Information Technology Reference
In-Depth Information
Chapter XVI
Empirical Inference of
Numerical Information into
Causal Strategy Models by
Means of Artificial Intelligence
Christian Hillbrand
University of Liechtenstein, Principality of Liechtenstein
aBstract
The motivation for this chapter is the observation that many companies build their strategy upon poorly
validated hypotheses about cause and effect of certain business variables. However, the soundness of
these cause-and-effect-relations as well as the knowledge of the approximate shape of the functional
dependencies underlying these associations turns out to be the biggest issue for the quality of the results
of decision supporting procedures. Since it is sufficiently clear that mere correlation of time series is not
suitable to prove the causality of two business concepts, there seems to be a rather dogmatic perception
of the inadmissibility of empirical validation mechanisms for causal models within the field of strategic
management as well as management science. However, one can find proven causality techniques in other
sciences like econometrics, mechanics, neuroscience, or philosophy. Therefore this chapter presents
an approach which applies a combination of well-established statistical causal proofing methods to
strategy models in order to validate them. These validated causal strategy models are then used as the
basis for approximating the functional form of causal dependencies by the means of Artificial Neural
Networks. This in turn can be employed to build an approximate simulation or forecasting model of the
strategic system.
Search WWH ::
Custom Search