Note: This post is part of my series on forecasting for MIRI. I recommend reading my earlier post on the general-purpose forecasting community, my post on scenario planning, and my post on futures studies. Although this post doesn't rely on those, they do complement each other.
Note 2: If I run across more domains where I have substantive things to say, I'll add them to this post (if I've got a lot to say, I'll write a separate post and add a link to it as well). Suggestions for other domains worth looking into, that I've missed below, would be appreciated.
Below, I list some examples of domains where forecasting is commonly used. In the post, I briefly describe each of the domains, linking to other posts of mine, or external sources, for more information. The list is not intended to be comprehensive. It's just the domains that I investigated at least somewhat and therefore have something to write about.
- Weather and climate forecasting
- Agriculture, crop simulation
- Business forecasting, including demand, supply, and price forecasting
- Macroeconomic forecasting
- Political and geopolitical forecasting: This includes forecasting of election results, public opinion on issues, armed conflicts or political violence, and legislative changes
- Demographic forecasting, including forecasting of population, age structure, births, deaths, and migration flows.
- Energy use forecasting (demand forecasting, price forecasting, and supply forecasting, including forecasting of conventional and alternative energy sources; borrows some general ideas from business forecasting)
- Technology forecasting
Let's look into these in somewhat more detail.
Note that for some domains, scenario planning may be more commonly used than forecasting in the traditional sense. Some domains have historically been more closely associated with machine learning, data science, and predictive analytics techniques (this is usually the case when a large number of explanatory variables are available). Some domains have been more closely associated with futures studies, that I discussed here. I've included the relevant observations for individual domains where applicable.
Climate and weather forecasting
- The best weather forecasting methods use physical models rather than statistical models (though some statistics/probability is used to tackle some inherently uncertain processes, such as cloud formation). Moreover, they use simulations rather than direct closed form expressions. Errors compound over time due to a combination of model errors, measurement errors, and hypersensitivity to initial conditions.
- There are two baseline models against which the quality of any model can be judged: persistence (weather tomorrow is predicted to be the same as weather today) and climatology (weather tomorrow is predicted to be the average of the weather on that day over the last few years). We can think of persistence and climatology as purely statistical approaches, and these already do quite well. Any approach that consistently beats them needs to run very computationally intensive weather simulations.
- Even though a lot of computing power is used in weather prediction, human judgment still adds considerable value, about 10-25%, relative to what the computer models generate. This is attributed to humans being better able to integrate historical experience and common sense into their forecasts, and can offer better sanity checks. The use of machine learning tools in sanity-checking weather forecasts might substitute for the human value-added.
- Long-run climate forecasting methods are more robust in the sense of not being hypersensitive to initial conditions. Long-run forecasts require a better understanding of the speed and strength of various feedback mechanisms and equilibrating processes, and this makes them more uncertain. Whereas the uncertainty in short-run forecasts is mostly initial condition uncertainty, the uncertainty in long run forecasts arises from scenario uncertainty, plus uncertainty about the strength of various feedback mechanisms.
With long-term climate forecasting, a common alternative to forecasting is scenario analysis, such as that used by the IPCC in its discussion of long-term climate change. An example is the IPCC Special Report on Emissions Scenarios.
In addition to my overviews of weather and climate forecasting, I also wrote a series of posts on climate change science and some of its implications. These provide some interesting insight into the different points of contention related to making long-term climate forecasts, identifying causes, and making sense of a somewhat politicized realm of discourse. My posts in the area so far are below (I'll update this list with more posts as and when I make them):
- Climate science: how it matters for understanding forecasting, materials I've read or plan to read, sources of potential bias
- Time series forecasting for global temperature: an outside view of climate forecasting
- Carbon dioxide, climate sensitivity, feedback, and the historical record: a cursory examination of the Anthropogenic Global Warming (AGW) hypothesis
- [QUESTION]: What are your views on climate change, and how did you form them?
- The insularity critique of climate science
Agriculture and crop simulation
- Predictions of agricultural conditions and crop yields are made using crop simulation models (Wikipedia, PDF overview). Crop simulation models include purely statistical models, physical models that rely on simulations, and approximate physical models that use functional expressions.
- Weather and climate predictions are a key component of agricultural prediction, because of the dependence of agricultural yield on climate conditions. Some companies, such as The Climate Corporation (website, Wikipedia) specialize in using climate prediction to make predictions and recommendations for farmers.
- Business forecasting includes forecasting of demand, supply, and price.
- Time series forecasting (i.e., trying to predict future values of a variable from past values of that variable alone) is quite common for businesses operating in environments where they have very little understanding of or ability to identify and measure explanatory variables.
- As with weather forecasting, persistence (or slightly modified versions thereof, such as trend persistence that assumes a constant rate of growth) can generally be simple to implement while coming close to the theoretical limit of what can be predicted.
- More about business forecasting can be learned from the SAS Business Forecasting Blog or the Institute of Business Forecasting and Planning website and LinkedIn group.
Two commonly used journals in business forecasting are:
- Journal of Business Forecasting (website)
- International Journal of Business Forecasting and Marketing Intelligence (website)
Many of the time series used in the Makridakis Competitions (that I discussed in my review of historical evaluations of forecasting) come from businesses, so the lessons of that competition can broadly be said to apply to the realm of business forecasting (the competition also uses a few macroeconomic time series).
