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Representation
Introduction
Why bother with
computer models and simulations?
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Our minds simulate the objects in
our world. To begin with our preconceptions color how we see the world
around us. Our eyes paint a picture of our surroundings. Our hearing,
touch and other senses modify that mental model. Through these sources
we perceive the world and we usually do not question our perceptions.
But there are times that remind us of our limitations. Driving along Mulholland
at night with two days deficit of sleep we might mistake a power pole
for the center line of the road with unsettling or disasterous consequences.
Optical illusions and stage magic offer more entertaining and benign examples
of our limitations.
Our minds simulate the processes
in our world. We enter this world with a "theory of mind," a
notion that other people have minds too. With some effort we can learn
to think like other people think. We can anticipate, to a degree, how
others will react to us and learn to modify their behavior. Actors are
very good at that. People with autism don't seem to have a "theory
of mind" about other people. They cannot model other people's minds.
When we dream, we see a simulated world inside our own minds. If we pay
attention to our dreams we see that they image objects and to some extent
they mimic processes. But the processes are often flat and shallow. They
often don't have long term coherence. (They are similar to Damashek's
n-gram sequential analyses.)
Our minds simulate both objects and
processes, but they have their limitations. It seems that our minds rely
to a great extent upon the external world and our constant stream of perceptions
from it in order to calibrate and adjust their simulations in real time.
It seems that vision is our most immersive source of information about
the world outside. It certainly carries the greatest bandwidth of information.
Visual illusions are difficult to uncover because of this. Even though
our rational mind knows what it sees is false, it has to struggle with
the compelling nature of what we see. This may be why it is so easy to
completely disorient a person wearing a head-mounted virtual reality display.
When we concentrate on reading literature, we try to isolate our senses
from the distracting sights and sounds around us. When we go to the movies
it's easy to suspend disbelief. We will even entertain the idea that we
have entered a world where the laws of physics are different, where Roadrunner
can paint a tunnel entrance on a rock face and escape through it, while
Wiley Coyote will run headlong into solid rock.
The arts, humanities, and sciences
have different ways of knowing the world. They often differ in how they
represent that knowledge, for example in paintings, in literature, and
in mathematics. They also often differ in how they rank the processes
and products of their practice. Do they priveledge the artifact itself
(the painting, work of literature, mathematical formula), the process
of its making (what went on in the mind of its maker), or the process
of its appreciation (what it means to researchers today)? If we take a
long view of the evolution of our biology and culture, we find that humankind
has adapted to different modes of representation, both genetically (biologically)
and memetically (culturally), at different times. It appears that natural
spoken language has been in place for 2,000,000 years. Visual symbolic
communication in the form of style and art has been in place for 2,000,000
years. Natural written languages appeared only 2,000,000 years ago. Formal
languages that included counting and simple mathematicswere in place for
2,000,000 years. As a result, we are more accustomed to certain modes
of representation than to others. For instance, we are more comfortable
with story telling than we are with logic. An interesting example is that
it is easier to understand a logical problem when it is presented in a
narrative than in an abstract form. These factors have a lot to do with
what we consider to be an good explanation for something that we see in
the natural world.
Most of us are more comfortable with
other people than we are with the products of our technologies. We consider
many seemingly intuitive forms of human interaction to be "natural"
and condemn the things we design and make by "artifice" (see
OED definition) as being "artificial"(see OED definition) or
simply "artifacts" (see OED definition). This distinction can
be useful, but the boundary between the natural and artificial is blurred.
The two are intimately interwoven. For instance, what constitutes a natural
environment? If we remove the vestiges of a logging camp does the forest
once again become natural? Which human relationships are natural? Is there
such a thing as a natural human right? Which human relationships are artificial?
What constitutes a cultural construction? All may be fair in love and
war, but which concepts of fairness are due to nature (what we are instinctively
born with) and which to nurture (what we are taught)? Having evolved for
2,000,000 years as hunters, foragers and gatherers in small groups, we
are probably more prepared to go along with the concensus in small groups
than to think independently in large complex ones. We are probably more
confortable with simple technologies and tools than we are with complex
ones. We have learned to be prejudiced towards simplicity. In many cases
this has served us well. In many cases it has not. Learning new things
is a difficult endeavor. If we could get away with it, it might seem easier
and more fulfilling to conjur up only sunny days at the beach, to eat
well, and to make love. But the world is more complex than that, and perhaps
more socially complex than it has ever been before. "Sex and drugs
and rock and roll" is another way of dealing with the complexity
of present times. Technologically, something complex happened during World
War II that became apparent around 1950. That was the advent of, or perhaps
the discovery of, computation. By computation, I am not simply referring
to email, the Internet, NAPSTER, DVDs, ATM machines, the news or the banking
system. By computation, I am referring to something deeper. I am referring
to the fact that for the first time here on Earth, or as far as we are
presently aware, the first time in the Universe, an intelligent and sentient
being has created an artifact that can, in some sense, think. As if that
were'nt enough of an accomplishment, we have been able to insert the entire
process of evolution through natural selection into that machine. Yes,
we have invented software that can write itself. But not only can our
software evolve, but so can the hardware that we run it on. This new technology
is called "evolutionary computation." The evolutionary hardware
is called "reconfigurable logic." So what is very new is the
fact that we have created a technology that has imbedded within it the
same evolutionary processes that created us. This is certainly one of
the greatest technological accomplishments on the face of this planet.
