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-A logical fallacy of “affirming the consequent” would be committed if a model is verified because empirical data match a predicted outcome.
- The greater the number in confirming the observed data, the flaw will be smaller.
-The confirming data will not demonstrate the veracity of the hypothesis/model but only support the probability.
More complex observations will always have more than one theory can be explained.Thus, verification of any hypothesis will be impossible.

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-donates the establishment of legitimacy
- useful for assertions about a generic computer code but its misleading if used to refer to actual model results (may/not be valid
-its used to interchangeably with verification to indicate the model predictions are consistent with observations
-often misleading thinking verification and validation are the same

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- The manipulation of the independent variables to obtain a match between the observed and distribution of dependent variables.

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-It is impossible to demonstrate the truth of any proposition, except in a closed system.
-Models are never a closed system as they require input parameters that are incompletely known.
-The scale on which input parameters are measured is typically much smaller, and the relation between those measurements and larger scale model parameters is always uncertain and generally unknown
-The embedded assumptions rendered a system to be open
-Model results are always underdetermined by the available data

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-crop model
-climate model
-flood inundation model
-landscape model

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-Considering the most likely consequences will be of alternative possible actions
-Give an informed appraisal of the possible outcomes of the choices
-Avoid issuing a quantitative outlook

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The  Role of Quantitative Models in Science

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The  Role of Quantitative Models in Science

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Limited to:
-Short-duration
-Repetitive system
-Small numbers of measurable variables

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Heuristic value is less well informed

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-The ideal system of a 'law' do not exist
-Can only make adjustments and modifications based on past experience and failed attempts

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example 2 - Celestial Mechanics and the Prediction of Planetary Motions

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example 2 - Celestial Mechanics and the Prediction of Planetary Motions

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Factors to make accurate and precise prediction
1. Measurable parameter
2. Highly repetitive
3. Enormous database

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example 1- Meteorology and Weather Prediction

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example 1- Meteorology and Weather Prediction

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1. Non-deterministic
- a range of plausible values
- probabilistic forecasting is used as specific, quantative prediction of a complex system is difficult
2. Involving of spatial ambiguity
- weather can be very local
- weather models have been repeatedly adjusted , unlike models being built in aid of public policy

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Confident assertions from the scientific community will soon be refuted.

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model testing, forecasting and scenario development

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model testing, forecasting and scenario development

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- natural sciences with complex non repetitive have no record of predictive success
- short term prediction : mean to evaluate & compare models
- long term prediction : cannot be tested and can’t improve scientific understanding

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-complex natural system, more different components is needed to the model
-more uncertainty
-complexity can improve the accuracy of the model output by minimizing the impact of errors in any one variable
-systematic bias from individual variables decreased as the number of variables in a model increased. - but the uncertainty might increase when errors from each variables are accumulated

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Hypothetico-deductive Model- only work reliably in closed system.

Equipment had advanced and therefore increase the experiment complexity

Omit Irrelevant
-Irrelevant at one point might become relevant in the future or vice versa.
- Exclude relevant factors due to various reason (lack of time or analytic data).

Even law equation is open as there is no logical ways to determine the assumption is correct and based on prior experience.

Besides, well measured model can fail to make reliable prediction as some important relevant parameter has not yet to be recognized.

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The Role of Quantitative Models in Science

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The Role of Quantitative Models in Science

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Three main functions of quantitative models in ecosystem science:
-Synthesis and integration of data (if they make sense and fit together)
-Guiding observation and experiment
-Predicting or forecasting the future