Decision Playground

The Decision Playground is an interactive component of the OpenRules IDE designed to help business users:

• Produce and compare multiple possible decisions: It allows for hands-on, visual experimentation with different scenarios to see how changes in the decision request affect the decision model’s outcome.
• Add last‑minute constraints right when a decision needs to be made—especially when there’s no time or expertise available to update the decision model itself.
• Find optimal decisions: Users can choose an optimization objective to Minimize or Maximize and see the proper optimal decision produced by the underlying decision model with additional constraints
• Create and maintain Decision Pools for various operational scenarios: Users can save different decision outcomes for further analysis and comparison.

The Decision Playground is particularly helpful for:

• Finding appropriate decisions under uncertainty and when a user allows a certain degree of tolerance in the recommended decisions.
• Supporting “What-iffing” by asking “What if” questions and exploring various decision model possibilities and outcomes.

Why is this important? When it’s time to actually make a decision, the user, who is usually a business specialist, has a much clearer understanding of the real‑time constraints and limitations than even the most advanced decision model can capture. So, the user needs a way to share these real‑time constraints with the model and quickly explore multiple alternatives.

How does it work? The scheme below shows a business analyst who is working with the graphical interface supported by the Decision Playground:

The user brings the new request in the JSON format, make changes in it if necessary, and selects the button “Optimize” on the right of the Decision Playground interface. Then the user may choose an optimization objective which should be Minimized or Maximized. The playground will run the decision model to produce the proper optimal decision.

Using the button “Filters“, the user may quickly add additional constraints to tune the decision model to the reality of a particular decisioning situation:

While experimenting with various possible decisions, the user may save the Decision Candidates into the Decision Pool, adding explanations to each of them:

Inside the Pool, a user can visually compare different Decision-Candidates, sort them by different KPIs, and choose the Decision-Champion that best fits the current situation. This decision can then be exported for further use and future Decision Tracing.

When the number of possible decisions is relatively small, users can also browse all available options using the buttons “First”, “Next”, “Previous”, and “Last Decision” to choose the “best” one without invoking any optimization.

The Decision Playground can be used with different underlying open-source or commercial Constraint Solvers or Linear/MIP Solvers. It is easy to switch between them without any changes in the decision model.

Whether your decision model uses a rule engine, constraint solver, or MILP solver, you may use the Decision Playground to experiment with various decisions to choose the one that you consider is the best fit for your current business situation.

The Decision Playground supports “What-iffing” by allowing a user to change the input request and add additional filters. Then they may consider best- and worst-case scenarios to evaluate potential impacts of different choices.

Decision Playground is included in the RuleSolver installation. The following sample projects contain the launch file “play.bat” that starts the playground for this decision model:

• Selecting a Loan
• Inside/Outside Production
• Website Features
• Making a Burger
• Arithmetic.