Christians Shouldn’t Be Dismissive of Scientific Modeling
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Projections from an IHME model.
Over the last several weeks I’ve encountered a range of negative views toward the models epidemiologists have been using in the struggle against COVID-19. Skepticism is a healthy thing. But rejecting models entirely isn’t skepticism. Latching onto fringe theories isn’t skepticism. Rejecting the flattening-the-curve strategy because it’s allegedly model-based isn’t skepticism either.
These responses are mostly misunderstandings of what models are and of how flattening-the-curve came to be.
I’m not claiming expertise in scientific modeling. Most of this is high school level science class stuff. But for a lot of us, high school science was a long time ago, or wasn’t very good—or we weren’t paying attention.
What do models really do?
Those tasked with explaining science to us non-scientists define and classify scientific models in a variety of ways.
The Stanford Encyclopedia of Philosophy, for example, describes at least 8 varieties of models, along with a good bit of historical and philosophical background. They’ve got about 18,000 words on it.
A much simpler summary comes from the Science Learning Hub, a Science-education project in New Zealand. Helpfully, SLH doesn’t assume readers have a lot of background.
In science, a model is a representation of an idea, an object or even a process or a system that is used to describe and explain phenomena that cannot be experienced directly. Models are central to what scientists do, both in their research as well as when communicating their explanations. (Scientific Modeling)
Noteworthy here: models are primarily descriptive, not predictive. Prediction based on a model is estimating how an observed pattern probably extends into what has not been observed, whether past, present, or future.
Encyclopedia Britannica classifies models as physical, conceptual, or mathematical. It’s the mathematical models that tend to stir up the most distrust and controversy, partly because the math is way beyond most of us. We don’t know what a “parametrized Gaussian error function” is (health service utilization forecasting team, p.4; see also Gaussian, Error and Complementary Error function).
But Christians should be the last people to categorically dismiss models. Any high school science teacher trained in a Christian university can tell you why. I’ve been reminded why most recently in books by Nancy Pearcy, Alvin Plantinga, William Lane Craig, and Samuel Gregg: Whether scientists acknowledge it or not, the work of science is only possible at all because God created an orderly world in which phenomena occur according to patterns in predictable ways. For Christians, scientific study—including the use of models to better understand the created order—is study of the glory of God through what He has made (Psalm 19:1).
Most of us aren’t scientists, but that’s no excuse for scoffing at one of the best tools we have for grasping the orderliness of creation.
Should we wreck our economy based on models?
The “models vs. the economy” take on our current situation doesn’t fit reality very well. Truth? The economy is also managed using models. A few examples:
- Calculating the unemployment rate
- Unemployment forecasting (also this)
- Business forecasting
- Cost Modeling
Beyond economics, modeling is used all the time for everything from air traffic predictions to vehicle fire research, to predictive policing (no, it isn’t like “Minority Report”).
Models are used extensively in all sorts of engineering. We probably don’t even get dressed in the morning without using products that are partly the result of modeling—even predictive modeling—in the design process.
Christians should view models as tools used by countless professionals—many of whom are believers—in order to try to make life better for people. Pastors have books and word processors. Plumbers have propane torches. Engineers and scientists have models. They’re all trying to help people and fulfill their vocations.
(An excellent use of predictive mathematical modeling…)
Why are models often “wrong”?
An aphorism about firearms says, “Guns don’t kill people; people kill people.” Implications aside, it’s a true statement. It’s also true that math is never wrong; people are wrong. Why? Math is just an aspect of reality. In response to mathematical reality, humans can misunderstand, miscalculate, and misuse, but reality continues to be what it is, regardless.
The fact that the area of a circle is always its radius squared times an irrational (unending) number we call “pi” (π) remains true, no matter how many times I misremember the formula, plug the wrong value in for π, botch the multiplication, or incorrectly measure the radius.
The point is that models, as complex representations of how variables relate to each other and to constants, are just math. In that sense, models are also never “wrong”—just badly executed or badly used by humans. That said, a model is usually developed for a particular purpose and can be useless or misleading for the intended purpose, so, in that sense, “wrong.”
When it comes to using models to find patterns and predict future events, much of the trouble comes from unrealistic expectations. It helps to keep these points in mind:
- Using models involves inductive reasoning: data from many individual observations is used in an effort to generalize.
- Inductive reasoning always results in probability, never certainty.
- The more data a model is fed, and the higher the quality of that data, the more probable its projections will be.
