Sciences of the Artificial Herbert A. Simon

Overview

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”).

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) Arrangement of gears Highly variable

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.

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.

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.

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.

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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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.

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.

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.

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)

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) If utility theory is to have application to real-life design problems, it must be accompanied by tools for actually making the computations.

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.” We might question whether the forms of reasoning that are appropriate to natural science are suitable also for design. One might well suppose that introduction of the verb "should" may require additional rules of inference, or modification of the rules already imbedded in declarative logic. How does the formalism avoid making use of a special logic of imperatives? It does so by dealing with sets of possible worlds: First consider all the possible worlds that meet the constraints of the outer environment; then find the particular world in the set that meets the remaining constraints of the goal and maximizes the utility function. But, a science of design is emerging. Natural science is concerned with how things are while design is concerned with how things ought to be. One can adapt ordinary declarative logic to describe the requirements of design. In particular Simon discusses the logic of optimize a nation methods, such as constrained optimization. The logic of optimization makes use of possible worlds; it moves from would to should. The issue is to develop efficient and practical methods for constrained optimization. This leads Simon to introduce satisficing, illustrating it with the traveling salesman problem.

Science of Design: The Search For Alternatives Heuristic search: factorization and means-end analysis GPS Example http://www.youtube.com/watch?v=SC5CX8drA tU Heuristic – experience based technique for problem solving. Goal-seeking system is that it is connected to the outside environment through two kinds of channels: The afferent or sensory channels through which it receives information and the efferent or motor channel through with it acts on the environment. Ability to attain goals depends on building associations, which may be a simple or very complex between particular changes in state of the world and particular actions. That will bring change about

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 First, conservation of scarce resources may be one of the criteria for a satisfactory design. Second, 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.

Science of Design The search for Alternatives Theory of structure and design organization: hierarchic systems Decomposing large problems into semi- independent components Example: Structured Programming

Science of Design The search for Alternatives Representation of design problems design should be transparent O X 4 9 2 3 5 7 8 1 6 That representation makes a difference is evident for a different reason. All mathematics exhibits in its conclusions only what is already implicit in its premises, as I mentioned in a previous chapter. Hence all mathematical derivation can be viewed simply as change in representation, making evident what was previously true but obscure. This view can be extended to all of problem solving. Solving a problem simply means representing it so as to make the solution transparent.

Social Planning – Designing the Evolving Artifact Problem Representation Representing Qualitative Problems Satisficing Outcomes Representing structure that permits functional reasoning Emission Control Example: the quantity of emissions is a function of the number of cars, how far they are driven, and their design (hence cost); the quality of air is a function of the level of emissions and of various geographical and meteorological parameters; effects on human health depend on the quality of the air and the population exposed to it.

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

Social Planning – Design without final goals Social design may not have defined long-term goals Evolution Theory – Small continual changes

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 Examples of hierarchy systems include: Social (Family, villages), Biological and Physical Systems, Formal Organizations (work places), Symbolic Systems (books, music)

The Architecture of Complexity: Time of Development Evolutionary Perspective Time depends on the number of intermediate stable forms Watch Maker Example

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 Room Example The third section explores the dynamic properties of hierarchically organized systems and shows how they can be decomposed into subsystems in order to analyze their behavior. If the decomposability condition is met, the efficiency of one component (hence its contribution to the organism's fitness) does not depend on the detailed structure of other components. Near decomposability is when components of a system are self-sufficient in the short-term, but have weak links in the long term. These are the links between decomposable systems and hierarchy. (room example)

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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