In the framework of complex dynamical systems we can view the world at the beginning
of this century as loosely connected with clusters of highly complex trouble spots
and the number of people with a global perspective and fast connections was very small.
Today we have multiple systems of global information exchange together with impressive
data and information systems accessible for a rapidly growing number of people. We claim
that the
a global computer network 
will have a much more dramatic impact on global issues
than other networks of global communication systems like television or telephone.
In the first case we obtain many features that we can associate with the development
of biological brains whereas in the two latter cases many
essential features are missing. The most relevant one is the creation of
the analog of cell assemblies.
We know from the theory of complex neural
systems (both artificial and natural) that the connectivity between
the individual information processing units is essential for
solving large, global tasks. For that reason we think that the conditions for the
emergence of a Global Brain will become a reality in the near future.
In this paper we want to discuss the current status of three ingredients of a new
modeling approach based on the Global Brain
paradigm: distributed, associative information servers, global computer communication
systems and its role as empirical research tool and finally distributed simulation
servers which will develop
into a complex, adaptive, and evolving global model of our world:
It has become clear that our individual brain is not the stimulus/response automaton
as that it was frequently seen. The adequate response to complex external stimuli
would be too slow to be effective. Therefore there is a continuous unconscious
modeling process going on in different levels of brain activity. If the modeling of an input stream is successful, we don't have to
pay too much attention to it: we can anticipate what will happen next. This phenomenon
at a very low level is known as habituation. Noteworthy new information is associate
with a P300-alert.
The same mechanism seems to be at work at the perception of music and for the distinction
between interesting and boring pieces of music
(see e.g. [36][35],[34], [37] and references therein).
If we want to succeed in the transition to a sustainable self-organized management of this planet, we have to do the transition from reactive fixing of problems to an active approach of modelling and control. In such a system the aspect of identification of situations with severely limited predictability is almost as important as the predictions themselves.
Recognizing that a complete solution to all problems will not be feasible for the foreseeable future, we take a pragmatic perspective. Under the assumption that we have access to a well developed, global computer network we identify the following steps: (i) Define targets for the solution of important problem areas (population, CO-2 level, violation of human rights,etc) and assign a relevance weight to each of the problem areas; (ii) Acquire qualitative information on the current status of the problem; (iii) Define sub-areas where a quantitative approach appears to be promising; for those areas (iv) obtain current quantitative data and identify models that deal with the solution of any of the sub-problems; (v) Create a conceptual model of the integrated system; (vi)Link data and simulation models to an interdependent, distributed network; (vii) perform simulation, sensitivity analysis and (viii) compare the results with the updated information from (ii) and (iii) and evaluate them with respect to the targets specified in (i). From the study of nonlinear and chaotic systems we know that only short-term predictions are possible if the system is complex and exhibits chaos. Therefore, a typical time scale of five years between formulation and verification of a global model appears to be too long in a world where time scales of e.g. regional conflicts as in eastern Europe are significantly shorter than one year. Future models will have to be object-oriented with links to other models and information systems and they will have to be adaptive to changing basic conditions. In these notes we want to mainly focus on items (ii),(iv), and (v) in the above list.