Gregory McNiff (4:10)

Excellent. Before we dive into your framework for describing evolution, I just want to define a few terms. Specifically, you label everything from biology to history and economics. The second science. What is the first science, and why are these disciplines considered the second science?

Mark Belland (4:31)

So the term the second science comes from a biologist named Graham Bell, who was essentially riffing off of Simone de Beauvoir's book called the Second Sex. And the second science is the science of evolution. The first science, which he never defines using that term, is physics and all of the other branches of science that flow out from physics. So to understand chemistry, you're essentially applying physical principles, astronomy, and even large parts of biology. So if you're interested in how the interaction workings of a cell, you know, go on, or physiology, really, we're talking about the first science. You know, it boils down to physical laws. But this idea that everything boils back down event ultimately to physics, is what he was pushing back against. And so if you want to know where, you know, pizza or giraffes or religion or any of these things come from, how they came to be in the first place, you need a fundamentally different set of processes. Physics did not tell you a lot about that. For that you need to evolution. And so Graham Bell has a fairly provocative proposition, which is that everything in the universe can be understood via these two sciences together and nothing else. And so, you know, the working title for basically the entire writing process was the Second Science. We decided against putting that in the title of the book itself, but that's where the term comes from.

Gregory McNiff (6:00)

Nice. Just expanding on your answer there. You write towards the beginning of the book, quote, in the game of life, the first science establish the constraints, the second science sets the rules. Could you clarify or expand on that?

Mark Belland (6:14)

Certainly. So if I say that the second science explains how we end up with language or pizza or blue whales, I don't mean that physics is irrelevant to understanding those species, objects, phenomena, what have you. The laws of physics place important constraints on what is possible in every realm. And all of these things have physical manifestations. So the physical properties of the raw ingredients that go into pizza or any other type of thing you're making in your kitchen constrain the possible things you can produce with it. The shapes of a blue whale that are permissible are constrained by the physics of movement through water. That nature of language is constrained by the capacity of our brains, our physical capacity of our brains. And so the idea is that in evolution, physics and physical laws present constraints that obviously you cannot get around. But if you're interested in understanding how these things come to be and change over time, the real driver there is the second science, evolution.

Gregory McNiff (7:21)

Perfect. I think most people associate evolution candidly with one person, namely Charles Darwin. And what I found fascinating about your book is a. You suggest not only is evolution bigger than Darwin and biological evolution, but perhaps the relationship specifically with language might not be as causal as one might expect. And specifically here you suggest that, quote, proto evolutionary biologists relied on the development of language to make the case for evolution. Do you think, and forgive me if I'm wrong, I think you suggest, I'm sorry, maybe it was Gould, that Darwin may have cribbed evolution from economics. And that may have been said in jest. I feel like we would have been taught that. But maybe if you could just define the relationship between biological evolution and other sciences at the time in Darwin and how it's since evolved. I realize that's a big question.

Mark Belland (8:20)

Yeah, we can tackle it in, in parts, I suppose, but indeed, yes, Stephen Jay Gould is quoted as saying that Darwin may have cribbed the idea of natural section from economics I think the point is that the basic idea that, you know, variants are generated so people have different ideas about how you might go about producing or selling something. And then the ones that are ultimately, you know, there 10, 20, 30 years from now are the ones that passed through this filter of selection of some sort. You know, it worked, it made a business competitive. And so, you know, the core idea really is there. And whether or not, you know, Darwin, you know, got the idea from Adam Smith is debatable. But the core idea was clearly there in advance. You specifically mentioned language. And there's a great book by someone called Stephen Alter called Darwinism and the Linguistic Image where, know, it lays out the whole history of interaction between linguistics and evolutionary thinking. And this is outside of my own immediate area of expertise. So I found it fascinating, fascinating to learn about one of the controversial aspects of evolution as applied to biology was the idea that of common descent with modifications so that humans, you know, evolved from some primate and ancestor which was, you know, different, perhaps something like chimpanzees and ultimately if you trace it back to some sort of bacteria like thing. And so this idea of common descent was established fairly clearly in the 1700s for languages. When you look at, you know, all of their overlap and similarities to various degrees, you know, a very clear case was made that they have a common ancestor and they've diverged over time into these different languages. And yeah, so Darwin and other biologists with evolutionary developing evolutionary ideas in the 1800s sort of used that analogy to try to explain to people how biological evolution might work as well. And so, yes, this sort of flow of ideas from some of the social sciences into biology fascinated me because since the time after Darwin published the Origin of Species, flow of ideas has clearly been more in the other direction where people have taken the sort of Darwinian version of these ideas and applied them elsewhere. But there's a very long history of interaction and ideas that date, you know, to long before the 1800s that from which you can identify some of the key components of what we call evolutionary theory now.

Gregory McNiff (11:02)

And yeah, as I was mentioning, you do a nice job comparing biological evolution with language. And you do point out, I think prior to the mid-1800s, the analogies came from the social sciences into biology. So it's almost as if they were looking to the social sciences to describe biological evolution. You also basically suggest Darwinian evolution is a distraction when trying to understand the larger process of evolution. Could you talk about this Darwinian distraction?

