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Principles of Complex Systems

How to think like nature: Part II

Russ Abbott

Does nature really think?

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Complex systems overview

Part 1.Introduction and motivation.Overview – unintended consequences, mechanism, function, and purpose; levels of abstraction, emergence, introduction to NetLogo.Emergence, levels of abstraction, and the reductionist blind spot.Modeling; thought externalization; how engineers and computer scientists think.Part 2.

Evolution and evolutionary computing.Innovation – exploration and exploitation.Platforms – distributed control and systems of systems.

Groups – how nature builds systems; the wisdom of crowds. Summary/conclusions – remember this if nothing else.

Lots of echoes and repeated themes from one section to another.

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Are there autonomous higher level laws of nature?Fodor cites Gresham’s law.

The fundamental dilemma of science

How can that be if everything can be reduced to the fundamental laws of physics?

The functionalist claimThe reductionist position

It can all be explained in terms of levels of abstraction.

My answer

Emergence

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Game of Life Gliders

A 2-dimensional cellular automaton. The Game of Life rules determine everything that happens on the grid.• A dead cell with exactly three live neighbors becomes alive. • A live cell with either two or three live neighbors stays alive. • In all other cases, a cell dies or remains dead.

The “glider” pattern

Nothing really moves. Just cells going on and off.

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The Game of Life

Click Open

File > Models Library > Computer Science > Cellular Automata > Life

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• Gliders are causally powerless.– A glider does not change how the rules operate or which cells will be

switched on and off. A glider doesn’t “go to a cell and turn it on.”– A Game of Life run will proceed in exactly the same way whether one

notices the gliders or not. A very reductionist stance.

• But …– One can write down equations that characterize glider motion and

predict whether—and if so when—a glider will “turn on” a particular cell.– What is the status of those equations? Are they higher level laws?

Gliders

Like shadows, they don’t “do” anything.The rules are the only “forces!”

Good GoL website

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• Amazing as they are, gliders are also trivial.– Once we know how to build a glider, it’s simple

to make as many of them as we want.• Can build a library of Game of Life patterns and

their interaction APIs.

By suitably arranging these patterns, one can simulate a Turing Machine. Paul Rendell. http://rendell.server.org.uk/gol/tmdetails.htm

Game of Life as a Programming Platform

A second level of emergence.Emergence is not particularly mysterious.

What does it mean to compute with shadows?

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Downward causation

• The unsolvability of the TM halting problem entails the unsolvability of the GoL halting problem.

– How strange! We can conclude something about the GoL because we know something about Turing Machines.

– Yet Turing Machines are just shadows in the GoL world.– And the theory of computation is not derivable from GoL rules.

Downward causation entailment

“Reduce” GoL unsolvability to TM unsolvability by constructing a TM within the GoL.

Paul Davies, “The physics of downward causation” in Philip Clayton (Claremont Graduate University), Paul Davies (Macquarie/NSW/Arizona State University), The re-emergence of emergence, 2006

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A GoL Turing machine … • … is an entity.

– Like a glider, it is recognizable; it has reduced entropy; it persists and has coherence—even though it is nothing but patterns created by cells going on and off.

• … obeys laws from the theory of computability.

… is a GoL phenomenon that obeys laws that are independent of the GoL rules while at the same time being completely determined by the GoL rules.

Reductionism holds. Everything that happens on a GoL grid is a result of the application of the GoL rules and nothing else.

Computability theory is independent of the GoL rules.

Just as Schrödinger said.

Living 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. — Schrödinger

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Level of abstraction:causally reducible yet ontologically real

A collection of entities and relationships that can be described independently of their implementation. A Turing machine; biological entities; every computer

application, e.g., PowerPoint.

When implemented, a level of abstraction is causally reducible to its implementation. You can look at the implementation to see how it works.

Its independent description makes it ontologically real. How it behaves depends on its description at its level of

abstraction, which is independent of its implementation. The description can’t be reduced away to the implementation

without losing information. If the level of abstraction is about nature, reducing it away is

bad science.

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Supervenience

• A set of predicates H (for Higher-level) about a world supervenes on a set of predicates L (for Lower-level) if it is never the case that two states of affairs of that world will assign the same configuration of truth values to the elements of L but different configurations of truth values to the elements of H.

– In other words, L fixes H.– Or, no change in H without a change in L.

• Think of L as statements in physics and H as statements in a Higher-level (“special”) science.

• Or of L as statements in a computer program and H as the specification of the program’s functionality.

• Or of L as a description of cells on the GoL grid (which are on and which are off) and H as a description of the patterns (like gliders) on the grid.

Developed originally in philosophy of mind in an attempt to link mind and brain.

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Supervenience example

H: {An odd number of bits is on:. True, False. The bits that are on are the start of the Fibonacci sequence: False, False. The bits that are on represent the value 27: False, True. …}

H supervenes over L1.The truth value of a statement in H depends on the truth values of the statements in L1.

But not over L2. The H statement“An odd number of bits is on.”

can be either true or false (by varying bit 2) without changing the truth values in L2—since L2 ignores bit 2.

The world in two different states

L1: {Bit 0 is on., Bit 1 is on., Bit 2 is on. Bit 3 is on., Bit 4 is on.}

t, t, t, t, tt, t, f, t, t

L2: {Bit 0 is on., Bit 1 is on., Bit 3 is on., Bit 4 is on.}

t, t, t, t

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Evolution as a level of abstraction• Darwin and Wallace’s theory of evolution by natural selection is

expressed in terms of – entities– their properties– how suitable the properties of the entities are for the environment– populations– reproduction– etc.

• These concepts are a level of abstraction. – The theory of evolution is about entities at that level of abstraction.

• Let’s assume that it’s (theoretically) possible to trace how any state of the world—including the biological organisms in it—came about by tracking elementary particles

• Even so, it is not possible to express the theory of evolution in terms of elementary particles.

• Reducing everything to the level of physics, i.e., naïve reductionism, results in a blind spot regarding higher level entities and the laws that govern them.

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How are levels of abstraction built?• By adding persistent constraints to what exists.

– Constraints “break symmetry” by ruling out possible future states.• Should be able to relate this to symmetry breaking more generally.

• Easy in software. – Software constrains a computer to operate in a certain way.– Software (or a pattern set on a Game of Life grid) “breaks the

symmetry” of possible sequences of future states. • How does nature build levels of abstraction? Two ways.

– Energy wells produce static entities.• Atoms, molecules, solar systems, …

– Activity patterns use imported energy to produce dynamic entities.• The constraint is imposed by the homeostatic processes that

the dynamic entity employs to maintain its structure.• Biological entities, social entities, hurricanes.

