Wolfgang Höhl (2015): Network Structures in Virtual Environments
Does network theory help to understand collaborative virtual environments? Which ideal network model is best suited for a collaborative virtual environment? Are “scale-free networks” better than “random” or “hierarchical networks”? What does modularity, efficiency and economy mean in terms of network qualities? How can spatial centrality and urban design tools be seen in this respect? This paper investigates these issues using the depiction of a collaborative virtual environment as an abstract network.
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It was demonstrated that the interpretation of all five analyzed use cases remain inconclusive in some terms. But there are certain interesting tendencies.
Simulation and virtual environments meet in many issues. To illustrate, this paper will take a closer look at the philosophy of science, simulation and scientific modelling. We will learn that simulation is an interdisciplinary technique and collaborative virtual environments are a part of general simulation systems. Simulation itself works in three stages and supports the innovation process in its early stage. Qualities, advantages, disadvantages and application areas of simulation will be shown. Then we will have a closer look at the structure and principles of collaborative virtual environments. It is shown how time, space and organization influence the usage and behaviour of a collaborative virtual environment. General evaluation criteria of real-time systems are shown. Ongoing applications and projects will illustrate topical application areas.
Network theory does indeed help us understand collaborative virtual environments. One can depict all elements and relations of a virtual environment in an adequate and operational manner. Network simulation allows for the manipulating of the elements and gives a view on changes in the structures and behaviour of the whole network. Simulation allows us to go backward and forward in time, explore possibilities and understand scenarios of virtual environments, diagnose problems and constraints, understand behaviour and processes, find consensus and prepare for change, and gives possibilities for visualizing how to proceed.
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