Liberty, Equality, and Fraternity, After All?

It’s too early to say.

Markovian Processes

(1) A Bernoullian trial — in which a person flips a coin — has has a stationary probability distribution. There is no current state. (2) A fictive Markovian process — in which teams having one-week-sprints are only 1% likely to decide that the next sprint should last for three or more weeks — has a current state, but no memory of prior states. (3) A path-dependent process never returns to the same state because every event from the past, potentially influences the future. We continuously add new learnings.

Bernoullian Processes

Path-Dependent Processes

  • Bernoulli and Markov processes are independent of history; the latter is affected by a current state, though. Yet, when all history matters, we deal with a path-dependent process. It will most certainly never return to an old state.
  • We may deduce the output from a general rule and a specific input. And a general rule can be induced when we have several observations. However, when humans are involved, most processes are path dependent. This is why we should start with an abductive approach.



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