I am quite amazed at the fact that the stuff that I write is almost always and universally seen as controversial, because the way I see it, it’s the common sense interpretation of the available fact pool. I’m not making stuff up, I’m not making low-probability leaps of faith and logic. The fact that I’m seen as controversial means you’re all smoking the wrong shit, I would say.
If anything, I would say that my ability to see what the facts actually are, and what follows from them is controversial only in the sense that everybody else lags behind because they have some kind of emotional or intellectual resistance to accepting the available facts; it’s not that I’m making wild guesses or working with fringe theories. I’m working with the same dataset everybody else can access. I’m not even that smart; if you IQ-test the statistical sample of some demanding science-based university, a percentage of students would match my raw cognitive power, or exceed it. So, if I’m not using an alternative dataset, and I don’t possess alien brainpower, how is it that I’m routinely ahead of the “main stream” to the point where people look at me as if I have two heads when I make a statement, and then months, years or decades later it’s “fuck, how did he know that”. It’s actually very simple. I don’t care what people think. I don’t care what they believe. I don’t care what they consider to be “main stream”. I don’t care if something will be accepted as true by others. I basically don’t care, I just take in the widest available pool of data, I do several attempts at normalizing the dataset (for instance, when thinking about bone shapes of hominid fossils, I ignore those obviously suffering from arthritis; when analysing the political picture, I eliminate obvious wishful thinking), and basically let the data speak to me with as little coloration as I can manage. I’m letting the raw data speak to me, so to say, and tell me about the world it lives in. Then I try to imagine the world the data lives in, and I try to predict stuff, and as I gather more data, I check whether it confirms or rejects my predictions, and so I iteratively refine my simulation until it fits all the available facts, and I allow for paradoxes; things don’t have to be neatly arranged and they don’t have to make sense. I don’t reject a Platipus just because it appears to make no sense. I don’t reject evidence of an extinction event just because it’s a one-off thing. I don’t reject the possibility of rare events just because they don’t happen in anyone’s living memory that introduces the kind of recency bias that allows people to build cities and farms in close proximity of active volcanos that just happened not to erupt in recent memory, or participate in economic bubble hysterias just because the last bubble-popping disaster was decades ago.
Basically, I’m doing science the way people used to do it, before it got so formal and rigid. I’m gathering data, using it to make a model, and then I test the predictive ability of the model with new data, and I either revise it or abandon it, depending on whether it works or not. And I don’t care whether it works or not; I don’t care about my reputation, or other people’s opinion, or about sunk investment in the existing model. I just want to find out how stuff works, and as far as my understanding improves, I don’t care whether I was right or wrong, as long as I get better understanding in the next iteration.
Let me demonstrate this with a few examples.
There used to be a controversy about whether the Neanderthals could speak, because in order to conclude that they could speak there should be a fossil finding that confirms both sufficient tongue mobility (hyoid bone) and brain capability (Broca’s region). My logic was that the common ancestor (Turkana boy) of both modern man and the Neanderthal man had a developed Broca’s region and this had to be present in the evolutionary successors, basically assuming that stuff that didn’t change much across the evolutionary tree was developed early on and then inherited in the perfected form. What follows from this logic is that speech was developed quite early on in the hominid evolutionary tree and it is actually the driving force behind the later brain development, because once you have speech, you can communicate complex ideas, and therefore more complex ideas can actually be an evolutionary advantage, and lack of complex ideas can be an evolutionary pressure. Basically, you can communicate complex things, such as storing food for the winter season, or migrating to where the salmon will be, or ambushing a herd of bisons in order to drive them off a cliff, or tell an educational story to the next generation in order to expand the level of inherited knowledge compared to the baseline of personal experience. Basically, my model of hominid brain development assumes that speech was developed quite early on, and that it created a positive feedback loop that both motivated and rewarded brain development.