• A constrained system operates differently (has additional laws—the constraints) from one that isn’t constrained.

Isn’t this just common sense?A TM GoL acts differently from a random configuration.

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Not surprisingA constrained system is likely to obey special rules

How can you use two tablespoons of water to break a window?

Russ Abbott

4. Hurl the “water stone” at the window.

2. Freeze the water, thereby constraining its molecules into a rigid lattice structure.

3. Remove the frozen water from the tray.

1. Spoon the water into an ice cube tray.

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Not surprisingA constrained system is likely to obey special rules

So if we constrain the GoL to act like a TM, it shouldn’t be surprising that it is governed by TM laws.

How can you use two tablespoons of water to break a window? Russ Abbott

4. Hurl the “water stone” at the window.

2. Freeze the water, thereby constraining its molecules into a rigid lattice structure.

3. Remove the frozen water from the tray.

A phase transition often signals the imposition or removal of a constraint.

1. Spoon the water into an ice cube tray.

Frozen water implements a solid. It can be used like a solid, and it obeys the laws of solids. (That’s because it is a solid—which is an abstraction.) Is this a trivial observation? Is it just common sense?

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Categories of entitiesNaturally occurring Human designed

Energy Status

Static. At an energy equilibrium; in an “energy well.” Supervenience is useful.

Atoms, molecules, solar systems, …Homeostatic mechanisms: lowest energy state.

Tables, boats, houses, cars, ships, …Homeostatic mechanisms: few; generally dependent to “maintenance” processes,

Dynamic. Must import energy (and usually other resources) to persist. Supervenience is not useful.

Hurricanes(!), biological organisms, biological groups, …Homeostatic mechanisms: specialized for individual cases.

Social groups such as governments, corporations, clubs, the ship of Theseus(!), …Homeostatic mechanisms: specialized for individual cases, ranging from force to incentives.

Subsidized. Energy is not relevant since it is provided “for free” within a “laboratory” which has built-in support for entities.

Ideas, concepts, “memes,” … The elements of a conceptual system. (This paper is not about consciousness. This category just fits here.)Homeostatic mechanisms: don’t understand how memory works.

The “first class” values—such as objects, classes, class instances, etc.—within a computational system.Homeostatic mechanisms: generally not required since no natural degradation.

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Does nature use levels of abstraction?• Given the imposition of some (random) constraints,

what entities result? Two possibilities.–There are none, or they don’t persist. Back to nature’s drawing board.–They persist and by their interaction create a new level of abstraction.–Nature then asks: what can I build on top of that? (Think James

Burke’s Connections.) • Software developers do the same thing.

• It’s all very bottom-up—and in nature’s case random. Each new entity or level of abstraction creates a range of possible laws/mechanisms that didn’t exist before.

• These could not have been “deduced” from lower levels—except through exhaustive enumeration—any more than a new piece of software can be “deduced” from the programming language in which it is written.

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Principle of ontological emergence.

Extant levels of abstraction are those whose implementations have materialized and whose environments enable their persistence.

In some sense it is possible to “deduce” the theory of every natural process and reconstruct the universe, but the reconstruction will involve random constructions or exhaustive trials.

Recall Einstein vs. Anderson[Starting with the basic laws of physics] it ought to be possible to arrive at … the theory of every natural process, including life, by means of pure deduction. — Einstein

The ability to reduce everything to simple fundamental laws [does not imply] the ability to start from those laws and reconstruct the universe. — Anderson

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Practical corollary: feasibility ranges

• Creating or breaking a level of abstraction frequently corresponds to a phase transition.

• Physical levels of abstraction are implemented only within feasibility ranges.

• When the feasibility range is exceeded a phase transition generally occurs.

Require contractors to identify the feasibility range within which the implementation will succeed and describe the steps taken to ensure that those feasibility ranges are honored—and what happens if they are not. (Think O-rings.)

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Principles of Complex Systems: How to think like nature

Modeling, the externalization of thought, and how engineers and computer scientists thinkRuss Abbott

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Modeling problems:the difficulty of looking downward

• It is not possible 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.– 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.

EpiphenomenalNature is not segmented into a strictly layered hierarchy.

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• 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.

– 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

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Turning dreams into reality• Computer Scientists and Engineers both turn

dreams (ideas) into reality—systems that operate in the world.

• But we do it in very different ways.

• Humanists turn reality into dreams. —Debora Shuger.• Mathematicians turn coffee into theorems. —Paul Erdos.

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How do we externalize thought?

Which one is different? Why?

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Intellectual leverage in Computer Science: executable externalized thought

• Computer languages enable executable externalized thought—different from (nearly) all other forms of externalized thought throughout history!

– Software is both intentional—has meaning—and executable.– All other forms of externalized thought (except music) 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.– Absolutely solid and concrete—in a virtual sort of way.– 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.

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• Engineering is both cursed and blessed by its attachment to physicality.

– There is no reliable floor (like the bit) in the material world.• 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 don’t create ontologically independent entities.

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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, but we

may have a hard time saying what it is—emergence.– When designing systems, Computer scientists start with the

bit and build 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 down to the physics—using functional decomposition and successive approximation.

• Engineering is (proudly) applied physics. Engineering is the art of directingthe great sources of power in naturefor the use and convenience of man. —Thomas Tredgold (1828) 

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Principles of Complex Systems: How to think like nature

Evolution: how nature thinks

Russ Abbott

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Peppered moths: evolution in action

• Originally, the vast majority of peppered moths in Manchester, England had light coloration—which camouflaged them from predators since they blended into the light-colored trees.

• With the industrial revolution:– Pollution blackened the trees.– Light-colored moths died off. – Dark-colored moths flourished.

• With improved environmental standards, light-colored peppered moths have again become common.

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Try it out

File > Models Library > Biology > Evolution > Peppered Moths

Click Open

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The evolutionary process

• There is a population of elements.• The elements are capable of making copies

of themselves– perhaps with variants (mutations) and – perhaps by combining with other elements.

• The environment affects the likelihood of an element surviving and reproducing.

• This results in “evolution by natural (i.e., environmental) selection.”

– Darwin likened it to breeding. The environment plays the role of the breeder.

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The nature of evolution

Moth coloring confers survival value (fitness)—which depends on the environment. • Hence Darwin’s “natural selection,” i.e., environmental selection. • The environment selects the winners.

There may be multiple “winners.” All one needs is a niche, not domination.

