Sciences of the Artificial
Herbert A. Simon
Overview
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Understanding the Natural and the
Artificial Worlds
Using imperfect tools, we model and modify our world to
understand and control it.
• Physical science is about things.
• Complexity masks simplicity. Unmasking beautiful
complexity can lead to new beauty.
• Most things that we interact with are man-made “artifacts.”
These artifacts are discussed in terms of their purpose, have
distinct properties, and exist in their environment.
• Artificial, as opposed to but not separate from natural, refers
to things that are man-made.
• Artifacts made to imitate nature are considered synthetic.
• Engineers synthesize (“ought”). Scientists analyze (“are”).
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Understanding the Natural and the
Artificial Worlds
• Clock example:
Clock
Purpose
Properties
Environment
Sundial
(Arizona)
Tell time
Plate with
indicators
Sunny, stable
climate
Watch (on
ship)
Tell time
Arrangement Highly variable
of gears
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Understanding the Natural and the
Artificial Worlds
• An artifact’s adaptability is determined by the functionality of its
properties in its environment.
• Given properties (or incentives), we can often predict behaviors of
individuals. It is often useful to ask “how would a rationally
designed system behave under these circumstances?”
• Artifacts designed imperfectly will reflect their purpose in their
environment and some of the components of their properties. The
less perfect the design, the more of their properties will show
through. Ideally, artifacts appear identical to their non-artificial
equivalent from the outside.
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Understanding the Natural and the
Artificial Worlds
• Simulation is the artificial performance of a process. Modern
examples often involve computers, however, the use of simulation
predates computers.
▫ A simulation is no better than the assumptions built into it.
▫ A computer can do only what it is programmed to do.
▫ New knowledge can be gained by determining unknown
implications.
• Computers are useful for simulation, even though their parts are
not. The organization of their parts makes them meaningful. This
organization is designed to imitate human behavior and is useful for
simulating it.
• Symbols (letters, numbers, expressions) represent information.
Intelligence is the work of symbol systems.
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Economic Rationality:
Adaptive Artifice
• Scarcity forces us to allocate resources. Economics aims
to understanding this task of rationing.
• Positive rationality models behavior assuming
individuals are behaving to optimally achieve their goals.
Normative rationality advises individuals on how to
optimally achieve their goals. Risk and uncertainty
(which are always present) complicate this process.
• The process used to discover and implement this type of
decision is procedural rationality. Refining this decision
process to a series of mathematical calculations allows
computers to automate it.
• Perfect real-world optimization is not possible with or
without computers. The use of computers to aid in
finding “good enough” solutions is reasonable.
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Economic Rationality:
Adaptive Artifice
• Society uses mechanisms to allocate resources among its
members. One mechanism is the market-based approach;
another is a planned, socialist hierarchy.
• Market-based assumptions for economic research work as a
result of the seemingly automatic pattern of price
optimization with or without a planner.
• Rationality is effectively undefinable when incentives are
opposed. In the prisoner’s dilemma, tit-for-tat tends to be
most effective. Most economies have both cooperative and
competitive components.
• When individuals act as a part of an organization, they
develop loyalty to that organization and make decisions as
though they are that organization.
• Cooperation can be, in and of itself, selfish.
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The Psychology of Thinking:
Embedding Artifice in Nature
• An adaptive system may deviate from a direct path if
there are obstacles between it and its objective. With
knowledge about the path, system, and objective, we
may be able to infer information about the obstacles.
• Human behavior is simple. Their environments are
complex.
• Human thought takes longer to repeat calculations
than does a computer, but is capable of developing
ah-hoc heuristics to find solutions that circumvent
the necessity to perform those calculations. Humans
can discover these heuristics, but do not always,
sometimes they must be taught.
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Memorization Game
• dfwxowmrxomrhzpafkck
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Memorization Game
• bobjillfranksuebety
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Memorization Game
• batmicedogcatgiraffe
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The Psychology of Thinking:
Embedding Artifice in Nature
• To discover any pattern in nature precisely, we must
know all factors that we need to control for when
measuring that pattern.
• Humans deal with information in chunks. Conceptual
familiarity causes larger amounts of information to be
“chunked” together. For example, cat is a chunk, but the
nonsense syllable quv is 3 chunks, “q”, “u”, and “v.”
• People can generally remember two things in short-term
memory across interruption. Seven plus or minus two
typically only works without interruption.
