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Criteria for Convincing Modeling: Assumptions and Logical Necessities

This course covers key criteria for creating convincing models in various fields, focusing on the importance of reducing assumptions and ensuring logical coherence. Models gain credibility when assumptions are logically deduced from simpler principles or grounded in established laws. The course explores examples from physical, economic, and biological systems, emphasizing the necessity of empirical data for validation. Students will learn to assess the consistency of assumptions and identify when simplified formal models or empirical models can be constructed.

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Criteria for Convincing Modeling: Assumptions and Logical Necessities

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  1. A core Course on Modeling Introductionto Modeling 0LAB0 0LBB0 0LCB0 0LDB0 c.w.a.m.v.overveld@tue.nl v.a.j.borghuis@tue.nl P.16

  2. Criteria for modeling: convincingness Convincingness: a model is more convincingifitcontainsfewerand/or lessimplausibleassumptions.

  3. Criteria for modeling: convincingness Convincingness: a model is more convincingifitcontainsfewerand/or lessimplausibleassumptions. • Most convincing: are assumptionslogicallydeduciblefromother, lessproblematicassumptions? • (example: If we assume on average 1 kW power consumption, • andif we assume 100 hours of work, then the energy consumption is 100 kWh: this is a logicalnecessity, givenby the definition of ‘power’, ‘energy’ and ‘average’.)

  4. Criteria for modeling: convincingness Convincingness: a model is more convincingifitcontainsfewerand/or lessimplausibleassumptions. • If no logicalnecessity, are thereany first-principle ‘laws’ to back up assumptions? • (example: ifthis component behaves as a lever, we canuse the physicallaws of torque in a lever.)

  5. Criteria for modeling: convincingness Convincingness: a model is more convincingifitcontainsfewerand/or lessimplausibleassumptions. • Ifthere are no lawsavailable, canwe construct a simplifiedformal model system? • In a formal model system laws do apply (e.g., replace the world + oceans by a homogenous ball covered by a sheet of water). • The measure of correspondence between these two is left out of the discussion; convincingness relies on the agreement with empirical data.

  6. Criteria for modeling: convincingness Convincingness: a model is more convincingifitcontainsfewerand/or lessimplausibleassumptions. • Ifformal model system is not possible, canwe get support fromcomparisontoobservations on anempirical model system? • Examples of empirical model system • physical: water tank, wind tunnel • economical, social: comparative populations, historical survey data • biological: the guinea pig!

  7. Criteria for modeling: convincingness Convincingness: a model is more convincingifitcontainsfewerand/or lessimplausibleassumptions. • If even anempirical model system is not possible, is there at leastany argument forconsistency of the assumptions? • (example: priceelasticity, ‘fun’ in the taxi model.)

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