Nature is not necessarily “red in tooth and claw.” The dark and light moths don’t compete directly with each other. • “Survival of the fittest” doesn’t

mean survival of the strongest. It means survival of those that best fit the environment.

• There are no moth-on-moth battles.

• Nor do the dark moths attempt to convince the light moths that it’s better to be dark — or vice versa.

Moths (and their colors) are rivals, not adversaries. • It’s more like a race than a boxing

match.• They are rivals with respect to

their ability to survive and acquire resources from the environment.

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Six time scales of evolution

• Social/economic/cultural systems evolve at medium speeds.

– As rivals: a social system that does well for its members thrives and expands.

– As adversaries: social systems sometimes compete for resources—land in the past; now other resources.

• Markets are evolution speeded-up.– Coke and Pepsi are rivals for

consumer dollars, not adversaries.

– They don’t attempt to kill each other’s CEOs or to sabotage each other’s delivery trucks.

• Warfare: often super fast evolution.– IED tactics and counter tactics.

• Biological evolution is generally slow.

• Thought: thinking through options is even faster.

– Let one’s hypotheses die in one’s stead. —Karl Popper

• Simulation: computer modeling of evolutionary processes is faster yet.

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Application to engineering problems

The Traveling Salesman Problem (TSP). Connect the cities with the shortest tour that is a permutation of the cities.• Starts and ends at the same city.• Includes each city exactly once.

In this case the problem is easy to solve by inspection. In general, it’s computationally explosive since there are (n-1)! possible tours.

B

A

D

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C20

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1412

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• The obvious tour will include the sequence ACED-54 (or its reverse).

• No diagonals: A-E or C-D.• The question is where to put B: ABCED-

55, ACBED-57, or ACEBD-56?

Why not n!

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An exchange (or reverse or mutation) solves this problem in one step. ACBED-57 → ABCED-55

Genetic algorithm approachCreate a population of random tours.• AEBCD-59, ACBED-57, ADCBE-59, ACDEB-71, …• In this case there are only 4! = 24 possible tours. • Could examine them all. Usually that’s not possible.

Repeat until good enough or no improvement. But beware local optima.• Select one or two tours as parents, e.g., AEBCD and ACBED.

− Ensure that better tours are more likely to be selected.• Generate offspring using genetic operators to replace poorer

elements.− Exchange two cities: ACDEB-71 → ACBED-57− Reverse a subtour: ACBED-57 → AEBCD-59− (Re)combine two tours: AEBCD-59 & ACBED-57 → AEDCB-71.

• Possibly mutate the result: ADCBE-59 → ACBDE-70

B

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C20

13

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1412

79

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Try it out: TSP.jar

• After starting a run, double click in the display area to add a city or on a city to remove it.

– New cities are added to the tour next to their nearest neighbor.• Stop and restart for new random cities.

– The number of new cities will be the same as the number of old cities.

• The differences between the current best and its immediate predecessor are shown by link color.

– New links are shown in green. – Removed links are in dashed magenta.

• No “geographical” heuristics are used. Just the structural ones shown on the previous slide.

Tsp.jar

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Genetic algorithms: parameter setting/tuning• The number of variables is constant.

– Both the TSP and the peppered moths examples illustrate genetic algorithms.

• Peppered moths: one parameter (color) to set.• TSP: N variables. As a parameter setting problem think of each

tour as consisting of N variables, each of which may contain any city number. The additional constraint is that no city may repeat.

• Often there are hundreds of variables (or more) or the search space is large and difficult to search for some other reason.

• There is no algorithmic way to find values that optimize (maximize/minimize) an objective function.

Terrile et. al. (JPL), “Evolutionary Computation applied to the Tuning of MEMS gyroscopes,” GECCO, 2005.

Abstract: We propose a tuning method for MEMS gyroscopes based on evolutionary computation to efficiently increase the sensitivity of MEMS gyroscopes through tuning and, furthermore, to find the optimally tuned configuration for this state of increased sensitivity. The tuning method was tested for the second generation JPL/Boeing Post-resonator MEMS gyroscope using the measurement of the frequency response of the MEMS device in open-loop operation.

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Genetic programming: design• The number of variables (and the structure of the possible

solution) is not fixed.• Original goal was to generate software automatically.

– Not very successful, but hence the name. • Applied successfully to other design and analysis problems.

– Circuit design– Lens design

Bongard and Lipson (Cornel), “Automated reverse engineering of nonlinear dynamical systems,” PNAS, 2007.

Abstract: Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable. …

“Symbolic regression”

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The Human-competitive awards: “Humies”

• Each year at the Genetic and Evolutionary Computing Conference (GECCO), prizes are awarded to systems that perform at human-competitive levels—including the previous two slides.

– See http://www.genetic-programming.org/hc2005/main.html• An automatically created result is considered “human-competitive” if it satisfies at

least one of the eight criteria below.

A. The result was patented as an invention in the past, is an improvement over a patented invention, or would qualify today as a patentable new invention.

B. The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal.

C. The result is equal to or better than a result that was placed into a database or archive of results maintained by an internationally recognized panel of scientific experts.

D. The result is publishable in its own right as a new scientific result — independent of the fact that the result was mechanically created.

E. The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions.

F. The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered.

G. The result solves a problem of indisputable difficulty in its field. H. The result holds its own or wins a regulated competition involving human contestants (in the

form of either live human players or human-written computer programs).

John Koza

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Genetic Algorithm for Constellation Optimization (GACO)

• Finds optimal constellation orbits using a genetic algorithm under multiple design constraints and with multiple sensor types.

4 Satellite ConstellationsGlobal Coverage, elmin = 0

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Earth Central Angle (degrees)

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Walker

For low number of sats, GA arrangement is significantly better than Walker

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Principles of Complex Systems: How to think like nature

Organizational innovation

Russ Abbott

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Innovative environments

Net-centricity and the GIG• Inspired by the web and the internet• Goal: to bring the creativity of the web and the internet

to the DoD

What do innovative environments have in common?How can organizations become innovative?

Other innovative environments• Market economies• Biological evolution• The scientific and technological research process

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The innovative process: exploration and exploitation

Innovation, including human creativity, is always the result of an evolutionary process.

If I were to give an award for the single best idea anyone has ever had, I'd give it to Darwin, ahead of Newton and Einstein and everyone else. In a single stroke, the idea of evolution by natural selection unifies the realm of life, meaning, and purpose with the realm of space and time, cause and effect, mechanism and physical law.

Daniel Dennett, Darwin's Dangerous Idea

• Generate new variants (e.g., ideas)—typically by combining and modifying existing ones.

