COMPLEX SYSTEMS
ALiCE:
Artificial Life,
Artificial Culture &
Evolutionary Computation

Representing the Complex Adaptive Systems in the natural world with
Evolving Multiagent Models & Simulations.

Our knowledge of the world is indirect. It's based on senses, perceptions and beliefs evolved over countless generations. Our ways of knowing began with conscious and unconscious thought (intuitive and quick) to which we added natural language (slow and reasoned). Along the way we've added other ways to re-present reality, new technologies of description, understanding and explanation, which we have invented, or discovered in nature. We can think and reason through diagrams (maps & charts) and art, with physical objects (architectural models) and mental models. The sciences have perfected formal languages such as mathematics, algebra and calculus. Yet only since the second World War have we added complex computational models and simulations to our ways of knowing.

This course will provide you with some degree of critical fluency in these new technologies of thought, in their philosophical and epistemological underpinnings, and in hands-on engagement with their methods.

ISIS 170 / CompSci 107 / VMS 172
Information Science & Information Studies Program.
Computer Science / Visual Studies

Spring 2016

Tu/Th 10:05 - 12:55
Perkins LINK Classroom "6"
Nicholas Gessler
nick(dot)gessler(at)duke(dot)edu
Justin Bryant
job13(at)duke(dot)edu

The Use of Complexity Science
A Report of the U.S. Department of Education

"The challenges of the 21st century will require new ways of thinking about and understanding the complex, interconnected and rapidly changing world in which we live and work. And the new field of complexity science is providing the insights we need to push our thinking in new directions."

Flier
Poster

what participants have said...

KEEP YOUR OWN COPIES OF EVERYTHING YOU TURN IN

OFFICIAL Description:

Philosophy and epistemology of emergence, computation and evolution applied to describing, understanding and explaining complex multiagent processes in nature, society and culture.  Critical exploration of computer simulations informed by practice in building and visualizing them in C++ for PCs.  From minimal worlds, like the chaos game and cellular automata, we design richer representations of growth, assimilation, segregation and flocking, finally creating agents, ecologies, societies and cultures that evolve and serve as desktop labs for evaluating theories in the arts, humanities, natural and social sciences.  Included is work with sensors, actuators and early computing devices.  No programming experience required. Instructor: Gessler

SYNOPSIS

“Artificial Life, Culture and Evolution” is a fusion of three emerging practices in the “new sciences of complexity.”  Artificial Life is the simulation of large numbers of creatures (each with its own perceptions, beliefs, goals and behaviors) living in a complex environment (with resources and other agents situated differentially in space).  You create the creatures (agents).  You create the environment (physical and social).  Then you turn them loose by pressing “run” to see what they will do.  Often you find that counterintuitive and surprising global patterns of behavior emerge from relatively simple local rules.  “The whole is greater than the sum of its parts.”  The whole is not the linear sum, but the parallel processual interaction, of its component parts.  Artificial Culture builds upon this paradigm, with agents that are more human, populations that are less numerous and environments that include physical technologies, material artifacts, social interactions and the natural world.  Culture in this sense includes that complex whole of thoughts, ideas, behaviors and real world things.  Artificial Culture is a test-bed for developing models and theories of cultural change and cultural evolution, just as Artificial Life is a test-bed for developing models and theories of biological change and evolution.

These approaches are called agent-based or simply multi-agent, where “agent” in the philosophical sense refers to causation.  An agent of causation may be a solution to a problem, a thought, a person, a group of people or any other thing that interacts with others.  What all these approaches offer to us is the ability to describe, understand and explain complex processes in the natural world and culture.  They do so by forcing us to express our ideas about how things in the world work clearly and by enabling us to observe the consequences of the complex worlds we just created almost instantly.  Unlike the human mind and discourse, computation can spin out the entailments of any complex system you describe, keeping a history of the unfolding of those events, a history that can be reviewed and analyzed at leisure.  You create the agents, their interactions and their environments.  You set the players to their initial conditions.  The computer will take care of the rest.  “The proof of the pudding is in the tasting.”  If you think you know how something works, then build it and we’ll see.  Since it may be too risky and expensive to build a house, a country, or a culture in the real world, and keep trying until we get it right, the safer and economical alternative is to build it in simulation.  This is being done historically from the “big bang” to “globalization and foundationally from “quark to quasar.” 

