 Take two and welcome back To be on networks the evolution of living systems So let's talk about truth about time Perspectival truth how Can scientific theories be true or wrong in this sort of me? Let's quickly recall the sort of basic complex structure of the universe that's Occurring in these sort of levels of organization and Not just the fundamental levels of organization are real but all of them. They're populated by different entities That are interesting to study and as you get further up here into the realm of biology and the social sciences The separation of these levels the organization of the world becomes much less clean cut and Simple than in those very small realms The physics is studying here and chemistry we've talked about this sort of idea the very end of last lecture that We need some sort of criteria To decide what what sort of insight what entity out there is real What insight is trustworthy? We need in other words a swimsuit rights bill when sat to secure the real Reliability of our conceptual structures Otherwise what we're saying becomes just opinion becomes relative And it's important that there cannot be any privileged level of organization Entities and all kinds of levels of organizations are real so The fundamental criterion for trustworthiness is not that you're at the bottom of the hierarchy like in reductionism But it's what when said calls robustness the robustness of an idea of a theory of an entity of a process and this whole idea of tackling a complex Universe with many levels is motivated By this simple insight that there should be more ways of interacting with a spouse than with a quark you have Entities that are at your own level if you have a partner that's the sort of entity you interact With probably the most If you have friends, why shouldn't you consider them real? I'm sitting at a table here. I consider this table real and not just a collection of atoms I'm in my house Again, it's much more important for me To deal with this entity house than with the particles That are completely abstract and irrelevant in my level of organization The world we see we live in we respond to and act upon is too important too central to our way of being to be dismissed And this is what reductionism is based on it says all these things are not real What's real is just the underlying sort of chemistry the underlying physics and nothing else And we have to get rid of this idea because it's absurd and it's it's it's counter our own Daily experience. So instead of adjusting the world to some theory Let's adjust it to our real-world experiences and come out with a view of the world that fits that experience better But how do we decide then what is true and what is not? What is trustworthy knowledge and what is not and so when that comes up with this criterion called robustness and It's quite simple. I'll give you the definition as it is written in his book Wim said says things are robust if they are accessible and that can be detectable Measurable drivable definable producible or anything like that in a variety of and that's very important independent ways Well, what does that mean? I'll give you a bunch of examples throughout the course But let's just think about it. So robustness is something that may apply Not just the things or ideas but the properties relations proportions Propositions so theories are made of about propositions models levels and perspectives themselves So you can have a robust perspective Or that's not robust. I have to admit the genetic perspective from biology is very robust while some of this stuff I'm going to tell you During this lecture is not it's an exploration But every perspective starts out being fragile and only grows into a robust sort of idea later on The second point to make is robustness does not require experimental measurement It's much more broadly defined. It can rely on observation derivation production So you have to produce a certain phenomenon to make it robust And and this is very very important and it comes back to our first lecture this criterion does not give certainty because nothing does There are no magic bullets in science or anywhere else for that matter So we're staying inside this fallible list view of knowledge. No empirical knowledge is ever a hundred percent certain Okay, so Can be realists and fallible lists? That's no problem There's that goes on to argue and we've encountered this already That limited beings like us cannot hope to have a complete explanation of reality. It's irrational to think That some monkeys descended from a tree can understand the universe in its entirety just like that And he calls this is a beautiful name the myth of Laplacian omniscience Laplace of course was a French mathematician and he imagined his demon That would be able to measure The state of the universe at a given moment all the elements of the universe at a given moment And would then be able to predict not only the future but also post stick the entire past of The universe it was one of the strongest arguments for what we call determinism and What we've said is saying here is this omniscient being Basically, if you think about it, it must be God it cannot be a real being that is limited through its by its capacities and its its context So it doesn't make sense to even dream this dream of Laplacian omniscience that we have this view from nowhere in Gary's term or the God side Instead our scientific theories serve to answer a given question or address a given problem So we do science because we have a problem to solve Think about the matter of prioritizing to there is so much to know about the world How do we make priorities? How do we choose our questions and the problems we want to work on? This is not a rational or scientific process itself. It comes from within From the things we care about. How do we choose? further so this this sort of local problem-solving The complexity scientists famous complexity scientists Herbert Simon will have more to say about him in the future in this series Calls is satisfying so our theories. They don't have to be a hundred percent True certain Cover everything they just have to solve the problem That we have So this is satisfying Therefore our scientific perspectives are not like algorithms that always yield the correct solution So you cannot just follow a model or a law and apply it everywhere But they're more like heuristics heuristics are algorithms that mode work most of the time But not all of the time and they have to know when they work and when they don't The advantage of those heuristics is they take a lot less time to calculate than true algorithms that give you the correct solution a hundred percent of the time They require much less effort, but they only work under specific circumstances So all the models all the so-called laws we have in science are built to solve specific problems and they are perspectives in that sense When you said comes up with which is my favorite chapter title in his book That chapter title is called false models as means to truer theories So he says all of the models the models that that we're using are like tools will come back to