– This is a random process in nature. – But random or not isn’t the point. – The point is to generate lots of possibilities, to explore the landscape.

• (Select and) exploit the good ones– Allow/enable the good ones to flourish. The hard part!

The easy part!

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Exploration and exploitation in nature

• Evolution.• E. Coli navigation.• The immune system.• Ant and bee foraging.• Termite nest building (to come).• Building out the circulatory and nervous systems.

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Exploration and exploitation:like water finding a way down hill

Quite a challenge! We are very well defended. But we still get sick!If there is a way, some will inevitably find it. (Murphy's law?)The trick is to make the inevitability work for you, not against you.

Microbes attempting to get into your body must first get past your skin and mucous membranes, which not only pose a physical barrier but are rich in scavenger cells and IgA antibodies.

Next, they must elude a series of nonspecific defenses—and substances that attack all invaders regardless of the epitopes they carry. These include patrolling phagocytes, granulocytes, NK cells, and complement.

Infectious agents that get past these nonspecific barriers must finally confront specific weapons tailored just for them. These include both antibodies and cytotoxic T cells. From a tutorial on the immune system from the National Cancer Institute.

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Exploration, exploitation, and asymmetric warfare

• It is the nature of complex systems and evolutionary processes that conflicts become asymmetric. 

• No matter how well armored one is …• there will always be chinks in the

armor, … and something will inevitably find those chinks.

• The something that finds those chinks will by definition be asymmetric since it attacks the chinks and not the armor.

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Exploration and exploitation:groups and individuals

• Successful group exploration typically requires multiple, loosely coordinated, i.e., autonomous, individuals.

– That’s because nature is not regular; one can’t fully plan an exploration.

– If one knew in advance what the landscape looked like, it wouldn’t be an exploration.

• Much exploration is wasted effort. – One may hit the jackpot while the others find

nothing.

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Exploration and exploitation:groups and individuals

• For a group to benefit from the discoveries of individuals, there must be mechanisms to bring the discoveries back and allow the group them to use them.

– Mechanisms to internalize successful/promising discoveries must be built into a group’s process.

– This frequently requires “creative destruction,” which may be more difficult to accept—especially if it’s your job that is being destroyed.

– Markets are how we integrate creative destruction into society.

Recall ant foraging and pheromone following.

Joseph Schumpeter, Capitalism, Socialism, and Democracy

It’s amazing how well we have tamed destruction. It’s now an accepted part of our normal processes.

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How does this apply to organizations? To ensure innovation:

Sounds simple doesn’t it?

Exploration: creation and trial • Encourage the prolific generation and trial of new

ideas.Exploitation: establish the successful variants• Allow new ideas to flourish or wither based on

how well they do—rather than for political reasons.

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Initial funding Prospect of failure Approvals Establishment

Biological evolution

Capitalism in the small.

Nature always experiments.

Most are failures, which means death. (But no choice given.)

None.

Bottom-up resource allocation

defines success.

EntrepreneurLittle needed

for an Internet experiment.

Perhaps some embarrassment, time, money; not

much more.Few.

Entrepreneur wants rewards.

Bottom-up resource

allocation.

BureaucracyProposals,

competition, forms, etc.

When 100% Mission Success is the group goal,

who wants a failure in his/her personnel file?

Far too many.

Managers have other priorities.

Top-down resource

allocation.

New ideas aren’t the problem.

Trying them out

Innovation in various environmentsGetting

good ideasestablished

We save ourselves by spin-doctoring and benign neglect

Viagra: making friends in Afghanistan

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Garages and laboratories, workbenches, and scribbled napkins are filled with brilliant ideas unmatched with determination, resources, and market sensibilities.

Jack Russo, Silicon Valley intellectual-property lawyer.

• In 1999, when Nathan Myhrvold left Microsoft (formerly CTO; brilliant, but he missed the importance of the web) he set himself an unusual goal.

• He wanted to see whether the kind of insight that leads to invention could be engineered.

• He formed a company called Intellectual Ventures. • He raised hundreds of millions of dollars. • He hired the smartest people he knew. • It was not a venture-capital firm.

– Venture capitalists fund existing insights. They let the magical process that generates new ideas take its course, and then they jump in.

• Myhrvold wanted to make insights—to come up with ideas, patent them, and then license them to interested companies.

Malcolm Gladwell (May 12, 2008) “In the Air,” The New Yorker, http://www.newyorker.com/reporting/2008/05/12/080512fa_fact_gladwell

Matt Richtel (March 30, 2008) “Edison ...Wasn’t He the Guy Who Invented Everything?,” New York Times, http://www.nytimes.com/2008/03/30/weekinreview/30richtel.html

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Planned invention?• When Myhrvold started out, his expectations were modest. • Although he wanted insights like Alexander Graham Bell’s, Bell was

clearly one in a million, a genius who went on to have ideas in an extraordinary number of areas—sound recording, flight, lasers, tetrahedral construction, and hydrofoil boats, to name a few.

• Invention has its own algorithm—some combination of genius, obsession, serendipity, and epiphany. How can you plan for that?

• The original expectation was that I.V. would file a hundred patents a year. • It’s filing five hundred a year and has a backlog of three thousand ideas. • It just licensed off a cluster of patents for $80,000,000. • Its ideas are not trivial.

– Improved jet engines – New techniques for making microchips– A way to custom-tailor the mesh “sleeve” used to repair aneurysms– Automatic, battery-powered glasses, with a tiny video camera that reads the

image off the retina and adjusts the fluid-filled lenses accordingly, up to ten times a second.

Why doesn’t every technical organization do this?

56

1

10

100

1000

Raw

Promisi

ng

Fund

ed

Home ru

n

Ideas

VC arithmetic

It’s hard work to harness the power of innovation.

57

Practical organizational innovationHamel and Skarzynski: an innovation architecture.• An innovation pipeline for managing and opportunities• A core set of people trained in the processes of innovation • A systematic process for generating and managing strategic insights• The right evaluative criteria at every stage of the development process

to prevent potentially valuable ideas from being killed off prematurely• Ideas that are sufficiently radical to deliver breakthroughs• Mechanisms for rapidly reallocating resources behind new opportunities • Mechanisms to manage growth opportunities with different timescales

and risk profiles • Metrics to measure innovation performance• Linkages between innovation and management compensation • A self-sustaining enterprise capability and a tangible core value

Prediction (20 years). To survive outside a protected environment an organization will need a successfully functioning innovation architecture.

Corollary. Some organizations will focus on preserving their environments.