Confusion of terms, analogies, different
media.
By "artificial" we simply
mean constructed by human beings. In the field of "artificial intelligence"
the term meant one of two things: a system that looked intelligent on
the outside even though its insides had no obvious relationships to human
brains, or a system that tried to be intelligent on the outside because
its insides did try to mimic known brain processes. From the field of
artificial intelligence sprang the fields of artificial life, artificial
societies, and artificial culture. In these instances, the term "artificial"
meant the computational model or simulation as opposed to the "real"
or "natural" phenomenon it tried to represent. When we use the
term "artificial" it will refer to the computational model.
We will not use the term to mean "cheap, phoney, or ...
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Minimalist
models, toy problems, reductionsist/ Analytical
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Analysis
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Synthesis
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Reductionist
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Constructionist
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Relationship to Virtual Reality and
Computer Games.
You will notice that I have jumped
around a lot in preparing this introduction. This is, in part, a consequence
of the view that many of us have about reality. We find that spoken and
written natural language are inadequate to represent the complexities
of the real world. We would like NOT to impose these limitations on our
understanding of that world. Rather, we would like to find a way to represent
the complexities of that world in a way that's closer to what they actually
are. We believe that multiagent simulation can help us achieve that goal.
| Variations
in Emphasis |
Conceptual Dimensions of
a Simulation Space
Simulations may be characterized
as occupying a point cloud in a three dimensional conceptual space
enclosed among three axes: agent complexity, agent density, and
environmental detail. The space is bounded limitations in the overall
computational effort (e.g. number of CPU cycles) required to run
the simulation. Simulations occupying regions of this space close
to the origin may be said to be "minimalist." Those occupying
regions far from the origin may be said to be computationally rich.
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Agent Complexity
Most of the work in Artificial
Intelligence and Distributed Artificial Intelligence
falls into this category. The catchphrase might be: "Compelling
and immersive intellectual environments."
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Agent Density
Most of the work in Artificial
Life and Cellular Automata falls into this category.
The catchphrase might be: "From simple local individuals to
complex global populations."
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Environmental Detail
Most of the work in Virtual
Reality, Virtual Environments, and Augmented Reality
falls into this category. The catchphrase might be: "Compelling
and immersive visual environments."
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Analogies...
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noun
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verb
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object
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process
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discourse
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argument
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computer
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computation
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Why should
we learn about simulation?
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The job of
brains is to simulate reality.
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We can learn
from mental experiments instead of real world experiments where
failure could be costly.
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We can learn
from computer experiments instead of real world experiments where
failure could be costly.
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Like virtual
reality, simulation allows us to explore alternative worlds.
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Simulation
mimics our knowledge of the world.
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Simulation
is natural, like a thought experiment, a theory of mind, a dream.
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The human
mind has not evolved to understand complex interactions.
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Simulation
provides us with insights into societal phenomena.
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Simulation
enables communication between researchers.
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Simulation
serves as predictive theories of social interaction.
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Simulation
gives us compelling and immersive experiences.
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Simulation
enhances education.
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Simulation
is fun.
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Why construct
minimal experiments on toy models?
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They enable
us to search for key parameters that effect the system.
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They provide
us with the greatest explanatory power for the least investment
of time.
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They are consistent
with Occam's Razor - a problem should be stated in its basic and
simplest terms.
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They allow
us to track cause and effect more easily.
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They give
us the greatest leverage - they give us the most from the least.
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KISS - Keep
It Simple Stupid.
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phenomenon itself. The real event. The external world. |
Individual
perceptions, concepts, thoughts, ideas, internal language |
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A
considered collective reconstruction or representation of the event
itself. |
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Raw Description
Unedited so as not to "bias" information.
