Believe Science? What Science Do You Want to Believe? Empathy in the Time of the Coronavirus (X)

One thing about being a bona-fide Space Alien is that, an any given time, I can hop on the Close-to-Light spaceship and see what’s going to happen in the future. If I could fix my FTL drive, I’d just get off this rock. But this is actually less fun than it may seem, because what it also means is I can’t get those years back (time dilation only works in the forward direction, which is why the original Planet of the Apes movie was such a bummer!) and it ages my brain.

Thus it is with the coronavirus, which is declining pretty dramatically across Europe, and in most of the early infected places in the U.S., following a similar pattern. Yep — functionally off-the-beaten track places in the U.S. will continue to see rises, but you’ve heard it here first. I’m giving solid odds that there will be no vaunted Second Wave, and while COVID-19 will function at some endemic level in our population, it will fade as a political driver. If I’m wrong, well, I’ll be shown to be wrong pretty soon — the mass demonstrations across the world for Black Lives Matter certainly had potential for super-spreader status, considering that combo of crowds and jail cells.

What we’re also going to start to see in the next couple of weeks is a crescendo in conflict between scientists, who enlightened folks are telling us we should believe. “Believe science!” is the battle cry, and as a scientist myself, I am supportive. Believing science, even at its most rudimentary, is more appealing than believing astrology, and for those that follow this blog for any length of time know, I am not into magical thinking of any stripe, other than understanding it as narrative scaffolding for how we live our lives. For that, if you need to believe “don’t mess up the environment because the Mountain God will whoop on you!” I’m down. But for using Magical Thinking problem-solving complex scenarios in the near-term, eh, not so much.

The problem is that I’ve also spent an entire career in the Science Sausage Factory, and after that, well, like any sausage factory, you’d be a little less sanguine about consuming the prospect. Lots of stuff goes into “science” from all the different knowledge structures, and I’ve written about this here, among other places. Short version, empirical measurement-based science does great when you can draw a hard boundary around the problem, and set up controlled experiments. That fits perfectly within the context of the Legalistic/Authority-Driven Relatively Rigid Hierarchy that such science functions under. When you match methods (complicated algorithmic processes for data collection and transformation) with the social structure (a closed hierarchy, which scientists are always beating on you to recognize as the only source of knowing!) you’re creating knowledge in, you can be sure the results are as coherent as they can be. And if you’re collecting data from the real world, there is a natural validity/grounding that also occurs. Short version — the data is reliably collected, the problem is closed, the scientists are trained, and IMPORTANTLY — the phenomena has already happened.

And here is the thing. For those circumstances, none of the higher thought processes of empathy are really required. You aren’t required to link outside of discipline, no agency for the researcher collecting the data on a small scale is required, no judgment calls, no synthesis with other fields, or lay audiences, and heaven forbid any reflection. That’s not going to make it through peer review. We even have a name for this in the Sausage-Making business — “turning the crank.” Which is exactly what you do when you make real sausages.

I think it might be useful to lay out that last paragraph as a quick list with the different Knowledge Structure Levels here so you can see how useful some of that work is. Kinda “it’s my blog, and I’ll cry if I want to!” NOTE — this is for closed systems!!!

  1. No link outside of discipline (driven by Authority value set)
  2. Collect that data properly and follow the rules! (driven by the Legalistic/Absolutistic value set)
  3. No agency for the researcher (no Performance/Goal-Based thinking values)
  4. No synthesis with lay folks or other disciplines (no Communitarian value set!)
  5. No reflection (Yellow- Systemic thinking value set!)

Short version — you’re trying to know something that you can know, within the context of the structural memetic system you’ve set up. Perfect!

But here’s the rub. It gives you poor predictive ability if the exact same closed system, albeit with different parameters, isn’t what you’re trying to figure out the next time. Which is EXACTLY what COVID-19 is. And to make matters worse, if you’re an epidemiologist, you’re stuck in an open system. And THAT open system is continually changing. Big time. To the point where even history (like the Spanish Flu) is a very poor guide to how these things work. Last time I checked, there were no Boeing 777s criss-crossing the globe in 1918. Short version — you’re stuck in a closed social system (they don’t call it the Ivory Tower for nothing!) that’s poorly equipped to give you projective ability for larger, open system problems.

