Ventity is software for modeling dynamic systems. It combines a friendly, pretty interface with the power of modular entities, data-friendly architecture and dynamic model structure changes.

Featured

Some features & applications of Ventity:

View More Featured Items
  • Dynamic Structure

    In Ventity, you can introduce new entities on the fly during a simulation. You can introduce them via data, or programatically with discrete Actions. Introduce a firm into a market when you need it, or delete a cohort of products when they’re all sold. Simulate and review just your real behavior, without lots of clutter

  • Causal Tracing®

    Like Vensim®, which created the tool, Ventity has Causal Tracing for rapid model analysis:  

  • Data-Friendly Architecture

    Ventity fits relational and ad hoc data sources naturally, because it represents detail with collections of entities rather than arrays. You can enter data easily, with convenient builtin tables or spreadsheet links.

News

We invite you to read about recent releases and other news:

View More News
  • Ventity Release 1.0.2

    Ventity is just getting better – release 1.0.2 is now available for evaluation and purchase. Download a copy now. Entity Picker Improvements The revised Entity Picker is much faster than the initial version, which makes it easier to analyze large populations of entities. You can easily pick out individuals, or slice collections by attributes, to

  • Data science meets the bottom line

    Ventity can be used to put big data in context, by incorporating the learnings from big data in simulations that account for organizational structure and finances. Ventity can also do data intensive simulation, with an architecture friendly to relational data and dynamically changing lists, optimization for calibration, and much more  on the road map.

  • What can you do with entities?

    This video demonstrates the use of entities to: represent markets with sparse offerings and new product introductions, create new structure on the fly, from data or random events model multiple scenarios simultaneously for an aggregate infection model, model the same infection process on an agent based social network, and calibrate a function to time series