The Haunted Swamps of Heuristics: Uncertainty in Problem Solving - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 50

Fig. 5.2 Three well known optical illusions: Zöllner illusion ( a ), bulging checker board illusion
( b ), and blur and picture content illusion ( c )
apply heuristic and approximate approaches if they have to solve complex problems.
In many cases they do not have the complete information for a precise solution.
Heuristics are about finding a good enough solution where an exhaustive search
would be impractical. One of the most commonly used heuristics, which can initiate
a problem solving process, is trial and error. Other common examples of heuris-
tics are, e.g., drawing a figure for better problem understanding, working backward
from an assumed solution, or examining a concrete example of an abstract prob-
lem. Previous experiences and known information result in such heuristic concepts
as prejudices and stereotypes. By evolution some heuristic approaches are firmly
anchored in perceptual and mental processes. Heuristic problem solving may work
in many circumstances, but in some cases fails to deliver the correct solution. This
can lead to cognitive biases in decision making or to imperfections in perception like
optical illusions. Three of these optical illusions are shown in Fig. 5.2 . Long black
lines with horizontal and vertical marks in the Zöllner illusion [ 20 ] (Fig. 5.2 a) are
parallel to each other but do not seem to be. The apparent bulging of the checker
board (Fig. 5.2 b) is not real: the board is planar. The third example (Fig. 5.2 c) shows
how frequencies affect the perceived content of a picture. If the viewer examines
the image from a short distance, Albert Einstein's face is seen, but, if looking from
farther away, the face changes to that of Marilyn Monroe. These three examples
are synthetic, but sometimes optical heuristics can fail in real life situations as well.
For instance, the size of the moon seems to vary depending on its distance from the
horizon.
Algorithmdevelopers use heuristics for problem solving inmany ways. Whenever
the information available for a task is incomplete or exact solutions are too expensive,
an algorithm or some algorithmic part is supplemented with heuristics. Often the
heuristic portions of an algorithm are encoded in coefficients or parameters. The
following section will elaborate more on the heuristic nature of parameters, issues
connected to parameters and ways to explore the uncertainty of an algorithm by
studying its parameters.