This ability of computation to quickly and accurately synthesize the outcome of the interaction of a myriad of processes operating simultaneously and in parallel, is something that our minds and brains have difficulty doing.  Much of what’s inside our heads does compute in parallel, but much more slowly than computers and often at a pre-conscious level.  How those calculations reach our level of awareness and how they are communicated through language, as internal monolog, spoken discourse or in writing is another matter.  Natural language is mostly linear and serial.  This raises an acute problem:  How do you describe, understand and explain complex parallel processes in linear and serial sentences?  Great cinema, literature and poetry all try to do this, and they do it reasonably well considering the limitations inherent in these modes of representation. 

Agent Based Modeling, Artificial Life, Artificial Culture and Evolutionary Computation provide us, as investigators of the world in which we live, with insights into the complexities of real world phenomena.  They may provide us with reliable answers to specific questions, but they will always make us question and refine our assumptions as to how the world works.  These are desktop laboratories in which we can development and test theories in a variety of disciplines.  We can evaluate them for internal consistency and against real world data.  They provide us with the cloud of possibilities that may emerge from certain given starting and changing conditions.  They provide us with a suite of starting and changing conditions that may lead to a desired outcome.  They can tell us what is possible and what is not, and lead us to a better understanding and more perceptive appreciation of real world events around us.  This course is designed to implement some of the models of complex human interaction which we will develop in a companion course in networks of trust, secrecy and deception “Espionage, Cryptology and Psychological Operations” (ISIS-135).

Participants must keep copies of everything that they turn in.  None of it will be returned.  Selected research findings will form the basis of a growing body of material which we will collaboratively develop for future versions of this and related courses.  In addition to lecture, discussion and laboratory formats, the course also serves as a research seminar focused on the generation of new knowledge.

GRADING:

Grading is based upon demonstrated engagement and involvement with the course, its philosophy, subject matter and activities, as evidenced by attendance, participation, assignments, challenges, tests, quizzes and a course project.  Since there are NO prerequisites, consideration will be given to improvement.  Excellence in certain facets of the course may outweigh weaknesses in others.  The course cumulatively integrates theory with practice, building upon material presented weekly.  Consequently, the course project and later work may carry significantly greater weight in grading.  There is NO final exam,                                     

COSTS:

Participants will be expected to purchase their own writable optical disks (15 CD-ROMs), soft paper sleeves for the CD-ROMs (15 sleeves), open top 8.5 x 11 sheet protectors (15) and one USB memory stick, and to cover the costs of printing (including color) of the materials they turn in.

ACTIVITIES OUTSIDE THE CLASSROOM:

Some hands-on activities will be held in our ALiCE laboratory in Bay 12 of the Smith Tobacco Warehouse on East Campus.  Participants will be expected to work with sensors and actuators in the later part of the course and most of the larger and more interesting pieces of equipment are too large to bring to the computer classroom in Link Classroom #6.  We have available a number of plotters, 3d pick-and-place robots which we may convert into rapid prototype machines, one MakerBot Cupcake rapid prototype machine, one Z400 rapid prototype machine, some vintage computers and a variety of other sensors and actuators.  A recent arrival is a PARROT 4-rotor helicopter which will be a companion for our more robust model.

COURSE RESOURCES:

There are no required textbooks.  Insofar as possible, we will make all readings available online.  Much material is already hosted on the Web by government and private sources and new ones are appearing every day.  We will upload current content regularly.  We will try to make rare and out-of-print material available as well.  Listed first are articles which participants may be called upon to discuss.  Clearly, there is much more material available for this course than we can cover in a single term, so we will include only a selection sampled from sources like the following books, URLs, museums and audio-visual material which follow and which form the philosophical and epistemological foundations of the course:

ARTICLES FOR FURTHER READING: (You may be called upon to discuss one or more of these.)

