What Is "Idea Chemistry" and Why's It So Important for Ideonomy?

A confession: I’ve been waiting a long time to write this article. It’s just taken me awhile to get my thoughts together well enough to finish it.

By the end of this article, you should be able to understand the core empirical methodology Gunkel was proposing for ideonomy (pronounced IDEA-onomy). In other words, you should be able to understand why Gunkel believed he’d created a new science.

But first, I have an important disclaimer to make.

What Gunkel called “idea chemistry” is not actually the methodology I’m describing here.

Rather, this is a minor communications flourish I’ve recommended—one of only a few—in an attempt to make ideonomy more suitable for the lay reader.

In fact, Gunkel referred to the methodology I will describe here as “idea combinatorics” or the even-more-difficult-to-meme “ideocombinatorics.”

Gunkel felt that the central principle of his science was the somewhat ordinary recognition that existing ideas can be combined to produce other ideas.

It was the methodology, however, and the implications of that methodology, where Gunkel’s innovation can be truly seen as a leap of genius.

With the phrase “idea chemistry,” Gunkel was actually imagining a much more formal process with a direct analogue to chemistry.

Since the ability to write up idea-combination formulae in the same way chemists present chemical reactions is probably a long way off, however, I think it’s better to just use the term “idea chemistry” and leave the rest to future students of ideonomy to sort out.

What is an “idea”?

We’ve avoided addressing this until now.

When I first read through Gunkel’s materials, I was pretty confused by his repeated references to “ideas.”

So were a lot of other people.

Not only does Gunkel refuse to define “idea,” he seems to deliberately avoid defining it anywhere.

This was one of Gunkel’s biggest errors in my opinion.

Specifically, Gunkel failed to realize it’s possible to have an empirical (i.e., testable) definition for a studied phenomena while simultaneously acknowledging that anything close to a “final” definition will require a lot more study.

Science is constantly revising and debating the definitions of the things under examination. Gunkel totally knew this, but for some reason he froze up when it came to his own science.

There is in fact a substantial history of authors trying to pin down and define the nature of an “idea.”

Of course this has been an extremely challenging area of research.

Ideas are invisible.

They “appear” inside our heads.

Often there is an external stimulus behind the idea, but other times they emerge from our minds unprovoked.

Some ideas seem genuinely new, but many others are commonplace or cliché.

In many cases the attempt to identify what an idea “really is” has been tied to language, visual imagery, or the concept of novelty. This is a vast theoretical space. It points toward what I was saying in Part 1 of my 8-Part series (“Why Humanity Desperately Needs a Theory of Ideas”), which is that a science of ideas can’t get off the ground until someone acknowledges there is a phenomena here that can and should be tested.

But let’s side-step this theoretical problem for a moment and ask ourselves what experimental methods we have at our disposal.

Since we can’t test the space inside our heads—not yet, anyway—what is the phenomena that a science of ideas actually can test?

The answer is this:

Words, or even more precisely:

Fragments of speech that represent concepts.

Although words and speech-fragments represent only a portion of what we might know as an “idea” given that they’re a projection or manifestation of what happens inside our heads, language is where the most objective empirical evidence regarding ideas can be gathered.

Before we can understand idea chemistry, the method Gunkel was proposing to use in order to experiment on ideas (as represented by words and word fragments), it’s critical to first understand the existing methodology he was building on.

Gunkel didn’t invent idea chemistry from scratch. He was, to paraphrase Isaac Newton, standing on the shoulders of giants.

Fritz Zwicky (1898 - 1974) was a Swiss-American astrophysicist who Gunkel called the “grandfather” of ideonomy.

What did he mean by this?

What I’m about to explain is not intuitive for someone who is casually or even fairly familiar with Gunkel’s work.

Gunkel should have foregrounded the connection between ideonomy and Fritz Zwicky’s work, but he did not. This was another major error.

Zwicky was a best known for the discovery of dark matter. He also invented new kinds of jet propulsion systems.

But Zwicky also invented a problem-solving methodology called morphological analysis, which he first described in a 1948 article called “Morphological Astronomy.”

Morphological analysis was for a time incorporated into creativity studies throughout the 1960s and 1970s.

Then it was absorbed into an offshoot of creativity studies called Design Thinking.

Today morphological analysis has been largely forgotten or renamed and incorporated into other disciplines as a native technique. Many scientists today use a form of morphological analysis without even realizing it.

So how do you conduct morphological analysis?

1) The first step is defining the problem.

2) The next step is creating a problem space (also called a “solution space” or “morphological field”) by decomposing the problem in the following way:

3) The last step in morphological analysis involves searching through all possible combinations of the elements to understand the total number of possible configurations for the problem.

To illustrate, we can use an example from Zwicky himself.

As described in Zwicky’s 1948 article, morphological analysis can be used to imagine the construction of a new telescope. Not a copy of an existing telescope, but a totally new one that has never been made before.

Rather than trying to wrack our brains to imagine a new kind of telescope, why not start by asking what key things combine together to make a telescope? These are the parameters.

The examples Zwicky provided for parameters were the ratio of energy entering the telescope, the photographic plate used to capture the image, and the way the energy entering the telescope interacts with its optical parts.