- When data is missing for parts of the model, assumptions have to be made.
- Changes in a model’s predictions are not really evidence of “failure.” As the quantity and quality of data changes, and assumptions are replaced with facts, good models change their predictions.
- True professionals, whether scientists or other kinds of analysts, know that models of complex data are only best guesses—and they don’t claim otherwise.
- The professionals that develop and use models in research are far more tentative and restrained in their conclusions than people who popularize the findings (e.g., the media).
In the case of COVID-19, one of the most influential models has been one of IHME’s (Institute for Health Metrics and Evaluation). Regarding that model, an excellent Kaiser Family Foundation article notes:
Models often present “best guess” or median forecasts/projections, along with a range of uncertainty. Sometimes, these uncertainty ranges can be very large. Looking at the IHME model again, on April 13, the model projected that there would be a 1,648 deaths from COVID-19 in the U.S. on April 20, but that the number of deaths could range from 362 to 4,989.
Poor design and misuse have done some damage to modeling’s reputation. Some famous global-warming scandals come to mind. But in the “Climategate” controversy, for example, raw data itself was apparently falsified. The infamous hockey stick graph appears to have involved both manipulated raw data and misrepresentation of what the model showed. Modeling itself was not the problem.
(XKD isn’t completely wrong … there is such a thing as “better garbage”)
Why bother with models?
Given the uncertainty built into predictive mathematical models, why bother to use them? Usually, the answer is “because we don’t have anything better.” Models are about providing decision-makers, who don’t have the luxury of waiting for certainty, with evidence so they don’t have to rely completely on gut instinct. It’s not evidence that stands alone. It’s not incontrovertible evidence. It’s an effort to use real-world data to detect patterns and anticipate what might happen next.
As for COVID-19, the idea that too many sick at once would overwhelm hospitals and ICUs, and that distancing can help slow the infection rate and avoid that disaster, isn’t a matter of inductive-reasoning from advanced statistical models. It’s mostly ordinary deduction (see LiveScience and U of M). If cars enter a parking lot much faster than other cars exit, you eventually get a nasty traffic jam. You don’t need a model to figure that out.
You do need one if you want to anticipate when a traffic jam will happen, how severe it might be, how long it might last, and the timing of steps that might help reduce or avoid it.
Leaders of cities, counties, states, and nations have to manage large quantities of resources and plan for future outcomes. To do that, they have to make educated guesses about what steps to take now to be ready for what might happen next week, next month, and next year. It’s models that make those guesses educated ones rather than random ones.
Highly technical work performed by exceptionally smart fellow human beings is a gift from God. Christians should recognize that. Because we’ve been blessed with these people and their abilities (and their models) COVID-19 isn’t killing us on anywhere near the scale that the Spanish Flu did in 1918 (Gottlieb is interesting on this). That’s divine mercy!
(Note to those hung up on the topic of “the mainstream media”: none of the sources I linked to here for support are “mainstream media.” Top image: IHME.)
Aaron Blumer 2016 Bio
Aaron Blumer is a Michigan native and graduate of Bob Jones University and Central Baptist Theological Seminary (Plymouth, MN). He and his family live in small-town western Wisconsin, not far from where he pastored for thirteen years. In his full time job, he is content manager for a law-enforcement digital library service. (Views expressed are the author's own and not his employer's, church's, etc.)
- 180 views
[Aaron Blumer]“Whenever the models come in, they give a worst-case scenario and a best-case scenario. Generally, the reality is somewhere in the middle. I’ve never seen a model of the diseases that I’ve dealt with where the worst case actually came out. They always overshoot,” Dr. Anthony Fauci, a key member of the White House’s coronavirus task force, told CNN’s Jake Tapper on “State of the Union.”
This is consistent with what I’ve been saying and said in the article. I explain there both why models work this way and why they’re still useful.
Aaron, your explanation fails. Models that always overshoot are worse than useless. People in authority make decisions based on the worst case scenario, and in this case they are bankrupting the country, causing unnecessary harm. If the models were more accurate, more reasonable decisions could be made.
Maranatha!