But scientists, organized in those Legalistic (at best)/Authority-driven (typical) hierarchies DO manage to converge to the truth. But it takes a while. It usually happens, in happier times, through a process of subdivision micro-specialization, and endless bickering (some folks call it ‘peer review’,) which is how those hierarchies create knowledge. The ladder of subdivision goes down, and down (think about those particle physicists, blasting apart atoms with higher and higher energy!) until finally synergy is reached through overlap. We lock smaller and smaller hunks of stuff inside colliders until our need for seemingly infinite precision yields a God Particle. Or something.

For those, though, that can’t blast, they create models using mathematics. I wrote a longish Twitter thread on this — how scientists create models, which are what they do when faced with a real world that can’t be captured and measured. Now we start seeing problems. Scientists get trapped outside of their Ivory Tower, uh, I mean v-Meme, uh, I mean social structure. You get the picture. And as I’ve alluded before, some do it better than other. Now their discipline requires metacognition — knowing what they don’t know — which is what their social structure absolutely sucks at. For those that doubt me (almost always academics) stand up in your next faculty meeting and watch what happens to your status when you tell your colleagues you don’t know. Not pretty. (Yes, I’ve gained tons of insight into social systems in faculty meetings!)

How those models come into existence now matters a lot. They, too, are based on given Knowledge Structures, and dependent on the social structure that creates them, they map. There’s a ton to write on this, but the short version is the list below:

  1. Deterministic models based on fundamental principles. These are what we use, for example, to figure out asteroids running into the Earth, or stress concentrations in airplane wings. Same meta-class. We know the physics well, and can model the physics using various numerical techniques, and simulate on a computer
  2. Semi-deterministic models based on parametric estimation (usually from some data set out there that ostensibly describes the phenomenon.) Basically all the epidemiological models fall into this category. There’s some physical assumption about how the virus is spread and how fast (this is the whole R0 thing you hear about) and then people take data sets, and estimate parameters. Various people receive chops for fads, like using Machine Learning (Artificial Intelligence has to be more intelligent!) and the circus continues.
  3. Monte-Carlo simulations based on running probabilistically generated trials for various scenarios. These are often done for looking at performance efficacy of a given system — I used to do this back when I helped hunt submarines and pioneer new radar detectors.

One of the interesting things about my career as a bona-fide aerospace engineer is that I’ve used all three of these things. The first involved the basic research I did for my Ph.D. The second was an extension I used of my Ph.D. work that led me into attenuating helicopter noise using signal processing techniques called wavelets. And the final was my Master’s degree work on signal detection theory (radar and sonar) that gave me a toolkit to combine all these things into looking at wavefront modeling of forest fires.

It was arguably the first that taught me the limits of the other two. As one of the folks working some 35 years ago to understand how chaotic dynamics worked, I got taught early on the power of metacognition — knowing what you didn’t know, and realizing what you couldn’t likely know. How? You’d run a given simulation one way, and get an answer. Then you’d change one thing one teensy-tiny bit, and the answer that would come out would be totally different. This phenomenon (short version for the scientifically adept — sensitivity to initial conditions) was poorly understood at the time. For me, wanting to finish my Ph.D., it became a stern master in my fundamental ignorance. If anything, it taught me that Yellow v-Meme Reflection thing. Alone in the lab, running simulations (remember, this was 35 years ago, and we didn’t have iPhones that could do this stuff in their sleep!) I was forced to ponder my deep inadequacy in completing the work I had promised my advisor, who was (and is) an awesome human whom I did not want to disappoint.


Fast forward to understanding how the street-fighters, uh, I mean ‘respectable scientists’ are lining up regarding the COVID-19 predictions, and how, and importantly when, the pandemic is supposed to end. On the one side you have the standard immunological/epidemiological established community (“Believe science!”) crowd who originally, with their models (some mix of all 3 archetypes, but heavily weighted toward #2) lined up and broadcast 10x-100x greater fatalities/spread/whatever than actually occurred.

As time has passed, their models have gotten more precise as far as predicting things like death totals. No question. But that’s also mostly because when you’re doing Type 2 modeling, you’re really working on principles of interpolation, which ALSO really means you’re forcing the model to be more like a closed system. So of course, more data would lead to better paradigmatic estimation. The curve would fit tighter.

But you’d still be stuck in your Legalistic social structure, and your v-Meme. Which would mean two big things.

