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The Role of Quantitative Models in Science定量模型在科中的作用
/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? Powerpoint by: Fernanda Rossi Jessica Matthews 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 “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 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? There may be several possible configurations of nature that could produce a given set of observed results Therefore, any empirical data we collect in
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