Here’s a core concept of my emergence engine: model based evolutionary value creation. Basically it means the following:
- You get stories depicting some domain, i.e., user answers or tweets
- You translate them to semantic concepts & statements
- You give these concepts & statements (model parts) behavior, which basically tries to create value for the end users, or the end users organization
– Simplest example is when some statement can bring value to some user if he learns about it
– Another example is when several statements reason the causal relations between them & infer what is the root cause to some phenomenon
- Having all of the model parts behave all the time is compute intensive, which has a cost
- Not all of the model parts have the potential to create value
- So, an evolutionary process can take place, in which only the model parts that succeed in creating value survive & get resources.
See also my initial post on the base concept.
