Modular 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

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.

Interactive Charting

You can pop up a chart on any variable in a diagram or list from a context menu. Charts feature smart scaling, brushing for values and legends, and drilldown, all a right-click away.


The Ventity equation editor, and other forms, are non-modal and use predictive typing. That makes it easy to navigate, view diagrams, and enter just what you need, without a complex dialog. Because a model has a limited set of terms, predictions are good, and model-diagram and units consistency checks further help to prevent errors.

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

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. Collections are based on flexible lists of attributes; you can supply a table of entities without having to enumerate all possible combinations of