Wspólnotowy Serwis Informacyjny Badan i Rozwoju - CORDIS

FP5

ICITIES Streszczenie raportu

Project ID: IST-1999-11337
Źródło dofinansowania: FP5-IST
Kraj: Greece

Tool to visualize interactions and highlight emerging structures

In iCities project we develop and study the behavior of non-analytical models with emerging and self-organizing properties. Our models simulate a web economy where patterns of organization (mostly, aggregation of visitors within particular web sites behaving as information cities) emerge within two populations of boundedly rational agents, Internet consumers and web sites, who interact locally or globally. In particular, we study problems of "product-choice" (i.e. web-site choice) in an environment characterized by increasing returns and various other behavioral rules. Placing agents with particular behaviors in such an environment proves sufficient for the emergence of various aggregation and segregation structures. Such structures have characteristics that are suggestive of information cities. Agent-based computational models are used to study the non-equilibrium dynamics, in which aggregated structures are perpetually born, growing, prospering or diminishing.

Our approach consists to observe model's outcomes and conclude on their properties, stability and their dependence on initial conditions and other key-parameters, to "measure" aggregation trends, to evaluate how this "aggregation index" varies as a function of different model parameters and compare model's output to aggregate statistical data. The Tool provides all the necessary functionality to monitor the above-described tasks, to visualize interactions among models' populations and highlight emerging structures.

ICities project has the objective to run various simulation experiments (by modifying key model parameters or by playing scenarios using various combinations of simulation infrastructure's models) and observe emergence and stability of aggregated structures. Essentially, the Visualization Tool helps the Model Analyst to proceed with information obtained from the experiments. It gets basic functionality from MATLAB to elaborate and accordingly present data, which are imported, after each experiment, from the iCities simulation platform built on top of Mozart/Oz.

Each simulation experiment generates a number of output files, which include statistical information, for example the number of users that visit web sites at each time step. The Model Analyst generates in each experiment several output files according to experiment's needs and the specific model under study. Files that are generated from the simulation environment and saved from the Model Analyst according to experiments' requirements are read inside MATLAB. A mat-File is generated for each experiment. The Model Analyst can then launch the main figure of the visualization tool, load the mat-File that relates to the experiment, and start the analysis. The functionality and the user interface of the visualization tool main figure changes as a function of the model under experimentation.

The components (pop-up menus, pushbuttons, edit-boxes) of the Visualization Tool User Interface relate to information data for the experiment the Model Analyst is studying. The pop-up menus carry information for the name and the version of the experiment. Edit boxes display experiment's parameter values and pushbuttons compute and plot statistical information for the loaded experiment. Model analyst can easily load a second experiment and load it in the Visualization Tool then, both experiment files are available to the Model Analyst for comparative analysis. The User Interface and the functionally it provides, automatically change depending on the loaded experiment version. Simultaneous analysis of different experiments can be easily performed by using the pop-up menu that displays experiment's name and selecting, from a list of names, one particular experiment among the loaded experiments.

By clicking to a pushbutton, the Model Analyst obtains a figure that displays a particular statistical graph for the selected experiment. For example a figure that displays the histogram of the occurrences of sites by popularity, etc.

Extensions of the Visualization Tool can be used for the analysis of any discrete time agent based computational model as i) the main infrastructure for the parsing of experiment output files in MATALB Mat-files are already implemented, and ii) the main statistical graphs are common for this class of agent-based models. However, programming skills are needed to implement model specific statistical graphs and measures. In the near future we plan to publish the Visualization Tool user manual and tutorial that will clearly describe tool functionality and the process of new models integration in the tool.

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Reported by

University of Crete
Knossos Avenue
71409 Heraklion
Greece
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