Do we have an innovation

architecture?

Most companies want to make money. What metrics would we use?

58

Successful innovative organizations: W.L. Gore, Best Buys, Whole Foods, GE, Whirlpool, P&G, CEMEX, Google

• Lower levels discover opportunities through exploration.– New initiatives often grow from the “edges,” where perception occurs. – Constrained by “rules of engagement,” which protect them from harm.– Must be possible for initiatives to originate at all levels—even the top.

• Higher/broader levels provide perspective, impose constraints, shape direction, and add or withhold resources as events develop.

– They do not primarily issue commands.

• This is primarily a bottom-up model of resource allocation. – Decisions about increasingly significant commitments are made at

increasingly higher/broader levels. If the entire organization commits, becomes an organization/organism-level goal.

• Top-level strategy: stay healthy and build skills, resources, and capabilities that can be recruited/applied/committed when needed.

Just what your mother always told you: eat right, exercise, get plenty of sleep, study hard, practice, and save money.

59

Innovation in the military• Our military is deliberately mission driven—where the

missions are determined by civilian authority. • We don’t want our military to take the initiative to find

new missions for itself.• What kinds of innovation does it make sense for the

military to attempt?– Innovation that makes it more effective at doing what it

is charged to do.– How can success be made self-validating in the way

making money or reproducing are? • Innovations that save lives (e.g., anti-IED techniques)

are self-validating and are adopted relatively quickly.• Need a way to aggregate resources/success bottom-up.

– Establish a military-specific innovation architecture.

60

Principles of Complex Systems: How to think like nature

Design: platforms, services, cycles, and energy flows

Russ Abbott

61

How would you gather wood chips into a pile?Probably not like this.

File > Models Library > Biology > Termites

Click Open

62

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 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

Wikipedia commons

Run the program and watch what happens.

Exercise: prove that this will always happen

63

No horizontal communication.No dashed lines. (Is that good?)

It’s not accurate as a communication or

operational structure.

It may represent how authority is delegated,and it may represent how responsibility is assigned,but it doesn’t represent how communication occursor how organizations really work.

Downward pointing arrows: commands.Upward pointing arrows: results/reports.

Can be implemented with point-to-point communication links.

Organizational/system structure:What’s wrong with this picture?

64

A somewhat more realistic picture

The focus is on interaction among participants in the organization.

David Sloan Wilson,Evolution for Everyone

Everything is both an entity and a group.

65

From point-to-point links to platformsNeed more than fixed point-to-point communication channels

The communication system(even if just a telephone system) is the start of net-centricity

Must distinguish between communication structure and command hierarchy.

Becomes reified as an additional component—not just a collection of interfaces.

“Platform”

But a network/platform may do nothing on its own.

The fundamental question How will the organization use the network/platform?

Enabling communication neither eliminates responsibility nor undermines command intent.

As a common resource, how does it fit into the hierarchy? How is it governed?

66

Layered architectures — not functional decomposition

PresentationSession

TransportNetworkPhysical

WWW (HTML) — browsers + serversApplications, e.g., email, IM, Wikipedia

Asymmetric warfare

Each layer is a platform that a) is built on the layers below itb) enables higher level layers to

be built on top of itc) is vulnerable to disruption.

67

How does Aerospace send mail to Aerospace?

El Segundomail routes

(carts)

Chantillymail routes

(carts)

LAX ↔ IAD(commercial

aircraft)

AerospaceUSPS

(trucks)

AerospaceUSPS

(trucks)

Sort &route

Sort &route

Sort &route

Sort &route

Many interlinked processes.

The various systems provide platforms for each other.

Infrastructure

A system of (many!) systems. • Aerospace, USPS, commercial airlines,

airports, traffic, road maintenance, …

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Multi-sided software platforms• Evans, Hagiu, and Schmalensee (2006) Invisible Engines: How

Software Platforms Drive Innovation and Transform Industries, MIT Press. (freely downloadable)

– Operating systems, the web browser.

• Markets/mechanisms that connect disparate groups. – A stock exchange matches buyers and sellers.– A credit card system matches merchants and cardholders. – Shopping centers, dating websites, TV channels, TV talk shows,

Amazon resellers, telephone & telegraph systems.– Large retail stores (Wal-Mart, supermarkets) “rent” shelf space.

• Not your usual business model: buy; add value; sell.• The value to each group increases as the size of the

other group(s) grow. (Also known as network effect.)

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Platforms as refactorings

A multi-sided platform may be understood as the standardization and factoring out (refactoring) of a hard part of an interaction and providing it as a service.

The hard part is done by theplatform.

USPS: sending & receiving materials. Credit card: paying and being paid. Dating service: finding the other

party and making an initial contact. Robert’s Rules of Order: the interaction

protocol

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Standards as (ephemeral) platforms

• Since a platform is a level of abstraction, it can be characterized by a specification.

• The specification can then serve as the definition of the platform, e.g., HTML, SQL, … .

• Multiple vendors can be encouraged to compete to implement it.

• Defangs platform owners.• Empowers platform users. • Some platforms are single-sided: programming

languages, automobile & public transportation system, (woodworking, etc.) tools. Have similar value.

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Platforms as infrastructure and environments • Sometimes platforms define an environment.• The free market economic system is defined primarily

by two platforms.– The monetary and banking system.

• Factors out the economic notion of value. Allows value to be abstracted, stored, exchanged with minimal overhead.

– The legal and judicial system.• Factors out agreements (contracts) and enforcement

mechanisms. Overhead not so minimal (lawyers) — but better than hiring your own “enforcers.” Used to rely more on reputation. Still do in eBay.

• Much too important to be controlled privately.• In general, the set of platforms available in an

environment is the environment’s infrastructure.

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Governance and change• Once a platform has been established, what

mechanisms are available so that it can evolve as needed?

– Since its use is embedded in the workings of many users, it’s difficult to change.

– Since its use is central to the survival of many users it must be able to change as needed.

• Platforms – Open at the bottom. It’s the interface that matters,

not the implementation. – Open at the top. New uses are encouraged.– Stable but slowly changing.

• Must be stable enough to be relied on but flexible enough to change as needed.

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An unusual platform-based design

• E. coli can produce lactase which digests lactose.

• But for efficiency sake 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?

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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

systemSwitch

Off

On

Is it really top-down?Assumes platforms of components and framework.Engineers can always design a lower level if needed.