  1. Because life as an Authoritarian/Legalist means that everything ought to be perfectly predictable, your sense of consequentiality would still be shit. You wouldn’t be able to predict what things might come along to mess up your model. And you wouldn’t be particularly happy to see that happen, either, since the accuracy of your model isn’t tied to the Guiding Principles (still evolving) of the pandemic. It would be tied to increasingly accurate schemes of parametric estimation. And here’s the rub — you’d be super-comfortable with that, since you’d be satisfying that “way your brain is programmed Legalistic v-Meme itch” in how your psyche works. “Extensive testing is proving me right!” you’d holler! Well, yeah — because extensive testing is finally feeding your parametric estimations so your model doesn’t look totally awful.
  2. You would be openly hostile to anyone from any other discipline giving insight, especially at a higher level, on why your model is wrong. You’d argue that you’re the REAL scientist, operating only on what the known information (that awful COVID-19 data) is. And because you’re stuck inside a social structure that whomps you on the head if you say you don’t know, you’re far less likely to stand up and entertain that there are “Invisibilia” — factors that you just don’t understand — are screwing up the highly refined model you’ve just staked your reputation on. Trust me that all those Ph.D. students who helped you build it aren’t gonna utter a peep. They want to graduate.

#2 is the reason far too many scientists are stuck on what I call Intellectual Flatland. And the COVID-19 pandemic is a great exemplar of how that works. From a virus perspective, without a vaccine (which is a tool for building uniform herd immunity, FWIW) we are stuck with naturally generated “herd immunity” like it or not. And because our Legalistically organized hierarchical epidemiologists are stuck with the brains they have, there’s only one way that can manifest (that’s that whole dichotomous thinking thing that comes out of the social structure) and that’s with measurable antibody counts. Either you’ve got ’em or you don’t — and if you don’t, because all we’ve got is hindsight, and the awful data, well, the virus must not have continued to spread, because those seropositive antibody counts have to be up about 60-70% of the population in order to really have it.

Never mind the observable phenomenon that even in places like Lombardy, Italy, or New York City, places so obviously saturated with COVID-19, are displaying antibody counts around 20-25% of the population (that are detectable.) There aren’t any other options, according to this community, and that’s that. Because they’re the experts. Never mind the fact that they were wrong last week. T-cell immunity? What’s being now called ‘Denatured/Barriered, or Innate/Cleared’ (from Ivor Cummins podcast) which basically means you make so much snot than the virus can’t get through, or the second, your immune system is so bad-ass the virus doesn’t even cause a ripple — doesn’t exist. As well as the obvious hypothesis testing ‘False Negative/Miss’ flaw. Mistakes, even small ones? They’ve got those under control.

And here’s the critical thing — we are SO conditioned to believing that independent subject matter must all be processed through different parts of our brains, there simply can’t be overlap — we can’t believe that our brains would use the same circuits, using domain-independent knowledge in similar ways. We CAN’T believe that whole ‘As we relate, so we think’ thing this blog harps on constantly. THAT messes us up with understanding this pandemic. Because, well, it’s not just my personal affectation. It’s true.

How does this manifest itself with our Legalistic/Authoritarian epidemiology friends, who now have major reputation stakes riding on being right? They’re going to start insisting on actions that flow naturally out of the v-Meme were ALSO major factors in stopping the spread of the virus.

What actions map out of the Legalistic/Absolutistic v-Memes? First off, they have to be low-agency, meaning individuals only help the situation if they listen to the boss, or follow the rules. Individual health or immune response really can’t come into play. And because these folks know what they’re doing, those might be the reason the virus finally dies out. Certainly not factors that they don’t understand. And true to the dominance of Externally Defined Relationships in the social structure, only Externally Defined Factors can really make a dent in this virus.

How this manifests is fascinating. Here are some examples.

  1. The coronavirus is receding because increased sunlight in the Northern Hemisphere is KILLING more coronavirus. (External) This as opposed to people’s individual immune systems are being strengthened by Vitamin D levels increasing because of their own ability to strengthen their immune systems in the summer.
  2. Lockdowns HAD to work, as opposed to social distancing. There’s no more powerful command and control than having everyone stay in their houses. Being courteous and not coughing in other people’s faces is NOT something the Value Set trusts you to do.
  3. Asymptomatic cases are perilous (Authorities operate out of a limbic, fear-based perspective,) because they can unwittingly contaminate other people with the virus and cause them to die (empathetic, agency-based relational contact is not something the social system tolerates at all!) as opposed to entertaining what I’ve promoted — a dose-dependent understanding of spread that says appropriately managed asymptomaticity is actually what is building immunity across a population.