There is a mix of explicit forecasting models and individual judgment-based forecasters in the macroeconomic forecasting arena. However, unlike the case of weather forecasting, where the explicit forecasting models (or more precisely, the numerical weather simulations) improve forecast accuracy to a level that would be impossible for unaided humans, the situation with macroeconomic forecasting is more ambiguous. In fact, the most reliable macroeconomic forecasts seem to arise by taking averages of the forecasts of a reasonably large number of expert forecasters, each using their own intuition, judgment, or formal model. For an overview of the different examples of survey-based macroeconomic forecasting and how they compare with each other, see my earlier post on the track record of survey-based macroeconomic forecasting.
Political and geopolitical forecasting
I reviewed political and geopolitical forecasting, including forecasting for political conflicts and violence, in this post. A few key highlights:
- This is the domain where Tetlock did his famous work showing that experts don't do a great job of predicting things, as described in his book Expert Political Judgment. I discussed Tetlock's work briefly in my review of historical evaluations of forecasting.
- Currently, the most reliable source of forecasts for international political questions is The Good Judgment Project (website, Wikipedia), which relies on aggregating the judgments of contestants who are given access to basic data and are allowed to use web searches. The GJP is co-run by Tetlock. For election forecasting in the United States, PollyVote (website, Wikipedia), FiveThirtyEight (website, Wikipedia), and prediction markets such as Intrade (website, Wikipedia) and the Iowa Electonic Markets (website, Wikipedia) are good forecast sources. Of these, PollyVote appears to have done the best, but the others have been more widely used.
- Quantitative approaches to prediction rely on machine learning and data science, combined with text analysis of news of political events.
Forecasting of future population is a tricky business, but some aspects are easier to forecast than others. For instance, the population of 25-year-olds 5 years from now can be determined with reasonable precision by knowing the population of 20-year-olds now. Other variables, such as birth rates, are harder to predict (they can go up or down fast, at least in principle) but in practice, assuming level persistence or trend persistence can often offer reasonably good forecasts over the short term. While there are long-run trends (such as a trend of decline in both period fertility and total fertility) I don't know how well these declines were predicted. I wrote up some of my findings on the recent phenomenon of ultra-low fertility in many countries, so I have some knowledge of fertility trends, but I did not look systematically into the question of whether people were able to correctly forecast specific trends.
Gary King (Wikipedia) has written a book on demographic forecasting and also prepared slides covering the subject. I skimmed through his writing, but not enough to comment on it. It seems like mostly simple mathematics and statistics, tailored somewhat to the context of demographics.
With demographics, depending on context, scenario analyses may be more useful than forecasts. For instance, land use planning or city development may be done keeping in mind different possibilities for how the population and age structure might change.
Energy use forecasting (demand and supply)
Short-term energy use forecasting is often treated as a data science or predictive modeling problem, though ideas from general-purpose forecasting also apply. You can get an idea of the state of energy use forecasting by checking out the Global Energy Forecasting Competition (website, Wikipedia), carried out by a team led by Dr. Tao Hong, and cooperating with data science competitions company Kaggle (website, Wikipedia), some of the IEEE working groups, and the International Journal of Forecasting (one of the main journals of the forecasting community).
For somewhat more long-term energy forecasting, scenario analyses are more common. Energy is so intertwined with the global economy that an analysis of long-term energy use often involves thinking about many other elements of the world.
Shell (the organization to pioneer scenario analysis for the private sector) publishes some of its scenario analyses online at the Future Energy Scenarios page. While the understanding of future energy demand and supply is a driving force for the scenario analyses, they cover a wide range of aspects of society. For instance, the New Lens Scenario published in 2012 described two candidate futures for how the world might unfold till 2100, a "Mountains" future where governments played a major role and coordinated to solve global crises, and an "Oceans" future that was more decentralized and market-driven. (For a critique of Shell's scenario planning, see here). Shell competitor BP also publishes an Energy Outlook that is structured more as a forecast than as a scenario analysis, but does briefly consider alternative assumptions in a fashion similar to scenario analysis.
Many people in the LessWrong audience might find technology forecasting to be the first thing that crosses their minds when the topic of forecasting is raised. This is partly because technology improvements are quite salient. Improvements in computing are closely linked with the possibility of an Artificial General Intelligence. Famous among the people who view technology trends as harbingers of superintelligence is technologist and inventor Ray Kurzweil, who has been evaluated on LessWrong before. Website such as KurzweilAI.net and Exponential Times have popularized the idea of rapid, unprecedented, exponential growth, that despite its fast pace is somewhat predictable because of the close-to-exponential pattern.
One other point about technology forecasting: compared to other types of forecasting, technology forecasting is more intricately linked with the domain of futures studies (that I described here). Why technology forecasting specifically? Futures studies seems designed more for studying and bringing about change rather than determining what will happen at or by a specific time. Technology forecasting, unlike other forms of forecasting, is forecasting changes in the technology that we use to operate our lives. So this is the most transformative forecasting domain, and naturally attracts more attention from futures studies.