Archive for the ‘Epistemology’ Category

Researchers found mathematical structure that was thought not to exist [Topic Map Epistemology]

Tuesday, November 15th, 2016

Researchers found mathematical structure that was thought not to exist

From the post:

Researchers found mathematical structure that was thought not to exist. The best possible q-analogs of codes may be useful in more efficient data transmission.

The best possible q-analogs of codes may be useful in more efficient data transmission.

In the 1970s, a group of mathematicians started developing a theory according to which codes could be presented at a level one step higher than the sequences formed by zeros and ones: mathematical subspaces named q-analogs.

While “things thought to not exist” may pose problems for ontologies and other mechanical replicas of truth, topic maps are untroubled by them.

As the Topic Maps Data Model (TMDM) provides:

subject: anything whatsoever, regardless of whether it exists or has any other specific characteristics, about which anything whatsoever may be asserted by any means whatsoever

A topic map can be constrained by its author to be as stunted as early 20th century logical positivism or have a more post-modernist approach, somewhere in between or elsewhere, but topic maps in general are amenable to any such choice.

One obvious advantage of topic maps being that characteristics of things “thought not to exist” can be captured as they are discussed, only to result in the merging of those discussions with those following the discovery things “thought not to exist really do exist.”

The reverse is also true, that is topic maps can capture the characteristics of things “thought to exist” which are later “thought to not exist,” along with the transition from “existence” to being thought to be non-existent.

If existence to non-existence sounds difficult, imagine a police investigation where preliminary statements then change and or replaced by other statements. You may want to capture prior statements, no longer thought to be true, along with their relationships to later statements.

In “real world” situations, you need epistemological assumptions in your semantic paradigm that adapt to the world as experienced and not limited to the world as imagined by others.

Topic maps offer an open epistemological assumption.

Does your semantic paradigm do the same?

Automated science, deep data and the paradox of information – Data As Story

Saturday, March 31st, 2012

Automated science, deep data and the paradox of information…

Bradley Voytek writes:

A lot of great pieces have been written about the relatively recent surge in interest in big data and data science, but in this piece I want to address the importance of deep data analysis: what we can learn from the statistical outliers by drilling down and asking, “What’s different here? What’s special about these outliers and what do they tell us about our models and assumptions?”

The reason that big data proponents are so excited about the burgeoning data revolution isn’t just because of the math. Don’t get me wrong, the math is fun, but we’re excited because we can begin to distill patterns that were previously invisible to us due to a lack of information.

That’s big data.

Of course, data are just a collection of facts; bits of information that are only given context — assigned meaning and importance — by human minds. It’s not until we do something with the data that any of it matters. You can have the best machine learning algorithms, the tightest statistics, and the smartest people working on them, but none of that means anything until someone makes a story out of the results.

And therein lies the rub.

Do all these data tell us a story about ourselves and the universe in which we live, or are we simply hallucinating patterns that we want to see?

I reformulate Bradley’s question into:

We use data to tell stories about ourselves and the universe in which we live.

Which means that his rules of statistical methods:

  1. The more advanced the statistical methods used, the fewer critics are available to be properly skeptical.
  2. The more advanced the statistical methods used, the more likely the data analyst will be to use math as a shield.
  3. Any sufficiently advanced statistics can trick people into believing the results reflect truth.

are sources of other stories “about ourselves and the universe in which we live.”

If you prefer Bradley’s original question:

Do all these data tell us a story about ourselves and the universe in which we live, or are we simply hallucinating patterns that we want to see?

I would answer: And the difference would be?

To Know, but Not Understand: David Weinberger on Science and Big Data

Wednesday, January 4th, 2012

To Know, but Not Understand: David Weinberger on Science and Big Data

From the introduction:

In an edited excerpt from his new book, Too Big to Know, David Weinberger explains how the massive amounts of data necessary to deal with complex phenomena exceed any single brain’s ability to grasp, yet networked science rolls on.

Well, it is a highly entertaining excerpt, with passages like:

For example, the biological system of an organism is complex beyond imagining. Even the simplest element of life, a cell, is itself a system. A new science called systems biology studies the ways in which external stimuli send signals across the cell membrane. Some stimuli provoke relatively simple responses, but others cause cascades of reactions. These signals cannot be understood in isolation from one another. The overall picture of interactions even of a single cell is more than a human being made out of those cells can understand. In 2002, when Hiroaki Kitano wrote a cover story on systems biology for Science magazine — a formal recognition of the growing importance of this young field — he said: “The major reason it is gaining renewed interest today is that progress in molecular biology … enables us to collect comprehensive datasets on system performance and gain information on the underlying molecules.” Of course, the only reason we’re able to collect comprehensive datasets is that computers have gotten so big and powerful. Systems biology simply was not possible in the Age of Books.

Weinberger slips twix and tween philosophy of science, epistemology, various aspects of biology and computational science. Not to mention with the odd bald faced assertion such as: “…the biological system of an organism is complex beyond imagining.” At one time that could have been said about the atom. I think some progress has been made on understanding that last item, or so physicists claim.

Don’t get me wrong, I have a copy on order and look forward to reading it.

But, no single reader will be able to discover all the factual errors and leaps of logic in Too Big to Know. Perhaps a website or wiki, Too Big to Correct?