This is one of the reasons for the existence of many different conceptual models in the literature.
In many instances conceptual models can undoubtedly be more influential in directing human thinking and action than, for example, complicated mathematical and simulation models, which of course have their scientific and practical merits.
This assumption sits underneath many of the conceptual models of consumer decision making that have been adapted in an attempt to model consumer behaviour in a pro-environmental context.
The Rasch model has many conceptual and mathematical advantages over more complex model, and is the standard model used in statistical equating [ 26].
Similarly, while many existing student engagement conceptual models have expanded to include factors that influence student engagement, none highlight the overt role of technology in the engagement process (Kahu, 2013; Lam, Wong, Yang, & Yi, 2012; Nora, Barlow, & Crisp, 2005; Wimpenny & Savin-Baden, 2013; Zepke & Leach, 2010).
Many professionals have experience with conceptual models, participatory planning processes, threats assessments, and methods for making decisions under uncertainty.
However, designing a real-world actor framework that subsumes full scalability, strong reliability, and high resource efficiency requires many conceptual and algorithmic additives to the original model.
Evaluation of maps generated from different conceptual models or data processing approaches at spatial level has importance in many geoenvironmental applications.
However, bridging the gap between conceptual models of landscape dynamics and their simulation on a computer can lead to many pitfalls.
With these, conceptual models can reach a very high degree of complexity while trying to integrate as many aspects as possible from the simulated process/studied phenomena.