Next, we identify the elements. These are the specific choices we can make within the parameters.

So if a particular ratio of energy entering the telescope is defined as “A,” we can then have different types of ratios represented as A1, A2, A3, and so on.

If the photographic plate used to capture the image from the telescope is defined as “B,” we can then have different types of photographic plates represented as B1, B2, B3, and so on.

If the interaction between the entering energy and the optical parts of the telescope is defined as “C,” we can then have different types of interactions represented as C1, C2, C3, and so on.

The result of this exercise will look like the below: a matrix from which “all possible telescopes” can be made.

With 3 parameters and 3 elements per parameter, the total number of possible telescopes is 27 (3³).

If you add just one more parameter—let’s say, for fun, the color of the telescope—and that parameter has three possible variations, the number of possible telescopes you can invent explodes to 81 (3⁴).

Before we move on to Gunkel’s innovation, let’s mark one last thing about morphological analysis—namely, that it uses words, rather than numbers, to define the problem space.

Morphological analysis is a non-quantified modeling method.

This means that it produces different configurations of reality using words rather than numbers.

Gunkel probably encountered Fritz Zwicky’s work via Herman Kahn.

That’s because morphological analysis is a scenario-building method used by futurists, and Kahn was the pioneer for these methods.

The suggestion is that we can “design” and examine future scenarios using morphological analysis just as we can design potential telescopes.

The fluid nature of morphological analysis as a modeling method that could be used both in the hard sciences like engineering (telescopes) as well as the soft sciences (futurology) impressed the hell out of Gunkel.

In fact, this was a feature of morphological analysis that Zwicky himself noted, writing in his 1969 book, Discovery, Invention, Research, the following critical statement:

In short, Zwicky was saying that morphological analysis can be used to study the relationship between anything and any other thing.

But this proposal remained simply that—a proposal—until Gunkel came along and took up the call Zwicky presented to the world.

Gunkel saw how morphological analysis could be generalized and extended, and he made this methodology the centerpiece of his science of ideas.

But there’s a reason Gunkel didn’t just call it morphological analysis.

There’s a reason he called it something else.

Idea chemistry is an innovation over morphological analysis, although this has never been documented in the literature and it’s one of my #1 goals as the world’s foremost Gunkel scholar to make this happen.

To understand what this innovation was, let’s think back to my earlier discussions of lists and list-making.

Because what Gunkel realized is that the parameters and their associated elements, the A1, A2, A3 and B1, B2, B3, etc., of morphological analysis, are essentially lists rotated 90 degrees clockwise.

The subject heading of each list is the parameter; the list items are the elements:

Moreover, Gunkel realized that you do not need a problem to solve in order to take advantage of morphological analysis.

Instead, the problem space can be represented as whatever list items you feel like trying to discover a relationship between.

What this means is that you can theoretically “configure” any idea in the same way you can “configure” a telescope or a future scenario—by joining discrete elements from lists together into a unified whole.

As with my Part 1 explanation for Gunkel’s science of ideas, which considered the importance of an underlying theory of ideas, today’s discussion of the empirical aspect of Gunkel’s science is just scratching the surface.

I’m currently writing several books and articles about ideonomy, and that’s where the more detailed discussions and examples are going to end up. This is just a bare-bones overview.

I do want to address one last point, however, which returns us to the concept of experimentation, empirical evidence, and science.

One of the most important features of science is the ability to describe and predict.

Another feature of science is the use of instruments to extend our observations.

With idea chemistry in its final or mature form, Gunkel was proposing to use artificial intelligence to run through and interpret the infinite number of ideas that can be derived from the combination of lists (parameters) and the items on them (elements). So that’s how Gunkel imagined technology being used to extend human capabilities in service of this science.

What’s important not to overlook as well, however, is ideonomy’s ability to validate ideas that already exist.

For example, consider these two very short matched lists:

Although each element matches each other element in a very direct way, in fact there are potential relationships between other elements that are not bounded by the specific associations.

Below, we can see that “skin” can have “leaks” and “pipes” can have “weeds.”

In fact, weeds are the #1 destroyers of pipes.

And leaking skin can be fatal.

This demonstrates in a very brief way how the universe of ideas is in fact more modular, unified, interrelated, and patterned than it first appears.

By matching together lists, idea chemistry is able to reveal these relationships—both in the description of existing things and in the prediction of new things that do not yet exist but could.

For another example, consider the following unorthodox relationship:

Do gardens have leaks?

At first this doesn’t seem likely.

But the more I think about it, the more I realize that water quickly “leaks out” or percolates into the surrounding dirt during and after a rainstorm.

This makes me wonder whether someone could invent a smart irrigation system that captures water from saturated garden soil before it “leaks” away and then returns the water to the garden during times of drought.

Here, I’ve not only used idea chemistry to “predict” the existence of a new concept that we might not think much about, but the novel framing for a rather mundane concept has also inspired a potential invention.

Creativity research shows that new ways of framing old problems can lead to innovation.

This is just one of many ways Gunkel imagined idea chemistry working as the empirical core of a science of ideas—demonstrating both the utter simplicity and incredible utility that we often demand of genius.