COVID19 may kill social security

The continued obsession on COVID19 is shining a bright light on this very fundamental fact: we are asking the young to sacrifice their future calendars for the sole sake of preserving the future calendars of the retirement-age group. Eventually, that light will shine on the massive qualitative difference of those calendars.

COVID19: need for future-tense science

Another advantage of the dedomenocracy is that it would allow more vigorous future-tense science of making risk based decisions.  Like for present-tense science, this is a subtle consequence of how the dedomenocracy would operate.   The dedomenocracy only enforces rules for the short period of time that the population expresses an urgency for such action.   This gives the population to opportunity to decide that when the urgency is over.

An upper limit to the number of vaccines

Vaccination works by using the body’s natural immune response systems.   The history of the evolution is that any particular individual would only encounter a few viruses that it would need to find an immune response to.   We should worry that there is an upper limit of the number of immunities that the body can have at the same time.

Business activity tracking can improve requirement analysis for maintaining legacy applications

Legacy applications can benefit from big data approaches without the need to replace the legacy architecture with new technologies. Instead the big data can augment the application by collecting higher volume, variety, and velocity data about the user’s activity using the application. Analysis of this data can inform decision makers where there may be problems with the work-products. Correspondingly, it can provide requirements analysts with information about where improvements are needed or with more complete library of edge cases to consider for new designs.

Dedomenocracy: unsupervised government

When we look to data technology to solve problems, we should permit the technologies to identify the problems that can be solved with the current capabilities instead of demanding that the technologies evolve to solve the hard problems we have been working on. There are many opportunities to make progress even if we don’t touch the hard problems. Allowing technology to solve what it can solve now may transform the hard problems to be narrower, or possibly even less visible. For example, there are other ways we can improve overall life expectancy without curing any cancers, perhaps with investments in areas unrelated to health care. It is our nature to focus on objectives that catch our attention. This focus can blind us to immediate opportunities that are realistic given our current situation.

Big Data as a ship on a sea of missing data

If someone wants to cause trouble for the big data owner, they can leverage the known missing data to raise accusations that the big data owner will not have any data to use in defense. The accusations can suggest cheating, fraud, criminal activities, etc that can harm reputations or invoke costly and lengthy investigations that can deny the owner of realizing the potential benefits of the big data analytics.

Datum Governance: Distinguishing bots from real world

Data deception is a concern for automated decision making based on data analytics (such as in my hypothetical dedomenocracy). I think it is already a concern with our current democracy. I fear the current enthusiasm for data technologies because I do not see much in the way of appreciation for the possibility of deception. There is a huge confidence in the combined power of large amounts of data and sophisticated statistical tools (such as machine learning). Missing from our consideration is how well the data actual captures the real world. The data is not necessarily an honest representation of what is happening in the real world. It is very possible that the data may include deliberate deception.

Data governance vs Datum governance

I’m describing this as the security of the datum instead of the data. Specific observations are vulnerable to exploitation instead of everything observed by sensors. The malware is in the population being observed instead of in the IT systems.

To combat this kind of problem, we are going to need an additional approach of datum governance to protect the observed population from deliberately inserted biases.