Accelerating evolution in your knowledge ecology

Although the amount of available data is increasing exponentially, the usefulness of our knowledge is evolving only slowly. To accelerate the evolution (and so the usefulness) of our knowledge for understanding and addressing global and local problems, we need a new understanding of what knowledge is, what makes it useful, how it evolves, and what we might do to accelerate the pace of that evolution. In this article, we draw a parallel between the evolution of knowledge and evolution of life. Then, drawing on recent research in the science of conceptual systems, provide two simple tools, and a few handy tips, you can use to accelerate the evolution of useful knowledge in your operational environment.

To understand the evolution of knowledge, let’s go back about four billion years, to when the first single-cell organisms emerged on Earth. Over time, many different forms evolved; and numerically, they covered the globe – much like the rapid increase in available data today. But it took about two billion years for them to evolve into multi-cellular creatures. Another billion years before they evolved into something we would call animals. Worms, sponges, jellyfish, and such simple creatures filled the seas; interacting with (and eating) each other. As they did, something strange and interesting occurred. There was an absolute explosion of life forms! Within that last billion years we went from fish, to dinosaurs, to mammals, to humans, and you.

Yes, within the great big ecology of the planet Earth, the pace of evolution seems to be accelerating. While our own human lives are too short to notice changes on a day to day basis. There is another ecological system we can see – and it is changing very quickly.

Some of the earliest “proto-writing” included symbols written on cave walls about 40,000 years ago. Certainly by 9,000 years ago, early civilizations were carving symbols into tortoise shells and bones. They mean nothing to us today, but back then must have communicating something interesting – they were sharing ideas. Only 5,000 years ago, “real” writing was spreading around the planet. This enabled the communication of complex ideas (and the recording of interesting information). Today, of course, we have vast amounts of writing in books, and online, with social media messages and video clips streaming all over the world 24/7.

All of that communication represents an ecology of knowledge. Like the cellular life of billions of years ago, our many ideas and insights are interacting, multiplying, and evolving.

To see the differences between types of cellular life, you need a good microscope. It’s different when looking at the very large clumps of cells such as dogs and cats, humans and whales. We can look at ourselves and say, “Wow, that’s a lot of cells!” How many? The average person has about 37 trillion cells. So, it is very easy to see the difference between very simple life-forms (few cells) and highly advanced life forms such as yourself (many, many cells).

It is a different situation when you are looking at a knowledge ecology. How do we see the difference between knowledge that is relatively primitive (not so useful) and knowledge that is highly evolved?

This is an important question because we are constantly bombarded by information, insights, ideas, and instructions. Product advertisements, political speeches, and just daily chit chat. How do we know what is important – what is useful?

A key perspective here is that we live in a world of systems. Those systems are more effective they have more structure. For example, an organization is more successful and sustainable when the component people are connected through their social and communication connections. Similarly, life forms are also more effective when they are more systemic – when their many cells are better connected.

To help us understand, let’s dive into the world of knowledge to see what happens in a knowledge ecology.

Let’s start where world of knowledge overlaps with our human world. Knowledge lives in printed pages – books, magazines, journals, file folders, journals, notes jotted down on napkins. Knowledge also lives in electronic form on disks, data sticks, online databases, and a place called the cloud. In a sense, the knowledge there is in a kind of “suspended animation.” Waiting to move to that most fertile environment, the human mind.

Knowledge may be formed in the human mind as a result of life experiences (or, more formally, research), or knowledge may move into minds from print and/or electronic sources. When it does, something magical happens.

Bits of knowledge interact; and, as they do, knowledge MAY evolve.

Research in the science of conceptual systems has explored the structure of knowledge and shown how knowledge evolves over time¹ and identified basic building blocks² of knowledge. During the scientific revolution, researchers were conducting experiments, gaining new ideas, and sharing them with others. The bits of knowledge evolved over just a century or two into the basic laws of physics – the same laws that give us the ability to launch communication satellites into Earth orbit, make cell phones, provide advanced medical care, and more.

But knowledge doesn’t always evolve. Sometimes it sits still. Imagine the great ideas in books and papers you’ve never read. Also, during difficult times, people become stressed. Under stress and fear, people don’t think very clearly. For example, the “fight or flight” response is rather limited compared with, “What delicious dish might we make for dinner?” Some politicians have deployed simple “us against them” messages as a way to encourage fear, limit critical thinking, and suppress generative discourse.

Complex problems have evolved over many years and so require highly evolved knowledge to be solved.

No microscope can identify “how evolved” knowledge is. And, for most people, it can be quite confusing. Some people look at a tiny idea like, “be nice to everybody” and see that as a big and important idea while others may ignore it as foolishly simplistic. The point is not to argue which it might be, but to find a way to “classify” knowledge.