Mark Belland (11:33)

Yeah, so there's a couple components to it. One which perhaps is less of an issue at the moment as it has been in the past, is that in the social sciences, you know, at times the word Darwin or Darwinian seems to immediately channel thoughts towards what people call social Darwinism. You know, the idea that, you know, Darwin's ideas lead us to conclude that it's only natural for some people to dominate or marginalize others, you know, especially based on race or gender or some such thing. And this is a patent misrepresented of evolutionary theory. And so that to me distracts from an entirely legitimate effort, an important effort, to build a more generalized evolutionary theory. The other aspect of it, which sort of maybe more surprising and more of an original contribution, original idea that I put forward in the book is that biologists themselves and people who are in favor of applying evolutionary theory more broadly, sort of almost like obsessed with Darwin to the point where it does become a distraction. So as an example, you know, Darwin himself knew nothing of how inheritance worked in biology. We knew nothing about genes. The DNA hadn't been sequenced until, you know, a century later. And yet what we now call neo Darwinian evolutionary biology includes all kinds of details about genetics, for example. And so when we think of applying evolutionary theory more broadly, people quickly are looking for analogs to genes. And people have put forward the meme as some little unit of culture, which is a bit like a gene. But generally speaking, those efforts end up in dead ends. And then people think, okay, the whole project is dead because that analogy didn't work. But we were able to work out the evolutionary process without even knowing what genes were. So why on earth would we need analogies for genes outside of biology anymore than we need, as I say in the book, an analogy for verb tenses in a genome. You just don't need it. Darwin also stressed the idea that new variants arise at random in biology. And so if they arise via non random processes in culture, which they clearly do, somehow that's not evolution. But regardless of where these new variants come, we're really talking about the same type of evolutionary process. So, you know, in short, arguments about whether cultural evolution is specifically Darwinian suggest a sort of false dichotomy between biological and cultural evolution, which I think distracts us from the goal of identifying a more unified set of processes that apply much more broadly.

Gregory McNiff (14:10)

Perfect. And I want to move to that because you write again in the first part of the book, you lay out your description of evolution and then you present a pretty detailed model called the evolutionary soundboard. And I want to get there, but just to state Your definition of evolution, you write, quote, in the realms of life and culture, everything evolves. And that would include man made technology or processes.

Mark Belland (14:35)

Most definitely. And actually, some of the easiest examples to explain or to use to illustrate this idea come from technology. If you start with any of the technologies around you, so your cell phone, whatever kind of vehicle used for transportation, a car, a bus, or the electricity that was used to warm up your morning coffee, and you trace them back in time, you'll find a long series of trial and error steps that get you from past to present. We tend to think that genius inventors like Steve Jobs had an instantaneous flash of insight and built the iPhone out of thin air. But if you really think of all the components in an iPhone, even the way that they're combined, all of them have precedence, you know, in the near past and then those in the more distant past. And all of them were developed via this fairly incremental process of trial and error. People, inventors, other people in labs trying out different, you know, types of glass to make the right screen and a touchscreen, different types of microprocessor, you know, gps, everything in there has a long evolutionary history. And if you really think about how that process works, it's very clear for technology as a evolutionary system. Yeah.

Gregory McNiff (15:57)

As I was reading your book and you make that thesis, my first thought was, are we talking about the scientific method here or are we just talking about normal progress over time? But you really drill down and give your specific definition of evolution and includes these three characteristics, variation, inheritance, and differential success. Could you maybe talk about each of those variables and how you view evolution in a larger context? Not biological, but again, really throughout life. As you said, both nature and man made how. It's sort of the overall theme for really our life.

Mark Belland (16:35)

Sure. So it's worth emphasizing that these, putting forward these three components of a general evolutionary process is not my own contribution. So many, many people have sort of put forward that as an idea just to make sure I don't falsely claim credit for that. But, but yeah, so, so if we start with variation, you know, nothing can ever change if there are multiple options, for example, for how a piece of technology might work. If you can't change it at all, if there can't be multiple options, you can't change it. So you need variation that includes, you know, multiple options for how a political institution might work or how, you know, an animal might locomote through space. So variation is, you know, the first key ingredient to allowing, you know, change over time in populations of any of these types of things. So if, when we also need a means for passing on those new options to future inventors, governments, fruit flies, whatever the case may be, if those were to be getting erased immediately and not being able to pass on, well, then you couldn't have the cumulative change over time in these populations. So that's where you need inheritance. And then finally, to produce anything that is adapted for a particular function, you know, a boat for sailing, a bacteria for metabolizing, you know, whatever it is that it does, we need for some variants to succeed and others for fail to fail. This is what economic economists call creative destruction. So this is where differential success comes in. And so when you put all those together and you repeat the cycle, you know that that's, that's where evolution comes from. And as we've seen, it occurs in a great many different realms.

Gregory McNiff (18:26)

Perfect. And I want to get to those examples you give because they're very illustrative of your evolutionary thinking. But specifically, what I believe is your contribution and sort of the first half of this book, is this notion of the evolutionary soundboard, which has eight dials to determine or evaluate evolution, again, both biologically as well as man made. Could you talk about this soundboard, this framework that you use to evaluate evolution?