Don Johnson
Jer 33.3
Stop. Take a breath. Consider the breadth of the reporting on COVID-19. Now, be honest. Is most of it in the national media (NYT, LA Times, CNN, MSNBC, CBS, NBC, etc. ) telling a totally impartial view of data and modeling. Or, is it being used to drive an agenda that somehow the federal government under Donald Trump is not doing enough. Even when something is positive, do they show it that way? For example, we are testing 250,000+ people a day for COVID-19. That is a tremendous success. Listen to Dr. Birx talk about that. This is hardcore testing looking for nucleic acids of the COVID-19 virus. Only HIV and HPV testing does that. Most rapid flu tests use antibodies. COVID has resisted a fast antibody test up until recently, and even then it is iffy. Did the government drop the ball on testing early on? Yes. that is undeniable. But they have ramped it up to a level no one on the Earth has ever done before. How does the media report that? They talk about percentages of population being tested to hide any success…
So, modeling is one thing. It is obvious to any honest observer that the modeling is being used by the media to drive their narrative. Is that the model’s fault? No. It is the media’s fault.
Now, Don talks about a different problem. I think along with others that some models are deliberately high to try to drive an outcome with the model. That is separate from media bias.
Stop. Take a breath. Consider the breadth of the reporting on COVID-19. Now, be honest. Is most of it in the national media (NYT, LA Times, CNN, MSNBC, CBS, NBC, etc. ) telling a totally impartial view of data and modeling. Or, is it being used to drive an agenda that somehow the federal government under Donald Trump is not doing enough. Even when something is positive, do they show it that way?
Never stopped breathing. :-D
Mark, these are two different topics:
- the problem of what people (including the media) do with the information models produce
- the question of whether the models themselves are helpful and whether those who develop and use models are trying to accomplish some political goal
I’m not interested in #1 at all. My article isn’t about that, and I’m not at all interested in that because it’s a given. We all know that the majority of the largest and loudest news outlets frequently show a lot of leftward bias (not always, though!). Been there, done that (for me this happened in the 1980s!). Don’t care.
Nothing said about topic 1 has any bearing on topic 2… which is my concern here and is a far more important question.
Views expressed are always my own and not my employer's, my church's, my family's, my neighbors', or my pets'. The house plants have authorized me to speak for them, however, and they always agree with me.
[Aaron Blumer]Nothing said about topic 1 has any bearing on topic 2… which is my concern here and is a far more important question.
I think most of us here understand the importance of doing modeling apart from how they are used politically. The bigger question to me though, is this: why keep using (and praising) models that are known to be inaccurate? When Dr. Fauci says the worst case scenarios have NEVER come to pass, that means two things. 1. Given that the worst case will be used politically, it’s irresponsible to keep using models with a false “worst case,” and 2, more importantly, knowing that the worst case never comes to pass, the model needs serious adjustment. I completely understand that the most likely scenario is down the middle, but if one of the best or worse cases is never reached, then the model MUST be wrong (or at best incomplete), because if it were accurate, then occasionally either the best or worst case would come to pass, even if not statistically often.
Dave Barnhart
Just this morning I am going through my news feed on my phone, which, try as I might, is still biased towards leftest news… (thanks Apple). I see this “news story” which contains this paragraph:
“A new model prepared by epidemiologists and computer scientists at Harvard and the Massachusetts Institute of Technology in partnership with the Daily Beast provides an estimate of what that might look like in Georgia, where fitness centers, tattoo and massage parlors, bowling alleys and hair salons were allowed to reopen last Friday and restaurants and other businesses began operating on Monday. As of the end of last week, at least 871 people statewide had lost their lives to COVID-19. According to the model, Georgia would have logged a total of between 1,004 and 2,922 coronavirus fatalities by June 15 if it had maintained its pre-Friday lockdown policy. But that range shoots up to 1,604 to 4,236 deaths if approved businesses return to just 50 percent of their pre-pandemic activity (or “contact”) levels — and 4,279 to 9,748 deaths at 100 percent of pre-shutdown activity.”
This is what I mean by bias in models is inherent. Daily Beast asked MIT and Harvard to make a model to look at Georgia. Why Georgia you might ask, and not Massachusetts, or Illinois, or Colorado? Because the aim is to make Republicans look bad and to make Trump look bad and incompetent. They also want Georgia to pay for not electing Stacey Abrams their governor, and to make sure they do it right the next time. So modeling is about 10% math/statistics, and 90% desired result.
The only time this is not true is in modeling for business operation and such things. Say I own a production plant and I want to model profit and cost with how my plant works. I believe that model… it has no bias other than profit.
You get into the public policy realm, and someone is trying to influence something. That includes climate change modeling.