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Creative Description
Edited to capture the "key" features. |
| John's experience of it. |
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Talking |
Narrative |
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Writing |
Literature |
| Mary's experience of it. |
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Mathematics |
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Film & Video |
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| Jennifer's experience of it. |
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Photos, graphics, drawings |
Art |
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Computation |
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Representation
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Mode
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Geometry
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Example
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Thought (broadly
construed)
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Various
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Various
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Conscious
& Unconscious
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Natural Spoken
Language
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Discursive
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Linear &
Sentential
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Narrative
& Story Telling
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Natural Written
Language
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Discursive
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Linear &
Sentential
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Narrative
& Story Telling
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Visual Language
& Diagrammatic Reasoning
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Diagrammatic
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Diagrams,
Maps & Art
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Formal Mathermatical
Language
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Formulaic
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Formulas &
Statistics
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Computer Language
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Computational
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Programs,
Simulations & Runs
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Readings
- Time Magazine, January 23, 1950.
- Counterintuitive Nature of Social
Systems
- Review of Axtell & Epstein's
Growing Artificial Societies
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review
DVD Chapter 5, 16:29-17:54
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How do we know the world? Biologically,
we simulate the world through our senses. Culturally, we simulate the
world through constructions. The status of different representations:
the world itself, mental constructions, natural discursive language, art,
mathematics, computation.
- Example: differing representations
of animal physiology, i.e. locomotion (multimedia).
- Video: MIT Leg Lab robotic demonstration
and simulation.
- Video: Karl Sims, Evolved Virtual
Creatures.
What is simulation? A bottom-up perspective:
from simple local interactions to complex global behaviors. Self-organization
and the emergence of levels of complexity.
- Website: Temple of Alife, six
programs. alife.fusebox.com
- Example: Sierpinsky's gasket.
- Example: Cellular Automata - Conway's
"Game of Life." Ed Fredkin's project of rewriting the laws
of physics as CAs.
- Are we programs running on some
giant computer? (Cuts from the film "The Thirteenth Floor.")
Lab - Help students get online
with the proper accounts. Have students introduce and photograph each
other. Introduce HTML (DreamWeaver), PhotoShop, and CuteFTP. Have them
build a webpage for the course comprising a personal introduction and
a survey of their beliefs about computation and society. Re-run the tournaments
from the Karl Sims' video. Re-run Conway's "Game of Life." Open
discussion...
Assignment - Karl Sims' work
clearly shows that the structure, cognition and behavior of an organism
may be evolved by computer simulation. Closely observe the behavior of
the creatures in three tournaments of your choosing. What can you discern
about the sensory and cognitive processes going on in the "minds"
of each of the contestants?
Readings
- Karl Sims, Evolved Virtual Creatures
- Film Clip - Dark City.
- Film Clip - The 13th Floor.
- STOW and ModSAF.
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How brains evolved to re-present
the world. How we think they think. The limits of human knowing.
The natural evolution of thinking
things. Computational models of the evolution of thinking:
Braitenberg's perspective:
Rodney Brooks' perspective:
Robot communities, reviews/videos:
- Sarita Thakoor, JPL
- Luc Steels
- Maja Mataric, MIT
What is a computer? How technology,
including culture-as-a-technology, evolved to re-present the world. The
limits of technological knowing. The evolution of the technology of computing:
- Danny Hillis' "Tinker Toy"
computer.
- Norden bombsight (analog computers).
- Cryptography - The Jefferson wheel,
M-209, Enigma, Bombe, and the advent of the Universal Turing Machine
(digital computers).
- Biochips: growing neurons on silicon
(cyborg computers).
- DNA/RNA, molecular, optical, quantum
computers and beyond... (other computers).
- Edwin Hutchins' "Cultural
Cognition" - Cognition
in the Wild. Coevolution
of human and technological computation. (techno-cultural computers).
How are computers and brains alike
or different? Computation exists
across many media. An evolutionary and computational way of knowing the
world. A technological understanding of the world.
Lab - Open discussion on the
uses and entailments of differing natural and artificial modes of computation.
Run excerpts from the films:
- The Matrix
- The Thirteenth Floor
- Dark City
- Ghost in the Shell
- Fast, Cheap and Out of Control
Assignment - View and then
write a critique of one of the films above based upon a comparison of
popular cultural ideas of computation, the ideas portrayed cinematically
(artistically, visually, metaphorically, and through dialog) in the film,
and the "scientific reality" of computation as you have come
to understand it. Turn in your critique to your TF electronically. Upload
a copy to your website AFTER the due date.
Readings
- Lamprey Eel brain robot controller.
- Neuron - Silicon Interface.
- Biochips - Gene Chips
- Edward Fredkin, Universal Cellular
Automata Computer.
- DNA Computers.
- Reconfigurable Logic.
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