Philoinformatics and Categories of Informatics

How does philoinformatics relate to general informatics?

I will once again answer this using MS Paint. I think informatics (information science) can be usefully looked at as a kind of fan-like spectrum from general informatics to specific informatics. On the pointy handle end, you have fundamental informatics. As you move along to the edges of the fan, you find general domain informatics. At the edges on the right you have specific informatics fields such as bioinformatics, socioinformatics, and philoinformatics. Those are the grey and beige slices.

You can think of any very specific topic being placed on the edge of the fan under the title of "Informatics of X" or "X informatics" or, if you're lucky enough to have a nice prefix representing the topic, even "Xoinformatics"!

Domain Informatics
Just taking a look at the wikipedia page on informatics begs for some subcategories to help organize the discipline. The problems that are solved by the same mechanism in multiple specific informatics disciplines are more appropriately put deeper (to the left) in this fan picture, in the direction of general fundamental informatics. This is the realm of domain informatics. Domain informatics is arguably the most interesting area of informatics. Fundamental informatics is quite stable and almost completely content-neutral. While most advances in a specific informatics domain can usually be generalized to a certain point under certain conditions. I'd put things like information entropy, communication channel theory, cloud computing, and generic encryption issues in the 'fundamental informatics' category. In order to explain where philoinformatics lies in this picture, I'm going to try to identify and categorize the domain informatics.

Qualitative vs Quantitative
I think a major distinction to make when categorizing the growing amount of domain informatics is between qualitative and quantitative content. All disciplines, of course, need to deal with both quantitative and qualitative data, but some disciplines (like physics) have quantitative measurement at their cores, while other disciplines (like history) have qualitative reports and observations at their cores.

A Third Q?
Abstract disciplines like philosophy, law, math, economics, and computation, which are "removed" in a sense from direct empirical observation are interesting cases. They all seem to allow for more rigid models than qualitative observations but are generally not amenable to numerical models in the way quantitative measurements are. Unfortunately I can't think of an appropriate catchy word that starts with a 'Q' to add to the Quantity/Quality (false) dichotomy. But I think we can roughly partition all of domain informatics into Feature, Model, and Measurement Informatics. These are the yellow, blue, and red parts of my beautiful map of informatics above.

Categorizing Philoinformatics
Content in philosophy is published in chunks on the paper and book level. Some of these papers can get heavy in symbols but generally we're talking about free text and almost does a paper get heavy on numbers. Philoinformatics to handle this traditional form of philosophy becomes largely encompassed by general Publishing 2.0 initiatives which is a part of domain informatics. Registries of philosophers, registries of papers, and construing bibliographies as dereferencable (aka "followable") URIs are not unique to philosophical publications. This initiative involves simple feature informatics (by which I mean 'simple features' not 'simple task'). It's also a task that is extrinsic to philosophy in the sense that it is neutral to the content.

The more radical goal of philoinformatics that I mentioned in my philoinformatics manifesto draft involves cracking into the content itself whether by extracting from traditional publications or inventing new types of publication. Much of this content will involve trying to serialize identified ideas, concepts, and definitions that would be only available as unstructured freeform text in regular publications. As important as this is, even these items are somewhat general in that they are going to be used in all kinds of publications. But I should stress that these are the kinds of things that are currently rarely captured in a formal machine-readable kind of form, and would be a major enhancement to the entire domain.


So what content is unique to philosophy, or at least almost unique? The motivation for distinguishing philoinformatics (and any subdiscipline of general informatics) is that there is a different quality of the content that makes it somewhat unique. The content that is almost unique to philoinformatics is the handling of thought experiments, the free use of 'Xian' where X is any philosopher's name, and possibly the modeling of widespread but uniquely philosophical notions like internalist/externalist, foundational/coherentist, objective/subjective, absolute/relative, contingent/necessary. With a proper foundation with terms and links combining these items with to publications and endorsement and rejection statements, we could start computing over philosophical notions to find general properties of philosophical positions such as hidden inconsistencies, distance from evidence, robust multidirectional support and other relations that could potentially be defined in terms of this foundational data. Basically, traction and then real progress may finally be possible.

