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1、Why Believe a Computer? The Role of Quantitative Models in Science,Naomi Oreskes,Powerpoint by:Fernanda RossiJessica Matthews,Why Believe a Computer? The,Overview,Models are used to:Organize dataSynthesize informationMake predictionsModels never fully represent so therefore make uncertain prediction
2、sAdded complexity in model decreases certainty of predictionsShort-time frame model vs. long-range deterministic model,OverviewModels are used to:,Models as a Science?,“Testing is the heart of science. Although there is no foolproof way to define science, testability is the most commonly cited demar
3、cation criterion between scientific theories and other forms of human explanatory effort.”A single test is rarely, if ever, sufficient to convince anyone of anything,Models as a Science?“Testing i,The Role of Quantitative Models in Science,Purpose of model is to gain understanding of natural worldSc
4、ientists have sought understanding to:Advance utilization of earths resourcesFoster industrializationPrevent or treat diseasesGenerate origins storiesReflect on worlds creatorSatisfy human curiosity,The Role of Quantitative Model,Until 20th century, the word “model” in science referred to physical m
5、odelNow “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 future,Until 20th century, the word “,ge
6、nerated 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 lit
7、erature. Are these claims legitimate? Can a computer be proved true or false? How can we tell when to believe a computer?,generated predictions are used,The Problem of Verification,There may be several possible configurations of nature that could produce a given set of observed resultsTherefore, any
8、 empirical data we collect in support of a theory may also be consistent with alternative explanationsFor this reason, many scientists except the view that theories can be proved false but not true (falsified but not verified),The Problem of VerificationThe,Purpose of essay is therefore to challenge
9、 the utility of models for predictionQuantitative model output has been used in issues such as global climate change and radioactive waste disposalBut it is open to question whether models generate reliable information about the futureShould we create new policies based on the prediction of models?,
10、Purpose of essay is therefore,Naomis Opinion,The 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 accurate,Naomis OpinionThe prediction,Example of Assumptions we Make:,Stellar parallax in the es
11、tablishment of the heliocentric model of planetary motion by Nicolaus CopernicusFlaws present in instruments we use,Example of Assumptions we Make,Another Example:,Earth was thought to be billions of years old based on the concept of uniformitarianism the assumption that presently observable geologi
12、cal processes are representative 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 nat
13、ural selection and for several decades evolutionists were in nearly full retreatTHEN, radioactivity was discoveredproving Kelvin wrong.,Another Example:Earth was thou,In hindsight, it is easy to see where others have gone wrong: Astronomers thought their instruments were better than they were; Kelvi
14、n thought his knowledge more complete than it was. It is harder to 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 assu
15、mptions can be very hard to see. How DO we test computer models?,In hindsight, it is easy to se,The Complexity Paradox,The more complex the natural system is, the more different components the model will need to mimic that systemComplexity decreases systematic bias but increases uncertaintyShould we
16、 use complex or simple models to make predictions?,The Complexity ParadoxThe more,Models are Open Systems,Models are Open Systems,Hypothetico-deductive model (deductive-nomological model)Generates hypotheses, theories, or laws and compare their logical consequences with experience and observations i
17、n the natural worldPROBLEM: only works reliably in closed systems,Hypothetico-deductive model (d,Another way to understand,2 + 2 = 4 therefore 4 2 = 2Is a straight line the shortest distance between two points?,Another way to understand2 +,Open Systems,All models are open systems3 general categories
18、 into which this openness falls:ConceptualizationEmpirical adequacy of the governing equationsInput parameterization,Open SystemsAll models are ope,Successful Prediction in Science,Successful prediction in science is less common than most of us thinkEx. 1: Meteorology & Weather PredictionsWeather pr
19、ediction is not deterministicSpatially averagedRestricted to the near termTrial and error,Successful Prediction in Scien,Ex. 2: Celestial Mechanics and the Prediction of Planetary motionInvolve a small number of measurable parametersSystems involved are highly repetitiveEnormous database with which
20、to work,Ex. 2: Celestial Mechanics an,Ex. 3: Classical MechanicsScientific laws create an imaginary world that requires adjustments and modifications based on past experiences and earlier failed attempts,Ex. 3: Classical Mechanics,Model Testing, Forecasting, and Scenario Development,Short-term predi
21、ctions 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 understanding,Model Testing, For
22、ecasting, an,Complexity is the Strength and Weakness of Numerical Models,Computer models 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
23、, their predictions may be error, and the scale of their predictions make them difficult if not impossible to test,Complexity is the Strength and,Continued,“No sensible person would wish to court disaster by ignoring the threat of global warning, but neither would any sensible society wish to spend
24、large sums of money solving a problem that does not exist.” Computer models are only as strong as their weakest link.,Continued“No sensible person,Has your opinion of models now changed?,Has your opinion of models now,VIDEO TIME!,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?,VIDEO TIME!youtub,
