  Despite the emergence of event driven business process management, smart
business networks, social networks, etc. as important research areas in
management, for all the attractiveness of these concepts, two major challenges
remain around their design and the partner selection rules while learning from
interaction events.While smart business networks should provide advantages due
to the quick connect of business partners for selected functions in a process
common to several parties, literature does not provide constructive methods
whereby the selection of temporary partners and functions can be done. Most
discussions only rely solely on human judgment. This paper introduces both
computational geometry, and genetic programming, as systematic methods whereby
to identify, characterize, and then display on a continuing basis from event
monitoring such possible partnerships; such techniques also allow to plan for
their effect on the organizations and thus to carry out selection. The two
methods are being put in the context of emergence theory. Tessellations address
the identification and categorization issues; business maps address the display
and monitoring challenge with the use of Voronoii diagrams. Cellular automata
mimicking living bodies, with genetic algorithms of which parameters are
estimated by learning, address the selection and effect issues. To illustrate
the approach, some experimental results from the sourcing function in a high
tech industry, are discussed; they address the case of how to determine the
selection process for a systems integrator to set up joint ventures with
smaller technology suppliers.