Adleman, Leonard.  “Computing with DNA.”  In SCIENTIFIC AMERICAN, August 1998, pp. 54 ff.
Anon.  “The Thinking Machine.”  In TIME, January 23, 1950, cover and pp. 54ff.
Bentley, Peter.  “An Introduction to Evolutionary Design by Computers.”  In Bentley, Peter, editor.  EVOLUTIONARY DESIGN BY COMPUERS.  Morgan Kauffman (1999).  Pp, 1-34.
Egan, Greg.  “Prologue: (Rip, tie, cut toy man).”  In Egan, Greg, PERMUTATION CITY, HarperPrism (1994). Pp. 2–14.
Forrester, Jay.  “Counterintuitive Behavior of Social Systems.”  In SIMULATION, February 1971, pp. 61-76.
Fredkin, Ed.  “A New Cosmogony.”  Working paper, 1992. http://www.digitalphilosophy.org/Home/Papers/ANewCosmogony/tabid/107/Default.aspx
Gessler, Nicholas.  Varios publications on the Web at: http://www.duke.edu/web/isis/gessler/cv-pubs/
Gessler, Nicholas.  “ALiCE: Simulation, and Programming Guide – C++, Embarcadero and the Windows API.”  Circa 30pp.
Glass, Robert.  “Software Conflict 2.0 - The Cognitive View: A Different Look at Software Design.”  In Developer.* Books (2005).  http://www.developerdotstar.com/mag/articles/glass_cognitive_view.html
Lem, Stanislaw.  “Non Serviam.”  In A PERFECT VACUUM, Harvest / HBJ (1978), pp. 167-196.
Lohn, Jason.  “Design b Darwin – Artificial Evolution Beats an Engineering Degree.”  In WIRED, 02/2004, P. 114.
Keats, Jonathon.  “John Koza has Built an Invention Machine.”  In POPULAR SCIENCE, May 2006, pp. 66ff.
Reeves, Jack.  “Code as Design – Three Essays.”  http://www.developerdotstar.com/mag/articles/PDF/DevDotStar_Reeves_CodeAsDesign.pdf
Shasha, Dennis and Cathy Lazere.  “A Finely Mottled Universe.”  In OUT OF THEIR MINDS, Copernicus, pp. 21-27.
Tooby, John and Leda Cosmides.  “The Psychological Foundations of Culture.”  In THE ADAPTED MIND, Oxford (1992).

BOOKS FOR FURTHER READING:

Axelrod, Robert.  THE EVOLUTION OF COOPERATION.  Basic Books (1984).
Bäck, Thomas, David Fogel and Zbigniew Michalewicz.  EVOLUTIONARY COMUTATION 1 – BASIC ALGORITHMS AND OPERATORS.  Institute of Physics Publishing (2000).
Bäck, Thomas, David Fogel and Zbigniew Michalewicz.  EVOLUTIONARY COMUTATION 2 – ADVANCED ALGORITHMS AND OPERATORS.  Institute of Physics Publishing (2000).
Ballard, Dana.  AN INTRODUCTION TO NATURAL COMPUTATION.  MIT Press (1997).
Banzhaf, Wolfgang, Peter Norden et al.  GENETIC PROGRAMMING – AN INTRODUCTION.  Morgan Kauffman (1998).
Batty, Michael and Paul Longley.  FRACTAL CITIES.  Academic Press (1994).
Bentley, Peter and David Corne.  CREATIVE EVOLUTIONARY SYSTEMS.  Morgan Kauffman (1999).
Bentley, Peter, editor.  EVOLUTIONARY DESIGN BY COMPUERS.  Morgan Kauffman (1999).
Bertalanffy, Ludwig von.  ROBOTS, MEN AND MINDS – PSYCHOLOGY IN THE MODERN WORLD.  George Braziller (1967).
Braitenberg, Valentino.  VEHICLES – EXPERIMENTS IN SYNTHETIC PSYCHOLOGY.  MIT Press (1994).
Brooks, Rodney.  CAMBRIAN INTELLIGENCE – THE EARLY HISTORY OF THE NEW AI.  MIT Press (1999).
Casti, John.  WOULD-BE WORLDS – HOW SIMULATION IS CHANGING THE FRONTIERS OF SCIENCE.  John Wiley (1997).
Casti, John.  ALTERNATE REALITIES – MATHEMATICAL MODELS OF NATURE AND MAN.  John Wiley (1989).
Dautenhahn, Kerstin and Chrystopher Hehaniv.  IMITATION IN ANIMALS AND ARTIFACTS.  MIT Press (2002).
Epstein, Joshua and Robert Axtell.  GROWING ARTIFICIAL SOCIETIES – SOCIAL SCIENCE FROM THE BOTTOM UP, MIT Press (1996).
Flake, Gary William.  THE COMPUTATIONAL BEAUTY OF NATURE – COMPUTER EXPLORATIONS OF FRACTALS, CHAOS, COMPLEX SYSTEMS, AND ADAPTATION.  MIT Press (1999).
Fodor, Jerry.  THE MODULARITY OF MIND.  MIT Press (1987).
Fogel, David.  BLONDIE24 – PLAYING AT THE EDGE OF AI.  Morgan Kauffman (2002).
Fogel, David, editor.  EVOLUTIONARY COMPUTATION – THE FOSSIL RECORD.  IEEE Press (1998).
Fogel, David.  EVOLUTIONARY COMPUTATION – TOWARD A NEW PHILOSOPHY OF MACHINE INTELIGENCE.  IEEE Press (1995).
Glass, Robert.  SOFTWARE CONFLICT 2.0 – THE ART AND SCIENCE OF SOFTWARE ENGINEERING.  Developer.* Books (2005).
Hillis, Daniel.  THE PATTERN ON THE STONE – THE SIMPLE IDEAS THAT MAKE COMPUTERS WORK.  Basic Books (1999).
Holland, John.  EMERGENCE – FROM CHAOS TO ORDER.  Addison Wesley (1998).
Hutchins, Edwin.  COGNITION IN THE WILD.  MIT Press (1995).
Kaandorp, Jaap and Janet Kübler.  THE ALGORITHMIC BEAUTY OF SEAWEEDS, SPONGES, AND CORALS.  Springer (2001).
Kauffman, Stuart.  THE ORIGINS OF ORDER – SELF-ORGANIZATION AND SELECTION IN EVOLUTION.  Oxford (1993).
Koza, John.  GENETIC PROGRAMMING – ON THE PROGRAMMING OF COMPUTERS BY MEANS OF NATURAL SELECTION.  MIT Press (1993).
Koza, John.  GENETIC PROGRAMMING II – AUTOMATIC DISCOVERY OF REUSABLE PROGRAMS.  MIT Press (1994).
Koza, John et al.  GENETIC PROGRAMMING III – DARWINIAN INVENTION AND PROBLEM SOLVING.  Morgan Kauffman (1999).
Langton, Christopher, editor.  ARTIFICIAL LIFE – AN OVERVIEW.  MIT Press (1995).
Mandelbrot, Benoit.  THE FRACTAL GEOMETRY OF NATURE.  Freeman (1983).
Meinhardt, Hans.  THE ALGORITHMIC BEAUTY OF SEA SHELLS – ENLARGED EDITION.  Springer (1998).
Minsky, Marvin.  SOCIETY OF MIND.  Simon & Schuster (1986).
Mitchell, Melanie.  COMPLEXITY, A GUIDED TOUR.  Oxford (2009).
Prusinkiewicz, Przemyslaw and Aristid Lindenmayer.  THE ALGORITHMIC BEAUTY OF PLANTS.  Springer (1990).
Resnick, Mitch.  TURTLES, TERMITES, AND TRAFFIC JAMS – EXPLORATIONS IN MASSIVELY PARALLEL MICROWORLDS.  MIT Press (2000).
Sawyer, Keith.  SOCIAL EMERGENCE – SOCIETIES AS COMPLEX SYSTEMS.  Cambridge (2005).Schelling, Thomas.  THE STRATEGY OF CONFLICT.  Harvard (1997).
Schelling, Thomas.  MICROMOTIVES AND MACROBEHAVIOR.  Norton (1978).
Simon, Herbert.  THE SCIENCES OF THE ARTIFICIAL.  MIT Press (1998).
Sun Tzu.  THE ART OF WAR.  Translated by Samuel Griffith.  Cambridge (1971).
Szfranski, Colonel Richard.  “Neocortical Warfare? The Acme of Skill,” in MILITARY REVIEW, Novermber (1994), pp. 41-55.
Trappl, Robert et al, editors.  EMOTIONS IN HUMANS AND ARTIFACTS.  MIT Press (2002).

