|
Space exploration/colonization and Chaos theoryAnonyme, Lunes, Junio 21, 2004 - 13:35
Carl Zimmerman
http://www.suite101.com/discussion.cfm/101_fun_stuff/32939/latest/2 Subject: Chaos theory and successful innovations by Carl Zimmerman "...The opportunity to create and build our own worlds from scratch..." Imagine this dialogue between a parent and teenager in the not too distant future: Parent: Why does your generation want to leave "mother" Earth and colonize Mars? They will have to take great risks and pay enormous costs in lives lost and material destruction if initial attempts fail. This imaginary dialogue depicts the direction that the U.S. and many other developed countries are taking today--space exploration and colonization. Currently, the main goal is to develop new technologies that will improve the quality of life on Earth, but as the dialogue suggests, the colonists may "fall in love" with living on other planets. This will especially happen if the cost of solving major problems, such as ecological disasters, on Earth becomes prohibitive. Models which predict the innovations that are likely to succeed will improve the quality of life on Earth as well as for our colonists on distant planets. In some previous innovations, such the airplane, the consequences of early failure were limited (e.g., a biplane crashing on the side of a barn). For innovations needed for space exploration and colonization, the costs of failure would be enormous, including, for example, ecological disasters during testing on Earth and destruction of entire colonies on other planets. Predicting changing needs and their impact on populations is required to predict successful innovations. Ideally, in order to produce a successful innovation, we must predict the essential needs of the space colonies, and identify what does and does not exist today that will meet these needs. The "what does not exist today" will tell us which innovations will have to be developed. However, nnovations based solely on "guesstimates" of future needs and technologies run a high risk of costly failure if these conflict with actual future needs. Innovations that reflect only the present state-of-the-art without any assumptions of the future will almost certainly fail. Innovations should be based on the right balance between what we assume is needed tomorrow and what we know will work today. How can this be accomplished? The following We could apply the traditional approach of physics, that is, identify the components and the interactions operating on these, both to and from the outside world. From this, we can predict the functioning of the system at that time based on the causal relations between components, which we presume are present. However, the predictions can be correct for as long as the taxonomy (classifications) of the system remains unchanged (Aristotle fans, please take note). The mechanical model of deterministic equations for a given time cannot produce new types of objects and variables. Its predictions will only be valid until some moment, unpredictable within the model, when there is an adaptation to innovation, and new behavior emerges. Consequently, we need models that not only predict what future needs will be, but when they will change and what change in population behavior will result. Equilibrium does not exist in the real world The basis of scientific understanding has traditionally been the mechanical model. In addition to focusing on causal relations between components at a particular time, it is assumed that this system has run itself to equilibrium, so that the correspondence between object and model is made through balance of variables at equilibrium. In economics, for example, equilibrium is represented by optimal utility for the actors, where consumers minimize cost of goods and services, and producers maximize profits. This approach assumes that all the actors know what they want and how to get it, and are doing what they want given the choices available. However, we know that equilibrium does not exist in the real world. Today, system dynamics is available to replace this oversimplified static approach, which is based on unrealistic assumptions, in developing models for rational prediction of population behavior, need and innovation. These are based on the following parameters: "The clouded mind sees nothing." (The Shadow, a fictional character) Since system dynamics models required for predicting successful future innovations are concerned with evolution, these must be model systems in which adaptive and structural changes can occur. The internal characteristics of the actors change endogenously, and new variables and mechanisms of interaction can appear spontaneously within the system itself, leading to a changed taxonomy. In order to make the model work, it must be simplified. This can be accomplished with two assumptions: 1. Events occur at an average rate. Chaos equations are better predictors of successful future innovations than bell curve equations Errors introduced by assumption #1 (above) can be corrected by probabilistic dynamics, which assumes that all individuals are identical to an average type, but that events of different probabilities occur. Consequently, probabilistic dynamics includes sequences of events that correspond to runs of good or bad "luck" and their probabilities. Systems with nonlinear interactions between individuals eliminate the concept of a simple, constant trajectory. Evolution of the system will be described by a probability distribution that gradually changes in shape from a single modal "bell curve" (sharply peaked and centered on a mean) to spreading and splitting into a multi-modal distribution with peaks that correspond to different attractors of the dynamics (attractor=position where the dynamic system converges). These attractors could be point or cyclic, but most likely, based on previous experience, will be chaotic. Since unpredictable runs of good or bad "luck" will occur, a precise trajectory of the system does not exist for predicting future behavior. Also, these deviations from the average rate of events means that a system can "tunnel" through apparently impassable potential barriers, and can switch between attractor basins and explore the global space of the dynamic system in a manner that the system cannot predict. Adaptations For Space Colonization Since the innovations for future space exploration and colonization will be dynamic systems, the relationships of the system variables will be nonlinear. Consequently, we expect chaos equations to provide very useful descriptions of relationships for mission-critical biological, ecological and economic systems. Already electronics hardware such as solid state lasers, oscilloscopes and analog computers make extensive use of chaos equations. We've only scratched the surface of their potential. In future issues, Carl Zimmerman will expand on the promising applications of chaos theory and its advantages for future pioneers. About The Author Carl Zimmerman is a techno-marketing writer in the U.S. for a major global manufacturer of animal nutrition and health products. He has a B.S. degree in chemistry and an M.B.A. in marketing.
Chaos theory, Physics, Astronomy
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Ceci est un média alternatif de publication ouverte. Le collectif CMAQ, qui gère la validation des contributions sur le Indymedia-Québec, n'endosse aucunement les propos et ne juge pas de la véracité des informations. Ce sont les commentaires des Internautes, comme vous, qui servent à évaluer la qualité de l'information. Nous avons néanmoins une
Politique éditoriale
, qui essentiellement demande que les contributions portent sur une question d'émancipation et ne proviennent pas de médias commerciaux.
|