Another example is the relationship between climate and the K-T extinction. I started by looking a graph of all known great extinctions in the history of life on Earth:
The shape of the graph surprised me, because I expected the mass extinctions to be independent, Poisson-distributed events, something akin to the background radiation. What I found useful is the ability to look at the graph and ignore all the scientific labelling that concentrates only on a few spikes, naming them the P-T extinction, K-T extinction etc. In fact, the extinctions follow a pattern of an elevated baseline of extinctions, followed by a spike, which means that some evolutionary pressure was in the environment for quite a bit of time, usually millions of years, and then either the pressures exceeded the survivability threshold for a huge number of species and caused a supermassive extinction all at once, or a discrete event aggravated the situation to the same effect. I also had to ignore human ways of perceiving time, because humans are a very short-lived species of even more short-lived beings, and our perception of time and change is inherently flawed. If something doesn’t change for 10 KY, we think it’s forever. If something stays the same for longer than our species has been around, we think it was designed in this perfect and static form by God. This is the motivation behind thinking of great extinctions in terms of discrete events – a supernova explosion, a giant asteroid strike, a supervolcanic eruption and so on. We don’t think in terms of continental drift that takes hundreds of millions of years to change the configuration of continents relative to sea currents, and when a radically fatal configuration is established, it takes 60-70 MY for the effect to manifest itself fully. We also don’t connect the events intuitively across such vast chasms of time, observing the long-term trends and ignoring the very visible spikes, but that’s exactly what I did with the data. I made an assumption that is opposite to every other analysis I’ve seen, and said “what if the spikes don’t actually matter?”, because the dinosaurs were in a process of mass extinction due to the slow process of reduction of global temperature, increased aridity and increase in seasonal climate variances. By “normalizing the data” I mean ignoring the biggest elephants in the room in order to see whatever is left when the distractions are removed, and then I saw that the climate has been cooling for more than 65MY, and a few MY ago it reached the point so extreme it started throwing the planet into ice ages, alternating between glacial and interglacial cycles, where the interesting fact is that it conforms to the Milankovich’s cycles, but only within Pleistocene, only after something cooled down so much it started throwing the climate off balance, and I decided that the amount of buffers in the atmosphere must have gone below the critical level, which allows for the extremes; most likely, the atmospheric CO2 was extracted into the oceans due to greater solubility of the gas in cold water, which put the climate into its death-throes, with the anticipated stable condition of a global glaciation that might last until the continental drift gradually changes the position of continents relative to the sea currents away from the current configuration that promotes cooling. My analysis is that the anthropogenic increase in CO2 emission actually helped stabilize the situation a bit, increasing the buffer levels to a more long-term sustainable value, but the long-term prognosis is unchanged. The problem with human thinking is that, due to our short life span, we assume that the Earth was perfect “the way we found it”, while in fact it was in a configuration that is fatal for life in the long-term, because of the cooling trend, and that we are in the last, terminal phase of this transition, and this terminal phase is called “Pleistocene”, the phase in which even the extremely small variances in orbital parameters can introduce an ice age, or pull the planet out of it. The next phase, I could call it Cryocene (in order not to repeat the “Cryogenian” label), would take place when the buffer levels in the atmosphere fall below the amount necessary for the orbital variances to thaw the planet out of the glacial phase, instead allowing for the progressive increase in glaciation until it reaches the “snowball Earth” phase again. How long until then? It’s hard to tell, but my intuitive interpretation of the graph says that the error of 5MY is acceptable. Translated to human language, the next ice age might be the one we never get out of, or we might have 5MY until that point, because the industrial CO2 emissions introduced so much unexpected buffer it’s hard to anticipate the consequences, to the point where it might delay the onset of the new ice age by several MY, or it might actually destabilize the system, create an unexpected Dansgaard-Oeschger event and pull us into an ice-age sooner. The margin of “I don’t know” is the size of 5MY, which is double the size of Pleistocene. One of the instability-modes that my model predicts is that the plants are normally restricted by the scarcity factors, such as CO2 or Phosphorus in the environment, and when you remove the restrictions, their growth suddenly expands exponentially to the point where they suck up and “bury” all those factors from the environment, basically turning atmospheric CO2 into coal deposits. This means that human-induced CO2 spike can produce a plant-induced CO2 drop which can, in some kind of a perfect storm of conditions, trigger a glaciation. However, the number of unknowns is so vast that my simulation has no predictive abilities within the stated margin of uncertainty. What is quite certain is that my model of a long-term cooling trend, driven by continental distribution that allows for a Coriolis-powered circumantarctic sea current, essentially “liquid cooling” the planet more efficiently than the Sun can warm it up, and promoting gradual buffer-extraction that destabilizes the global climate, is valid, and long-term predictive. The “problem” is that the process started more than 65MY ago, and that the Chicxulub asteroid produced a very visible extinction-spike that masked the actual problem. Or, we could say that human psychological attraction to discrete spikes is the actual problem. I think it has something to do with predatory genetics, where a lion or some other animal is perceived as a significant event, and grass growing is perceived as background noise that is ignored. Well, in my attempt to become less blinded by human biases, I started ignoring the lions and zebras and paying attention to the grass. This is why my analyses start by ignoring the things “everybody knows”, and going back to the raw data, normalizing it against distractions, and letting it tell its own story.
This article is too long already so I’ll stop here, although I could cite a dozen or so additional examples. In any case, you can see the outlines of my method – absorb the raw data, ignore biases and distractions, trust the known-to-be-valid mechanisms, such as thermodynamics, inertia and so on.
But, that’s also how I model politics – it’s not that much different. See who has better debt-to-GDP ratio, who has foreign trade sufficit, who has cheaper energy and more of it, who has better access to the basic natural resources, who is less sensitive to isolation from the global economic and political systems, who has more robust and reliable basic technological systems, and who has population that has a healthier attitude towards reality, and then model interactions and time-graphs. When you do that, not only do my assessments no longer look like some fringe conspiracy theory, but you start asking yourself why is nobody else following such common-sensical principles?
Good question, I guess.