Mark Belland (18:56)

Definitely. When scientists develop theory of any kind, they very often write down equations, or at the very least, they propose verbally cause and effect relationships that we could in principle write down as equations. This is an essential component of the science itself. But to understand the essence of what's happening, we don't need the formal math. And I think if you were to proceed with the formal math in a book for a general audience, you'd lose a lot of that audience. And so what I'm trying to do is capture the essence of how this works in what I call the evolutionary soundboard. So if we're interested in understanding how things change over time, maybe it's the lending practices of banks or the degree of antibiotic resistance in bacteria, There's a core set of things that you need to know. And I characterize each of those things as a dial, you know, like, like a volume knob on a stereo. And so we picture eight of them on a soundboard. Some of them characterize the nature of variation generation. You know, how fast does it happen? Are we generating new variants, you know, very quickly, many different ones in a given period of time, or much more slowly? How do those new variants differ in their characteristics from the old ones? Are they big differences? Are they little differences? There's some others that characterize the process of inheritance. You know, from how many other entities is inheritance happening and do they tend to be, you know, just your parents are also, you know, contemporaries. Some of them characterize the nature of selection. And then finally there's one for the amount of movement between semi independent systems. And so the key point in putting forward that evolutionary soundboard is that when we switch, for example, from characterizing the evolution of bacteria to the evolution of a financial system, we adjust the same set of knobs rather than acquiring a different set for one or, you know, a knob itself that toggles between, you know, biology and culture. All of them are driven by variation, generation, inheritance, differential success. And the one we haven't mentioned yet, which isn't a necessary component, but it's sort of ubiquitous out there in the real world, is movement between some semi independent parts of that system.

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Gregory McNiff (22:39)

Terms apply, right? I want to as you're walking the reader through this soundboard, this framework, you make some, I would say, commentary on surprising assumptions. And one of them is this idea that evolution happens at multiple levels. But we focus only on one level and make simplifying assumptions about the other levels. How do we decide what level of evolution to focus on? Is it based on a particular discipline that we're looking at, the size of evolution? Could you help us think about what we focus on and maybe where the simplifying assumptions come into play, what we.

Mark Belland (23:18)

Choose to focus on is an entirely practical decision. So if I'm a agronomist, one of the examples I have in the book has pea plants at the center of a bunch of options. And pea plants were chosen because they were one of the organisms that were used to work out how inheritance works in biological systems. So if I'm an agronomist, the level of evolution I might be interested in is the evolution of the nutritional content and the size and the number of, of these seeds or peas that are produced on pea plants. Whereas, you know, somebody else might be interested in how the practices of. Of agriculture, you know, how we work the soil, nutrient addition or not intercropping, this kind of thing, how those practices themselves evolved over time to produce cultural practices that, you know, maximize agricultural yield, for example. And so there can be any one of these levels that somebody is interested in. None of them are the other correct level to work at. It's a practical decision. What are we trying to understand in terms of how it evolved? And when we do that, whenever we choose a level to focus on, we have to make some simplifying assumptions about what happened at other levels. So let's say I'm interested in understanding how agricultural practices themselves have evolved over time. As far as farmers try different techniques, some work, some don't, and over time, those practices evolve. You know, I might assume that during that time, you know, the genetic properties of my pea plants or my wheat plants are staying the same. But of course, that's not literally true. They might change over. Over time as well, in which case maybe I'll couple a model of how the crops themselves evolve in the agriculture. But we can never include everything, and so simplifying assumptions are always necessary.

Gregory McNiff (25:14)

Another observation you offer is this idea. In the short term, we can have multiple indicators of success, but only one in the long term. What would be a sample use case where you have several notions of success in the short term, but only one in the long term?

Mark Belland (25:33)

Yeah, the biologists refer to success more specifically as fitness. And it's. And it's simultaneously a fairly simple concept and also one of the most confounding things to try to wrap your head around. Ultimately, you know, the key thing for assessing, you know, the fitness or the success of a given entity, whether it's an organism, a technology, a Business practice is how much of some future population traces its ancestry back to that given entity. But you know, how far ahead in the future do we go when we, when we measure fitness? There's no correct answer to that. You might get a different answer over the relative fitness of different kinds of cell phone in the next five years versus over the next 10 or over the next 50 years. And I give some examples in the book where they're sort of. Clearly, it's really difficult to come up with one easy answer. So if you want to predict the future of some economic market, one indicator of success might be a company's market share. If greater market share that predicts it's going to have a bigger impact in the future. But it might be their profitability. Maybe they're more profitable, but don't have as big a market share. I also like to use this example of slime molds, which are maybe less familiar to people, sort of fascinating. They spread around on surfaces a bit like a fungus. And it's only a single cell, but they have different nuclei. And so we can imagine that one type of slime mold both takes, takes up more space than another, which is a degree of success, but another one that takes up less space but actually has more genetic copies of the nucleus that have been produced inside, which is actually the typical measure of biological fitness. Eventually, if one of those types is literally gone and extinct, well, clearly now we can, you know, declare that one of them indeed have long term higher fitness than the other. But along the way, you know, we can definitely think of success in different ways and, you know, use different proxies to predict what might be happening in the near and in the more distant future.