that Okay, they're always false. There's a famous quote by statistician George box He says all models are false, but some of them are useful. So it's very similar here Our models are false and they fail in certain ways and the cool thing is that what we do is We learn from those failures This is a feature of our way of knowing not above that all our models are false is a good thing Remember we've had this already a couple of times at the very beginning I said when I talked about Nassim Talib's view of antifragility in the world so antifragile systems are the ones that learn from errors They get smarter over time and this is a metaphor for evolution evolution is The most fundamental process that is antifragile Okay, learning from errors. So we need to make errors Learn if we have a perfect theory that never fails. We don't learn anything Okay Also, this is argument by Michael Polanyi. If you remember that our unique perspective This sort of tacit knowledge that we have that we cannot even talk about that is a unique connection We have the universe needs a good thing that every one of us has a different connection And we should take advantage of this instead of erasing these differences between our perspectives, which is is madness Okay, but this is what we're doing especially in biology It's very sort of a uniform. It's the North Korea of academia. It's sort of, you know If you don't have a genetic Mechanism in your paper the reviewer will send it back and say you don't have a mechanism You know explanations are only allowed this one level. It's crazy Especially in a field that is so rich with so many different But I digress So the point here is that we learn from making errors again Nietzsche said that Two lectures back remember he said you have to have the sort of guts to fail Okay, and then to know to own your failure if you do that if you remember that quote Then you learn something so systematic bias in the errors our theories produce contains information about how they fail okay, and This is really cool. When said calls this the metabolism error So we process this error like our metabolism processes our food analyzing metabolizing these errors therefore is our guide to new knowledge So we need to take a step back and see where Thus the system fail what we do nowadays in this crazy academic system that we Is we need to produce produce produce produce more in the tradition of our fields because the people who review our grants Who review our papers are in our field and if we don't fit in boom, you're dead and you don't get published You don't get your grant. I can tell you that and there's also Quantitative evidence for this if you try to go beyond the current perspective You get shot down. This is fatal especially in a field where we have basically so much that is not yet known So much of the complexity that we don't understand Completely crazy. So we need to stop with that and we need to take a step back Look at our perspectives Try to see where they fail. But how do we do that? It's often when you're stuck when you're just looking for your keys under the cone of light And it's difficult to see the limitations of the claims based on your own perspective But again, so the problem is we still have to come back to this question of truth, right? Robustness is okay So we have many different independent ways I mean, let's let's look at this whole thing the question of truth and in Perspectivism from a different end The truth matters. So the reason I'm very critical of sort of some ways of doing science here, but Having scientific knowledge and understanding why it works better than other kinds of knowledge in many ways is crucial today I've been advertising doubt radical doubt doubt everything Tried to probe the limitations of your perspective to get out of it. On the other hand Truth matters a lot. So here's a book that I started reading lately By Immaculata de Meadow, Martin and Kristen Intiman the fight against that. It's about how the public relates To science and how there's a lot of anti-science sentiment anti-intellectual sentiment out in the general population This is a real problem. Okay science is our guide To better the truer knowledge about the world Not true So how so so we have to sort of walk Balance is a balancing act, right? So we have to walk this tight line between Not falling into hubris and saying, okay, I know for fat This is what's happening and also not falling into relativism and saying, okay Scientific knowledge is just another way of knowing I can also go Take a trip on acid and have this Magnificent revelation and come back and say, okay, this is how the world is this doesn't work. Why doesn't it work? So we had wingsets robustness criteria We can also say to come back To an argument that we had before It can be very pragmatic and say just like William James American pragmatist philosopher of the 19th century He says the true is only the expedient in the way of our thinking just as the right is only the expedient in the way of our Behaving so to behave in the right way. It's just what you ought to do so Truth is just what scientific knowledge ought to be about That doesn't have really it doesn't it's expedient it works. How does it work? It doesn't really have a wrong gear You remember I gave you this for two lectures back Here he's writing But a method it's good to the extent that it tends to select hypotheses with desirable characteristics such as agreement with data Or wide applicability over hypotheses that lack these characteristics. Okay, so but then it works somehow, right? But but in what way does it work? We have to get a little better than this If you want to if we want to talk about scientific truth and how you can have different perspectives at the same time get in some way at reality in this way and Here I want to introduce the first female philosopher. I'm showing this. I'm sorry That there's so many men here. The humanities are usually better in their gender ratio, but but philosophy is peculiar There are a lot less female philosophers and philosophers and this is a problem. So if you know female or other Philosophers from for minorities, for example, or whatever that contribute important, you know Work to the topics can present it here. Please write to me. I'm always looking for those but Mikaela Massimi is hard to overlook. She's great. She has an ERC grant philosophers also get such grants And that is on the topic of Perspectival realism and its relation to truth and she's done some really interesting work on this She's at the University of Edinburgh And she says just like in the spirit of James William James before she says Realism is normative. Okay, a scientist. We have to get things right. This is what we ought to do Is what we should be doing And so we have to think about what it is What sort of perspective what would come with content in our perspectives? What sort of claims we make are getting things right? We can't just say this is my point of view So I'll try to do that during the rest of the semester. I'm not just trying to say hey This is how things are and say this is my point of view and this is why Justifies So