One of the advantages of machine intelligence over human intelligence is that machines are not driven toward poetry. To me, poetry captures the scientific appreciation for the simplest explanations with the fewest number of terms. Humans are innately poets by nature, and even the objectivity of science can not escape the human delight in well-crafted poetry, or human disdain for inelegance in descriptions.
These cases are often described as open-secrets. Many people in the community are aware of the information about individual cases and about the pattern of behavior, but there has been some kind of understanding that the past events are resolved in some acceptable terms, and that ongoing behavior is restrained by certain conditions. The oxymoron of open-secrets can be resolved by defining the open-part as being observed data, while the secret-part is restraints on how this data may be used in future decision making.
Even including super-intelligent machines into the concept of dedomenocracy, there will remain the present-day complaint that the government needs to get lucky every day but the criminal human needs to get lucky only once. This problem will remain long after we replace democracy with dedomenocracy. The most dangerous criminal is the non-criminal who immediately acts on his newly discovered hypothesis. Even superhuman intelligent dedomenocracy may not be able to discover this hypothesis first.
Instead of assigning teams of excellence certain sprints to build minimally viable products, we could be employing individuals to freely explore the issue with independent determination. This determination and independence is characteristic of following the muses where the muses will lead a path into areas of incompetence or uncertain capabilities. The muses will lead to discoveries that we can not anticipate. It is those discoveries that offer the promise to solve the big problems we face not only within government but also in business. These discoveries are the antithesis of the minimally viable product that can be selected in advance. Pursuit of discovery (or following the muses) means we can’t anticipate the results. The results may involve new skills we did not bring to the project. The results may involve something incompatible with any existing team of excellence.
In modern data science projects with automated data collection and analytics, the hypothesis-discovery occurs at the beginning of the process. The modern decision maker participates at this early stage of the process to select discovered hypothesis that are self-evidently persuasive. The following data collection and analysis that supports this hypothesis will lead to a simple decision that does not require any last-minute invention of a story to earn the decision-makers approval. After the decision, additional invented stories will serve only the purpose of illuminating the underlying non-fiction of the data and analysis.
In order to be successful, a program of activity tracking of government employees would need their good-faith cooperation to capture useful data. This will require overcoming the natural objections to the micromanagement of their daily activities. I doubt this will ever be fully successful because people naturally resent being watched too closely (I know I would resent it), even in this case where it is justified to maintain social order by providing democratic insight into the independent bureaucratic processes that are not otherwise accountable to democratic participation.