Encyclopedia of Computer Science and Technology

432 singularity, technologicalTogether with physicist Enrico Fermi, two mathematicians,John von Neumann (see von Neumann, John) andStanislaw Ulam, devised a new way to simulate complexsystems. Instead of trying fruitlessly to come up with somehuge formula to “solve” the whole system, they appliedprobability formulas to each of a number of particles—in effect, “rolling the dice” for each one and then observingtheir resulting distribution and behavior. Because ofits analogy to gambling, this became known as the MonteCarlo method. It turned out to be widely useful not onlyfor simulating nuclear reactions and particle physics but formany other activities (such as bombing raids or the spreadof disease) where many separate things behave according toprobabilities.A number of other models and techniques have madeimportant contributions to simulation. For example, theattempt to simulate the operation of neurons in the brainhas led to a powerful technique for performing tasks suchas pattern recognition (see neural network). The applicationof simple rules to many individual objects can result inbeautiful and dynamic patterns (see cellular automata),as well as ways to model behavior (see artificial life).Here, instead of a system being simplified into a simulation,a simulation can be created in order to see what sort of systemsmight emerge.Software ImplementationBecause of the number of calculations (repeated for a singleobject and/or applied to many objects) required for an accuratesimulation, it is obviously useful for the simulationdesigner to have as much computer power as possible. Similarly,having many processors or a network of separate computersnot only increases the available computing power,but may make it more natural to represent different objectsor parts of a system by assigning each to its own processor.(This naturalness goes the other way, too: Simulation techniquescan be very important in modeling or predicting theperformance of computer networks including the Internet.)However, it is also important to have programming languagesand techniques that are suited for representing thesimultaneous changes to objects (see also multiprocessing).Using object-oriented languages such as Simula orSmalltalk makes it easier to package and manage the dataand operations for each object (see object-oriented programming,Simula, and Smalltalk).ApplicationsSimulations and simulation techniques are used for a tremendousrange of applications today. Besides helping withthe understanding of natural systems in physics, chemistry,biology, or engineering, simulation techniques are alsoapplied to human behavior. For example, the behavior ofconsumers or traders in a stock market can be exploredwith a simulation based on game theory concepts. Artificialintelligence techniques (such as expert systems) canbe used to give the individual “actors” in a simulation morerealistic behavior.Simulations are often used in training. A modern flightsimulator, for example, not only simulates the aerodynamicsof a plane and its response to the environment andto control inputs, but detailed graphics (and simulatedphysical motion) can make such training simulations feelvery realistic, if not quite to Star Trek holodeck standards.Whether for flight, military exercises, or stock trading, simulationscan provide a much wider range of experiences ina relatively short time than would be feasible (or safe) usingthe real-world activity. Simulations can also play an importantpart in testing software or systems or in predicatingthe results of business decisions or strategies.Simulations are also frequently sold as entertainment.Many commercial strategy and role-playing games as wellas vehicle simulators contain surprisingly complex simulationsthat make the games both absorbing and challenging(see computer games and online games). Such games canalso have considerable educational value.Further ReadingGilbert, Nigel, and Klaus G. Troitzsch. Simulation for the SocialScientist. 2nd ed. Maidenhead, Berkshire, U.K.: Open UniversityPress, 2005.Laguna, Manuel, and Johan Marklund. Business Process Modeling,Simulation, and Design. Upper Saddle River, N.J.: PrenticeHall, 2004.Rizzoli, Andrea Emilio. “A Collection of Modelling and SimulationResources on the Internet.” Available online. URL: http://www.idsia.ch/~andrea/simtools.html. Accessed August 21,2007.Ross, Sheldon M. Simulation. Burlington, Mass.: Elsevier AcademicPress, 2006.Shelton, Brett E., and David A. Wiley, eds. The Design and Use ofSimulation Computer Games in Education. Rotterdam, Netherlands:Sense Publishers, 2007.singularity, technologicalThe idea that an incomprehensible future is rushing downon us goes back at least as far as Alan Toffler’s book FutureShock (1970). Toffler suggested that fundamental changesin society brought about by industrial and postindustrialdevelopments were creating psychological stress and disorientation.Future shock can be thought of as a steep line on a graphthat represents the complexity of technological society. Butwhat if the line were asymptotic, approaching the verticaland then disappearing? This is what science fiction writerVernor Vinge described in the 1980s as the “technologicalsingularity.” In physics, a singularity is a place wherelaws break down, such as at the center of a black hole. Byanalogy, Vinge suggested that the development of artificialintelligence and related technologies would reach a pointwhere intelligent machines would drive their own furtherdevelopment, with their design and operation far outstrippinghuman understanding. Once intelligent machinescreate even more intelligent machines (and so on), moretechnological progress might occur in a decade or two thanin the preceding thousands of years.An obvious question is whether the singularity is in factcoming, and if so, when. Inventor and futurist Ray Kurzweilargues that history (including the accuracy of Moore’s lawof doubling computational power) shows that technological