Aaron, you said that most of these models were produced before COVID so they weren’t biased. OK… but I think there is a bias in them to overinflate so politicians could not ignore them. That is why they told Trump 2.2 million, or whatever. To make it so horrible he had to act…
The bigger question to me though, is this: why keep using (and praising) models that are known to be inaccurate? When Dr. Fauci says the worst case scenarios have NEVER come to pass, that means two things. 1. Given that the worst case will be used politically, it’s irresponsible to keep using models with a false “worst case,” and 2, more importantly, knowing that the worst case never comes to pass, the model needs serious adjustment. I completely understand that the most likely scenario is down the middle, but if one of the best or worse cases is never reached, then the model MUST be wrong (or at best incomplete), because if it were accurate, then occasionally either the best or worst case would come to pass, even if not statistically often.
I’m sorry but I can’t make sense of this. Can you clarify what you mean?
These models do not claim that their best or worse cases are particularly likely to ever happen. If the model claimed to say x will sometimes happen and it never does, it would be faulty, but they don’t attempt to do that.
Aaron, you said that most of these models were produced before COVID so they weren’t biased. OK… but I think there is a bias in them to overinflate so politicians could not ignore them. That is why they told Trump 2.2 million, or whatever. To make it so horrible he had to act…
Not quite what I said. Models are just math. They can’t be biased. Humans can be biased. It’s possible (though not easy!) to build a bias into a model, but there is no rational reason for a team of scientists who’s only purpose is to make projections as accurately as they possibly can to intentionally make them inaccurate. That doesn’t pass the reality test.
A dose of reality here: IHME and a bunch of other organizations are competing globally with one another to be the go-to tool for actionable infectious disease/hospital capacity projections. So when a deadly new virus hits the entire world, they’re going to intentionally get it wrong in order to defeat the American president?
I’m continually amazed that anyone finds that to be remotely plausible.
Do people do biased things with all forms of information (including projections from models)? Of course. Not in dispute.
Some of the models are going to turn out to have had some bad calculations or bad assumptions and/or bad data. They’re not all making the same assumptions so they can’t all be right.
IHME already knows it has a lot of questionable data… but it works with the best data it can get.
The COVID Tracking Project has given up on certain kinds of data for the time being…
States are currently reporting two fundamentally unlike statistics: current hospital/ICU admissions and cumulative hospitalizations/ICU admissions. Across the country, this reporting is also sparse. In short: it is impossible to assemble anything resembling the real statistics for hospitalizations, ICU admissions, or ventilator usage across the United States. As a result, we will no longer provide national-level summary hospitalizations, ICU admissions, or ventilator usage statistics on our site.
Because this data is so spotty, IHME and others have to try to estimate likely hospital needs vs likely hospital capacity from death rates and other data. It’s one reason that projected ranges are often large.
I’ve never claimed these models were producing really great predictions… only that they’re the best predictions we have and that leaders of cities, counties, states, and nations have to do their best to plan for what might happen.
The other thing I’ve claimed is that there is no reason to believe these folks are any less honest than any other professional off the street. Are there some idiots and liars? Well, human nature being what it is, of course. But we have no more reason to believe the COVID-modelers are deceitful than we have to belive that, for example, the economics modelers are deceitful.
Why all the hostility toward IHME and others and not toward economic forecasters (who are famously wrong so much of the time)? It doesn’t make any sense.
I’ve got nothing against economic forecasters. They do their best and their predictions often turn out to be wrong. Why should I think medical professionals trying to save lives would try less hard to get their projections right? But are they going to be wrong sometimes? Of course!
Views expressed are always my own and not my employer's, my church's, my family's, my neighbors', or my pets'. The house plants have authorized me to speak for them, however, and they always agree with me.
Aaron - are the modelers deceitful, or politically motivated? I don’t think so, but I can’t know for sure.
However, their projections seemed to be wildly overstated from the beginning and the progress of the situation seems to confirm that. IHME seems closer now, with more data in, but time will tell. However, the initial models were so outrageous that the whole world stampeded into “we’ve got to do something” even though that meant economic suicide. When we come out of this, things are going to be really bad. I don’t think we need a model to be able to predict that. Our great grandchildren will be paying off the debt from these two months, if the world lasts that long.
Maranatha!
Don Johnson
Jer 33.3
Here’s an article I think offers a balanced view. My own view is pretty close to this.
Maranatha!
