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subjective. The deductive-nomological account of explanation requires deductions, which can
be obtained from laws such as those of physics. Nevertheless, since about the 1990s the
putative direct relationship between theory and phenomena has been challenged and model-
based accounts of explanation have been developed, largely in the context of theories of
physics. For example, Cartwright’s (1983) simulacrum account claims that explanations are
models that mediate between a theory and the world. This view of model as mediator has also
been developed by Morrison and Morgan (1999) and others to fit phenomena to theory.
However, these accounts of explanation might work well when theories are robust, but when
theories are developing – in the context of discovery – they do not work very well.
Theory-centric accounts of explanation also do not operate effectively in the
biosciences. Here a new account of explanation has developed over the last 15–20 years: the
mechanistic account. Machamer, Darden, and Craver (2000) note, “Mechanism are entities
and activities organized such that they are productive of regular changes from start or set up
to finish or termination conditions.” This is a linear account that cannot capture the complex
non-linear dynamical phenomena that take place in biological fields, thus Bechtel (2006) has
put forward a different view: “A mechanism is a structure performing a function in virtue of
its component parts, component operations, and their organization. The orchestrated
functioning of the mechanism is responsible for one or more phenomena.” This chapter
focuses on cases from within the biosciences domain in which scientists are attempting to
develop an understanding or representation of phenomena for which there is no theory, and
thus no theoretical basis from which models could be construed. I want to ask the following
questions: Is mechanism something in which these scientists are interested? Are they able to
develop a mechanistic explanation? Do they need to do so in order to achieve some important
scientific successes? My plea for understanding is based on the fact that much of the science
in the area of bioscience simulations does not provide mechanistic accounts, although it does
provide objective understanding. It gives scientists control of various kinds of phenomena,
which is their primary aim.
Catherine Elgin (2010) writes, “Although knowledge involves belief, no one is
inclined to say that knowledge is merely psychological and not epistemological… There’s no
justification for simply assuming without argument that understanding is subjective, keyed to
historical circumstances or interrelated to a feeling of understanding if, indeed, understanding
has a specific feel.” In my view understanding is related to skills and judgment, which are not
subjective. Explanation does not subsume understanding. In the cases discussed in this
chapter, scientists get a grip on phenomena through the model-building process. They
develop a mathematical understanding of dynamical relations among variables in the system.
This understanding does not provide an explanation in any of the senses of explanation that
are currently in the philosophical literature, but it does enable them to control various