Gregory McNiff (27:35)

Mark, just expanding on your definition of fitness later in the book, you write, it is the shapes describing traits and fitness differences that matter, not the underlying causes. What exactly do you mean there?

Mark Belland (27:48)

Yeah, so fitness has been very tricky in biology because on the surface it seems sort of circular. So fitness is about leaving more offspring. And to know who leaves more offspring, you measure fitness. And the circle was broken by, by five theoreticians and philosophers played a role here too, by considering fitness as a propensity to leave more offspring. Now, that may or may not be realized, that propensity to leave more offspring, depending on how important stochastic or random forces are. But it can be a challenge to implement. Now, the reason to focus on fitness differences rather than their causes is that the number of possible causes for why two organisms or two pieces of technology, or two alternative, you know, policy Options. You know, there's just, there's just an endless number of possible causes that those two things might have differential fitness. So the fitness of a bird might be influenced by the availability of different foods, or the presence of predators or climate, or the availability of nest sites. The success of a new type of car battery might be influenced by the types of vehicles that are currently in demand, cost of component materials, any number of things. The list could go on forever. But what's important for predicting an evolutionary trajectory is the nature of the ultimate fitness differences or the differential success of those entities and how they might depend on the current prevalence of those entities too. Now, I don't want to say that causes aren't important things to study. So if you really want to know, if that's what you want to know, you want to know what are the causes of this difference, well then absolutely people need to study that. So I don't want to say that it's not important to know them. But when you're trying to build a theory that applies more broadly, what really becomes important is the nature of those differences, not precisely what underlies them.

Gregory McNiff (29:43)

Yeah, that was a really nuanced distinction. I want to move to another term or relationship that you also address in the book, namely the relationship between random and non random or directed variation evolution. Could you talk about that both in biological and more man made technologies or systems?

Mark Belland (30:03)

Yes. So it's a really important distinction at one level. But when we're thinking about a generalized evolutionary process, all of a sudden it doesn't seem quite as important. And in addition, while we tend to think of biological variation as being generated entirely randomly, it turns out that that's not always true. And when we think of cultural variation being generated non randomly, people directing it, that's not entirely true either. And so there's this fascinating overlap. One of the most fascinating things that I learned about during the writing of the book was how CRISPR works. So CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats. It doesn't really matter. It's been in the news because this technology can allow people to edit genomes with incredible precision and relative ease. But they've basically co opted something from bacteria. And so in bacteria, here's how it works. A virus attacks a bacterial cell. There's machinery inside the bacteria that can take a portion of that virus's DNA and incorporate it into its own genome. And so when a similar or the same virus comes back later, it has a little tag in its library that allows it to recognize it and it can go chop up that virus. So in short, a mutation, so a change in the genome of this bacteria was triggered by a challenge, the attack from a virus in the face of which that mutation itself is beneficial. So this is decidedly non random. That mutation did not happen at random in that genome and it does not fit comfortably at all in what we might think of as the neo Darwinian paradigm of evolution. Perhaps it's a relatively rare occurrence in the scheme of things, of genetic evolution, but its presence as a very real phenomenon tells us that not all biological variants arise randomly in culture. It's very clear that we have non random generation of variants. When someone proposes a change to how to make that car battery, they're not coming up with ideas purely at random. But in the history of, you know, technological advances, there's a surprising number of discoveries that did actually happen by pure random chance. One of the most famous is the discovery of antibiotics by Alexander Fleming based on a contaminated plate. And there's, and there's a lot more out like out there, like that. The bottom line is that new variants can be generated in non random or random ways both in culture and biology. So there's really no black and white distinction to make. This is where the dials and the soundboard come in. So for all of these things we can sort of tune them higher or lower, regardless of what kind of system we're looking at.

Gregory McNiff (32:50)

I'm curious, do you think directed variation plays a larger role or is more prevalent in man made rather than biological systems?

Mark Belland (32:59)

Yeah, I think that's a pretty safe bet. I mean when we come up with new variants for, you know, as we were saying, whether it's a government policy, a business practice, you know, technological advancement, you know, we sort of think a bit before we propose something and that we, that we hope or think is going to improve the design, not that's going to, you know, cause it to be worse, but if you trace it all the way back to just sort of pure brainstorming, we generate a whole lot of ideas that in fact are horrible. You know, I mean, you know, we hope, we tend not to put them into practice. If we, if we learn that, you know, that make, you know, your car, your phone, whatever, function less well than it did before. So there's definitely a difference there. I think, you know, in cultural systems, variation generation is more directed, but I think there's a lot more randomness there perhaps than we tend to think.

Gregory McNiff (33:59)

Yeah, I just my own limited understanding of mathematics, for example, the discovery of vectors and matrices and non Euclidean geometry were both instrumental with Einstein and space time. But I think the developers, those mathematicians weren't aware, remotely aware of the applications for the tools. So it does feel sometimes like the tools are developed and then later on down the road the uses develop. Just want to again move on to another topic. Variation. Basically another assumption you make or observation you make that I thought was surprising was sometimes more variation is suboptimal. I, you know, more can be less and use the example of bird beaks, for example, and that you also suggest altering design is more likely to do more harm than good. Could you talk about why that is?

