1. Introduction to Complex Systems: How to think like nature
Organizations: emergence and innovation
Russ Abbott
Sr. Engr. Spec.
Rotn to CCAE
The Aerospace Corporation. All Rights Reserved.

2. Flocking
Craig Reynolds wrote the first flocking program two decades ago:
Here’s a good current interactive version:
A soccer game based on “forces.”
Download, execute.
After it starts, click Console tab and reduce speed to

3. Group/system-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.
Emergence in ant and termite colonies may seem different from emergence in E. coli following a nutrient gradient because we see ant and termite colonies as groups of agents and E. coli as a single entity.
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 the ant/termite examples, the colony is the system. In the case of E. coli, the organism is the system.
In Evolution for Everyone, David Sloan Wilson argues that all biological and social elements are best understood as both groups and entities.
You and I are each (a) entities and (b) cell colonies.

4. Breeding groups/teams/systems
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.
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.
Wikipedia commons

5. Wilson on groups
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
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.
E.g., most corporations, military organizations, etc.
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.)
Group selection (not just individual selection) now accepted as valid.
These traits may be transmitted genetically (by DNA). They may also be transmitted culturally (by training/parenting/indoctrination/mentoring/…).
Human groups are much more complex because it’s not all built-in.

6. Clearly fundamental. How are we autonomous?
We’re smart because we are “programmable,” i.e., able to learn—both information and norms
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.
As humans we’re successful because we’re smart. We’re smart because we operate in complex groups. We can operate in complex groups because we have strong reciprocity. We both share and are willing to punish non-sharers. Take bees. You always think of the hive as the big social collective. Not true. Workers often try to lay eggs, even though only the queen is supposed to lay eggs. If workers lay eggs, other workers run around, eat the eggs, and then punish the workers that laid the eggs. Wherever you find cooperation, you’ll also find punishment. Think of your own body. Each cell has its own self-interest to multiply. Why don’t they go berserk (cancer)? How do you get cells to cooperate? You punish cells that don’t cooperate.
Next slide
What does it mean to say that we can learn?
The word may sound cold and robotic, but it means that we are “programmable,” i.e., capable of internalizing new skills and ideas.
Socialization is a form of learning.
Herbert Gintis
Clearly fundamental. How are we autonomous?

7. 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.
Strong 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 conditional cooperators
They behave altruistically as long as others are doing so as well.
and altruistic punishers
They apply sanctions to those who behave unfairly according to the prevalent norms of cooperation.
Moral Sentiments and Material Interests: The Foundations of Cooperation in Economic Life Herbert Gintis, Samuel Bowles, Robert T. Boyd and Ernst Fehr (eds), MIT Press, 2005

8. A far from equilibrium system. New energy is supplied “for free.”
Experimental “games” C D
3/3
0/5
5/0
1/1
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—e.g., from TAI. 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.
Version 1. The winning table is the one with the greatest total.
Version 2. A table survives if it winds up with at least $50.
A far from equilibrium system. New energy is supplied “for free.”

9. Which category dominates depends on modeling assumptions.
The Public Goods Game
Contributions to a common pot grow—via emergence. The result is divided among everyone, even free-riders.
Free riders do better than cooperators/contributors.
But then cooperation (and public goods) will vanish.
Punishment is important in sustaining cooperation.
But how can punishment emerge if it is costly?
Categories of players
Loners do not participate; they neither contribute nor benefit.
Defectors do not contribute but benefit.
Cooperators contribute and benefit but do not punish.
Punishers are contributors who also (pay to) punish defectors and simple cooperators—to prevent simple cooperators from free-riding on punishers.
Which category dominates depends on modeling assumptions.
Games of Life
Hannelore Brandt, Christoph Hauert†, and Karl Sigmund, “Punishing and abstaining for public goods,” PNAS, Jan 10,

10. Wise crowds: more than the sum of their parts
Traditional wise crowds
Teams
Juries
Democratic voting
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.
(James Surowiecki, The Wisdom of Crowds) (Scott Page, The Difference)
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 free to speak his/her own mind and make own decision.
Distillation mechanism: to extract the essence of the crowd's wisdom.
Emergence.
Participant autonomy.
Second slide ahead

11. A wise crowd as assistant and companion

12. Distillation: 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)
Knowledge extraction: prediction markets.

13. Prediction markets
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. Statement issued by 25 world-famous academics. May Including: Kenneth Arrow, Daniel Kahneman, Thomas Schelling, Robert Shiller, Cass Sunstein.