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E. coli lets lactose flip its own switch

LactoseRepressor Lactose itself binds

to the repressor, pulling it out of the way.

lacY lacAlacXRNAP

RepressorThree lac genes

RNA polymerase can’t bind to DNA. Transcription blocked.

lacY lacAlacXRNAP

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?

LactoseRepressor

lacY lacAlacX

RNA polymerase can now bind to DNA. Transcription enabled. The genes are expressed.

RNAPWhere’s the platform?

The

DNA → proteinprocessing system.

Once that processing cycle was built, nature found out how to turn it on and off with gene switches. It then became possible to use that mechanism to allow lactose to turn on generations of its own digestive enzyme.

76

Principles of Complex Systems: How to think like nature

Energy, platforms, cycles, and complex systems

New section. Preliminary thoughts.

Russ Abbott

77

• No general framework for describing the organization or functioning of a complex system.

• Let’s make a list of complex systems. Distinguish complex from complicated.

– An economy.– A biological organism.– An ecological system.– Most large organizations.– Systems that give rise to (dynamic) emergent phenomena(?).

• What is it about such systems that allows that to happen?- (Initially) unplanned interactions—that (generally) aren’t one-shot affairs.

• Multiple autonomous agents interacting within an environment.– That’s why agent-based simulations tend to be useful.

Is that why they’re called complex?Is that why they’re called complex? No.

Cycles, Design, and Requirements

Salt doesn’t count.

Will this really provide one? Ambitious. Audacious.

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• Complex systems are often said to be on the boundary between chaos and order, i.e., on the “edge of chaos.”

– Cosma Shilizi thinks it’s a terrible term.• In other words, they are neither (easily) predictable nor chaotic—

where easily predictable means that it can be predicted more easily than simulating it, what Bedau called weak emergence.

– Are there other alternatives? Shalizi doesn’t offer one.– Something that is growing at a predictable (even exponential) rate is

still (easily) predictable. • A Universal Turing machine is neither chaotic nor easily predictable

since it must be run to determine what it will do.– Most people wouldn’t consider a Turing machine a complex system.– Most people wouldn’t consider the computation of a Turing machine

(even weakly) emergent. • But recall levels of abstraction!

What about stability?

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How is stability defined?

• Stability implies some relatively constant features.– What about a pot of steadily boiling water—or most

of the other “dissipative structures” of Prigogine?– What do dissipative structures dissipate? Energy.

• Energy flows through a dissipative structure.• Will return to this in a few slides.

• At maximum entropy and hence stable with respect to its internal energy content—even though it is experiencing a constant flow of energy?

• E.g., Rayleigh-Bénard cells?

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Basins of attraction

• Two kinds of attractors—with respect to energy considerations– Homeostatic mechanisms, e.g., a economy/biological organism/ecology.

• Require continual supply of energy.• But also at max entropy?

– Energy wells, e.g., a lake/ocean.• At energy equilibrium.

• The Lorenz attractor is neither.– Energy isn’t a consideration.– This is an important (and often

overlooked) feature of computation.

Lorenz attractor

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The agent as a Persistent Turing Machine

• If you ignore quantum/random/“creative” phenomena, each agent can be characterized by a computer program—a computation.

– Or agent-based simulations wouldn’t work.• Each computation is an endless loop—or the agent dies.

– The agent’s program is not an algorithm—since (by definition) to be an algorithm a computation must terminate.

• See Goldin and Wegner, “The Interactive Nature of Computing: Refuting the Strong Church-Turing Thesis” (Minds and Machines, 18/1, March 2008, 17–38) on Persistent Turing Machines.

• A TM’s state transition rules and execution cycle are both finite. • The Turing machine itself may do arbitrary computation if unlimited

memory is available. • For agents that represent biological organisms internal memory is

limited. Any unlimited memory must be external—in the environment.

The “code,” a finite description of the program.

What it does when it runs.

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Cycles: the underlying mechanism

• Based on the previous considerations, agents with internal cycles—e.g., the TM state transition cycle—are the underlying mechanism for complex systems.

• Cycles are also central to platforms. A platform most generally is a service—in the sense of Service Oriented Architecture. It can be modeled as an agent.

• If a platform/service could not be described finitely, we couldn’t build it.

– So a platform/service is a PTM.• Every complex system is built on a collection of basic PTMs.

Some—the widely used services—are more central than others. • Each PTM is defined by its underlying state transition cycle.• So each complex system is built on top of a set of basic cycles.

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But not the usual way we think of cycles.The “Nitrogen cycle.” What are the real cycles?

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

84

Impossible cycle: can’t always go downhill

85

What powers the cycles?The invisible element: energy

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

Each processing step is a service powered by an internal cycle.

86

“Far from equilibrium”• Generally applied to systems that have energy flowing

through them.• Not at equilibrium.• But often stable.

• Important—and often overlooked—features of complex systems are the energy flows through them.

– Follow the flows, especially the energy flows. – Money is a way to store value—including energy.

Grasslands Conversation Council of British Columbia, Canada

But I don’t see these three as equivalent.Energy powers the mechanisms that perform the other cycles.

Is water different from nutrients or are they all resources?

Not a cycle!Need source and sink.

87

Infrastructure

• So the key to many systems is to understand the system’s primary services/platforms, what flows they enable, and how they are powered.

• These make up the system’s infrastructure.• Not all flows are cycles—especially energy. • Resource flows increasingly are—and generally

require energy for recycling.

88

Morowitz theorem:A system with an energy flow must have a cycle.

• The flux [produced by the flow of energy through a system] is the organizing factor in a dissipative system. When energy flows in a system from a higher kinetic temperature, the upper energy levels of the system become occupied and take a finite time to decay into thermal modes. During this period energy is stored at a higher free energy than at equilibrium state. Systems of complex structures can store large amounts of energy and achieve a high amount of internal order.

• Therefore, a dissipative system develops an internal order with a stored free energy that is stable, has a lower internal entropy and resides some distance from thermostatic equilibrium. Furthermore, a dissipative system selects stable states with the largest possible stored energy.

• The cyclic nature of dissipative systems can be seen in the periodic attractors. Their cyclic nature allows them to develop stability and structure within themselves.

– As paraphrased by Piero Scaruffi.

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The fundamental cycles• Most systems have fundamental cycles (services) that all

the other processes “ride on top of.”– In a computer, it’s the instruction execution cycle.– “Ride on top of” means that higher level processes are built

by running the basic cycles to implement the higher processes. (Recall emergence and levels of abstraction.)

• A good way to understand a complex system is to identify those fundamental services and to understand how they are powered.

• In the ecology/nitrogen example, one of the fundamental cycles is the DNA → Protein generation cycle.