Mark Belland (34:49)

I. So it depends on the situation. But if we imagine that we've already, you know, natural selection, let's say, has honed the shape of a bird beak to be, you know, very well adapted to local conditions. And then we generate, you know, we generate variation, that is, you know, we have birds that have somewhat smaller or somewhat larger beaks. The ones with the larger, smaller beaks are going to tend to perform less well in that environment than those that have that single, you know, best beak size. And so in that sense there are cases in which, you know, jittering that variation can in fact, you know, reduce the average fitness of the individuals in that population. And you can imagine the same thing for a piece of technology. You know, I use the example of Japanese swords in the book where the, you know, the tempering of the metal, they have this extremely exquisite, exquisite set of processes and if you deviate it from a bit, you're going to get a knife or a sword that just doesn't work as well. So adding variation there is really not going to not very unlikely to lead to an improvement. The sort of flip side is that if we never add variation, if new things are never tried, well then you can't evolve and potentially find that better solution. And so, uh, it's actually thought that the rate of variation generation, so the mutation rate in, in biology or even the rate at which, you know, people generate new ideas can sometimes or, or try new things can actually be close to optimized to balancing both this cost of variation, which is, you know, the odds of that most likely you're going to do something worse. But the potential benefit of once in a while discovering something that even is much better.

Gregory McNiff (36:44)

Mark, another key theme you address is feedback. Can you discuss the role of positive and negative feedback in evolution?

Mark Belland (36:51)

Definitely. The way that evolution can feedback on itself is extremely important. So we can think of some types of technology where success amplifies success. So familiar Examples are things like word processing software or social media platforms. So an initial success might be due to consumer satisfaction with the functionality of Microsoft Word or if you're old enough to remember WordPerfect or something, you know. But each of these kinds of things, you know, a word processing software or a social, you know, network application, become more attractive the more people use them, because we want to be able to share files and we want to connect with many other people. In the world of biology, we have simpler kinds of phenomena. For example, where various tree species can modify the soil beneath them in ways that favor the seedlings and the saplings of those same species. And so it's a bit of a runaway process that can lead to a rapid change in a system and one that can sort of kind of get stuck, if you will, in a state that might be hard to bump it out of. Those are. Those are positive feedbacks, and they're also negative feedbacks, which tend to keep things more stable. So, at least in principle, free markets involve negative feedbacks that push back against, either over or under supply of particular goods and services. Ecosystems have species that have different needs for resources, for example. So if one of them gets extremely abundant, it's going to be pushing down the quantity of resources that that one species needs, which will actually give an advantage to the less common ones whose resources haven't been so depleted. So there's sort of the systematic tendency for something that's become less prevalent to increase and something that's become dominant to decrease, sort of bringing things back to a more kind of stable situation. And then their combination can help explain some of these, you know, what we call critical transitions or rapid changes in a system that are difficult to reverse.

Gregory McNiff (39:04)

Moving on to selection. How do you define it and what are its two main forms?

Mark Belland (39:10)

So in the book the simplest, just sort of most straightforward definition I found was from a philosopher named Eugene Earnshaw White, who called it the systematic advantage of a type. And it seemed too simple at first, but it really pretty much captures the essence of it. And so if we were to elaborate a bit, you know, we can say that if, if the differential success of. Of entities, you know, birds with different beak sizes, tele, you know, cell phones with different functionalities. If that differential success depends systematically on their characteristics, that's what matters. It's because the beak was bigger that the bird did well. For example, we call that selection. And in terms of the forms of selection, you know, sometimes it just sort of pushes in one direction. You know, it just sort of favors a certain variant out there. So at a certain moment in time, you know, call it direction or constant selection, you know, favored the iPhone over the BlackBerry and it became dominant in the cell phone market. But then sometimes fitness depends on the prevalence itself. So that's what we were talking about with the feedback. So positive feedbacks imply that success is a function of past success, and so current prevalence and negative feedbacks imply that past success will actually diminish future success. So those are the key forms that selection can take.

Gregory McNiff (40:37)

In your evolutionary soundboard, you have eight dials, and the first seven are subsets of variation, inheritance, and differential success. The eight dial, you introduce this concept of movement. Could you talk about what is.

Mark Belland (40:53)

Yeah. So movement is not often described as a necessary feature of an evolutionary system. So I could have, you know, an entirely contained system where variation is generated, there's differential success and inheritance, and we can have evolution. But out there in the real world, whether we're talking about, you know, human cultures with different languages and cultures, or any, almost any species that you can think of out there in nature has some sort of spatial structure to where it lives. You know, there are sugar maple trees that grow in southern Quebec, but also in Georgia. And, you know, their dynamics are coupled to some degree, but, you know, also semi independent. And the movement of, you know, entities or ideas, species, you know, what have you between those places can have a big impact on the, the evolutionary trajectories that we see in those different places. And, you know, at the moment, you know, the movement of people and they bring along species, their ideas, their cultures across the earth is really a dominant force of evolutionary change at the moment at all these levels.

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Gregory McNiff (43:34)

Third part of the book, the second science in action, you go through a number of examples or use cases again, both biological and more man made. I just want to talk about a few of those. Obviously the, I would say the topic these days that has gotten top of mind in public opinion is AI. And I think you have a really nice description of AI as it evolved from the first science to the second science. Could you talk about AI and the framework of your evolutionary soundboard?

