Why Believe a Computer? The Role of Quantitative Models in Science Naomi Oreskes Powerpoint by: Fernanda Rossi Jessica Matthews
Overview • Models are used to: • Organize data • Synthesize information • Make predictions • Models never fully represent so therefore make uncertain predictions • Added complexity in model decreases certainty of predictions • Short-time frame model vs. long-range deterministic model
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 demarcation criterion between scientific theories and other forms of human explanatory effort.” • A single test is rarely, if ever, sufficient to convince anyone of anything
The Role of Quantitative Models in Science • Purpose of model is to gain understanding of natural world • Scientists have sought understanding to: • Advance utilization of earth’s resources • Foster industrialization • Prevent or treat diseases • Generate origins stories • Reflect on world’s creator • Satisfy human curiosity
Until 20th century, the word “model” in science referred to physical model • Now “model” refers to a computer model • A numerical simulation of a highly parameterized complex system • Quantitative models in ecosystem science have 3 functions: • Synthesis and integration of data • Guiding observation and experiment • Predicting or forecasting the future
generated predictions are used as a basis for public policy • government 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. Are these claims legitimate? Can a computer be proved true or false? How can we tell when to believe a computer?
The Problem of Verification • There may be several possible configurations of nature that could produce a given set of observed results • Therefore, any empirical data we collect in support of a theory may also be consistent with alternative explanations • For 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 prediction • Quantitative model output has been used in issues such as global climate change and radioactive waste disposal • But it is open to question whether models generate reliable information about the future… • Should we create new policies based on the prediction of models?
Naomi’s Opinion… • The predictions models offer to us do not aid in basic scientific understanding • Our use of them does not make them important • More complex models tend to be less accurate
Example of Assumptions we Make: • Stellar parallax in the establishment of the heliocentric model of planetary motion by Nicolaus Copernicus • Flaws present in instruments we use
Another Example: • Earth was thought to be billions of years old based on the concept of uniformitarianism – the assumption that presently observable geological processes are representative of Earth’s history in general • Then, 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 invalid • This dismissed Charles Darwin’s theory of natural selection and for several decades evolutionists were in nearly full retreat • THEN, radioactivity was discovered…proving 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 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 Complexity Paradox • The more complex the natural system is, the more different components the model will need to mimic that system • Complexity decreases systematic bias but increases uncertainty • Should we use complex or simple models to make predictions?
Hypothetico-deductive model (deductive-nomological model) • Generates hypotheses, theories, or laws and compare their logical consequences with experience and observations in the natural world • PROBLEM: only works reliably in closed systems
Another way to understand… • 2 + 2 = 4 therefore 4 – 2 = 2 • Is a straight line the shortest distance between two points?
Open Systems • All models are open systems • 3 general categories into which this openness falls: • Conceptualization • Empirical adequacy of the governing equations • Input parameterization
Successful Prediction in Science • Successful prediction in science is less common than most of us think • Ex. 1: Meteorology & Weather Predictions • Weather prediction is not deterministic • Spatially averaged • Restricted to the near term • Trial and error
Ex. 2: Celestial Mechanics and the Prediction of Planetary motion • Involve a small number of measurable parameters • Systems involved are highly repetitive • Enormous database with which to work
Ex. 3: Classical Mechanics • Scientific laws create an imaginary world that requires adjustments and modifications based on past experiences and earlier failed attempts
Model Testing, Forecasting, and Scenario Development • Short-term predictions can be helpful • Long-term predictions cannot be tested and therefore do nothing to improve the understanding of scientific knowledge • Naomi proposes that we focus away from quantitative predictions of the future and towards policy-relevant statements of scientific understanding
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 models • Weakness – complex models are nonunique, their predictions may be error, and the scale of their predictions make them difficult if not impossible to test
…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 large sums of money solving a problem that does not exist.” • Computer models are only as strong as their weakest link.
VIDEO TIME! • http://youtube.com/watch?v=hHkbmSjSjbg • Look 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?