"A model is a qualitative or quantitative representation of a process or endeavor that shows the effects of those factors which are significant for the purposes being considered. A model may be pictorial, descriptive, qualitative, or generally approximate in nature; or it may be mathematical and quantitative in nature and reasonably precise. It is important that effective means for modeling be understood such as analog, stochastic, procedural, scheduling, flow chart, schematic, and block diagrams." (Harold Chestnut, "Systems Engineering Tools", 1965)
"A model is an attempt to represent some segment of reality and explain, in a simplified manner, the way the segment operates." (E Frank Harrison,The managerial decision-making process", 1975)
"A mathematical model is any complete and consistent set of mathematical equations which are designed to correspond to some other entity, its prototype. The prototype may be a physical, biological, social, psychological or conceptual entity, perhaps even another mathematical model." (Rutherford Aris, :Mathematical Modelling", 1978)
"Modeling in its broadest sense is the cost-effective use of something in place of something else for some [cognitive] purpose. It allows us to use something that is simpler, safer, or cheaper than reality instead of reality for some purpose. A model represents reality for the given purpose; the model is an abstraction of reality in the sense that it cannot represent all aspects of reality. This allows us to deal with the world in a simplified manner, avoiding the complexity, danger and irreversibility of reality." (Jeff Rothenberg,The Nature of Modeling. In: Artificial Intelligence, Simulation, and Modeling", 1989)
"Model building is the art of selecting those aspects of a process that are relevant to the question being asked. As with any art, this selection is guided by taste, elegance, and metaphor; it is a matter of induction, rather than deduction. High science depends on this art." (John H Holland, "Hidden Order: How Adaptation Builds Complexity", 1995)
"A model is a deliberately simplified representation of a much more complicated situation. […] The idea is to focus on one or two causal or conditioning factors, exclude everything else, and hope to understand how just these aspects of reality work and interact." (Robert M Solow,How Did Economics Get That Way and What Way Did It Get?", Daedalus, Vol. 126, No. 1, 1997)
"More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills - skills that are also necessary for understanding biases in the data, and for debugging logging output from code. Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms - some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications." (Rachel Schutt,Doing Data Science: Straight Talk from the Frontline", 2013)
"A mockup shows what we should expect to take away from a project. In contrast, an argument sketch tells us roughly what we need to do to be convincing at all. It is a loose outline of the statements that will make our work relevant and correct. While they are both collections of sentences, mockups and argument sketches serve very different purposes. Mockups give a flavor of the finished product, while argument sketches give us a sense of the logic behind the solution." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Keep in mind that a mockup is not the actual answer we expect to arrive at. Instead, a mockup is an example of the kind of result we would expect, an illustration of the form that results might take. Whether we are designing a tool or pulling data together, concrete knowledge of what we are aiming at is incredibly valuable. Without a mockup, it’s easy to get lost in abstraction, or to be unsure what we are actually aiming toward. We risk missing our goals completely while the ground slowly shifts beneath our feet. Mockups also make it much easier to focus in on what is important, because mockups are shareable. We can pass our few sentences, idealized graphs, or user interface sketches off to other people to solicit their opinion in a way that diving straight into source code and spreadsheets can never do." (Max Shron, "Thinking with Data: How to Turn Information into Insights", 2014)
"Creating mockups to communicate is not intrinsically a bad idea. But, as we are subject to confirmation bias, there’s always a risk that we will stop at our first design attempt and become reluctant to ask if there are better ways to achieve the same goals. Making these first ideas very detailed; putting them into a document; and especially blessing that document with the label 'requirements' are all moves which make further revision less likely, and put us more at risk from confirmation bias."
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