Philoinformatics Manifesto

I wrote this draft philoinformatics "manifesto" in April and have been meaning to polish it up and post it.

Yeah... it's July now.

I still intend to clean it up, but I decided to post my draft, as is, just to get it out there for now. Call it the beta version... actually more like alpha. All comments very welcome at this point.

Philoinformatics is a scientific, philosophical, and most of all an engineering discipline with the single goal of radically enhancing philosophy using information systems. If you're thinking about Artificial Intelligence that does philosophy for us then you've got the wrong (but awesome) idea. I'm talking about philosophy being done by people, but being done better, with the whole philosophical process being enhanced from end to end with helpful software made possible by underlying information systems.

We all need to recognize that philosophy as a discipline is devastatingly problematic and requires new life. Philosophy is known to lack proper resolution to patently philosophical problems and is fraught with pervasive disagreement among its experts, among other major problems. Just consider the thousands of man-years of thought put into the Free Will and Determinism topic, for example. Many philosophers do recognize that these and other problems exist and even usually spend some time thinking about them and/or joking about them, but also essentially practice a kind of denial about there existence. Maybe it's because doing philosophy still feels important and individual philosophers are making personal progress in the sense that they are acquiring and refining their own philosophical concepts. Philosophy is a slow and difficult process and philosophers are wasting the vast majority of their time doing what I believe they themselves would agree is a waste of their time if they only had a better way to view the actual landscape of philosophy; the landscape of the content of philosophy.

Despite this heavily pessimistic view of philosophy, I am not advocating for Skeptical Metaphilosophy, the view that philosophy does not have intrinsic value. I also, of course, don't claim to be able to reliably recognize when I or anyone else is specifically wasting their time doing philosophy. What I am advocating is that we build the systems required to show whether there is, where there is, and when there is valuable philosophy to be done. After thousands of years of philosophers spinning their wheels on philosophical problems, I think it's fair to suggest that some focus needs to be put into novel methods for making discipline-wide progress. For those that are able, instead of spending time refining one's own philosophical positions and attempting to make personal philosophical progress, I advocate putting some work into progress for the discipline as a whole.

Speaking of traction, actually imagine a wheel spinning on a road. The wheel spinning is the effort of philosophers and the forward motion is the progress of philosophy. If philosophy isn't making progress because of the nature of the content of philosophy (e.g. "words on holiday" or some other confusion) then that means our wheel is slippery, the problem is intrinsic. If instead philosophy isn't making progress because of the way philosophy is done or the current environment of philosophy (e.g. pervasive repetition of ideas or high barrier to entry for publishing or unknown status of philosophical positions) then that means our road is to slippery, the problem is extrinsic. For progress we need traction, for traction we need both the wheel and the road to have grip. Skeptical Metaphilosophy is the view that our wheel can't be made grippy. Naturalism could be construed as the view that scientifically supported positions are are the only grippy parts of the wheel. Both are about the wheel, philosophy itself. Philoinformatics is an attempt to give the road grip. Set up an environment where philosophy can make all the traction it possibly can. Of course, the wheel could be incurably slippery and giving the road grip won't help, but at least now you know where the grip is missing. In other words, Philoinformatics at bare minimum can provide evidence for or against Skeptical Metaphilosophy.

Philoinformatics is an attempt to face the problems of philosophy head on by building mechanisms for helping people understanding the actual landscape of philosophy. Actually, I intend Philoinformatics to be more general than what I've been advocating. Here's the more general form:

  1) Identify Symptoms
  2) Identify underlying qualities giving rise to the symptoms
  3) Design Systems to modify that quality of philosophy
  4) Develop the Systems

As simple as these steps may sound, all four of these steps are quite difficult. You might also notice that despite what I started out saying, Philoinformatics doesn't necessarily need to radically enhance philosophy. Any enhancement will do and would count as work in philoinformatics. But I stand by the "radically" part of the goal because I think philosophy requires radical enhancement and I hope philoinformatics is an avenue for getting us there.

