Plan Prediction Services

Any intelligent fool can make things bigger and more complex.  It takes a touch of genius - and a lot of courage, to move in the opposite direction.      Albert Einstein

Our apps

The ordinary Planticipate app and its workshop version seek to predict, and explain different categories of people’s plan preferences.

Earlier prototypes correctly predicted people’s top-ranked plan at least 70% of the time, and the current apps should improve upon this.

They constitute an extremely rare, if not entirely unprecedented application of scientific rigor to the intensely human process of people-oriented policy making.

Self improving:

Every app user is asked to consider some “situation”, comprising a client, a goal and two or more alternative plans for achieving that goal.  Their situation can be either one of the examples embedded within the app, or a new situation entered by the app user.

App users then score their situation's plans, both for twelve, key plan-evaluation criteria and for overall desirability, and their scores are automatically sent to the cloud where they are closely protected and NEVER revealed to any third party (Privacy statement).

This enables our cloud-based dataset to slowly accumulate knowledge about relationships between plans’ criterion scores and their overall desirability.  Since every app user has also supplied their demographic details, such relationships are updated for every demographic category to which the user belongs.

It follows that in ANY subsequent situation, provided that you first score alternative plans on the twelve criteria,  the apps will forecast each plan’s overall desirability according to up to 93 different demographic groups. 

The more people that use our apps the more accurate they should become - they are self-improving.

Self-monitoring:

Our apps always graphically locate their plan-desirability forecasts within margins of error.  This makes it obvious which forecasts are statistically significantly different from each other – if two plans' error margins do not overlap they are not forecast to be significantly different from one another in terms of overall desirability.  

The apps also calculate Bayesian probabilities that their plan predictions are correct – they are self-monitoring.

Self-explanatory:

Our apps always suggest why their forecasts turned out the way that they did.  For this they use our innovative 'face charts' method which is demonstrably superior for clarifying multi-criteria data. 

Specifically, a face chart showing the priority placed upon each criterion by the relevant category of people is set alongside all plans' face charts that show how each one scored on the criteria.  This highlights the important criteria and how well, or otherwise each plan scored on them.

What now?

To see whether or not Planticipate's accuracy is increasing, you should intermittently run it

More generally, if you think we could improve the empathy of your forward planning you should let us know by e-mail: admin@planprediction.org.