The-Role-of-Quantitative-Models-in-Science：定量模型在科学中的作用课件.ppt

1、Naomi OreskesPowerpoint by:Fernanda RossiJessica MatthewsModels are used to:Organize dataSynthesize informationMake predictionsModels never fully represent so therefore make uncertain predictionsAdded complexity in model decreases certainty of predictionsShort-time frame model vs.long-range determin

2、istic model“Testing is the heart of science.Although there is no foolproof way to define science,testability is the most commonly cited demarcation criterion between scientific theories and other forms of human explanatory effort.”A single test is rarely,if ever,sufficient to convince anyone of anyt

3、hingPurpose of model is to gain understanding of natural worldScientists have sought understanding to:Advance utilization of earths resourcesFoster industrializationPrevent or treat diseasesGenerate origins storiesReflect on worlds creatorSatisfy human curiosityUntil 20th century,the word“model”in s

4、cience referred to physical modelNow“model”refers to a computer modelA numerical simulation of a highly parameterized complex systemQuantitative models in ecosystem science have 3 functions:Synthesis and integration of dataGuiding observation and experimentPredicting or forecasting the futuregenerat

5、ed predictions are used as a basis for public policygovernment regulators and agencies may be required by law to establish their trustworthiness(How is this problematic?)Demand for“verification”or“validation”Claims about model verification are now routinely found in published scientific literature.A

6、re these claims legitimate?Can a computer be proved true or false?How can we tell when to believe a computer?There may be several possible configurations of nature that could produce a given set of observed resultsTherefore,any empirical data we collect in support of a theory may also be consistent

7、with alternative explanationsFor this reason,many scientists except the view that theories can be proved false but not true(falsified but not verified)Purpose of essay is therefore to challenge the utility of models for predictionQuantitative model output has been used in issues such as global clima

8、te change and radioactive waste disposalBut it is open to question whether models generate reliable information about the futureThe predictions models offer to us do not aid in basic scientific understandingOur use of them does not make them importantMore complex models tend to be less accurateStell

9、ar parallax in the establishment of the heliocentric model of planetary motion by Nicolaus CopernicusFlaws present in instruments we useEarth was thought to be billions of years old based on the concept of uniformitarianism the assumption that presently observable geological processes are representa

10、tive of Earths history in generalThen,Lord Kelvin calculated the time required for a molten body the size of earth to cool to its present temperature was at most 98 million years,declaring the entire science of geology invalidThis dismissed Charles Darwins theory of natural selection and for several

11、 decades evolutionists were in nearly full retreatTHEN,radioactivity was discoveredproving Kelvin wrong.In hindsight,it is easy to see where others have gone wrong:Astronomers thought their instruments were better than they were;Kelvin thought his knowledge more complete than it was.It is harder to

12、see the flaws in our own reasoning.(If we could see them,presumably we could correct them.)When computer models are involved,it can be more difficult still,because the systems being modeled are very complex and the embedded assumptions can be very hard to see.How DO we test computer models?The more

13、complex the natural system is,the more different components the model will need to mimic that systemComplexity decreases systematic bias but increases uncertaintyHypothetico-deductive model(deductive-nomological model)Generates hypotheses,theories,or laws and compare their logical consequences with

14、experience and observations in the natural worldPROBLEM:only works reliably in closed systems2+2=4 therefore 4 2=2Is a straight line the shortest distance between two points?All models are open systems3 general categories into which this openness falls:ConceptualizationEmpirical adequacy of the gove

15、rning equationsInput parameterizationSuccessful prediction in science is less common than most of us thinkEx.1:Meteorology&Weather PredictionsWeather prediction is not deterministicSpatially averagedRestricted to the near termTrial and errorEx.2:Celestial Mechanics and the Prediction of Planetary mo

16、tionInvolve a small number of measurable parametersSystems involved are highly repetitiveEnormous database with which to workEx.3:Classical MechanicsScientific laws create an imaginary world that requires adjustments and modifications based on past experiences and earlier failed attemptsShort-term p

17、redictions can be helpfulLong-term predictions cannot be tested and therefore do nothing to improve the understanding of scientific knowledgeNaomi proposes that we focus away from quantitative predictions of the future and towards policy-relevant statements of scientific understandingComputer models

18、 have helped us gain a better understanding o the Earth;s complex life-supporting processes.Strength-the ability to represent such systems is the obvious strength of modelsWeakness complex models are nonunique,their predictions may be error,and the scale of their predictions make them difficult if n

19、ot impossible to test“No sensible person would wish to court disaster by ignoring the threat of global warning,but neither would any sensible society wish to spend large sums of money solving a problem that does not exist.”Computer models are only as strong as their weakest link.youtube/watch?v=hHkbmSjSjbgLook for errors that could be found with this model process.What do we conclude about models?Are they useful but unreliable?Can we ever really know what is going to happen?