The base rate database project collects base rates for different categories of events and makes them available to researchers, forecasters and philanthropic organisations. Its main goals are to develop better intuitions about the potential and limitations of reference class forecasting and to provide useful information to the public. The data will enable research that enhances our understanding of the kinds of circumstances in which reference forecasting is a promising approach, what kinds of methods of reference forecasting work best, how to construct reasonable reference classes, and what potential caveats and pitfalls are. In addition to the raw data we will collect qualitative feedback on individual reference classes and on the overall process of building a base rate database, adding context to the data and developing comprehensive knowledge to build upon in the future. We aim to select categories of base rates in a way that makes the information we collect useful to decision makers and philanthropic organisations.
If one wants to predict whether some event will happen in the future, it is often helpful to look at the past. One can ask: "Ignoring all the specifics of the current event I'm trying to predict, what would I predict just by looking at the base rate of similar events happening in the past?". This is called reference class forecasting and helps forecasters to obtain an 'outside view' on the forecasting question at hand. This outside view, of course, is usually complemented by the 'inside view': what are the specifics of the current event at hand that distinguish it from other events?
Reference class forecasting is widely used among forecasters. To this date, however, there has been little systematic research done into how effective base rates are for forecasting future events, how they can best be used and what limitations apply. We aim to facilitate this research.
The main goal of this project is to develop a better understanding of the merits and limitations of reference class forecasting.
A secondary goal is to collect information that may be useful for forecasters and EA stakeholders in the future.
What we'll do
We want to achieve our goals by
- asking experienced forecasters to compile a public database with base rates for various categories of events
- collecting qualitative feedback on the process of collecting base rates, as well as the base rates themselves
- using the database to conduct and facilitate quantitative and qualitative research, especially with regards to the performance of various reference class forecasting approaches
- inviting others (you!) to suggest base rate categories that we should look into through this form.
Categories that we want to look into
We intend to look into categories as diverse as
- Violent and non-violent protests that have (or have not) led to regime change
- Elections with small margins of victory
- Zoonotic spillover events
- Development of new antibiotics
Specific research questions
The database is meant to be a resource for anyone who is interested in reference class forecasting. Please do feel free to use it for your own research as well as to reach out to us.
So far, we have thought of the following quantitative analyses we think may be promising:
- Comparison of the predictive performance of several reference forecasting approaches, for example:
- Naive Laplace's rule with different priors (uniform, Jeffrey, Haldane)
- Time invariant Laplace with different ways of treating the exponent
- Analysing how useful reference forecasting is overall, for example by
- constructing a reference class forecast based on the first x observations and scoring the forecast based on the last (n-x) observations
- arriving at an estimate for the robustness of reference class forecasting by obtaining a distribution of scores for different approaches across different base rate categories
- checking how robust forecasts / estimates are to changes in the observation period / the number of data points used.
- specifically investigating the relationship between accuracy and the number of data points available by constructing a forecast based on the first X data points and subsequently adding more data points to check consistency. The distribution of robustness would itself provide a base rate for how useful base rates are.
- Identifying patterns that make a base rate useful or less useful (e.g. if there is a dynamic over time, simply looking at the base rate may not be enough)
We also aim to obtain a better qualitative understanding of reference class forecasting by asking that forecasters who collect the base rates to reflect on the process as well as the individual base rate categories, for example
- How clear are criteria for inclusion / exclusion and the period that was looked at
- How trustworthy is the data?
- Are there any trends that can be identified?
- General thoughts / lessons learned
How you can help
Suggesting new categories
You can suggest new categories to include in the database here. Suggested categories should ideally be at least one of the following:
- helpful / useful / interesting
- easy to collect
If you have thoughts on anything presented here, please let us know in the comments or get in touch directly.