I gave a talk about my project, Poplar, at the FREECO workshop. At the same workshop there was a very interesting talk given by Klaus Ostermann, outlining some of the various challenges facing software composition. He linked composition of software components to concepts in classical logic, and informally divided composition into a light side and a dark side. On the light side are ideal concepts such as monotonicity (the more axioms we have, the more we can prove), absence of side effects and a single, canonical decomposition of everything. On the dark side are properties such as side effects, the absence of a single decomposition, knowledge that invalidates previously obtained theorems, and so on.

One of the ideas that resonated the most with me is the tyranny of the dominant decomposition. (For instance, a single type hierarchy). Being forced to decompose a system in a single way at all times implies only having a single perspective on it. Is this not platonism coming back to haunt us in programming languages? (Ostermann did indeed say that he suspects that mathematics and the natural sciences have had too much influence on programming). What we might need now is an antiplatonism in programming: we might need subjectivist/perspectivist programming languages. If components can view their peer components in different ways, depending on their domain and their interests (i.e. what kind of stakeholders they are), we might truly obtain flexible, evolvable, organic composition.

This is hopefully the start of a short series of posts in which I attempt to relate the concepts of value and value creation, in particular as they were understood by Friedrich Nietzsche, to the modern world, in some kind of way. Comments of all kinds are encouraged!

In the beginning (understood as ancient Greece), there was philosophy. That is to say, most systematic inquiry into matters worth thinking about was collected under this umbrella term. Ethics, politics, epistemology and metaphysics went side by side with physics, biology and astronomy. As millennia passed, the collective human knowledge and scholarly labour grew, and some philosophical disciplines got their own name, cut the umbilical cord, and got to stand on their own feet.

There are many definitions as to what a philosopher is; one definition would be those who study the academic subject of philosophy in academic institutions. The German philosopher and philologist Friedrich Nietzsche wrote at length about what a philosopher really is; in his definition a philosopher is someone who creates values. Nietzsche rejected morals and universal truth as laid down by a God or higher authority; instead they are created by subjective human beings, and by philosophers in particular.

Perhaps [the genuine philosopher] himself must have  been critic and sceptic and dogmatist and historian and also poet and collector and traveller and solver of riddles and moralist and seer and “free spirit” and almost everything in order to pass through the whole range of human values and value feelings [...] But all these are merely preconditions of his task: this task itself demands something different – it demands that he create values.

(Friedrich Nietzsche, Beyond Good and Evil, s. 211, Walter Kaufmann transl.)

We may understand a scholar to be a person who processes knowledge. Good scholarship entails marshalling what has been written and studied previously, perhaps with a view to settling a question or supporting a perspective. Scientists and philosophers can make use of scholars in their work. To the extent that the scholar does more than merely process knowledge, he or she is something more than a scholar.

In contrast, a scientist, as we understand him or her today, is someone who combines scholarship and primary investigation (in the form of calculation, experimentation, measurement and so on) in order to create models of nature and the world, in order to gain the power to explain. The classical scientific process involves repeated refinement of hypotheses until one that cannot be proven wrong has been found.

Today, science, which formerly was known as natural philosophy, has grown enormously large, and to most people probably appears to have much greater value than philosophy. The scientific mindset is widely appreciated and respected throughout the world — perhaps too respected. Scientists learn as one of their highest virtues to be skeptical and to reject assertions that are made without a basis in measurement or theory. Paralysis by skepticism is very much a possibility. To see the danger in this, we have to recognise that a great deal of valuable things in human history have been created without such a basis – by people who have been something like the ones Nietzsche describes.

The dangers for a philosopher’s development are indeed so manifold today that one may doubt whether this fruit can still ripen at all. The scope and the tower-building of the sciences has grown to be enormous, and with this also the probability that the philosopher grows weary while still learning or allows himself to be detained somewhere to become a “specialist” – so he never attains his proper level, the height for a comprehensive look, for looking around, for looking down. [...]

Indeed, the crowd has for a long time misjudged and mistaken the philosopher, whether for a scientific man and ideal scholar or for a religiously elevated, desensualized, “desecularized” enthusiast and sot of god. And if a man is praised today for living “wisely” or “as a philosopher”, it hardly means more than “prudently and apart”.

