Spontaneous Data

With big data, we end up with deep historical data from distant events. There will be something needed to fill in the gaps that were mysteries at the time. That gap filler will be spontaneous data whether we acknowledge it or not. Even if we as humans leave the gap unfilled, we can’t be sure that our data analytics or machine learning algorithms won’t fill it. When it does, how can we be sure it won’t come up with a supernatural explanation that it keeps to itself?

Fake News: A Dedomenocratic Perspective

What really makes legacy news fake is the tyrannical influence of past narratives that influence what future observations we accept. Fake news is the need to keep old narratives relevant when the such a narrative never would have emerged if started from scratch with the data available at the current moment.

Materialize the model to level the competition with observations

Having model data explicitly materialized into tables gives the data clerk to recognize the deficiency that this data is not observed data. This provides the data clerk the opportunity to ask whether there can be another source for this data. Perhaps, for example, some new sensor technology became available that provides observations that previously required models to estimate. The analyst can then revise the analysis to use that new data instead of the model-generated data.

Dark nothing hypothesis macro-sized particles

The popular dark-matter hypothesis takes for granted the existence of fundamental particles that are outside of human capacity to observe. The hypothesis in the first article is that these hidden particles are as-yet undetected peers of sub-atomic particles we already know. The lack of perturbation of post-collision dark matter implies that if such sub-atomic dark-matter particles exist, they do not collide individually like particles we know. My conjecture is that the entire blob depicted in ghastly blue in the visualization is a single particle, or an agglomeration of galaxy-sized fundamental particles. The collisions didn’t affect these particles because the collisions are trivial for the scale of these particles.

Exposing model generated information for public scrutiny

Sharing this model-generated data is not the same as sharing the models themselves. The source code for the models still can be hidden from the production system. The population will only have access to the the generated data captured in persistent tables instead of in temporary memory. The population can compare the model generated data with their own calculations to show that they can reproduce the results. Reproducing these intermediate model-generated results will provide confidence that the models are correct. Alternatively, the population can demand reconciling any discrepancies they find.

Truth as a confounding variable that interferes with interpreting data

Dedomenocracy is a scaled up version of modern data science practice using big data predictive analytics to automate decision making. As a data science project, there is a need to evaluate the data in terms of how closely it represents a fresh unambiguous observation of the real world at a specific time instead of a reproduction of a past observation through model-generated dark data. Darker data involves some level of contamination with historic observations or with our interpretation of past observations. The problem with darker data is that its use of old and potentially outdated data can discount more recent observations that can tell us something new and unexpected about the current circumstances of the world.