Of course, there is nuance to all of these, and the larger picture is assembled by understanding the larger guiding principles, and weaving together a tapestry of how the virus functions across the entire planet. Lockdowns may indeed have been effective, as I have argued, linked with asymptomatic spread, because it shunned people who were coughing (high dosage spreaders) from going out for any reason. Sunshine may indeed kill more coronavirus — remember, we started this pandemic with the belief that the virus was an invincible killer, virus- wise (something that also would lock into the fear-based modes of Authority-driven systems) — but we’re still not around to the point of disaggregating populations and understanding the very real effects on dramatically more impacted communities, like African-Americans. We can’t even bring ourselves to address the effect of our obvious metabolic/immune system crisis and how that might affect spread.

But enough about the epidemiologists. What is really interesting is that there are other heavy-hitting scientists that are wading in the fray. The main point here is that these individuals are OUTSIDE the In-Group (the epidemiologists) analyzing this situation.

I’ve written about Michael Levitt here, a Nobel Prizewinning scientist, combing through the data with a much greater humility than the mainstream epidemiology community. His background is in structural biology — far afield, really, from immunology — and the epidemiologists have been screaming for him to stay in his lane. His secret weapon, though is really just a greater metacognition. Yep — he is a thorough scientist, and relies on empiricism. But he also questions modes and mechanisms, and is searching for alternate mechanisms to explain immunity. It’s arguably easier to do this when one has a Nobel Prize hanging around one’s neck. No one’s going to call him stupid, and he’s not got the crazy tag that a couple of other Nobel Prizewinners have.

And because this problem is big enough, you also have more far-afield folks wading into the battle. Chief among these most recently is Karl Friston, a famous German neuroscientist, who, with his research team, is apparently doing a more first-principles modeling effort. Not surprisingly, his viewpoints and mine about the modeling efforts (and eventual outcomes) align. This dude is even using my Asymptomaticity as Dark Matter tagline! His quote from the Guardian piece:

How do the models you use differ from the conventional ones epidemiologists rely on to advise governments in this pandemic?
Conventional models essentially fit curves to historical data and then extrapolate those curves into the future. They look at the surface of the phenomenon – the observable part, or data. Our approach, which borrows from physics and in particular the work of Richard Feynman, goes under the bonnet. It attempts to capture the mathematical structure of the phenomenon – in this case, the pandemic – and to understand the causes of what is observed. Since we don’t know all the causes, we have to infer them. But that inference, and implicit uncertainty, is built into the models. That’s why we call them generative models, because they contain everything you need to know to generate the data. As more data comes in, you adjust your beliefs about the causes, until your model simulates the data as accurately and as simply as possible.

I’m absolutely NOT accusing this dude of ripping me off (you can check the dates — I didn’t rip him off either — but my blog has had these concepts out for months.) Rather, there is a convergence of value set that would cause us to generate similar insights, from similar value sets. This is what I talk about in this piece about the value of values. They serve as container sets for generation of similar, more complex information.

What IS interesting is that we now have the scene set for a major structural memetic war. Two camps, set firmly in their representative v-Memes, three v-Memes/value sets apart, at least tool-wise, aren’t going to reconcile any time soon. All three have a large (un?)healthy dose of Authority-driven Red Value Set in them — As a side note, I’ve written multiple Tweets to both Levitt and Bergstrom at the University of Washington, who could fairly be tagged as representing the mainstream epidemiological community. They don’t write back, though they will respond to snipers who are obvious trolls. Classic Authoritarian v-Meme – someone like me is, in their eyes, an unimportant authority. And Friston is utterly unreachable.

What this means — especially when you have Authority-driven personas, using toolkits from different value sets (Guiding Principles/Reflective for Levitt, and Legalistic/Absolutistic-Algorithmic processing for the University of Washington crowd) is you’re going to have both structural memetic conflict as well as a good old fashioned donnybrook.

One thing I can guarantee. Both (or all three) will argue ‘Science!” They will all claim the Holy Quest for Absolute Truth as their driver. But the reality is that it will be the v-Memes that will be doing the talking. We may start out with the Marquess of Queensberry rules. But trust me — this one’s gonna degenerate into Fight Club.

Bring popcorn.

11 thoughts on “Believe Science? What Science Do You Want to Believe? Empathy in the Time of the Coronavirus (X)

  1. I only skimmed your post this time. And I won’t give you a long comment as I sometimes do. I just wanted to note that I’ll be curious to see the uncontrolled experiment going on right now. We went from post-lockdown reopening to a worldwide mass protest movement. That is one way to get everything going again. It will be interesting to see how it develops.