to some of her spectable realism again in a nutshell very briefly and I like this way of putting it So we can assess here states of affairs about the world are perspective independent While our scientific knowledge claims about these states are perspective dependent Okay, so there again we sort of accept that there's a world out there It's independent of our minds But the knowledge claims that we can make about it are always dependent on perspective And so I see me that it defines Perspectival truth as tracking perspective independent states of affairs But the conditions that make the claim true Depend on your perspective. What does that mean? I want to give you an example So let's take an example from my own Discipline evolutionary developmental biology. It's one of the foundational principles of evolutionary developmental biology It's been assigned Attributed to a bunch of famous people. We don't quite know where it came from Richard Goldschmidt Walter Garstang Gavin the bear and Conrad Howe wanting to Depending on who you read. I will give it to you in a forum published by Porter in 1989 if you're interested in this topic read Ron Amitson's wonderful book About the changing role in the of the Ember and evolutionary thought will come back to this book over and over again And the quote says in order to achieve a modification in adult form evolution must modify the embryological processes responsible for that Therefore an understanding of evolution requires an understanding of development. Okay, he's closed This is a claim a scientific claim and the funny thing is It is fundamental and true for The discipline of you are the perspective of evolutionary developmental biology But it is false and actually irrelevant to population genetics because that perspective is based on the assumption that The outcome isn't really biased by the developmental process. So you can look just at distribution of of variabilities and phenotypes and Of course population distributions of genetic factors you can correlate those two without knowing anything about development So is development Important for evolution or is it not? How can we we track? The truth value of this statement Well, the thing is I mean the first perspective is not difficult because it Well, it is it's sort of assuming so this This claim is not based on Is it based on evidence or is it just saying look it has to modify? Embryological processes. I don't know I could modify metabolism behavior. I don't know. I'm not sure so Okay, but so it's an assumption It's based on assumptions in both cases Okay, so actually comparing Those two you find out that population genetics doesn't have to deal with development because it just Define it away. It didn't actually solve the problem saying okay. I can prove that development is important It just says again, I assume So basically the first perspective Says I assume that development is important the second perspective says I assume it's not important. Okay, so none of those Give us Sort of proof that it's important or not. That's one problem But the other problem is that we can actually we can reconstruct why it's not important in population genetics It's because of the aim of population genetics being different. So population genetics starts at the genetic and the phenotypic variability in a population and It tries to explain how selection works on that Variability that is a given and is supposed to be sort of random and not very much influenced by what's going on in development Okay, so there's a rationalization why? Development is not important, but there's no sort of scientific substance to this argument At all. This is what I was calling Bullshit argument before to come back to bullshit argument. So it's not very good argument instead We can look at those different perspectives and say, okay We have to look at this in a different way and this is what we're going to do We're going to have a different take on why development is important for evolution in this lecture series, for example So back to to Miguel and Massimine and truth in perspective She makes a really useful distinction and I want to end this lecture on this distinction She says there are two sort of context in which scientific claims have to be considered one Is the context of use, okay? So development by definition is Important in evolutionary development by all it right so in this context of use you can also justify Like the completeness principle does why it's important But if you stay within that one perspective, it doesn't help you make it a sort of any sort of statement It's not sufficient to establish that this is a perspective For that you have to consider a context of assessment So basically knowledge claims are sensitive to the context of use in other perspectives like like population genetics development may not be important and so Knowledge claims that can be assessed across perspectives are getting things right, okay, and this is a case This example we weren't quite sure we may be getting things right, but it's the first step we can compare the role of development in both perspectives and we can now look at the perspective Sort of from the outside we can step outside and get a god-side view but we can get a little bit of that by comparing perspectives and Learning from the differences between perspectives and that's very similar but not quite the same as robustness or Learning from a metabolism of errors They compliment each other I think I haven't really thought about this deeply enough, but I think Robustness sort of jibes with this so it would say that the more perspectives you have that use a certain Process entity the more robust it is as a piece of knowledge okay, but robustness doesn't say much about cross perspective comparison while the Learning from errors is one thing, but here you learn from differences between perspectives slightly So what we need to proceed with this lecture is we need a disruptive strategy So the way we are usually framing problems in biology is Okay, but it's within this one perspective of genetic rate reduction reduction is overwhelming There's some signs and systems biology or tissue mechanics that we're stepping out of this paradigm But there's still sort of margin, so we're still stuck in this one perspective And so we need to step out of that perspective to disrupt our framing to get to a new frame So we can have a diversity of perspectives again in biology and compare them and learn more about Much many many more questions in biology than we can learn from this one Reductionist genetic perspective below so what we're going to do next in the next module We're going to take Everything apart Literally everything apart and look at it as a process. We're going to look at the world and biology organisms and evolution, but also the process of doing science in this sort of dynamic perspective and That will hopefully demonstrate how you first of all can get a different perspective and how you can use it in a comparative way To learn more about the limitations of your own perspective Junin again Very soon when we talk about process thinking. Thanks for watching. See you next time