Situation Modelling
Analyzing complex situations is part of many professions. For example journalists writing stories, law enforcement officers solving crimes, historians studying events, scientists analyzing complex models or first responders assessing situations. Today, there are very limited tools available that would support complex situation analysis. Excel lists, mind maps, databases, document stores and content management systems all help but they fail at creating a complete picture. Even specialized tools are limited as they have specific feature sets that cannot be extended easily when a specific situation would require it. Most tools will be of limited help as soon as complexity reaches a certain level.
Using Havel, complex situations can be freely modelled much as CAD allows virtual drawings of mechanical parts or houses. Havel allows the modeling of involved entities, relations, events and temporal sequences. In a similar fashion as an architect would create a virtual model of a house, a journalist can create a model of a situation or foundation for a story. As a mechanical engineer would design a machine using 3D-CAD, a law enforcement officer can create a model of a crime, including all involved persons, entities, events and sequences. There is no limit in terms of detail modelling and complexity, every little bit of available information can become part of the model. Every part of the model can be freely annotated and qualified, e.g. with comments or information quality assessments.
These models have several purposes:
As a digital document storing available information.
The modelling process itself helps the user to understand the situation much better by having a constant visual representation of the information. A Havel model is a bit like a mind map on steroids.
The model can be visually and algorithmically analyzed. Data can be extracted and exported.
The model can be the base for simulations.
It is important to note that models are almost never stand-alone; they are dissolved in the user’s infoverse. Known information (e.g. persons, entities, relations, events) is referenced and re-used. Once the model is finished, it can become part of the infoverse itself, the containing information can automatically merge with it and be re-used in future situation models and data analyses.
This post is part of a comprehensive introduction into SRDM, graph languages and Havel.
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