Mark Belland (44:05)

Yeah, thanks. I spent quite a bit of time trying to figure out how the algorithms underlying AI work. You know, talking to some experts who understand it most definitely better than I do. And ultimately it is quite fascinating how we're essentially giving computers instructions on how to implement evolution. You know, before programmers did things like that, you know, a typical way to build a program, computer program would be to give the computer instructions on exactly what to do when it gets different commands. So in your word processor you click file and open and you know, a very predictable, very specific thing happens every time. But if we take, you know, a more complex task. So one of the ones that I'm still quite amazed by is the, is face recognition. If you're in Google Photos or I'm sure there's lots of other applications that do this. And I click, you know, my, my son and I say find all the pictures of him. I just can't believe how good it is at finding, you know, from weird angles and fuzzy at different ages and so on. And so the way that computer programmers ultimately succeeded in developing those algorithms was to basically feed data into the computer. So let's say, let's say millions of photos where you've already labeled them as saying which ones are of the same people. So maybe I've got a hundred photos of a given person and I've got, you know, thousands of different people. That photo is represented digitally in the computer. And you know, you basically write an algorithm that has tells the computer to try some, you know, random way of classifying these photos based on some characteristics of that digital file. And then there are algorithms that help it figure out what small change to this algorithm would do a slightly better job at classifying those images correctly. And step by step, it basically goes through an evolutionary process that ultimately finds an excellent, if not globally optimal solution for identifying which of those photos belong to which image of the, of the names in the database. And you know, no human could really ever come up with that algorithm on their own. And not only that, we actually now have difficulty even understanding once it's done, even what the, what the algorithm has, has done to, to, to accomplish the task. There's a whole field of study now dedicated to understanding the evolutionary products produced by computers. So there's a whole new field of evolutionary science retracing what happened in the development of these tools.

Gregory McNiff (46:50)

Yeah, I know we talked offline about Leslie Orgel's maxim or insight. You actually, I think quoted twice in the book, one beginning and one on this AI chapter that effectively evolution is cleverer than you. And oddly enough, that seems very much the case with AI. It really has evolved. And what I liked about your section is you summarized it so well at the end that AI started under the first science, which I, I guess would be more of a rules based or just algorithmic approach, but evolved by embracing the second science with neural networks. And you do a really nice job of just talking about that. Conceptually. I want to move on to genetic engineering because here again, I think some of us might associate more of a hard science. But you talk about why is genetic engineering closer to the second science than the first science? How is evolution, or what role does evolution play in genetic engineering?

Mark Belland (47:44)

Well, I just start by saying that evolutionary science can be a hard science as well. But I do, I do, I do think I know what you mean.

Gregory McNiff (47:50)

I give the physicists any more reason to feel proud than they do. There was a conference at the Santa Fe Institute where I think Warren Sumner set up and stood up and said, you physicists all have a Tarzan complex. So anything that brings them down a bit, I'm all in favor of.

Mark Belland (48:06)

That's funny, I've actually seen somewhere, I believe it was a physicist writing who had heard or read about, you know, the universality of evolutionary processes. And the very fact that perhaps a process was discovered that applied so broadly was now considered to be physics. Because if it is, if it is extremely general and applies broadly, well, it must be physics, which is almost laughable. But anyway, so if we get back to genetic engineering on one Hand, it feels like you're going into the organism and you're simply changing something physically, and that's the end of the story. And in a sense, certainly people do create mutations. Essentially there's a genome and we're going to change it somehow. But really, genetic engineering involves the generation of new variants. And when people have done this, especially using some of the older techniques, and even when they use newer techniques, it's quite unpredictable what exactly is going to happen. So, you know, you, you might have, have enough knowledge to know that when I make this genetic change, this plant should be able to now produce an enzyme it didn't produce before, but maybe that monkey's with some other metabolic pathway that, that leads to all of those plants dying. And so essentially, genetic engineering is one way of producing variants in a directed way, which is characterized in the evolutionary soundboard by there's both a rate of variation generation and a dial for the amount and the direction in which new variants differ from the old ones. But a great many products of genetic engineering fail, just as a great many natural mutations do not succeed either.

Gregory McNiff (49:56)

Just expanding on that, you also talk about adaptive evolution. What is adaptive evolution? And can it always, or does it always lead to optimal outcomes?

Mark Belland (50:06)

So adaptive evolution is when the evolutionary process enhances the fit of the characteristics of an entity, piece of technology, an organism, to some function. And whether or not it leads to an optimal outcome obviously depends on what we mean by an optimum. So I think, you know, often we can imagine that there is some better combination of components or, you know, some organism that has a, you know, vestigial leg bone in a whale, and it would just be more optimal if it didn't. But there are these historical constraints and so on that might prevent evolution from producing or maintaining what is truly optimal in a global sense. So there might always be a better design that hasn't been discovered yet. Some of these positive feedbacks we talked about could cause the system to be stuck in a state that it sort of is hard to get out of, even though somewhere else there's, you know, a state with higher fitness, let's say. And we always have to be careful. You know, we're people talking about this, and when we say optimal, some people might immediately read, well, like, optimal for me, optimal for all of society. You know, in an evolutionary perspective, you know, success means that for whatever reason, there is more likely to be greater prevalence of this given type or representation of something in the future than there is now not whether or not people are happy about it.

