Return on modelling effort (ROME) is the benefit resulting from a (supplementary) effort to create and / or improve a model.[1][2]
Purpose
editIn engineering, modelling always serves a particular goal. For example, the lightning protection of aircraft can be modelled as an electrical circuit, in order to predict whether the protection will still work in 30 years, given the ageing of its electrical components. More and more effort can be put in making this model predict reality perfectly. However, this perfection comes at a price: researchers invest time and money in improving the model. As a Return on investment (ROI), the ROME is a metric for the use of further modelling. It may therefore serve as a 'stopping criterion'.[2]
Typically, researchers will pull towards continuing modelling, while management will pull towards stopping modelling. Being explicit about the cost and benefits of continued modeling may help to make informed decisions that are understood by both sides.[citation needed] Continuous communication between model developers and model users increases the probability of models being actually put to profit.[3]
Domains
editROME is a metric, which can be evaluated wherever modelling is performed with a quantifiable goal. Examples include:
- Modeling the impact of federal policy on social problems.[3]
- Modeling a marketing mix to statistically correlate a number of inputs (or independent variables) – such as a marketing campaign – to outcomes (or dependent variables) – such as sales or profits.[4]
- Modeling the links between enterprise actors to make an informed choice on splitting organizations.[5][6]
- Modeling the coupling of an electromagnetic interference to a PCB to reduce its susceptibility by improving the routing of traces.[7]
Research
editThe initiative "Models at Work" studies the creation, management and use of domain models in scientific and industrial practice, aiming at a diversity of goals, varying from (as truthful as possible) representation of the conceptual structure of the domain that is modelled, via animation, simulation, execution and gamification, until automated (logic-based) reasoning.[8]
References
edit- ^ Op 't Land, M.; Proper, H. A.; Waage, M.; Cloo, J.; Steghuis, C. (2008). Chapter 4 of Enterprise Architecture - Creating Value by Informed Governance. Springer, Heidelberg, Germany. doi:10.1007/978-3-540-85232-2. ISBN 978-3-540-85231-5.
- ^ a b Proper, Erik (2009). "Models that matter; Return on Modelling Effort". erikproper.blogspot.fr. Retrieved 2016-10-06.
- ^ a b Fromm, Gary (April 28–29, 1977). Gass, Saul I. (ed.). "Review of the non-DoD modeling effort". Utility and Use of Large-scale Mathematical Models: Proceedings of a Workshop. 534 of NBS special publication. Gaithersburg, Maryland: Department of Commerce, National Bureau of Standards, 1979: 15–21.
- ^ Thomas, Jerry W. (2006). "Marketing Mix Modeling". Retrieved 2016-10-06.
- ^ Op 't Land, Martin (2008). Instrument for fast and effective splitting of organizations. Delft University of Technology. ISBN 978-90-71382-37-6.
- ^ Op 't Land, Martin (2008). Applying Architecture and Ontology to the Splitting and Allying of Enterprises. Delft University of Technology. ISBN 978-90-71382-32-1.
- ^ Op 't Land, Sjoerd; Mandić, Tvrtko; et al. (September 2013). "Comparison of Field-To-Line Coupling Models: Coupled Transmission Lines Model versus Single-cell Corrected Taylor Model". Proceedings of the 2013 International Symposium on Electromagnetic Compatibility (EMC EUROPE 2013): 276–281.
- ^ Proper, Erik. "Models at Work – Case reports from industry and science, on the creation and use of domain models". Retrieved 2022-02-22.