An analysis of the performance of a hierarchical wayfinding computational model using synthetic graphs

This paper describes a set of experiments, which use different levels of hierarchical shortest path computations. We investigate a graph-subgraph structural hierarchy as a mechanism imposed on an input data set, allowing a human or computer to access only a subset of the data necessary for a task like path retrieval. It challenges the selection of relevant data further used by people and, in turn, by computers for a particular analytical purpose. Consideration of the main principles for the design of such hierarchies raises a number of theoretical and practical research questions related to spatial information. The paper introduces the idea of adapting the principles of hierarchical wayfinding to modeling decision making, which is becoming increasingly important for advanced applications like Geographic Information Systems (GIS). The results of the experiment, which utilizes three kinds of synthetic graphs, are described. A number of important conclusions are presented, not leastwise that the benefits of hierarchical wayfinding over non-hierarchical wayfinding algorithms increases as the number of nodes in a graph increases, particularly in graphs with recognizable form