My other unsolved puzzle on this forum, I can keep unsolved for years... This puzzle, however, introduces patterns that have wide value to policy automation.
I want to introduce probabilistic determinations, so I can introduce the next step of merging AI and OPA determination at a conceptual level.
AI can go through our data and give us probable insights. Whether decisions are made directly by AI or whether AI interpretation / guidance is weighted within a policy is itself a determination. Someone has to decide (usually in policy) what to do with AI provided probabilistic determinations.
If no takers on this puzzle by the end of January, I will post a solution and a pattern to solve these problems on my blog, and get to the next problem -> high-level merging of AI (applied on top of big data) with human policy in determinations.
Anyone want a final go at this, before I post a solution at the end of next week?
No time to implement it, but looking forward to seeing Bayesian inference in OPA (perhaps a hint to others on one way to implement a solution).
Fair question! Good idea.
See the following: https://www.askiitians.com/iit-jee-algebra/probability/bayes-theorem.aspx
Note for those interested. This is one of the several mathematical approaches used in AI. It is also a good thing to understand in many real-life scenarios. I have been to many blogs where people say understanding this makes them stronger reasoners in life.
So, I had to provide the solution to this puzzle. Apologies. I may post an AI / Machine Learning blog later today.
Using this, we can take known statistics and combine it with OPA to have a probabilistic determination. I am sure this can be improved upon.
We can use OPA for everything from horse betting (based on horse attributes such as "first-time turfer") to security risk analysis. We just need the general stats. Those stats may be continually updated with big data, machine learning, etc.