User Based City: Agent Based Modelling

16.3.08

Agent Based Modelling

Voorbeeld van een simulatie in Mason

Na nog wat zoeken ben ik op het concept "Agent Based Modelling" gestoten. De website swarm.org linkt zo bijvoorbeeld naar een aantal open-source programmas waarmee men dergelijke 'agent based' simulaties kan doen.

Nu stelt zich echter de vraag of computer- simulaties wel een juiste keuze zijn. Deze simulaties vertrekken immers van agents die gedetermineerde beslissingen nemen. Dit leidt dus ook automatisch tot een op voorhand gedetermineerd eindresultaat. Als men een situatie 10 keer door een computer laat simuleren gaat die 10 keer hetzelfde resultaat leveren.

Dit gaat echter in tegen het idee van mijn ontwerp waarbij de mens en zijn creatieve input centraal staan. Het is mijn bedoeling een soort van 'framework' te creëren, dat dan door de gebruikers zelf wordt opgevuld. Het grote potentieel van dergelijk systeem zit hem volgens mij juist in het feit dat een mens locaal kan afwijken van de standaardaanpak; dat een mens creatief met zijn omstandigheden kan omspringen.

Voorbeeld van een simulatie in Ascape: Flockers

Het is dus essentieel dat een mens kan vrij beslissen wat hij doet. Het is echter ook belangrijk dat de gebruiker, buiten het feit dat hij locaal ageert, ook een zicht kan hebben op globale processen. Zo kan hij hierop inspelen of anticiperen (zie ook agency).

Stel dat ik deze laatste voorwaarde niet zou opnemen in het syteem. Dan zou het systeem de menselijke mogelijkheid tot inzicht (die een computer of dier veel minder hebben) totaal langs zich neerleggen. Het zou dus ook geen profijt kunnen halen uit deze menselijke intelligentie en de enige synergiën die zouden kunnen ontstaan zouden voortbouwen op mens als locaal agerend wezen, maar niet op onze mogelijkheid tot inzicht.

- Swarms.org: Agent Based Modelling
- Wikipedia: Free Will
- Wikipedia: Agency

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Begrippenindex

Complexity: In general usage, complexity often tends to be used to characterize something with many parts in intricate arrangement. In science there are at this time a number of approaches to characterizing complexity, many of which are reflected in this article. Seth Lloyd of M.I.T. writes that he once gave a presentation which set out 32 definitions of complexity.[1]

Complex systems is a subfield of a systems science or systemics, which studies the common properties of systems considered complex in nature, society and science. It is also called complex systems theory, complexity science, study of complex systems and/or sciences of complexity. The key problems of such systems are difficulties with their formal modeling and simulation. From such perspective, in different research contexts complex systems are defined on the base of their different attributes. At present, the consensus related to one universal definition of complex system does not exist yet.

In these endeavors, scientists often seek simple non-linear coupling rules which lead to complex phenomena (rather than describe - see above), but this need not be the case. Human societies (and probably human brains) are complex systems in which neither the components nor the couplings are simple. Nevertheless, they exhibit many of the hallmarks of complex systems.

Complex adaptive systems are special cases of complex systems. They are complex in that they are diverse and made up of multiple interconnected elements and adaptive in that they have the capacity to change and learn from experience. The term complex adaptive systems (CAS) was coined at the interdisciplinary Santa Fe Institute (SFI), by John H. Holland, Murray Gell-Mann and others.

Emergence: In philosophy, systems theory and the sciences, emergence refers to the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theory of complex systems.

For professor Jeffrey Goldstein, emergence can be defined as: "the arising of novel and coherent structures, patterns and properties during the process of self-organization in complex systems."(Corning 2002)

Systems theory is an interdisciplinary field of science and the study of the nature of complex systems in nature, society, and science. More specificially, it is a framework by which one can analyze and/or describe any group of objects that work in concert to produce some result. This could be a single organism, any organization or society, or any electro-mechanical or informational artifact.

Living systems theory is a general theory about the existence of all living systems, their structure, interaction, behavior and development. This work is created by James Grier Miller, which was intended to formalize the concept of "life". According to Miller's original conception as spelled out in his magnum opus Living Systems, a "living system" must contain each of 20 "critical subsystems", which are defined by their functions and visible in numerous systems, from simple cells to organisms, countries, and societies.