14. 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

15. Market mechanisms
Intrade uses a continual double auction (CDA) (Like stocks).
Aggregates information in price; can buy or sell any time; requires liquidity or market maker.
Pari-Mutual betting. Losing bets distributed to winning betters. (Like horse racing).
Aggregates information in odds. Can’t trade. Prices don’t vary.
kahst.
Market Scoring Rules (MSR). Combines pari-mutuel with CDA.
Robin Hanson
Inkling, QMarkets
Dynamic Pari-Mutuel Market (DPM). MSR variant.
The later you back a favorite, the more expensive.
David M. Pennock & Mike Dooley
Yahoo! Tech Buzz Game
Listing of markets: MidasOracle.org.

16. Slate’s Election Market Page
Split off from TradeSports
Prediction markets
Contracts: Intrade (UK-based): real money or play money.
But, there is evidence that prediction markets are not efficient.
Panos Ipeirotis
Slate’s Election Market Page
Other Intrade contracts: Current Events > Google Lunar X Prize
Land a privately funded robotic rover on the Moon that is capable of completing several mission objectives, including roaming the lunar surface for at least 500 meters and sending video, images and data back to the Earth.

17. Concerns and Myths Crowds don’t always beat experts
from Robin Hanson
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

18. Exploratory behavior: 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.

19. Exploratory behavior: like water finding a way down hill
From a tutorial on the immune system from the National Cancer Institute:
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.
Quite a challenge! We are very well defended. But we still get sick!
Some “invaders” will make it past these defenses. The problem is not even that some get through, it’s that they exploit their success.
How do they find the open pathways? It’s not “invaders” vs. “defenders.”
Through (evolutionary) exploratory behavior, if there is a way, some will inevitably find it.
Innovation is the (disruptive) invader not the defender.
Innovative organizations make that inevitability work in their favor.

20. Exploratory behavior: recall evolutionary processes
How can the human genome, with fewer than 25,000 genes
fill in all the details of the circulatory and nervous systems?
produce a brain with trillions of cells and synaptic connections?
Cell growth followed by die-off produce webbing in duck feet and bat wings but not in human fingers.
Military strategy of “probing for weakness.”
Ant and bee foraging.
Corporate strategy of seeking (or creating) marketing niches.
The general mechanism is:
Prolifically generate a wide range of possibilities
Establish connections to new sources of value in the environment.
Mechanism
generation
Function
explore
Purpose
use result
Bottom up

21. Innovative environments
The Internet
The inspiration for net-centricity and the GIG
Goal: to bring the creativity of the internet to the DoD
Other innovative environments
The scientific and technological research process
The market economy
Biological evolution
What do
innovative environments
have in common?

22. Innovative environments
Innovation is always the result of an evolutionary process.
Randomly generate new variants—by combining and modifying existing ones.
Select the good ones. (Daniel Dennett, Darwin's Dangerous Idea)
Requires mechanisms:
For creating stable and persistent design representations so that they can serve as the basis for new possibilities.
For combining and modifying designs.
For selecting and establishing better ones.

23. Designs in various environments
All bottom-up
Designs in various environments
Recorded as
Created by
How instantiated
Established
Internet
Software
Programmers who know the techniques
Self-instantiating
By users
Scientific knowledge
Publications
Scientists who know the literature
The publication is the instantiation
By peer review
Market economy
Trade secrets
Product developers who know the tricks
Entrepreneurial manufacturing
By consumers
Biological evolution
DNA
Combination and mutation
Reproduction
Whether it finds a niche
Entities: nature’s memes
Implicit designs
Construction, combination and mutation
Implementation of a level of abstraction

24. How does this apply to organizations?
To ensure innovation:
Creation and trial
Encourage the prolific generation and trial of new ideas.
Establishing successful variants
Allow new ideas to flourish or wither based on how well they do.
Sounds simple doesn’t it?

25. Innovation in various environments
New ideas aren’t the problem.
Trying them out
Getting
good ideas
established
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.
Entrepreneur
Little needed for an Internet experiment.
Perhaps some embarrassment, time, money; not much more.
Few.
Entrepreneur wants rewards. Bottom-up resource allocation.
Bureaucracy
Proposals, competition, forms, etc.
Who wants a failure in his/her personnel file—when “mission success” is the corporate motto?
Far too many.
Managers have other priorities. Top-down resource allocation.