(Friedrich Nietzsche, Beyond Good and Evil, s. 205, Walter Kaufmann transl.)

In fact, scientists today do not, in my experience, work like the ideal scientist described above. Scientists often use their own judgment and their own values in order to influence how their science is to be used. Einstein and Oppenheimer had opinions about the use and misuse of the nuclear bomb. Creators of vaccine may have opinions on how it is to be distributed and may be able to influence this. Sometimes these value statements made by scientists are pure judgments, applications of an ethic that the scientists already believe in. However, sometimes the situation is so new that the scientists effectively have to create values. To the extent that they do this, these scientists dabble in ethics, morality and philosophy, but this is often overlooked, as is the fact that scientific method itself was created by philosophy.

Nietzsche calls for philosophers to make use of scientists and artists, and create values in the service of mankind. He calls for a new recognition of the true role and dignity of philosophy, which does not at all need to mean a reduction of the value of science, but rather an expansion of the whole system. Philosophy stands naturally above science and scholarship and uses them as its tools. The activity of creating values based on philosophical insight by necessity goes on constantly and should not be confined to little nooks in the margins of society. The full extent of and need for this activity needs to be acknowledged.

Has the situation changed since Nietzsche wrote Beyond Good and Evil in 1886?

2. Skyscrapers in Ootemachi, Tokyo and the City, London. Buildings belonging mostly to banks and insurance companies. They appear, on some intuitive level, to have been there forever, though most of these buildings can now be built from the ground up in less than a year. It is hard to fathom how they could ever be destroyed, though the work could be done in a matter of months (?) with the right equipment.

3. What is permanent? Anything that we cannot perceive as changeable, we call permanent. But this is a linguistic and epistemological error. The inability to perceive something has led us to declare its absence.

4. The earth. 5.9736 x 10^24 kg of matter, likely fused into a planet about 4.54 billion years ago. The sun will enter a red giant phase in about 5 billion years and swallow or cause tremendous damage to it. The sun is also currently the source of all fossilised energy on earth and the energy used by most life forms on it.

5. A certain class of mathematical proofs often consist in converting facts from one basis (family of concepts) to another. Such proofs often have a hamburger-like structure: first the initial facts are rewritten into a larger, more complex formulation that suits both the assumptions and the conclusion, and then the complex formulation is collapsed in such a way that the desired results come out and the original formulation is lost. The “beef” in such a proof often consists in carrying out the correct rewriting process in the middle.

6. Facebook takes off and becomes enormously popular, in part because it facilitates, on a huge scale, something that human beings want to do naturally. Communication and the need to relate to crowds and individuals could be said to be universal among humans.

An incomplete version of the technology lattice, as suggested in this post, with human desires at the top and the resources available in the universe at the bottom.

7. We can imagine technology as a lattice-like system that mediates between the human being, on one hand, and the universe on the other. As a very rough sketch of fundamental human needs, we could list drives like communication, survival/expansion, power/safety and art. (In fact, an attempt to make some of these subordinate to others would constitute an ethical/philosophical system. Here we do not need such a distinction, and the one I have made is arbitrary and incomplete.) When we place our fundamental drives on one end, and the resources and conditions provided by the universe on another – elements and particles, physical laws and constants – we can begin to guess how new technologies arise and where they can have a place. The universe is a precondition of the earth, which is a precondition of animals and plants, which we currently eat. And food is currently a precondition of our survival. But we can imagine a future in which we are not dependent on the earth for food, having spread to other planets. We can imagine a future in which oil and nuclear power are no longer necessary as energy sources, because something else has taken their place. New possibilities entering the diagram like this adds more structure in the middle – more beef – but the motivating top level and the supplying bottom level do not change perceptibly. (Of course, if they did, beyond our perception, they could be made part of an even larger lattice with a new bottom and top configuration.)

8. Technology is a means to the establishment of permanence, and a re-encoding of human desires into reality.

9. New technologies arise constantly. But can this evolutionary process go on forever? Does the lattice converge towards a final state?

One way of making a name is stringing words together until the meaning is sufficiently specific. E.g. “morphism averse co-dependent functor substitutions in virtual machine transmigration systems”. Thus the abstruse academic research paper title is born.

