The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence
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.
Related Content
|
Bhargav Naidu Matcha, Sivakumar Sivanesan, K. C. Ng, Se Yong Eh Noum, Aman Sharma.
© 2023.
60 pages.
|
|
Lavanya Sendhilvel, Kush Diwakar Desai, Simran Adake, Rachit Bisaria, Hemang Ghanshyambhai Vekariya.
© 2023.
15 pages.
|
|
Jayanthi Ganapathy, Purushothaman R., Ramya M., Joselyn Diana C..
© 2023.
14 pages.
|
|
Prince Rajak, Anjali Sagar Jangde, Govind P. Gupta.
© 2023.
14 pages.
|
|
Mustafa Eren Akpınar.
© 2023.
9 pages.
|
|
Sreekantha Desai Karanam, Krithin M., R. V. Kulkarni.
© 2023.
34 pages.
|
|
Omprakash Nayak, Tejaswini Pallapothala, Govind P. Gupta.
© 2023.
19 pages.
|
|
|