– Requires both energy (ATP) and resources (amino acids).– Bacteria and all other living things depend on—and ride on

top of—it as well as other basic cycles.

90

Services/platforms/cycles and system acquisition

• Traditional system design starts with a “needs” statement, from which a requirements document is generated, from which the system specification and design documents are created.

• From requirements on down these documents provide a static view of the system—what will be sold and delivered.

• The system is often described in a top-down manner. – What else is possible?

• But what’s it all for? The original “need” (generally a capability, i.e., a service) is frequently lost.

• The use—Concept of Operations (CONOPS)—is not ignored, but it often takes second place to other requirements.

91

A CONOPS version of acquisition

• A capability-based approach—not a new idea!—starts with the system as a service and maintains that perspective.

– Terrible as he was as a foreign policy advisor, Rumsfeld had some good ideas with respect to defence acquisition.

• A capability-based, i.e., service-based view of a system to be acquired starts at the operational level and stays as close to that level as possible.

92

How would it work?

• How will it fit into the context of all other existing and expected systems?

• How will it be run day-to-day?• Who are its intended users?

– Can that user-set be enlarged?– Will it have a Community of Interest?

• What will the COI role be in defining requirements?

Envisage the use of the intended service in the context of all other services.

• What are its fundamental cycles?• How are they powered?• How is higher level functionality

built on top of them?• What are its operational resources

and how will they be supplied? • How will its operation be paid for?

• Do the people who will pay for operating it really want it?

• How will it be governed?– Who are the stakeholders?– Who are its intended owner/operators?– How will decisions be made about enhancements?

93

Principles of Complex Systems: How to think like nature

Organizations: how nature builds systems; the wisdom of crowds

Innovation required individual autonomy. What do groups add?

Russ Abbott

94

“Self-organizing” groups

• Craig Reynolds wrote the first flocking program two decades ago: http://www.red3d.com/cwr/boids.

• Here’s a good current interactive version: http://www.lalena.com/AI/Flock/

– Separation: Steer to avoid crowding birds of the same color. – Alignment: Steer towards the average heading of birds of

the same color. – Cohesion: Steer to move toward the average position of

birds of the same color.

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“Self-organizing” groups:how nature builds systems

• The bird, termite, and ant models illustrate emergence (and multi-scalarity). (See video Debora Gordon on ant colonies.)

• More recent talk (1 hour): http://www.youtube.com/watch?v=R07_JFfnFnY

• In both cases, individual, local, low-level rules enabled “the group” to achieve “emergent” higher level results.

– The birds flocked.– The wood chips were gathered into a single pile.– The food was brought to the nest.

• These systems are the product of the evolution of individual actions that resulted in coordinated benefits.

Emergence is successful group design. • Group exploration extends the

perceptual reach of any individual.• Group behavior extends the functional

capability of any one individual.

Virtually everything is both an entity and a group.

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Breeding groups

• 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 (because of the reduced mortality) 45% more eggs per group.

• Group (and more generally multi-level) selection is now accepted as valid.

Traditional evolutionary theory says there is no such thing as group selection, only individual selection. Bill Muir (Purdue) demonstrated that was wrong.

Wikipedia commons

http://www.ansc.purdue.edu/faculty/muir_r.htm

Groups are entities. You and I are both entities and cell colonies.

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• But then groups found that coordination, specialization, and coordinated specialization enabled emergence.

– Consider any multi-cellular organism, or any organism with multiple organs, or any society with any sort of specialization, or any social grouping with coordinated and/or specialized roles.

– These groups exemplify real emergence. Entirely new capabilities appear.

• Wind instruments can play melodies.• Piano and guitar can play chords as well.

Why groups?

• Perhaps groups formed initially because they increased survival value. A team will generally beat an individual of approximately the same skill level.

– This is not so much emergence as power in numbers.

Why groups? Two steps.

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David Sloan Wilson on social groups

• What holds for chickens holds for other groups as well: teams, military units, corporations, religious communities, cultures, tribes, countries.

• Successful groups are those that minimize within-group conflict and organize to succeed at between-group conflict.

• Groups with mechanisms for working together can often accomplish far more (emergence) than the sum of the individuals working separately.

– Corporations, military organizations; reproduction; mitochondria and “us.”• But if a group good is also an individual good (e.g., money, security), the

group must have mechanisms to limit cheating (free-ridership). • Group traits (although they are carried as rules by individuals) evolve

because they benefit the group. (E.g., insect behavior.)• These traits may be transmitted genetically (by DNA). They may also be

transmitted culturally (by training/parenting/indoctrination/mentoring/…).– Human groups can be more complex because it’s not all built-in.

Moral systems are interlocking sets of values, practices, institutions, and evolved psychological mechanisms that work together to suppress or regulate selfishness and make social life possible. —Jonathan Haidt

We evolved to be pro-social within groups but xenophobic between groups. – Michael Shermer

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Experimental “games”

• Prisoner’s Dilemma.– One shot. Defect is the only rational strategy.– Iterated.

• Tit-for-tat: Cooperate initially and then copy the other guy.• Pavlov: repeat on success; change on failure. (More robust.)

• Ultimatum Game. Proposer must offer to divide $100. Responder either accepts the proposed division or rejects it—in which case neither gets anything.

– Only rational strategy: proposer offers as little as possible; responder always accepts.

– Real experiments (world-wide). Responder rejects unless offer ~1/3.– Some societies are different, e.g., where giving a gift means power.– What would you offer/accept? Try it. (Played anonymously. Write offer.)

• Try it table against table. Each table prepares an offer.

C DC 3/3 0/5

D 5/0 1/1

A far-from-equilibrium system. New energy is supplied “for free.”

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Homo economicus vs. strong reciprocity

Homo economicus: individual selection• Agents care only about the outcome of an economic interaction

and not about the process through which this outcome is attained (e.g., bargaining, coercion, chance, voluntary transfer).

• Agents care only about what they personally gain and lose through an interaction and not what other agents gain or lose (or the nature of these other agents’ intentions).

• Except for sacrifice on behalf of kin, what appears to be altruism (personal sacrifice on behalf of others) is really just long-run material self-interest.

• Ethics, morality, human conduct, and the human psyche are to be understood only if societies are seen as collections of individuals seeking their own self-interest.

Moral Sentiments and Material Interests: The Foundations of Cooperation in Economic LifeHerbert Gintis, Samuel Bowles, Robert T. Boyd, and Ernst Fehr (eds), MIT Press, 2005.