Gregory McNiff (51:47)

In chapter eight, you talk about massive or critical transitions in nature and man made evolution. You give examples of coral reefs, the financial systems, obviously. But one I really found enlightening was the type of trees in the, I believe a national park you and your family visit. And you talk about how, I think it's broadleaf and maple trees, how they could dominate each section. Could you talk about how we have massive or critical or large transitions in evolutionary systems?

Mark Belland (52:21)

Yeah, as we've discussed, this is a mixture of the positive and the negative feedbacks. And the reason that these are of such importance and interest to people is because of their potential unpredictability and the difficulty of reversing them. So if we think about the example that you just brought up, and part of this story is a little bit speculative, we've sort of worked out some of the pieces, but, you know, if you stick with me anyway, as we go up this mountainside, you know, we begin in a forest dominated by sugar maple trees, so broadleaf deciduous trees. Leaves fall in autumn and so on. And then at a fairly abrupt spot halfway up the mountain, you switch to domination by coniferous trees, these native leaf trees that hold their leaves on all winter. And, you know, the environment itself, the climate, the soil conditions seem to change relatively gradually. So, you know, why the abrupt switch like that? And one of the ideas is that, you know, in those sort of intermediate conditions, so when it's warmer at low elevation, you know, the sugar maple is just favored kind of no matter what. Same thing. At the top, the coniferous trees are favored, but somewhere in the middle, it can really depend on the, you know, which of these types of trees became most abundant. So if the conifers, let's say, get past some threshold of abundance because of a disturbance or cutting or what have you, they actually change the soil in a way that makes it more difficult for some of these deciduous trees to get established. And so you can have a system where you can end up in one state dominance by coniferous trees or another state dominance by the deciduous trees. You know, they're purely, depending on which one happened via random chance to get sort of, let's say over a 50, 51 side or the other of a 50, 50 threshold. You know, there's. The coral reef case is also fascinating where, you know, if you, if you just sort of increase fishing pressure, you know, you can have a coral reef that sort of looks fairly similar as it did before you sort of started fishing. And then at some point these fish get to such low abundances that they can no longer keep down these big algae that can actually grow over the corals and cause the corals to die because corals need light to survive. And so you can have a very rapid transition from a coral dominated system to an algae dominated system. And the trick is that it's super hard to push it back because if you just stop fishing, well, it's kind of too late because there's nowhere for the fish to hide anymore. If the corals are no longer there, the algae is blocking the light, they can't necessarily control the algae when they're very large. And then in social systems, economic collapses or meltdowns that might have been triggered by relaxed lending practices and then a small economic downturn, you get this big spiral that you can't get out of just by tightening lending practices, for example. You need much more activist interventions to try to push in another direction. Yeah.

Gregory McNiff (55:20)

I think you conclude at some point in this chapter that these internal dynamics can lead to major and rapid changes. I guess once they exceed this tipping point or 50% threshold, is that.

Mark Belland (55:32)

Yeah, it's not always 50%, you know, it depends on the details of what's happening under the hood, so to speak. But definitely there's a positive feedback over a certain threshold, just keeps pushing it in that direction and then it can sort of be stuck, stuck somewhere, you know, not indefinitely. But the point is that the same thing that pushed it, the same pressure that pushed it there, you can't just push the equal amount in the other direction and get it out. You have to push much harder to get it over a different kind of hump.

Gregory McNiff (56:02)

Yeah, that was interesting, particularly the financial examples. I want to move. You talk about geographical and cultural diversity. How important is that in evolution? And does diversity or geographical diversity always have positive effects?

Mark Belland (56:17)

So diversity is in some senses a synonym for variation. So for evolution to work in the first place, we need some variation, so we need some diversity. Genetic diversity in a population, you know, diversity of products in a, in a, in a particular kind of market. You know, what I, what I talk about in that section of the book is actually some of the, also some of the short term term consequences of there being diversity. And both in ecological and in social systems. There's been a lot of work asking, for example, does an ecosystem, you know, let's say a forest, does it have greater overall productivity? So you know, how much wood is produced, if there's a diversity of species relative to a situation, if it's a monoculture. And often the answer is yes, in part because those trees are using resources in Complementary ways such that collectively they can grow more than they would if they were growing always next to other individuals of the same species. And in some kinds of work settings, scientific teams having a diversity of perspectives can lead to new insights, more efficient solutions to problems than in teams where there's very low diversity. It's not such a simple situation because it depends on, you know, diversity of what. So if I have, you know, a team of people trying to solve a problem, you know, in sustainable agricultural, and I have people with, you know, perspective from business and a perspective from agriculture and a perspective from ecology, you know, that diversity is almost certainly going to be helpful. But if I have people of a diversity of, you know, heights, you know, that's irrelevant whether a person is short or tall. So you need a match between what kind of traits we're talking about and what kind of outcome we're talking about. But many, many studies do show a benefit of diversity in social ecological systems. Some can show the opposite, because you can have diversity leading to internal conflict if, you know, different types of organisms or different types of people actually have difficulty working together. You know, the weight of evidence is in favor of the benefits, but it's. It's not quite simple. Just. It's not so simple. Just to say that diversity always has benefits.