We classify animals, people, organizations, and more. When we do, we give specific names to things. That way, we can say something like, “Let’s go to the restaurant and have some tasty pizza” and our friends will know what we are talking about.

To understand how to classify knowledge, we need to understand how knowledge has evolved. Much like life on Earth, and proto-writing, knowledge begins in the form of a “single cell.”

We can see single-cell knowledge with every concept (sometimes understood as a fact or piece of data). Or, as some philosophers say, a “truth claim.” There are certainly a lot of them! For example, if someone says “the sky is blue,” that statement may be seen as a single cell of knowledge.

By themselves, single cells of knowledge are not very useful – not a highly evolved knowledge entity. Much like the evolution of multi-cellular life on Earth and the evolution of real writing, knowledge entities are more useful when they contain more cells.

Improving those connections are key to supporting the evolution of knowledge. In an ecology of many single-cell bits of knowledge, there is no number (however large) of single-cell bits of knowledge that will be as useful as multi-cell knowledge. 37 trillion protozoa are not the same thing as a human being (so, don’t worry, they won’t take your job).

In exploring the knowledge ecology, you can seek more multi-cell knowledge. For example, let’s consider a knowledge entity that says, “more education can help me get a better job.” Notice there how one concept (education) leads to another concept (quality of employment). The connection between those two concepts is critical for identifying and evolved knowledge. But not just any connection will do. Importantly, the best kind of connection is a causal connection. If we know how doing one thing causes another thing to happen, that is more useful knowledge.

While we’ve identified a one-celled and two-celled knowledge entities, that is only a start. For fields requiring highly complex knowledge (business, economics, psychology, policy, etc.) knowledge entities of much greater complexity are required.

Even experts do not have the knowledge needed to solve the big problems of the world. So, we need to accelerate the evolution of useful knowledge.

To evaluate still larger knowledge entities, let’s start by saying you are reading something (academic paper, industry report, opinion/editorial, or other). That publication may include a lot of data, stories, metaphors, and such. The part we are interested in, however, is the part that says “how the world works.” Very basically, what might be called a theory, or model³.

To measure the connectedness of knowledge cells within a knowledge entity, you start by drawing a circle around each concept. Then, draw an arrow between the circled concepts where the author seems to suggest a causal connection. You might end up with something like this.

Next, you count the total number of circles (single cells). Here, there are six. Then, you count the number of cells that have more than one arrow pointing at them. Here, there is one (the use of performance information). That is what we call a “transformative” concept. A higher order of structure than a mere single cell (or even two cells with just one connection).

Finally, you divide the number of transformative cells by the total number of cells. Here, that is one divided by six, or 0.15. So, we can say that this knowledge-entity is about 15% evolved. That is not too good. That means this knowledge-entity is likely to be only about 15% useful for making decisions that help people to reach their goals. Clearly, this knowledge-entity needs help.

By becoming a “knowledge ecologist” you can help knowledge evolve. Even more so by becoming a “knowledge geneticist” where you learn to splice parts of knowledge-entities together to accelerate their evolutionary process within and between disciplines⁵.

It’s really quite simple to “splice” two (or more) knowledge entities together. In the following figure, you see Knowledge Entities A and B. Because they have a knowledge cell in common (here, it is: quality classroom experience) we can splice them together at that “overlap.” Thus, creating Knowledge Entity C.

Now, notice something else has happened here. Using the method above, you can see that Knowledge Entity A and B both are poorly evolved. With no transformative cells, they both have a zero level of evolution. Neither one, by itself, is likely to be very useful in guiding progress. When spliced together, however, we have created a transformative cell and so Knowledge Entity C has a 33% percent level of evolution (one transformative divided by three total cells). That is not awesome – but it does represent very rapid progress!

To support that process of evolution, and even to accelerate it, there are a few things you might do for yourself – and others. Generally, as a knowledge ecologist, you can:

1. Feed your knowledge ecosystem through research; reading the results of existing studies (or conducting new ones).

2. Classify those knowledge entities so you can see which ones are more evolved (as described above).

3. Splice knowledge entities together to accelerate their evolution.

4. Harvest the most evolved knowledge entities and share them through conversations, presentations, and publications (everything from academic journals to social media).

Don’t let a billion years go by while waiting for simple ideas to evolve into something amazing. Talk with your highly motivated colleagues and find ways to do this in YOUR environment. Wherever you are, you can accelerate the evolution of useful knowledge for making better decisions to reach your desired results.

Further information:

• Author’s textbook Practical Mapping for Applied Research and Program Evaluation – a comprehensive resource – with some no-cost guides.

• Author’s list of resources for researchers.

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