The Haunted Swamps of Heuristics: Uncertainty in Problem Solving - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 51

Fig. 5.3 Objects of desire in science. a Data (fromMultipathCPR [ 15 ]). b Mummy [Ötzi the Iceman
(© South Tyrol Museum of Archaeology— www.iceman.it ) ]. c Algorithm (courtesy of Heinzl [ 11 ])
5.3 Objects of Desire in Science
Every scientific discipline has its own object of desire, i.e., study focus. In some
areas like geology or medicine these are the physical and medical phenomena and
processes hidden inside the data. Data collection, classification, and analysis in
order to get useful insights are in the center of the scientific activity (Fig. 5.3 a).
In other areas artifacts, fossils and mummies (Fig. 5.3 b) are the investigated items.
In yet other disciplines pieces of text or poems might be in the center of attention. In
computer science algorithms (and data structures they work on) are the key entities
researchers and developers are designing and investigating (Fig. 5.3 c).
An algorithm is a set of instructions that operate on data which are given through
constants and variables. And then there are parameters. Constants are, as the name
says, constant during the execution of an algorithm. They are fixed and may be
mathematical or physical quantities like the cosmological constant. Variables con-
tain values that change during algorithm execution. So where do parameters fit into
this picture? Parameters (again a Greek term) are auxiliary measures which are
arbitrary but fixed. They are neither constants nor variables. If an algorithm simu-
lates a specific model within a class of models that share the same characteristics, the
parameter is fixed for this one model. Switching to another model in the class means
varying the corresponding parameter. Parameters are somewhat dual in their nature.
And they are just 'auxiliary'. Computer scientists and also visualization researchers
are very fond of the instruction part of their algorithms, which they dedicate a lot
of time to. Constraints, boundary conditions, approximations, and calibrations are
issues that often are encoded in parameters. Even more: if the algorithm still does not
work properly, it can only be in the parameters or more of them are needed. They are
our easy back-door out. Parameters in many cases do not get the necessary attention
and are all too often supposed to be specified heuristically. And it is exactly here
were heuristics get a bad reputation. Sometimes the inadequacy of an algorithm is
covered up by a set of unintuitive parameters which the developer himself cannot
control properly. So to make a virtue out of necessity, the parameters are declared to
be user-defined and this is sold as additional flexibility. In reality often this puts an