Conceptual Space Markup Language (CSML)

I've recently come across a great paper by Benjamin Adams and Martin Raubal called Conceptual Space Markup Language (CSML): Towards the Cognitive Semantic Web. I found their paper interesting on many levels because it lies at the nexus of many rather diverse topics that I’m interested in. CSML directly involves Computational Geometry and the Semantic Web, but indirectly involves the philosophy of meaning, mind, and color. Also, as it grows in popularity, I believe force-based organizational algorithms and neural networks will become a heavily used mechanism for generating and using CSML data. Basically, CSML takes Conceptual Spaces, which are already at an interesting intersection of multiple mind sciences and multiple strands of philosophy, and connects it with multiple threads of engineering and informatics.

Wait.
What are Conceptual Spaces?

Conceptual Spaces are multidimensional spaces made up of quality dimensions. Quality dimensions are basically just any property you can think of that has a (pseudo-) continuous range of values. Think: size, mass, brightness, beauty, craziness, unicornity or anything else that you think you can make sense of on some sort of numerical range. You can now consider points and (convex) shapes within your set of quality dimensions, which will correspond to concepts. Consider the classic-cool-colour-cone example to the lower right. That’s a representation of a conceptual space with quality dimensions of hue, value, and saturation. 

But this isn’t just a fancy mathematical model. Structures like this are in some sense actually “held” by neurons in your brain. You may have heard people talk about "levels of reality" (or "levels of description of reality") such as the physical level, biological level, psychological level, sociological level, etc. Well, the idea is that when you look into the brain, which is (of course) very complex, you can look at it "at different levels" (presumably of granularity in this case). Conceptual Spaces are one of those lesser-known but very useful levels between the neuronal level and the psychological level (and below the symbolic level if you think there is one). Other people have much more comprehensive and informative explanations that I’m not going to try to repeat here. If you’re interested in the multitude of potential philosophical implications check out Paul Chruchland’s State-Space Semantics and Meaning Holism. (He’s also got some good stuff to say about colors outside of the classic-cool-color-cone here.)

CSML describes Conceptual Spaces

CSML, the Conceptual Space Markup Language, is an XML serialization of conceptual spaces. CSML brings a whole new engineering dimension to conceptual spaces which fits into the realm of Semantic Web technologies and is actually analogous to OWL, but with radically different implications. In my previous post, which was actually written in June 2008, I dreamed of a “smooth semantic web” that didn’t always require rigid categorization. CSML looks like a better candidate to handle that kind of data. CSML is specifically designed to handle context-dependent meaning and (relative) similarity of concepts, which are both difficult to handle in OWL. (How would you represent a large squirrel and a tiny planet consistently? What about brightness, beauty, craziness, and unicorniness?) After trying to use a few units ontologies for measurement data (at iCAPTURE for Mark Wilkinson) I also have high hopes that CSML can help simplify the problems on that front.

The most exciting part for me is that conceptual spaces lend themselves to fancy techniques for being automatically generated by way of artificial neural networks and force-based organizational algorithms, which brings in a few more of the theoretical engineering topics I’ve been interested in over the years. Starting with similarity data, force-based (or tension reduction) algorithms could help identify quality dimensions. Neural networks can nicely use quality dimension coordinates as input and also learn to precisely place items into conceptual spaces. I can’t wait to see the tools that will be created to allow for generating, reasoning over, and visualizing CSML data and how they will integrate with existing semantic web technologies and machine learning techniques.

Putting my old philosophy papers online

I'm going to gather all the old philosophy papers I've written over the years, which isn't very many, and post them with my current opinions of the papers. I don't know how useful or interesting this will be, but it might be fun.