Don Johnson
Jer 33.3
[Don Johnson]Here’s an article I think offers a balanced view. My own view is pretty close to this.
I don’t think I disagree with anything there. I would recommend this one also: https://www.nationalreview.com/2020/04/as-new-data-improve-our-understa…
(If it doesn’t let you in, clear all your national review cookies and it probably will… or try a browser you don’t usually use)
I think it’s very likely that eventually, when we’ve got a whole lot more info and the benefit of hindsight, we’re going to see lots of mistakes were made. What I hate to see people do, though, is make the leap to the conclusion that leaders here and now should have made different decisions with the information they have now… or had in Feb or March. In some cases that might be true, but none of these decision-makers get to travel to the future then come back and make decisions now based on what they discovered.
This is true of Trump as well. Though I think he was slower to get serious than he should have been, and just about anyone at that level who is in the habit of being attentive to facts would have been quicker, he wasn’t late by a whole lot, given the ambiguities of the situation at the time. I’m not going to go into how he’s handled the situation since mid-March (you have the public Trump, and then the “what’s actually getting done” Trump; the former is obvious; the latter is hard to determine from the outside because most of the info comes through highly politicized filters.)
What should be clear to everyone by now is that there never was a single “the model” decision makers were relying on; the models they did use and continue to use are efforts to do the best we can with not very good data; the problem of all the politically-driven doing the things they usually do (on both the right and the left) is a real problem, but a separate one; the 2.2 million number came from the UK’s Imperial Model, and the model’s projections were very sloppily communicated to both the President and the media; providentially that number was big enough to wake the President up, even though he almost certainly didn’t understand it what it meant; and that the federal government has a very different role in all of this than the states and smaller jurisdictions do; also that the economy was self-closing well before all the gov’s got into action with their orders.
It’s likely that if all the restrictions were lifted at this moment, the vast majority of people would continue to socially distance, mostly stay home, voluntarily not work if they’re allowed to and aren’t out of money yet, etc. There is no “on switch” for the economy… short of a miracle cure popping up… because economies are ultimately driven by human minds in addition to human might. And the minds are not there yet, for the most part.
However, their projections seemed to be wildly overstated from the beginning and the progress of the situation seems to confirm that. IHME seems closer now, with more data in, but time will tell. However, the initial models were so outrageous
No, they really weren’t. See my post several comments up about where the 2.2 million came from. It was dumb to invent a fictional worst case scenario, but the Imperial Model report never said the worst case was likely. They specifically said it was not.
The more famous models’ early projections turned out to be quite different from reality, but this should be expected. The data is very even even now; it was far worse then. It’s like being a meteorologist who is trying to predict weather but the radar is broken and the barometers are all contradicting eachother, and observers on the ground are turning in different kinds of data under the same labels.
Of course that’s going not going to produce really good outcomes… still the best predictions anybody had at the time.
Views expressed are always my own and not my employer's, my church's, my family's, my neighbors', or my pets'. The house plants have authorized me to speak for them, however, and they always agree with me.
Aaron,
Respectfully, I think you’re missing an important part of the big picture.
How much of the professional scientific community believes “the world is going to end” due to climate change? How many of their predictions over the last thirty to fifty years have come to pass – or even close?
The scientific community may be “professionals” – they are professionals who are as leftist/liberal/anti-God as possible who push any contrarians out of the peer review community. Surely you know this. Their careers and reputations depend on them catering to anti-God positions, and this frequently leads them to conclusions that cater to their anti-God, academia colleagues and associated think-tank and political friends. They may very well be among the friendliest, most intelligent people we know and interact with, but their thinking and positions are completely skewed by deep humanism.
Ashamed of Jesus! of that Friend On whom for heaven my hopes depend! It must not be! be this my shame, That I no more revere His name. -Joseph Grigg (1720-1768)
[JNoël]Aaron,
Respectfully, I think you’re missing an important part of the big picture.
How much of the professional scientific community believes “the world is going to end” due to climate change? How many of their predictions over the last thirty to fifty years have come to pass – or even close?
The scientific community may be “professionals” – they are professionals who are as leftist/liberal/anti-God as possible who push any contrarians out of the peer review community. Surely you know this. Their careers and reputations depend on them catering to anti-God positions, and this frequently leads them to conclusions that cater to their anti-God, academia colleagues and associated think-tank and political friends. They may very well be among the friendliest, most intelligent people we know and interact with, but their thinking and positions are completely skewed by deep humanism.