    But other than protests, it does seem most people are still reluctant to fully re-enter normal economic activity. Many restaurants around here are still refusing to do indoor eating. The university is still closed down as well. Yet the protests are going full-throttle with thousands in the streets every night for a week now, which is impressive for a relatively small town with many of the students still away.

    What might make this most interesting of all is the demographics involved. Think about it. Who were those in the population most likely to be infected and die from COVID-19? Minorities and the poor. This demographic precisely describes a large part of the protesters. I thought about that when I saw all the people crowded into George Floyd’s funeral service, many of the attendees without masks.

    We’ll find out soon how much of a spike there will be in infections. But I’m still not sure what that might be able to tell us about a possible second wave in the fall and winter. There isn’t much we can do about it at this point. It’s a wait and see situation. I’m pretty sure, even if the second wave was worse, we’re not going to do a second large-scale lockdown. That cat is out of the bag.

    Most people have seemingly already forgotten about COVID-19. I had a coworker who was very worried about pandemic and about coworkers not wearing masks. Then the protests hit and she told me that she no longer thought about COVID-19 at all. She has become obsessed with and panicked about protest violence instead. It’s amusing.

    Liked by 1 person

  2. So I totally agree how some scientists act as gatekeepers to the truth (science), and how they revel in the prestige that exclusivity brings them. However if you put yourself in their shoes for a minute, with the universe demanding ever more certainty with the models and everything, I would find it hard to add a hundred asterisks that might affect my prediction, and if I increase my confidence interval people would just flock to other scientists who apparently figured it out and are smarter.

    Perhaps its because the public overestimates what science can do, or the scientists promised too much to begin with.

    Like

      1. So what you’re saying is that whatever the (authoritarian) epidemiologists come up with is only the truth from their perspective, not accounting for the complexities of the pandemic because of their closed circuit thinking.

        Like

      2. Pretty much. I think we’re getting there. How any one individual thinks, of course, is dependent on a host of factors, though the memetics are far more dominant than people realize — because, inside an organization, that’s what we practice. So you might be a reasonable communitarian on the outside, but once inside the walls of the academy, become pretty authoritarian. I myself have to guard against that. Here’s a piece on the whole enchilada. https://empathy.guru/2016/05/19/the-theory-of-everything-and-multi-scale-analysis/

        Like

  3. comments:

    1. these ‘science wars’ over COVID models from my fairly limited sample are already in full effect. I basically look at a few ‘high level sources’ (ie mathematical models i come across semi-randomly on physics preprint arxiv, in science web groups i’m in, etc… –and many of the better known groups like Friston’s, ones at JHU, Oxford, U Wash (funded by Gates, NIH, etc. )

    since i’m totally unqualified to judge any of the empirical science, and have only a passing knowledge of the theory, l only look at math models that look ‘interesting’ or ‘aesthetic’, and related ones like international comparisons of how countries have fared based on their use of lockdowns, etc.

    my view is basically nobody really knows and there are papers coming out each day which claim to have the answers. (I saw one very complex (impenetrable ) one from an Oxford-stanford-harvard, etc group which seemed to claim that the only thing that works is closing schools.

    one thing I will say is none of these groups attempt to present their results data in a way that i imagine few people outside their research group or ‘cult’ can understand. (the most recent paper for example does;’t have any ‘simple graph’ comparing countries COVID death rates with their policies.

    also despite my admitted lack of math ability, i sort of flagged that paper with its first equation—which was an assumption of ‘statistical independence’.

    since there are all these protests now, thissounds to me like assuming being a certain ‘race’ and having a certain ‘income’ and ‘home location’ are ‘statistically indpendent.)

    2. . I view these models and the pandemic as just interesting like history. Something i might trym to write a very simple model of. (i’ve been sort of reading biography of einstein recently–so it might be like that—ancient history exdcept with some math.)

    Since we have all the protests and some riots around here i view those the same way (i used to go to these but i’m non aligned now. Might be a nice modeling excercize. I’m aquainted with at least one of the ‘antifas’ who was in the news ( who struck me when i met her as a very upscale ‘radical anarchist’ –some call these people ‘protest hoppers’ –they go to protests the way others go to various ski resorts. the (not very many) black people i know around here don’t go to protests–they are the kind of people who get shot by the police if they don’t shoot each other.

    Maybe ‘we’ or ‘they will win or maybe not. The russian revoklution and 1968 riots had sort of mixed results—same with 1776 one.

