Databases Reference
In-Depth Information
Causal complexes may be described using nested granularity. A complex may
have several larger-grained elements. In turn, each of the larger-grained ele-
ments may be made up of a complex of more fine-grained elements. Recur-
sively, in turn, these elements may be made up still finer-grained elements. In
general, people are more successful in applying commonsense reasoning to a
few large-grain sized events than to many fine-grained elements.
Somebody waiting for a dinner companion in mid-town Manhattan
who observed a tra
c jam might presume that they might be late be-
cause their taxi would most likely be delayed. Only the large-grained
knowledge of the tra
c jam would be needed; knowing the finer-
grained details of the jam would probably not be needed.
When using large-grained commonsense reasoning, people do not always need
to know the extent of the underling complexity. This is also true for situations
not involving commonsense reasoning; for example:
When designing an electric circuit, designers are rarely concerned with
the precise properties of the materials used; instead, they are con-
cerned with the devices functional capabilities and take the device as
a larger-grained object.
Complexes often may be best handled on a black box, large-grained basis. It
may be recognized that a fine-grained complex exists; but it is not necessary
to deal with the finer-grained details internal to the complex.
1.4 Satisficing
The knowledge of at least some causal effects is imprecise for both positive
and negative descriptions. Perhaps, complete knowledge of all possible factors
might lead to a crisp description of whether an effect will occur. However, it
is also unlikely that it may be possible to fully know, with certainty, all of the
elements involved.
People do things in the world by exploiting commonsense
perceptions
of
cause and effect. Manipulating perceptions has been explored [33] but is not
the focus of this chapter. The interest here is how perceptions affect common-
sense causal reasoning, granularity, and the need for precision.
When trying to precisely reason about causality, complete knowledge of
all of the relevant events and circumstances is needed. In commonsense, every
day reasoning, approaches are used that do not require complete knowledge.
Often, approaches follow what is essentially a
satisficing
[1955] paradigm.
The use of non-optimal mechanisms does not necessarily result in undesired
ad hocism; Goodrich [4] states:
Zadeh [32] questions the feasibility (and wisdom) of seeking for opti-
mality given limited resources. However, in resisting naive optimizing,
