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TOWARDS A QUANTITATIVE FRAMEWORK FOR EVALUATING THE EXPRESSIVE POWER OF CONCEPTUAL SYSTEM MODELS
Author(s) -
Mordecai Yaniv,
Dori Dov
Publication year - 2018
Publication title -
insight
Language(s) - English
Resource type - Journals
eISSN - 2156-4868
pISSN - 2156-485X
DOI - 10.1002/inst.12187
Subject(s) - computer science , conceptual model , formality , systems engineering , function (biology) , representation (politics) , software engineering , artificial intelligence , engineering , linguistics , philosophy , database , evolutionary biology , politics , law , political science , biology
Conceptual models describe, explain, and specify the function, concept, structure, and behavior of complex systems. Quantifying the contribution of conceptual models to stakeholder understanding of the systems‐of‐interest has been a great challenge. This difficulty hinders justifying the use of formal modeling and simulation of complex systems and the adoption of a holistic model‐based systems engineering (MBSE) approach. The informativity of a model is the value of the information it conveys. Conceptual system model informativity is a key performance indicator for MBSE. We introduce MIA – Model Informativity Analysis – a quantitative, utility‐based, prescriptive approach for boosting conceptual models’ expressive power and measuring the value of the information they provide. We define an integrated informativity index, “I3”, which aggregates model scores of diverse informativity‐enhancing factors. We demonstrate various aspects of MIA with Object Process Methodology, OPM – a model‐based systems engineering paradigm and ISO‐19450 standard. OPM caters to quantitative informativity analysis due its formality, comprehensive function‐structure‐behavior modeling, and bimodal equivalent graphical‐textual representation.