Two things here: you’re confusing what scientists do with how their results are reported and popularized. The vast majority of climate predictions anyone has heard about are the ones politicians, advocacy groups, and highly politicized media outlets have made famous. Most of what scientists predict, they predict with all sorts of “if…” qualifications and caveats and probability scales. The world ending anytime soon is not a claim coming from very many scientists.
And they’re mostly interested in success in their fields, not political aims. Does bias creep in? Absolutely, but no more than it does for grocers, barbers, economists, truck drivers, farmers, pastors, school teachers (well, probably far less than most of them, given how political eduction has become), plumbers, you name it.
Two, it isn’t clear at all what sort of political bias would make sense when it comes to modeling the spread of disease. How would your bias even influence you? As I’ve already pointed out, these modelers have every reason to want to get it right regardless of their politics. Unless you’re willing to fall on your sword to try to—in some very complicated and unlikely way—influence an election, your success in your profession depends on doing what you do better than others do it.
So my question to those who think the modelers have acted with a political agenda is this: how would that work? Did all these independent and global organizations get on the phone in a big secret meeting and decide “Let’s all tell essentially the same story, even though we’re using different models. Because here’s our chance to smash the US economy and get Trump out of office! … sure, we’ll smash the economies in all the other countries of the world also, and we’ll make ourselves look like idiots because our projections look wildly innacurate, but hey, it’ll be worth it!!” … and then every single one of them agreed to this plot… along with a vow of secrecy?
No, occam’s razor, folks. Just people doing their best and sometimes failing.
Views expressed are always my own and not my employer's, my church's, my family's, my neighbors', or my pets'. The house plants have authorized me to speak for them, however, and they always agree with me.
Although not directly related to modeling, this piece expresses quite well what is happening now at the juncture I list in the subject field. You’ll note it was written by Matt Taibbi, a contributing editor to Rolling Stone, and hardly a friend of either conservatives or Christians. However, I think he gets what many of us are thinking as we consider what is happening with the coronavirus “crisis,” and why many tend not to trust models that are supposedly completely scientific and ostensibly not political. (Or at least, we don’t tend to trust their presentation, since they are often misused.) The Atlantic piece he references about why internet speech should resemble China’s censorship model more than the “free speech” model is particularly chilling.
https://taibbi.substack.com/p/temporary-coronavirus-censorship
Note: I don’t agree 100% with everything he says in this piece, but if you’re like me, it will make you think.
Dave Barnhart
I’m trying to stay on topic, but your post reveals significant flaws in your logic.
Academia, especially the scientific community, is, by a huge majority, anti-God, and, therefore, anti-American conservatism: because much (most?) of that is people who are pro-God and in complete disagreement with much of what the anti-God scientific community promotes. This means the scientific academia is immediately predisposed to hate Trump and the Republican party and do everything they can to promote liberalism/leftism and those Democrats who are anti-God. Why do they even bother modeling global warming/climate change? Why do their models always support the global warming/climate change alarmist positions? Because those are the positions that are as far from God and showing any support of a Creator as possible, and they are positions that support the liberal, leftist, anti-God agenda. The scientists themselves do not need to claim the world is coming to an end – they only need to model it in such a way that it generates the desired responses. You cannot tell me the scientific community that builds these models is not predisposed to only support and promote thinking that is anti-God. This is why the peer review process virtually eliminates any professional / expert thinking that would support or even come close to hinting at the possibility of the Divine, and why there really is, on a global scale, a sense of “let’s all tell essentially the same [wrong/anti-God] story. Ultimately, it is being driven by the enemy of God, and it is wonderfully easy for him to coordinate these attacks against God because of sinful humanity.
The political bias that would make the most sense would be that which would hurt American conservatism and help American liberalism. Again, this is easy to see when you recognize that most of scientific academia is ultra anti-God and pro-humanism.
And let’s not forget about the WHO, which is composed of “experts” all around the world and which failed to properly report SARS-CoV-2 because of political bias – toward China. China still claims to only have had 83,958 confirmed cases and 4,637 COVID-19 fatalities (laughably false statistics indeed), and yet the WHO still panders to China, despite the obvious lies they continue to propagate. Why haven’t the WHO “experts” done their jobs? Because they, too, have political motivations.
