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.
The Truth about Scientific Models
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.
Artist Zach Weinersmith teams up with FiveThirtyEight to break down and explain the challenges in building accurate models for the pandemic from differing assumptions, data collection, and just a general lack of information.
Interesting quote from the cartoon: “Every variable is dependent on a number of possible choices and gaps in knowledge.”
Click on the images for the full cartoon.
Reminds me of a funny tweet I saw, “Using the right denominator is 50% of data science.”
Mental models are how we understand the world. Not only do they shape what we think and how we understand but they shape the connections and opportunities that we see. Mental models are how we simplify complexity, why we consider some things more relevant than others, and how we reason.
A mental model is simply a representation of how something works. We cannot keep all of the details of the world in our brains, so we use models to simplify the complex into understandable and organizable chunks.