ON THE WEB:

EVOL.  Rob Sanders. http://web.arch.usyd.edu.au/~rob/applets/genart/GenArt.html
EVOLVING LOGIC – ROBUST ADAPTIVE PLANNING. http://www.evolvinglogic.com/
GENETIC ART.  John Mount. http://mzlabs.com/MZLabsJM/page4/page22/page22.html
INTRODUCTION TO ALIFE.  Jean-Philippe Rennard. http://www.rennard.org/alife/english/
NATIONAL GEOSPATIAL INTELLIGENCE AGENCY.  https://www1.nga.mil/Pages/Default.aspx
NATURAL SELECTION, INC. – EVOLVING INNOVATIVE SOLUTIONS. http://www.natural-selection.com/
PARROT AR-DRONE. http://ardrone.parrot.com/parrot-ar-drone/parrot-ar-drone/en/
ROBOTICS AT BOSTON DYNAMICS. http://www.bostondynamics.com/robot_index.html
RSA Conference 2009 Webcast: http://media.omediaweb.com/rsa2009/preview/webcast.htm?id=1_5 Cryptographers Panel, April 21, 2009:
-Lieutenant General Keith M. Alexander, NSA, April 21, 2009. 
-Melissa E. Hathaway, Homeland Security, April 22, 2009. 
-James Bamford, author of “The Shadow Factory,” April 22, 2009.
SIGEVO – SPECIAL INTEREST GROUP FOR GENETIC AND EVOLUTIONARY COMPUTATION. http://www.sigevo.org/
TEMPLE OF ALIFE.  http://alife.fusebox.com/
WORLD CONFERENCE ON COMPUTATION INTELLIGENCE – EVOLUTIONARY COMPUTATION. http://www.wcci2010.org/topics/ieee-cec-2010
WORLD WAR II - MIND OF A CODEBREAKER.  The story of the British code breakers in Bletchley Park.  NOVA, WGBH Boston (2009).  http://www.youtube.com/watch?v=-ITPAbYScIw

FILM, VIDEO, DVD:

ARTIFICIAL LIFE.  vpro Amsterdam (1994).
AVALON.  Mamoru Oshii, Anime Studio (19??).
DARK CITY.  Alex proyas, New Line Productions (1998).
FAST, CHEAP AND OUT OF CONTROL.  Errol Morris, Sony Pictures (1998).
GENETIC PROGRAMMING II - THE NEXT GENERATION.  John Koza, MIT Press (19??).
MIT LEG LAB DISTRIBUTION. MIT AI Laboratory (1994).
ROBOFEST OSAKA 2001.  Anonymous (2001).
SPOTWORKS – THE EVOLUTION OF VISUALS.  scott draves (2004).
THE THIRTEENTH FLOOR.  Josef Rusnak, Columbia Pictures (1999).

"A New Way of Knowing," Human Complex Systems Program Commencement Address.
Nicholas Gessler, UCLA, June 15, 2008:

In Donald Rumsfeld's controversial career, one statement of his stands out as an admonition against simplistic plans and idealized expectations:

"There are known knowns. These are things we know that we know.
There are known unknowns. That is to say, there are things that we know we don't know.
But there are also unknown unknowns. There are things we don't know we don't know."
Donald Rumsfeld

Complexity is a "known unknown," an unknown that was once thought to be unknowable.

Perhaps, one of these "unknown unknowns" for Donald Rumsfeld was complexity. Perhaps, he didn't know that he didn't know the importance of the complex variety of perceptions, beliefs, goals, plans and actions in the world of social and cultural affairs. For us, in the Human Complex Systems program, social and cultural complexity is a "known unknown." It is the focus of our work. We know that we don't know the often counter-intuitive processes at work in society and culture, processes that interact and co-evolve in a dynamically ever-changing world. It is this complex network of causes and effects that we seek to describe, to understand and to explain in our Human Complex Systems Minor.

The U.S. Department of Education writes: "The challenges of the 21st century will require new ways of thinking about and understanding the complex, interconnected and rapidly changing world in which we live and work. And the new field of complexity science is providing the insights we need to push our thinking in new directions." A Report of the U.S. Department of Education

Much of complexity science arose from the general discovery of computation in the natural world around us, and the specific quest to build machines, computers, on which to simulate these multi-agent systems. As the power of consumer-off-the-shelf computers has grown, as the languages that we use to talk to computers have become more available, and as both have declined in cost, the desktop computer has become the instrument of choice for exploring our own ideas through simulations written by our own hand. No longer must we exclusively rely on someone else's programmed applications; we can write our own. No longer must we passively accept vague verbal arguments pretending to tell us how the complex world works; we can translate those into would-be worlds. Now, albeit with unrelenting effort, we can build artificial worlds, artificial societies and artificial cultures on our own. We can experiment with the theories and hypotheses they embody on desktop laboratories, evaluating one "what-if" scenario after another. In doing this, we can tell which worlds are plausible and which are not, which ideas at their foundations are credible and which are not. Among a wide range of theoretical explanations we can separate those within the realm of possibility from those that lay outside reality. It is not an easy task, but it is both insightful and necessary. . .

