on April 25, 2018

4 Helpful Metaphors for Evolution

Evolutionary theory can be hard to wrap the mind around. But there are some metaphors that can go a long way toward helping us grasp this complex subject.

Metaphors can be powerful teaching tools, leading us to an understanding of new concepts by connecting them to ideas we have already grasped. As with any tool, choosing the right one is important. The best ones encompass key features of the new idea and make them easier to comprehend. They’re like a wrench that fits just right, providing a comfortable grip for your hand and allowing you to engage a nut or bolt that would slip right through your fingers.

Evolution can be hard to wrap your mind around. It’s also a big subject made up of several distinct ideas. We’ve already seen a few examples of metaphors that fail to capture this complexity. But there are other metaphors that can actually go a long way toward helping us grasp the complexity of evolutionary theory. Assembling a complete understanding requires multiple metaphors matched to the details of those ideas. Here are a few metaphors that I and other biology educators in the BioLogos network have found to be helpful.

Family Tree

An oldie but a goody, this metaphor was central to how Charles Darwin thought and wrote about evolution. Family genealogy is a miniature version of how evolution connects all life. Living creatures are like the siblings and cousins at the ends of the family tree branches. Closer relatives share more recent ancestors. Siblings share parents, cousins share grandparents, second cousins share great-grandparents, and so on.

Evolution did not have to work this way. Some conceived of it as a more direct progression through the existing kinds of living creatures. Our fossils and our genes would look differently in that scenario than the branching family tree scenario. What we observe about both fossils and genes is more consistent with a family tree, confirming Darwin’s theory over other kinds of evolution.

Family trees also help us answer the perennial question of why there are still monkeys if humans evolved from them. That’s like wondering how you and your fourth cousins can co-exist. Neither of you is an ancestor of the other, but you do have one in common.

The first folio of the heroic epic poem Beowulf, written primarily in the West Saxon dialect of Old English. Part of the Cotton MS Vitellius A XV manuscript currently located within the British Library.

Language

People need common language to communicate. When people who share a language drift apart and stop talking to each other, that common language can drift apart as well. Hence the joke about the United States and Great Britain being two countries separated by a common language. Some formerly shared words drop out of use in one group but not the other; in the UK they rarely use ‘fall’ to mean ‘autumn.’ Sometimes different words arise for a new concept encountered after the split, like ‘gas’ vs ‘petrol.’

This is similar to how species diverge after they separate. One group spreads out in space, eventually becoming two groups separated geographically. They might be on different sides of a mountain range, or on different islands, or in different bodies of water that used to be connected. After the split, biological features they had in common might drift apart, especially if the environments are significantly different. The classic example is the beaks of Darwin’s finches; different beak variants were seen on different islands, each related to eating the different available food sources.

Language evolution also demonstrates the family tree pattern. The more similar two languages are, the more recent their common source. American and British English are similar enough to be dialects of the same language, reflecting their recent shared origin. Romance languages like French, Spanish and Portuguese are more distinct because their shared origin in Latin is further back in time.

Magnetic stick-and-ball toys

Most objects we make need to be put together by human agency; by themselves, the parts just sit around. But life happens on the scale of molecules, and molecules are always in motion. And they all push or pull each other through electromagnetic forces. The balance of all that pushing and pulling can cause bigger structures to form “on their own.”

Magnetic toys will also spontaneously assemble into structures thanks to comparable push-and-pull forces. They may need a little jostle to get started, but they don’t need us to put each piece in the right place. Some of the structures that form will fall apart easily, while others will be quite stable. Some of those stable structures can even provide a framework that will make it easier for further copies of the same shape to form. The same is true of molecules. Once some of them come together, they make it easier for other molecules to do the same. This kind of self-assembly helps us understand how chemistry could have transitioned into biology.

Sowing seed

There are two ways to plant seeds: placing individual seeds deliberately, or scattering seed over an area. Evolution is more like scattering, generously providing life opportunities to flourish wherever it can. It is an act of discovery, revealing which ‘seeds’ are suited to which ‘soil’ by allowing them to grow.

Machine Learning

“Learning” can evoke memories of school and receiving knowledge from teachers and books. But someone had to generate that knowledge in the first place. That kind of learning, extracting genuinely new knowledge from experience, is the kind of learning that helps us think about evolutionary biology.

Studying how learning works has grown to include programming computers so that they can learn. One generally way computers learn is by proposing new solutions and then getting feedback on whether they work or not. For example, if a computer is learning to play chess, its solutions are moves and the feedback is whether those moves lead to winning the game. If a computer is learning to compose music, its solutions are songs and the feedback is how many people like listening to them. New scenarios provide opportunities to discover what ideas work and what don’t. What works is reinforced and so remembered.

This learning process is similar to “natural selection” in biology. In fact, the similarity is so strong that some machine learning techniques are directly inspired by evolutionary biology. What is being learned is what kinds of biology allow life to flourish. Each individual has its own biological solutions to the challenge of flourishing, and feedback comes via the number of descendents that individual produces. That feedback is what we call natural selection.

Conclusion

None of these metaphors captures the full breadth of biological evolution. They each provide insight into different parts, from common descent (family trees) to the creation of new species (languages) and natural selection (machine learning). But machine learning, for example, doesn’t help us understand where new species come from, since there’s no equivalent to species in computer programs.

We can also see where the boundaries between metaphors and descriptions get fuzzy. Is a family tree a metaphor for common descent, or an example of it? Is common descent itself a complete description of evolutionary natural history, or does it introduce some simplification as a metaphor does? Some features of bacteria, for example, can be swapped between ‘siblings’ or ‘cousins’ rather than only passed to descendants. To some degree, all models and descriptions simplify some details to allow us to achieve some understanding and explanation. In that sense “all models are wrong,” as statistician George Box put it. He then added “some models are useful.” Biologists have found these metaphors, or models, helpful for thinking about evolution, and evolution itself has proven to be a useful model for thinking about the rich diversity and complexity of biology. And as we continue to encounter new features of biology, we continue to refine that model and thus learn more about where life came from and where it is going.

As we learn new ideas via science, we can also use them as metaphors to understand other challenging subjects. Jesus used that approach in many of his parables, for example. He took what the people understood about creation and used it to teach them more about the Creator. I’m curious whether we can use contemporary science in the same fashion. And so I wrote a book exploring metaphors for science and metaphors from science. If you are interested, Faith across the Multiverse will be published in July by Hendrickson and is available for pre-order now.

About the author

Andy Walsh

Andy Walsh is the Chief Science Officer at Health Monitoring Systems where he develops statistical methods for public health surveillance. He has a PhD in molecular microbiology and immunology from the Bloomberg School of Public Health at Johns Hopkins University. He covers science and faith every Wednesday on InterVarsity's Emerging Scholars blog.