    I sometimes wish all these science and street warriors would take it elsewhere. I can live without being famous for some paper or liberator–can’t compete anyway. unfortunately there are no bystanders in this world — you are either in the front of the bus or the back. .

    3. I’ve seen some of Friston’s stuff before —he actually use some of the formalism which derives from nionequilibrium statistical mechanics etc though he sort of adopts it to ‘machine learning’ (ie something he calls something like ‘causal graphs’. That seems to be a new twist–or one i hadn’t seen before (or thought of exactly). )

    The SIR models i view as a more complex variant of the lotka-volterra equations. One of Feynman’s students in the 1960’s wrote those basically in the way Friston does, except Friston takes it a step further and uses quantum field theory formalism. I saw a PhD thesis on population genetics paper from maybe 10-20 years ago that did this –rewrote alot of classical population genetics as a quantum field theory.
    ‘free energy’ is a basic concept i have to try to find in my memory when i see it, but friston’s connection to information reduction requires further memory search. i think its really just ajnalogous to a ball rolling down a hill.

    As noted i look at these from an ‘aesthetic’ perspective. Alot of these ideas you can sort of get with little rigorous knowledge—–but not science—thats alot of work and requires both background and likely some iq. and motivation—-i’m not interested in creating a work of ‘art’ (ie scientific paper or result) unless its relevant.

    (The paper linked to in the Friston Guardian article actually references economist P Davidson on ’emotions ‘ who is known for his (flawed) discussions of ergodic theory; and XM Wang with the hardcore math.
    Most of the equations in Friston’s paper i find unrecongizable—the ‘master equation’ as he writes it is barely recognizable–sort of like a long lost relative. my brother once asked me why i didnt recongize him on the street. that was because i was younger and smarter —its best to move fast so you dont have time to regognize anything. if you do, then you’ll be snagged.

    4. I sort of dislike the ‘spiral dynamics’ or similar qualitative or conceptual approaches (very common in ‘systems’ and ‘management’ science, and psychology. While hard science goes too deep into ‘theorem-proof-data-statistical analyses’ the qualitative ones go too much into handwaving, metaphors, and feelings.

    Real scientists ignore me, because i dont follow or learn their ruels. the ‘handwavers’ welcome me at times, but want me to learn their gestures and terminology. (In a way i see some of this as sort of what some have called ‘physics envy’— books on how to have a succesful meeting or organization, or a ‘positive’ have as many terms as ones on theoretical physics. (Sometimes they use the same terms.

    I saw a ‘global empathy index’ recently (for companies). I think silicon valley ones like Facebook were in top 5 places for empathy. I think some oil companies also had alot of global empathy.

    This reminds me of the big push for mindfulness meditation which will lead to inner and outer peace. All the major companies i hear are quite interested in this. Meditate to make money and be happy.

    5. One thing i see is how the COVID resarch papers are picked up outside the science world—-some people in media or various groups say the anti-lockdown papers prove COVID is just a conspiracy to crash the economy so Trump loses. Other see them as a precaution. (I liked the lockdown in some ways —now partially lifted —i can;t get much broker or unemployed than i am. no money to spend and nowhere to spend it. Though it affected me as well, partially via street crime from poor people, from which i may not or only partially recover from–though i can’t really blame anyone. I made the decision to be outside when i had been warned it was imprudent. ) .

    I hear on the radio show i listen to partly for amusement and partly to keep track of the ‘memes’ (just as i look at physics arxiv, blogs, etc.) they are promoting as usual that everyone ‘take you wife, and daughters to your local gun store and have some fun target practice and pick up a few to take home.’

    last night a diffferent radio had on a distinguished Duke professor discussing his new book on reparations. Despite him having a PhD in economics from MIT the only mathematical discussion of how to fund reperations was mention of a number often promoted (found by multiplying people by dollars) . I guess he gave up on economics and prefers now writing how life in jail and the ghetto is hard. Stirs empathy—math doesn’t. (His view was govt can just print up the reperations—he may be correct.) I prefer more aesthetic stuff, but i also wouldn’t mind reperations and probably can make an argument for them. ‘ask not what you owe your country, ask what it owes you’—thats the math problem to solve

    Liked by 1 person

  4. What this pandemic has revealed is our dysfunctional relationship with mortality. In my lifetime it has gone from a taboo subject to something that can be prioritized and ‘fixed’ when certain demographics are vulnerable and virtue signalling opportunities are abundant.

    Liked by 1 person

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s