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Homo economicus vs. strong reciprocityStrong reciprocity: group selection

• A predisposition to cooperate with others, and to punish (at personal cost, if necessary) those who violate the norms of cooperation

– even when it is implausible to expect that these costs will be recovered at a later date.

• Strong reciprocators are both conditional cooperators

They behave altruistically as long as others are doing so as well.and

altruistic punishersThey apply sanctions to those who behave unfairly even at a cost to themselves.

Socialization: norm internalization. There's no such thing in biology, economics, political science, or anthropology.Humans can want things even when they are costly to ourselves because we were socialized to want them:to be fair, to share, to help your group, to be patriotic, to be honest, to be trustworthy, to be cheerful.

102

Wise crowds: more than the sum of their parts

Web wise crowd platforms• Wikis• Mailing lists• Chat rooms• Prediction markets

Condorcet Jury Theorem (18th century) example• Five people (a small crowd).• Each person has a 75% chance of being right.• Probability that the majority will be right: ~90%• With 10 people: ~98%. Simple if you think about it.

Traditional wise crowds• Teams• Juries• Democratic voting

103

Wise crowd criteria

Diverse: different skills and information brought to the table. • Decentralized and with independent participants:

• No one at the top dictates the crowd's answer. • Each person is free to speak his/her own mind and make own decision.

• Distillation mechanism: to extract the essence of the crowd's wisdom.

Participant autonomy.

James Surowiecki, The Wisdom of Crowds

104

Example from The DifferenceWhich person from the following list was not a member of the Monkees (a 1960s pop band)?

(A) Peter Tork(B) Davy Jones (C) Roger Noll(D) Michael Nesmith

Imagine a crowd of 100 people with knowledge distributed as follows:• 7 know all 3 of the Monkees • 10 know 2 of the Monkees• 15 know 1 of the Monkees• 68 have no clue

In other words, less than 10 percent of the crowd knows the answer, and over two-thirds are culturally deprived of any Monkees knowledge. We assume individuals without the answer vote randomly. The Condorcet Jury Theorem, then, doesn’t apply because only a small minority knows the answer. Still, the crowd will have no problem getting the right answer.

• The 7 who know all the Monkees vote for Noll;• 5 of the 10 who know 2 of the Monkees will vote for Noll; • 5 of the 15 who know 1 of the Monkees will vote for Noll; and • 17 of the 68 clueless will vote for Noll.

So Noll will garner 34 votes, versus 22 votes for each of the other choices.

Diverse groups of problem solvers outperformed the groups of the best individuals at solving problems. The diverse groups got stuck less often than the smart individuals, who tended to think similarly.

105

A wise crowd as assistant and companion

106

Distillation mechanism: prediction markets

Statement issued by 25 world-famous academics. May 2007. Including: Kenneth Arrow, Daniel Kahneman, Thomas Schelling, Robert Shiller, Cass Sunstein.Abstract: Prediction markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election. Using these markets as forecasting tools could substantially improve decision making in the private and public sectors. We argue that U.S. regulators should lower barriers to the creation and design of prediction markets by creating a safe harbor for certain types of small stakes markets. We believe our proposed change has the potential to stimulate innovation in the design and use of prediction markets throughout the economy, and in the process to provide information that will benefit the private sector and government alike.

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Often Beats Alternatives

• Vs. Public Opinion – I.E.M. beat presidential election polls 451/596 (Berg et al ‘01)– Re NFL, beat ave., rank 7 vs. 39 of 1947 (Pennock et al ’04)

• Vs. Public Experts– Racetrack odds beat weighed track experts (Figlewski ‘79)

• If anything, track odds weigh experts too much! – OJ futures improve weather forecast (Roll ‘84) – Stocks beat Challenger panel (Maloney & Mulherin ‘03)– Gas demand markets beat experts (Spencer ‘04)– Econ stat markets beat experts 2/3 (Wolfers & Zitzewitz ‘04)

• Vs. Private Experts– HP market beat official forecast 6/8 (Plott ‘00)– Eli Lily markets beat official 6/9 (Servan-Schreiber ’05)– Microsoft project markets beat managers (Proebsting ’05)

from Robin Hanson

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Prediction markets

Contracts: Intrade (Ireland-based): real money or play money.

Panos Ipeirotis

But, there is evidence that prediction markets are not efficient.

Slate’sElection Market Page

Split off from TradeSports

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Concerns and Myths

• Self-defeating prophecies • Decision selection bias• Price manipulation• Rich more “votes”• Inform “enemies”• Share less info• Combinatorics• Risk distortion• Moral hazard• Alarm public• Embezzle• Bubbles• Bozos• Lies

• Crowds don’t always beat experts.• People will not work for trinkets.• High accuracy is not assured.

from Robin Hanson

The prediction markets got both the New Hampshire and California primaries wrong.

110

Other distillation mechanisms:making the crowd’s “wisdom” “actionable”

• Elections, polls, etc. Traditional. Many possible processes, e.g., transferrable ballots, etc.

– Expression of preferences.– Many online options (and more options).

• Collaboration: wikis and other collaboration tools (shared spaces), mailing lists, chat rooms, etc.

– Explicit: Generation of new “work products.”• Here’s a (long!) list of collaborative work environments.

– Implicit: Google’s page rank, “reputations” (e.g., eBay), “recommendation engines” (e.g., Amazon)

A hard problem. Yet evolution and markets do it automatically.

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Principles of Complex Systems: How to think like nature

Remember this …

Russ Abbott

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Complex systems• Emergence: the creation of a new entity, one which has new properties (often a group

or a system), through interaction among multiple autonomous elements. – Multiscalarity: everything is both an entity and a group.

• A level of abstraction has both a specification (requirements) and an implementation. – Throwing away the specification once an implementation exists produces a

reductionist blind spot.– It’s the specification (of the interface) that ensures loose coupling.

• Interaction—even (or especially) intra-system—occurs through an environment.– An environment that provides functionality that facilitates interaction is a platform.– Architectures: agents and platforms vs. stovepipes and functional decomposition.– Platform governance becomes a fundamental issue. Who owns it, runs it, controls it?

• Evolutionary processes are unavoidable—leading to unexpected consequences. They are also the source of all creativity.

– Their essence combines exploration with exploitation of discoveries.– Organizations can plan to be innovative.

• Groups are nature’s way to build systems.– We can build powerful groups because we evolved to live in groups and we can learn. – How can a group’s wisdom be distilled as action? Bottom-up resource allocation.

• Nature and markets have self-validating criteria: reproductive success and profits.• By looking carefully you can see the world in a grain of sand.