Integrating Ethics Into Modeling Courses In Engineering
Author(s) -
Murali Krishnamurthi
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--7213
Subject(s) - computer science , process (computing) , data modeling , field (mathematics) , representation (politics) , conceptual model , domain (mathematical analysis) , blueprint , modeling and simulation , simulation modeling , session (web analytics) , process modeling , artificial intelligence , data science , industrial engineering , software engineering , work in process , simulation , programming language , engineering , mechanical engineering , mathematical analysis , operations management , mathematics , microeconomics , database , politics , world wide web , political science , pure mathematics , law , economics
1. Models and Modeling From an engineering perspective, a model can be defined as a representation of an object, system or an idea in some form other than itself. Models can be classified using a spectrum ranging from physical (exact) to mathematical (abstract). Physical models are actual “mock ups” of objects such as cars and planes. Scaled models are reduced version of physical objects such as dams and building used generally for the purpose of testing. Analog models use substituted properties of reality. Examples of analog models are gages and blueprints. Simulation models include computer simulation models and role playing games. Computer simulation models useful for analyzing dynamic situations in manufacturing or service industries, and role-playing games are useful for exploring scenarios of problem situations. Information models or data models represent information that will be stored in a database system and knowledge models capture expert knowledge for use in an expert system. Mathematical models are abstract models such as linear programming models or statistical models. In the engineering field, models are generally used for understanding, analyzing, predicting, or solving problems.
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