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Sara Lumbreras and Lluis Oviedo | Belief networks as complex systems
More importantly, belief systems have an internal structure that has not
yet been reflected in the workings of ANNs, or machine learning in general.
The most important difference we would like to highlight is that belief sys-
tems are built as a hierarchy of concepts or categories. A concept is a pat-
tern that is comprehended, that has meaning in the sense of understanding
the concepts that relate to it in the hierarchy and that are either at its same
level, correspond to a partial aspect of it or correspond to a generalization
of itself. The relationships between the concept and other concepts in the
network can be known partially or in full and can be subject to ambiguity.
Powerful concepts have a simple description and can be applied to a large
number of instances, while weaker concepts are more convoluted or am-
biguous. Some authors have postulated how belief emergence and change
maximizes explanatory power or minimizes cognitive dissonance. This
parsimonious quality of belief systems has a key property that has not been
noted previously, at least to the best of our knowledge: they are robust with
respect to generalization.
Any machine-learning engineer working with ANNs to solve classification
or forecasting problems knows that their main danger is overfitting (Hawk-
ins 2004). Overfitting happens when the ANN learns the examples too well
and is not able to generalize. For instance, if most of the pictures of cars we
show our network correspond to vehicles photographed on a sunny day, the
algorithm might have trouble recognizing a car when the surroundings are
dark, as it does not recognize that lighting is not a relevant feature when
identifying a car. ANNs (and any other machine-learning technique) do not
understand what a car is; they are not able to discern when a characteristic
is important and when it is not. They only infer this from the examples they
are given. Overfitting is an issue that has to do both with the examples we
show them and with the training methodology, for instance taking care to
select a representative enough set of instances. The fundamental trouble
with machine pattern recognition in general (not only ANNs) is that al-
though the pattern might be recognized correctly, it is not comprehended.
The pattern of a car, for a computer-recognized pattern, might be a com-
plex combination of light and dark spots on an image. The network does
not need to recognize the wheels or the engine to identify a car, so when
the situations vary slightly (such as in the lighting example) they might
lead to errors.
Belief systems are robust with respect to generalization.
Limina
Grazer theologische Perspektiven, Volume 3:2
- Title
- Limina
- Subtitle
- Grazer theologische Perspektiven
- Volume
- 3:2
- Editor
- Karl Franzens University Graz
- Date
- 2020
- Language
- German
- License
- CC BY-NC 4.0
- Size
- 21.4 x 30.1 cm
- Pages
- 270
- Categories
- Zeitschriften LIMINA - Grazer theologische Perspektiven