Rushworth Kidder, President, Institute for Global Ethics, reminds us: "In times of fear people turn to fundamentalist mindsets, and I don't mean that only in terms of religion. There's economic fundamentalism; there's political fundamentalism, and so forth. And that's really a reducing of the complexity to very clear black versus white, right versus wrong, issues. When that happens, it is very easy for people to take stark, and harshly polarized, points of view and simply lob bombs back and forth at one another verbally. I think there is no question that that is, to some extent, the nature of the discourse in this country right now. And I long to have us move to an understanding of the complex nature of these things." Rushworth Kidder (President, Institute for Global Ethics). Radio Interview, "The World," November 22, 2005

It is the little things that build the underpinnings for both our highest triumphs and our deepest failures. It is an aphorism of our field to say that complexity arises from the bottom-up: from the seemingly disordered chaos of local rules there arise ordered global patterns of behavior. In this interpretation of emergence, "Both God and the Devil are in the details."

Next year I will be leaving UCLA to build a program in "Artificial Life, Artificial Culture and Evolutionary Computation" at Duke University. I will miss my colleagues here, with whom I worked to build our program in Human Complex Systems. I will also miss the many students I have had who have inspired me with their new insights and ideas and who have pushed me towards confronting the new challenges in our field, just as I have pushed them towards confronting the smallest details of cultural processes. To the many parents who are here, I congratulate you on your daughters' and sons' accomplishments. Your investment in their education has reached one of many levels of fruition. Congratulations to you all. And many "thank you's" to my colleagues, to our students, to their parents, and to our friends…

On a lighter note, earlier this week I went to see the movie IRONMAN. I encourage you to see it. Two scenes took my breath away: In it you will see a marvelously compelling simulation of a 3D computer graphics terminal of the future. You will also see a wonderfully convincing simulation of a robotic suit. These are both the result of astronomically complex computer calculations. Think of the millions of bottom-up computations that went into this production. Think, for a moment, what might happen if this talent were turned towards modeling the social and cultural issues of our time?

I would also encourage you to attend the upcoming conference on computer graphics called SIGGRAPH, the Association for Computer Machinery's Special Interest Group on Graphics. It will be held at the Convention Center in Los Angeles from August 11th to 15th. There you will see the latest innovations in simulation, largely for the entertainment industry. It is those techniques that we must learn to master. Again, that is SIGGRAPH (spell it out). Some of us will be there…

In this talk I've focused mostly on simulation, the re-creation and re-presentation of social and cultural experience as a formal model. Both experience and reflection are essential to understanding; each informs the other. And, reflecting on reflection itself, you may come to realize that it too is another facet of simulation in… Again, "thank you all," my colleagues, our students, their parents, and our friends. . .

Artificial Life, Artificial Culture & Evolutionary Computation - Simulations using Multiagent and Evolutionary Computation

In many ways, computer languages and programs are better representations of reality than spoken and written words. Look at the complexity that surrounds us: The local properties of individual elements give rise to the global properties of chemicals, minerals and life itself. The local properties of cells give rise to the global behaviors of organisms. Local neurons interact to process information giving rise to the global consciousness of the individual. Individuals interact at the local level resulting in the vast complexities of societies and cultures. This is the phenomenon of emergence, the process by which individual actors, or agents, with limited knowledge of the whole, interact with one another simultaneously and in parallel to produce larger global patterns of behavior that may not be apparent to any single individual. This process is also described as "multiple causation" or "multiple agency." Close to the concept and phenomenon of emergence is the process of evolution, described a century ago by Charles Darwin and Alfred Russel Wallace. It is difficult to talk about these things in natural spoken language, because language is by nature a serial description, a narrative unfolding through time, usually told from a single individual's perspective. How do you describe a situation in which you have one dozen different personalities, each with a different background, each from a different culture, each pursuing different goals, located in various places and communicating along different networks? How do you do this in natural language? And once described in natural language, how will these individuals interact? What will this situation entail? This can be done in computer languages. The situation can be described with a minimum of ambiguity, and once that set of initial conditions is fixed, the researcher can press "run" to see what these individuals will do. Moreover, she can change the initial conditions at will, change the way those individuals sense and perceive the world, change their goals, and study the outcome. Computer simulations give us the opportunity to study the entailments of a variety of "what if" scenarios. Thy are like having a social science laboratory on your desktop. Whether we simulate the behavior of atoms or ants, quarks or quasars, individuals or cultures, the procedure is similar. They say, "the whole is greater than the sum of its parts," because it is the interaction of the parts that constitute the missing processes. We will wrestle with the philosophies of emergence and evolution through a critical hands-on engagement with artificial life, artificial culture, and evolutionary computation.

