GAP-JOURNAL 2012/13 - AFA

73
universal computing machine, aso-called Turing machine, wecan ask, whether computing
the solution ofagiven problem will stop at some point oftime with aresult.
Turings analogon of Gödels theorem reads asfollows:
For each Turingmachine programme that shouldcompute, whether agivenTuringmachine
programme will halt, there exist aprogramme and input data, sothat the algorithm cannot
decide.
This leads astothe assumption that we cannot arbitrarily exactly decide within more
complex system and thus we receive the borders of computability asthe borders of system
complexity. To summarize this point: Complex systems donot necessarily need to
be complete –not all questions we put into the model will lead us to an answer. And
this fact itselfisproven.
Still, if we perceive human intelligence being ofaholistic kind, we can think ofcomplex
systems that may not bedecidable themselves, but with our outside view seem to
be quite trivial (Weizenbaum 1978).
Using acomplex model instead of asimple one to map realitytoour system can create a
kind of blurring. The system is not any more providing information inhigh precision,
but still accuracy could beeven higher. Indeed, with the sophisticated statistical methods
we have recently developed ine.g. theoretical physics orthe big data industry, we
can access new kinds of knowledge. The availability and further development of these
statistical methods will be akey factor to theincreasingofcomplex system deployment.
From thesystemtothe business model
To point out the importance ofaholistic and intense understanding of complex systems
for future business innovations, let ustake alook at the evolution ofinformation technology
(IT). Irving Wladawsky-Berger, IBM Vice President for Technology and Strategy,
describes the development ofthe IT sector in thelast years:
“At first, ITwas primarily applied toautomate back office, payroll, and inventory management.
[…] Astime went on, IT was increasingly applied tointeractive applications that
enabled people todofor themselves tasks that previously required human assistance. […]
Customer self-service was arguably the commercial killer-app ofthe Web in the ‘90s. […]
Beyond back-office automation, personal productivity and customer self-service, Ibelieve
that recent advances in IT are now enabling us to applytechnology to significantly improve
the productivity of services, and isthus ushering usinto the next major phase in the evolutionofwork”
(Hefley2008, p.x).