Scientists around the world are biased for political purposes. This really shouldn’t even be a topic of conversation, but you seem to think scientists pretty much don’t care about politics and only care about science. Yet they consistently ignore Truth and even fight, war against it. Anyone can use any data he wants to paint any picture he wants, and when the majority of a community hates God, the picture they are going to paint will logically follow suit. The majority of the scientific community abhors Donald Trump (and conservatives, in general) because he represents, to them, a potential loss of decades of advances in liberal/leftist/anti-God thinking.
Ashamed of Jesus! of that Friend On whom for heaven my hopes depend! It must not be! be this my shame, That I no more revere His name. -Joseph Grigg (1720-1768)
@Dave: I’ll check it out. There’s no question that getting the specialists (experts), politicians, and the masses all effectively balancing one another is a difficult thing. I’m personally convinced that we’re way out of balance in the populist direction, but I’m also not a technocrat, so there need to be disciplined and rational (not mob driven or mood driven) ways to keep experts accountable and connected to reality.
I don’t know if the article talks about this, but there’s always the problem of groupthink as well among tight communities of specialists.
This goes both ways though: specialists get too insular and lose touch with realities outside their fields; “the masses” get reflexively hostile toward experts because they don’t know enough of them as human beings.
It’s so easy to blame the big, vague “Them” for things if you don’t know any of “Them.”
Old fashioned xenophobia.
I think I want to write an article on how my thinking about science has changed over the last couple of years… and the growing impression that conservative Christians/fundamentalists have largely become anti-science. Which is unfortunate, if not tragic. But I was doing some growing in this area in 2019, and events of 2020 have accelerated it. It grieves me to see so many people I know (most in real life, not here at SI) latch on to whatever the conspiracy theory du jour is.
So we have a deep failure on “the right” (theologically and in political philosophy… which is really also part theology) to teach critical thinking and science. Political polarization + COVID has really exposed a lot of alarming deficiencies in the conservative Christian worldview. (Here I mean the version of the worldview people actually hold to, not the essence of what it is. I don’t think the worldview itself is flawed.)
In addition to what I’ve read over the last year from Christian authors, projects at work have had me digging into and summarizing lots of research in the social sciences. The first-hand interaction with their work has changed how I look at “scientists,” — even social scientists, who are famously on the soft and messy end of the science spectrum. If there’s a place where bias has lots of opportunity to shape outcomes, it’s in the social sciences, but even there, I have seen much less of it than one would expect, based on so much of the anti-science rhetoric I’ve grown up hearing in our circles.
In short, I think part of the clash on this topic is due to deeper differences in how we view science in general.
Views expressed are always my own and not my employer's, my church's, my family's, my neighbors', or my pets'. The house plants have authorized me to speak for them, however, and they always agree with me.
[JNoël]Aaron,
Respectfully, I think you’re missing an important part of the big picture.
How much of the professional scientific community believes “the world is going to end” due to climate change? How many of their predictions over the last thirty to fifty years have come to pass – or even close?
The scientific community may be “professionals” – they are professionals who are as leftist/liberal/anti-God as possible who push any contrarians out of the peer review community. Surely you know this. Their careers and reputations depend on them catering to anti-God positions, and this frequently leads them to conclusions that cater to their anti-God, academia colleagues and associated think-tank and political friends. They may very well be among the friendliest, most intelligent people we know and interact with, but their thinking and positions are completely skewed by deep humanism.
That would be a basic genetic fallacy, and of that you ought to repent, Mr. Noel. Moreover, as someone who’s got two degrees in engineering, and who’s worked (in school and professionally) with engineers and scientists for over three decades, you happen to be wrong, too. There are people of all political stripes, and while you’ll get more liberals in government employment, there are people who will speak up when they believe the science is being misrepresented. You will find people as well from all kinds of religious perspectives; for example, about half the current group I work in is evangelical Christian homeschooling dads, and the guys down in the lab show their “liberalism” with a sign “make devices great again.” You sometimes get a near consensus when one entity (or a group of associated entities) funds most of the research, as in climatology, but even there, people not receiving funding have been instrumental in pointing out the weaknesses with the data and evidence.
I should also note, since apparently the detractors of the official models want to use divergence of model from reality as a reason to not only reject the model and its creators, that many of them were avidly pointing out a few weeks back that since COVID-19 deaths had not yet reached the level of typical flu seasons, that the models were to be rejected. Note; since that is not operative, are those who made that argument going to apply the same logic to their own comments?
Aspiring to be a stick in the mud.
Discussion