Most participants enter the course with no previous programming experience whatsoever, yet in the first week everyone creates an application from scratch which illustrates the surprising nature of algorithmic processes with colorful graphics and MIDI sounds. We work with Borland C++ which simplifies the Windows environment and describe the worlds we will create in C++. We write descriptions of interactive processes in detail and run them to explore the often counterintuitive and visually striking results. Among the canonical simulations we may enhance and explore are cellular automata, including Conway's Game of Life, Schelling's segregation model, models of assimilation, aggregation and dispersion, flocking, growth, networks and mapping. Using evolutionary computational techniques we may tweak the Traveling Salesman's Problem to optimize a concert tour, political canvassing, or the itinerary of a Ferenghi trader. We may create applications to evolve visual arts and/or musical compositions. We conclude the course with simulations which self-organize and learn in much the same way that cultural and biological systems do, through the mechanisms of evolution. Thus we end by including the creative force that gave rise to our intelligence in the simulations that we write. Participants are encouraged to work with problems from their own major and/or imagination.

Given my own background as an anthropologist, we may also apply simulation to study cultures, cultural change and cultural evolution. The argument for employing simulation to the social sciences might look like this: Empirically, culture comprises individuals, artifacts and groups embedded in social, technological and physical environments, all complexly interacting in simultaneous mutual causation. Although sharing many commonalities, each individual has a distinct identity and conception of the world, a specific repertoire of experiences, beliefs, perceptions, interpretations and behaviors. Each artifact similarly carries information in a distinct way. Describing, understanding and explaining culture thus necessitates representations which not only capture this complexity as a description, but also enact it as a process, thereby enabling the researcher to evaluate suites of theoretical experimental "what-if" scenarios. We may critically explore state-of-the-art multicausal multiagent simulations emphasizing dynamically materially intermediated cultural cognition. In other words, we think not just consciously and in words. We think unconsciously, and with our bodies, and with the material world around us. Where you work and how your work is arranged is also part of your cognition. Moreover, members of a given culture do not share a homogeneous cognitive structure. We form networks of friends and enemies. We will also look at examples of trust, secrecy and deception on international relations as well as at artifacts (material cognitive devices) from the evolution of computation, born from the weaver's loom and from the need for intelligence.

As we gain more confidence in writing code, we will connect our simulations and applications to real-world sensors and actuators, to make them aware of, and responsive to, their surroundings. We have a suite of robotic components to work with. Picture, for a moment, the slot machines in Las Vegas. Do the reels on those machines rotate at random? In fact they are driven by stepper motors, controlled by a computer that knows exactly where the wheel is and what icon is showing through the glass. They are no longer real reels, random by virtue of their mechanical construction, rather they are called "virtual reels," highly deterministic in their movements, made to simulate the reels of old. Finally, we will work with a number of rare and once classified cryptographic devices, machines which construct and deconstruct secret codes. These are early electromechanical computers. It was the attempt to decrypt the messages enciphered by these machines, specifically the NAZI German Enigma, that gave rise to the modern computer. From the Polish "Bombe," to the British and American "Bombes" and "Colossus" it was the intelligence community that paved the way for computation as we know it.

This course is a must for those who wish to understand the computational environment in which we live and the increasing role it plays overtly and covertly in our lives. Whether you go on to write simulations, supervise a team of programmers, or critically tease out the assumptions hidden behind arguments for policy decisions, we offer a look inside the social and technological processes embedded in our culture.