Systems Thinking: It is a unique approach to problem solving, in that it views certain 'problems' as a part of the overall system so focusing on these outcomes will only further develop the undesired element or problem. Systems thinking is a framework that is based on the belief that the component parts of a system will act differently when the systems relationships are removed and it is viewed in isolation. The only way to fully understand why a problem or element occurs and persists is to understand the part in relation to the whole.Standing in contrast to Descartes', scientific reductionism and philosophical analysis, it proposes to view systems in a holistic manner. Consistent with systems philosophy, systems thinking concerns an understanding of a system by examining the linkages and interactions between the elements that comprise the entirety of the system.
Systems thinking attempts to illustrate that events are separated by distance and time and that small catalytic events can cause large changes in complex systems. Acknowledging that an improvement in one area of a system can adversely affect another area of the system, it promotes organizational communication at all levels in order to avoid the silo effect. Systems thinking techniques may be used to study any kind of system — natural, scientific, engineered, human, or conceptual.

Autopoiesis literally means "auto (self)-creation" (from the Greek: auto - αυτό for self- and poiesis - ποίησις for creation or production) and expresses a fundamental dialectic between structure and function. The term autopoiesis was originally conceived as an attempt to characterize the nature of living systems. A canonical example of an autopoietic system is the biological cell. The eukaryotic cell, for example, is made of various biochemical components such as nucleic acids and proteins, and is organized into bounded structures such as the cell nucleus, various organelles, a cell membrane and cytoskeleton. These structures, based on an external flow of molecules and energy, produce the components which, in turn, continue to maintain the organized bounded structure that gives rise to these components. An autopoietic system is to be contrasted with an allopoietic system, such as a car factory, which uses raw materials (components) to generate a car (an organized structure) which is something other than itself (a factory).
More generally, the term autopoiesis resembles the dynamics of a non-equilibrium system; that is, organized states (sometimes also called dissipative structures) that remain stable for long periods of time despite matter and energy continually flowing through them. From a very general point of view, the notion of autopoiesis is often associated with that of self-organization. However, an autopoietic system is autonomous and operationally closed, in the sense that every process within it directly helps maintaining the whole. Autopoietic systems are structurally coupled with their medium in dialect dynamic of changes that can be recalled as sensory-motor coupling. This continuous dynamics is considered as knowledge and can be observed throughout life-forms.
An application of the concept to sociology can be found in Luhmann's Systems Theory.

Control theory is an interdisciplinary branch of engineering and mathematics, that deals with the behavior of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain reference over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system.

Cybernetics is the interdisciplinary study of the structure of complex systems, especially communication processes, control mechanisms and feedback principles. Cybernetics is closely related to control theory and systems theory.

The term cybernetics stems from the Greek Κυβερνήτης (kybernetes, steersman, governor, pilot, or rudder — the same root as government). Cybernetics is a broad field of study, but the essential goal of cybernetics is to understand and define the functions and processes of systems that have goals, and that participate in circular, causal chains that move from action to sensing to comparison with desired goal to action. Studies of this field are all ultimately means of examining different forms of systems and applying what is known to make the design and function of any system, including artificial systems such as business management, more efficient and effective.

Collective intelligence is a form of intelligence that emerges from the collaboration and competition of many individuals. Collective intelligence appears in a wide variety of forms of consensus decision making in bacteria, animals, humans, and computers. The study of collective intelligence may properly be considered a subfield of sociology, of business, of computer science, and of mass behavior — a field that studies collective behavior from the level of quarks to the level of bacterial, plant, animal, and human societies.

Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is no centralized control structure dictating how individual agents should behave, local interactions between such agents lead to the emergence of global behavior. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.
In SI systems, the agents follow very simple rules generally out of the need for survial which leads to very complex rules/algorithms at the systems level. The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms.

Game theory is a branch of applied mathematics which is used in the social sciences (most notably economics), biology, computer science and philosophy. Game theory attempts to mathematically capture behavior in strategic situations, where an individual's success in making choices depends on the choices of others. While initially developed to analyze competitions where one individual does better at another's expense (zero sum games), it has been expanded to treat a wide class of interactions, which are classified according to several criteria.

User Based City

16.3.08

Agent Based Modelling

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