"Science progresses mainly through experimentation, but to become useful, experimental results have to be scrutinized, interpreted, and placed on a proper intellectual scaffold. These two activities are not always carried out evenly by the same person. Some scientists become known for their impressive experiments, others for innovative theoretical syntheses." – M. Schaechter 2012
"Models allow us to make explicit, quantitative connections between processes and patterns, and therefore provide a framework for developing and testing hypotheses about the causes for observed patterns." – S. Ellner 2003 (lecture notes)
“Models are ways for our minds to make sense of observed phenomena in terms of concepts that are familiar to us, concepts that we can get our heads around (or in the case of string theory, that only a few very smart people can get their heads around).” – M. Mitchell 2009
“There is no single, best all-purpose model. In particular, it is not possible to maximize simultaneously generality, realism, and precision.” – R. Levins 1968
"An immediate conclusion that emerges from our analysis is that a model ... should be judged by its usefulness in particular applications, not by a priori criteria relating to whether every biological detail has been represented accurately." – Andow et al. 1990
“It is tempting to believe that the surest way to a better model is to get more and more accurate models of the pieces, and that if the pieces are right, all else will follow. Not true. Indeed, the pieces must contain some of the truth, but the surest way to a better model is to string together the pieces so that small errors in the fine-scale details don’t matter much.” – S. Levin 1999
"The apparent ease of simulation often tempts the modeler to put in every variable which might matter, leading to complicated and uninterpretable models of an already complicated world. Surprising results can emerge from simulations, effects that we cannot explain. In these cases, it's hard to tell what exactly the models have taught us. We had a world we didn't understand and now we have added a model we don't understand" – R. McElreath & R. Boyd 2007
"Like most mathematicians, he takes the hopeful biologist to the edge of a pond, points out that a good swim will help his work, and then pushes him in and leaves him to drown." –C. Elton 1935 (review of A.J. Lotka 1934)
“The art of model-building is the exclusion of real but irrelevant parts of the problem, and entails hazards for the builder and the reader. The builder may leave out something genuinely relevant; the reader, armed with too sophisticated an experimental probe or too accurate a computation, may take literally a schematized model whose main aim is to be a demonstration of a possibility.” – P.W. Anderson 1977
“As in all sciences, ecology has its theoretical and its empirical school. Perhaps because of the complexity and variety of ecological systems, however, both schools seem, at times, to have taken particularly extreme positions. And so the empiricists have viewed the theoretical school as designing misleading constructs and generalities with no relation to reality. The theoreticians, in their turn, have viewed the empirical school as generating mindless or mind-numbing analysis of specifics and minutiae.” –D. Ludwig et al. 1978
"What we find difficult about mathematics is the formal, symbolic presentation of the subject by pedagogues with a taste for dogma, sadism, and incomprehensible squiggles." –J.E. Gordon 2003
A model in its elegance Is better than reality Its graphical simplicity Denotes a rare intelligence.
The simple graph incites the wrath Of field men who, half undressed, Go rushing out to start a test Which culminates in aftermath.