. Wikipedia commons Exercise: prove that this will always happen 5 Reading the code: the Procedures tab to go search-for-chip find-new-pile put-down-chip 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 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 get-away rt random 360 fd 20 if pcolor != black [ get-away ] end to wiggle fd 1 rt random 50 lt random 50 end 6 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. Wood chips in one pile Termite behavior rules • That the lines of code together do that is an example of emergence. Lines of code • No individual rule is responsible for the gathering of the wood chips into a single pile. As we’ll see later, each layer is called a • That the rules together bring about that result is a second level of emergence. level of abstraction Notice the similarity to layered communication protocols 7 Let’s try another one File > Models Library > Biology > Ants Click Open 8 Simple ant foraging model 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. • population: number of ants • diffusion-rate: rate at which the chemical (pheromone) spreads • evaporation-rate: rate at which chemical evaporates In “to look-for-food”, change “orange” to “blue”. Turns plotting on/off. After running once, play around with the population, diffusion-rate, and evaporation-rate. 9 Group-level emergence • Both the termite and ant models illustrate emergence (and multiscalarity). • 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/ 10 Breeding groups/teams Evolutionary processes are fundamental to complex systems Traditional evolutionary theory says there is no such thing as group selection, only individual selection. Bill Muir (Purdue) demonstrated that was wrong. • Chickens are fiercely competitive for food and water. http://www.ansc.purdue.edu/faculty/muir_r.htm • 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. Wikipedia commons • 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. 11 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. 12 One more—because it’s so famous File > Models Library > Social Science > Segregation Click Open 13 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. 14 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) Want a separate slider with [color = [color] of myself] for %-other-wanted? 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. 15 More emergence: a satellite in a geostationary orbit In some ways the simplest possible complex system. • 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. period of the orbit = period of the earth’s rotation 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. Mechanism: ? Function: ? Purpose: ? 16 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? 17 A (quasi-top-down) functional analysis solution Lactose sensor Off Switch On Lactase production system The unasked questions • How does one know one can build these pieces? • What enables the interfaces? • What holds it all together? Is it really top-down? 18 RNA polymerase can’t bind to DNA. Transcription blocked. Let lactose flip its own switch lac operon Three lac genes RNAP Repressor Lactose itself binds to the repressor, pulling it out of the way. lacX lacY lacA RNAP lacX lacY RNAP lacX The unasked questions • How does one know one can build these pieces? • What enables the interfaces? • What holds it all together? Nature is notoriously bottom-up lacA lacY lacA RNA polymerase can now bind to DNA. Transcription enabled. The genes are expressed. • 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. 19 Emergence Naturally occurring and Designed Entities and Groups Naturally occurring Entity E. coli locomotion, virtually any biological entity (including ourselves), … Ant/termite colony, Group human family/tribe, … Human designed Computer, satellite, integrated system, … Combination Nation state, chicken group, market economy, religious unit, … Government, (Multi-national) corporation, system of systems, military unit (SMC), … … Don’t take these categories too seriously. There is lots of overlap. As Wilson says, everything is both an entity and a group. 20 The Game of Life File > Models Library > Computer Science > Cellular Automata > Life Click Open 21 Try it out Simple rule at each cell ifelse live-neighbors = 3 [ cell-birth ] [ if live-neighbors != 2 [ cell-death ] ] People love the Game of Life because one gets amazing complexity from a very simple rule. Try a few runs. • setup-random • go-forever What about you, me, Theseus’s ship? • 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? 22 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. Not just a component that • Open Glider Guns. performs a specified function • 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. 23 The reductionist blind spot: epiphenomena and downward entailment • 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. • Gliders and Game of Life (GoL) Turing Machines are epiphenomena. (They have no causal power.) – They are causally but not ontologically reducible. Wikipedia Commons – “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. Socrates • 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. 24 Complexity typically involves multiple autonomous components • When multiple autonomous entities interact within an environment, one often gets emergent results. • Systems of that sort tend to be called complex. • 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. Derogatory terms in the complex systems world Wikipedia commons Imagine having to program the Game of Life. Power to the edge!25