• Visual processing and algebraic processing may arrive at
the same conclusion, using different processes.
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Remembering and Learning: Memory
as Environment for Thought
• Increasing memory use associated with a thinking
task does not increase the complexity of the thinking
process.
• Both short-term and long-term memory are
associative.
• Recognition a relation of associated knowledge takes
time.
• A decade is the usual timeframe for acquiring the
knowledge of an expert in a given field. If sufficient
information cannot be gathered in a decade,
specialization is likely to occur.
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Remembering and Learning: Memory
as Environment for Thought
• Both knowledge expansion and compression into
parsimonious theories advance human knowledge.
• Memory can be stored in different forms of differing
efficiency specialization and recalled for processing later.
• To solve a problem about the property of an object, an
individual must be able to calculate the effect of forces
on and properties of relevant objects.
• It is easier to add information to an existing process than
it is to change the process as a result of new information.
• Problem solving generally works backward from a
desired goal.
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Science of Design:
Evaluation of Designs
• Theory of evaluation –
 Utility & Statistical Decision Theory – logical
framework for rational choice among alternatives
 Consider all possible alternatives (“worlds”) that meet
the constraints of the outer environment, and then find
the specific alternative that meets the constraints of the
inner environment and maximizes the utility
(Optimum)
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Science of Design:
Evaluation of Design
• Utility Theory Tools
• Computational methods
▫ Algorithms for choosing optimal alternatives such
as linear programming, control theory, dynamic
programming
▫ Algorithms and heuristics for choosing
satisfactory alternatives (search)
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Science of Design:
Evaluation of Design
• The Formal Logic of Design:
▫ Imperative Logic
 Design is concerned with how things ought to be
▫ Declarative Logic
 How things are.
▫ Reduction to Declarative Logic – Optimization
through constraint of environmental variables
• “We simply ask what values the command variables would
have in a world meeting all these conditions and conclude
that these are the values the command variables should have.”
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Science of Design:
The Search For Alternatives
• Heuristic search: factorization and means-end
analysis
• GPS Example
• http://www.youtube.com/watch?v=SC5CX8drA
tU
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Science of Design:
The Search For Alternatives
• Allocation of resources for search
▫ Conservation of scarce resources may be one of
the criteria for satisfactory design
▫ The design process itself involves management of
the resources of the designer, so that his efforts
will not be dissipated unnecessarily in following
lines of inquiry that prove fruitless
• Optimal vs. Satisfactory Solutions
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Science of Design
The search for Alternatives
• Theory of structure and design organization:
hierarchic systems
▫ Decomposing large problems into semiindependent components
▫ Example: Structured Programming
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Science of Design
The search for Alternatives
• Representation of design problems
▫ design should be transparent
O
X
O
4
9
2
X
O
X
3
5
7
O
X
O
8
1
6
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Social Planning – Designing the
Evolving Artifact
• Problem Representation
▫ Representing Qualitative Problems
▫ Satisficing Outcomes
▫ Representing structure that permits functional
reasoning
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Social Planning – Data for Planning
• Importance of data
• Dealing with poor data
• Forecasting – artifacts lies in the future
▫ Upper and Lower Bounds
• Feedback Control
▫ Homeostatic Mechanism – Insulated from the
environment
▫ Retrospective Feedback – Adapt to the
environment
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Social Planning – Design without final
goals
• Social design may not have defined long-term
goals
• Evolution Theory – Small continual changes
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The Architecture of Complexity:
Hierarchic Systems
• Complex System – One made up of a large
number of parts that have many interactions
• Hierarchic System – Composed of interrelated
sub-systems
• Complex systems can be better understood by
decomposing into hierarchic systems
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The Architecture of Complexity:
Time of Development
• Evolutionary Perspective
▫ Time depends on the number of intermediate
stable forms
▫ Watch Maker Example
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The Architecture of Complexity:
Decomposition
• Efficiency of one component does not depend on
other components
• Near decomposability
▫ Short-term sell sufficient
▫ Long-term – weak links with other components
 These are the links between components and the hierarchy
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The Architecture of Complexity:
Descriptions
• State Description – Characterizes the world as is
(pictures, blueprints, etc.)
• Process Description – Characterizes the world as
acted upon (recipes, differential equations, etc.)
• Problem solving uses process descriptions to
lead to a desired state description
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Questions?
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