The reason is simple: most of us, even those of us who are scientists ourselves, lack the relevant scientific expertise needed to adequately evaluate that research on our own. In our own fields, we are aware of the full suite of data, of how those puzzle pieces fit together, and what the frontiers of our knowledge is…
There’s an old saying that I’ve grown quite fond of recently: you can’t reason someone out of a position they didn’t reason themselves into. When most of us “research” an issue, what we are actually doing is:
formulating an initial opinion the first time we hear about something,
evaluating everything we encounter after that through that lens of our gut instinct,
finding reasons to think positively about the portions of the narrative that support or justify our initial opinion,
and finding reasons to discount or otherwise dismiss the portions that detract from it.
The problem with peer review is the peers. Who are “the peers” of four M.D.’s writing up an observational study? Four more M.D.’s who know just as little as the topic. Who are “the peers” of a sociologist who likes to bullshit about evolutionary psychology but who doesn’t know much about the statistics of sex ratios?
This concise video goes over a lot of important ground by first establishing basic ideas about observations, explanations, and scientific models and then moves on to the problems of models that don’t actually simplify anything. Ultimately this leads to a discussion of science and pseudoscience. She also has other great videos on her channel as well.
Extremely ambitious project from Propublica and the NY Times Magazine. What I really appreciate about this work is that it does not simply talk about alarmist conclusions and predictions but discusses at length its assumptions, methods, and different possible outcomes along the way. I think the TOK value here is less about climate change and its impacts and more about the ways in which we predict the future. What is the role of models? How do different disciplines build them? What is the value of interdisciplinary work? What are the limitations of these predictions?
In all, we fed more than 10 billion data points into our model. Then we tested the relationships in the model retroactively, checking where historical cause and effect could be empirically supported, to see if the model’s projections about the past matches what really happened. Once the model was built and layered with both approaches — econometric and gravity — we looked at how people moved as global carbon concentrations increased in five different scenarios, which imagine various combinations of growth, trade and border control, among other factors. (These scenarios have become standard among climate scientists and economists in modeling different pathways of global socioeconomic development.)
The results are built around a number of assumptions about the relationships between real-world developments that haven’t all been scientifically validated. The model also assumes that complex relationships — say, how drought and political stability relate to each other — remain consistent and linear over time (when in reality we know the relationships will change, but not how). Many people will also be trapped by their circumstances, too poor or vulnerable to move, and the models have a difficult time accounting for them.
All this means that our model is far from definitive. But every one of the scenarios it produces points to a future in which climate change, currently a subtle disrupting influence, becomes a source of major disruption, increasingly driving the displacement of vast populations.
They don’t necessarily try to predict what will happen—but they can help us understand possible futures
Scientists rely on models, which are simplified, mathematical representations of the real world. Models are approximations and omit details, but a good model will robustly output the quantities it was developed for.
Models do not always predict the future. This does not make them unscientific, but it makes them a target for science skeptics.
“We’ve arranged a global civilization in which most crucial elements profoundly depend on science and technology. We have also arranged things so that almost no one understands science and technology. This is a prescription for disaster. We might get away with it for a while, but sooner or later this combustible mixture of ignorance and power is going to blow up in our faces…
“I worry that…pseudoscience and superstition will seem year by year more tempting, the siren song of unreason more sonorous and attractive. Where have we heard it before? Whenever our ethnic or national prejudices are aroused, in times of scarcity, during challenges to national self-esteem or nerve, when we agonize about our diminished cosmic place and purpose or when fanaticism is bubbling up around us– then, habits of thought familiar from ages past reach for the controls. The candle flame gutters. Its little pool of light trembles. Darkness gathers. The demons begin to stir…
“Science is more than a body of knowledge; it is a way of thinking. I have a foreboding of an America in my children’s or grandchildren’s time — when the United States is a service and information economy; when nearly all the key manufacturing industries have slipped away to other countries; when awesome technological powers are in the hands of a very few, and no one representing the public interest can even grasp the issues; when the people have lost the ability to set their own agendas or knowledgeably question those in authority; when, clutching our crystals and nervously consulting our horoscopes, our critical faculties in decline, unable to distinguish between what feels good and whats true, we slide, almost without noticing, back into superstition and darkness. The dumbing down of America is most evident in the slow decay of substantive content in the enormously influential media, the 30-second sound bites (now down to 10 seconds or less), the lowest common denominator programming, credulous presentations on pseudoscience and superstition, but especially a kind of celebration of ignorance.”
Jacob Bronowski on the dangers of dogma
Relatedly, here is a clip from the 1970s television adaptation of the book, The Ascent of Man by Jacob Bronowski.
Jacob Bronowski explains why the pursuit of science is better than seeking absolute knowledge.
Seriously, this is the last time I’m posting anything about statues. The first is a thoughtful piece that compares how we think about morality in history to the progress of science. The second is a podcast that delves into the questions around why we care about monuments and statues. For previous posts about this topic, click here.
Are these long-overdue corrections in the name of social justice, or simply ideologically driven acts of anti-historical vandalism? The answer depends on how we judge the moral actions of figures from the past, a question that in turn requires us to consider the nature of morality itself…
Knowledge is a relay race. It is a fundamental misunderstanding about how it works to criticize a swift runner who effectively passed the baton because he did not complete the race on his own….
All of which to say, there is a vital difference between being wrong and being blameworthy….
When it comes to praise and blame, intention and context matter, not just a snapshot of the final result.
The Inquiry Podcast: Why do we care about statues?
The killing of African American George Floyd ignited anti-racist protests around the world – many centred on statues associated with colonialism and slavery. Why do these figures of bronze and stone generate such strong feelings? And what do they tell us about how countries deal with their past?
How can we grasp nature’s image and put it on a page? How do we judge the truthiness of images of nature? These questions are particularly challenging when it comes to images of the far reaches of galactic and oceanic space, which share a quality that we might call sensory distance…Visualising places of sensory distance requires distinctive approaches, not merely to collect information but also to interpret it, since the information gathered is patchy. In the absence of comprehensive and accessible information, acquiring knowledge about sea monsters and black holes calls for imaginative image-making…
Although today’s black hole image emerges out of different material technologies, it, too, relies on techniques of imaginative prototyping of distant objects – about which only limited evidence can be gathered, by circuitous means – and collaborative practices of visualising…
Natural objects exist in the world independently of our knowledge of them and – what is key here – independently of any particular community’s knowledge of them…
The imaginative extrapolation involved in prediction and expectation is also crucial to the discovery of black holes: the laws of gravity suggested that this species of space entity existed, in theory, long before the first black hole was discovered in 1971. Just as naturalists had to imagine the animal whose head had once sported a tusk…
The metaphors of neuroscience – computers, coding, wiring diagrams and so on – are inevitably partial. That is the nature of metaphors, which have been intensely studied by philosophers of science and by scientists, as they seem to be so central to the way scientists think. But metaphors are also rich and allow insight and discovery. There will come a point when the understanding they allow will be outweighed by the limits they impose, but in the case of computational and representational metaphors of the brain, there is no agreement that such a moment has arrived. From a historical point of view, the very fact that this debate is taking place suggests that we may indeed be approaching the end of the computational metaphor. What is not clear, however, is what would replace it.