Sciences sometimes give new meanings to existing words. This could be called overloading, following the example of object-oriented programming. E.g. a “group” in mathematics is something different from the everyday use of the term. A “buffer” in chemistry is something different from a software or hardware buffer, even though a fragment of similarity is there. And so on. This overloading of words gives newcomers to the field a handle on what is meant, but full understanding is still impossible without understanding the actual definitions being employed.

Sometimes new terms can be created using inventors’ names and everyday words. E.g. a “Lie group” or the “Maxwell equations”, or “Curry-Howard correspondence”. This is potentially useful, but perhaps not something you can do freely with your own research without seeming like you’re trying to inflate your ego excessively. (Even though researchers love inflating their egos, nobody wants to admit it.)

There’s a similar problem in software development. When we invent names of functions, classes and variables, the lack of words becomes very clear. Intuitively, what is an “adapter registry”? An “observer list”? Or an “observer list mediation adapter?” My feeling is that we often end up compounding abstract words because we have no better choice. And here lies a clue to some of the apparent impermeability of difficult source code. We need better ways of making names. We’re inventing ideas faster than our language can stretch.

On the software side, from a user perspective, it gets tougher. In terms of document formats, everybody is expected to have a web browser, to be able to read PDF documents, and to play MP3 files. There’s also a certain expectation that people can read and edit Microsoft Office documents, but in general, unless people have Microsoft Office itself, they are likely to use some program that does an imperfect job of converting to and from the office formats. More exotic formats people do not expect to share unless they know that the receiver has the same software.

One of the worst situations (and maybe one of the sources of these troubles) is software architecture. To look at just one subset of this universe, let’s consider some popular Java frameworks. There are many Java frameworks. I can think of Struts, Spring, Enterprise JavaBeans, Tomcat, JBoss, Websphere, OSGI. Each one is trying to be a universal, or near universal, component based framework for large scale Java applications. (Some of these are intended specifically for web applications, but the distinction is a bit arbitrary these days). Each one defines a particular behaviour and API that its users need to fit into.

One problem is that once you’ve developed your application in one of these frameworks, it’s tough to move to a different one. At best it’s “just” a matter of using a different API. At worst, the new framework has different expectations of component behaviour and forces you to either refactor your old application majorly or write lots of adapters.

Software has this particular kind of stickiness: objects designed in one context tend to be difficult or impossible to move to other contexts. Here are some problems brought about by this fact:

• Across several different devices and services, I have many different lists of my various contacts. For instance, I have an address book on my computer, which fortunately is shared with my iPhone. However, there’s also a list that Gmail maintains, one list on MSN messenger, one on ICQ, one on Facebook, one on the Playstation Network… etc. In a world with perfect software, I would have a single contact list that could be updated and accessed from any of these services (and I would still be able to selectively hide things from any one service, if I wanted to).
• Most social networks support some kind of status update feature (Twitter, Facebook, …) but they generally can’t share this information with each other. There’s a tool called ping.fm which is able to update lots of networks at once, but this is just for a single feature. And it took a third party tool to do it. And I have to give it all of my logins and passwords.
• The health care systems in many countries are a mess (UK’s NHS and probably most US systems fall in this category). Different medical offices in different medical IT systems establish different journals, but they have no way of sharing the journal if the patient wants to use a different care provider. As experiences in the UK showed, achieving the ability to share these records can be very difficult sometimes.

So how can we design really future proof standards? Here are my intuitive, unqualified answers:

Favor openness over closedness. Allow unknown entities to enter your protocol at many different stages and in many different roles. Even if you don’t design for this, it will tend to happen. Better to design for it.

Allow extensibility in many directions. Frameworks often allow you to make more specific versions of their components, but it’s rare that you can generalize or inject behaviour at a middle level of a concept hierarchy.

Use semantic metadata. Ontologies are under heavy research in computer science. They are formal classifications of concepts and their relations. One popular format for developing ontologies is RDF(S); for an example of what the concepts can look like, check this RDF(s) directory. I will have reason to come back to this topic in the future.

One example of a brilliant long-lasting standard: TCP/IP. Somehow in this case, the designers got something miraculously right at the first try. I’m amazed at how we keep building on this standard every day.