Gregory McNiff (58:40)

That's interesting. I guess the corollary is, is homogenization, or I think you used the term McDonald's vacation as American. I recognize that. Is that ever a good thing?

Mark Belland (58:51)

So in the book, I try as hard as I can to steer clear of value judgments. This is good. It's bad. My goal is understanding how, for example, if we start moving things across the world so the people call globalization McDonald'sification or McDonaldization because there's McDonald's restaurants in all corners of the globe. Is it a good thing? It obviously depends on the perspective. But, you know, one thing we can say is that it's not a universal sort of diversity killer of febrile. So we tend to bemoan the fact that we have the spread of certain kinds of cultural cultures, culture across the globe, and it's quashing local cultures. And while that's definitely, you know, part of what's happening there when, you know, as I explain in the book, when a McDonald's restaurant sets up in a new place, they actually often, you know, kind of incorporate some of the local culture, producing products that simply wouldn't exist anywhere on earth had they not shown up and made this kind of hybrid, you know, Terry Tommy sandwich in Japan, for example. So I'm not going to comment on whether it's a good thing or a bad thing, just that there's some really interesting outcomes that some of which are not intuitive.

Gregory McNiff (60:06)

Well, I doubt you'll be promoting McDonald's for them in the near term, but that's very nice to hear that you have a nuanced, thoughtful approach to their expansion. You put forward a pretty interesting proposal at the end, namely structuring the educational system to teach the first and second sciences more effectively. Could you talk a little bit more how you would do that, I think at the university level and why you think students should be exposed to this approach?

Mark Belland (60:37)

Yeah, so the proposal, which, I mean, no time soon am I going to start some institute where we try to encourage all universities to undergo a major restructuring. So let's think of it as a thought experiment where instead of having faculties of natural sciences and social sciences, which in one form or another is a very common, let's call it a high level structure in universities and in funding agencies, certainly in Canada, and I'm quite sure in the United States too, if you're doing social science research, you apply to one place, natural science, which includes physics, chemistry, biology, you apply somewhere else. And so that's an important distinction, human affairs versus natural science. But if we really think that there's sort of a fundamental threshold crossed when we start talking about systems that require evolutionary processes to really understand, perhaps there's just a different way to split these things up. And so as like an ecologist and evolutionary biologist. If I were in a faculty of evolutionary sciences, I'd end up, you know, my sister departments might be sociology and economics instead of physics and chemistry. And there's always a cost to these things. You know, I have colleagues in those discipline, chemistry and physics, that I, that I like crossing paths with. Perhaps there would actually even be more synergies if I was now, you know, neighbors are matched with a different set of people. Given that there's this common set of underlying processes, the evolutionary processes, whether anybody's ever gonna try that, who knows? But I definitely think that this idea that evolution is a process that applies to any system where you have variable entities that have inheritance and differential success over time is absolutely something that can be appreciated, let's say by high school students. And so next week I'm giving a talk to a group called Teaching and Evolution and Society, maybe ties, I can't remember what the S is for, but anyway, and the idea is going to be here's a way of thinking about evolution that I think is Understandable by a great many people. So if science really involves these two big pillars, I don't see any barrier to teaching that to people from the outset of their scientific education.

Gregory McNiff (63:06)

Yeah, at a minimum, you make a very strong case for cross disciplinary interaction and certainly some freshman level course with your book on understanding the relationship between the first and second sciences better. Last question. And with the obvious caveat that you are not a cosmologist. And what I'm about to ask you is, I would say speculative. There is a one theory in cosmology called cosmological natural selection in which black holes create universes and the universe is somewhat, I'm sorry, yeah, the universe is fine tuned for this notion of black holes creating universes. Throughout this entire conversation, we've talked about evolution on planet Earth. Do you think there's any sense or any possibility that evolution could be one of the larger forces? And if somebody said, hey, I think black holes are creating universes and the universe is fine tuned for this process, how would you respond?

Mark Belland (64:07)

I have to say that sounds super cool and that I'm very much intrigued, unqualified to evaluate the likelihood that they're right or wrong. Uh, you know, when, when I, I read a couple of, you know, popularized articles about this exact topic from which I felt like I, I grasped the essentials. You know, universes get created and some of these fundamental constants in physics have to be just so for our universe to neither collapse in on itself or, or, or, or, or expand, essentially blow up. Uh, and so, you know, the universes that are going to stick around are the ones that have some of these physical constants just like ours. So if in fact there's some evolutionary process happening at that level, I mean, that would be pretty cool. Whether or not we could ever learn that with any confidence, I have no idea. But it would certainly suggest that we might need to then flip the labels and call evolutionary science the first one and physics the second.

Gregory McNiff (65:07)

We'd love to see the reaction from the physicists on that. On that note, that concludes our interview. The book is Everything why Evolution Explains More Than We Think, From Proteins to Politics by Mark Belland. Mark, thank you so much for your time in writing such a thought provoking and enjoyable book.

Mark Belland (65:25)

It was a pleasure. Thank you so much.

Gregory McNiff (65:27)

Thank you.

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