Authors:
Assaf Marron
1
;
Irun Cohen
2
;
Guy Frankel
1
;
David Harel
1
and
Smadar Szekely
1
Affiliations:
1
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, 76100, Israel
;
2
Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, 76100, Israel
Keyword(s):
Modeling, Simulation, Emergent Entities, Rule-Based Specifications, Incremental Development, Evolution.
Abstract:
Models of complex systems, both human-made and natural, assist in making critical decisions with regard to function, safety, economy, the environment and more. In this position paper, we explore the difficulty in modeling several innate properties of such systems, including: (i) the frequent emergence of new entities (like group effects and temporal patterns) and the system’s reaction to such emergence; (ii) the essence of the system’s reactive behavior as a rich composition of stand-alone rules; and (iii) the vast number of internal and external interactions that the system engages in. For each of these challenges we propose some implications to modeling—methodological approaches that can help address it and potential support in modeling languages and tools. We introduce the concept of unmodeling—formally defining model entities and behaviors that are excluded from model execution—and discuss how unmodeling enhances model quality, supports incremental enhancement, and facilitates ev
aluation. This report emanates from our research and development in modeling languages and methods and our present research in biological evolution. We believe that analysis and implementation of these principles have general applicability in model development and assessment.
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