List formats
Sometimes, you will see lists written inline, like this:
The first five leaders of the Stoic school were 1) Zeno of Citium, 2) Cleanthes, 3) Chrysippus, 4) Zeno of Tarsus and 5) Diogenes of Babylon.
We can instead format it as a block:
Prediction Scorer REST API
Using forecasting to help decision-making is tricky. To make it slightly easier, I set up a REST API that lets you score predictions. Check out the documentation on https://predictionscorer.herokuapp.com/docs.
This API does the exact same thing as the Python Prediction Scorer library. Using the REST API is convenient if you are not using Python or if you are on a Python version that the library does not support.
Python Prediction Scorer v1.0.0
I just released version 1.0.0 of Python Prediction Scorer.
I wanted to make it simple, so I removed all the complicated parts in favor of a few simple functions to score predictions. Hopefully, I will be able to solve the more advanced use cases later.
Python Prediction Scorer v0.3.0
Scoring predictions is useful, but non-trivial. It can take quite a bit of work to implement and test. In order to make it easier, I have started working on Python Prediction Scorer, an open source library to score and compare predictions.
Modular empirical science
In this article, I suggest a process for empirical science. It is an alternative to the peer-reviewed journal. The overarching goal is scientific progress, and I think this is one way to accelerate it. My suggestion is inspired by modular design, the open science movement and the success of free and open-source software.
Epistemic status
- 80 percent confident that it would produce more scientific progress given the same input.
- 70 percent sure that the idea is not objectively original. I certainly have at least parts of it from other people, and at least parts of it is already being done.
- 70 percent confident that I haven’t found the best name for the concept.
- Many implementation details are unknown to me.
The problem
Imagine that you are a scientist, and you have dreamed up an experiment to answer a question. To illustrate, let us use the hilarious Stanford marshmallow experiment:
Empiricast post mortem
In September 2018, Martin Holten and I founded Empiricast. As I’m writing this, it’s April 2020, and we have stopped working on it. I’d like to share what we did and what we learned so that both we and others can learn from it.
Why We Sleep — a tale of institutional failure
How to write a paper
With the help of a colleague, I recently wrote a white paper about forecasting research and how we want to use it to improve decision-making in organizations. Since people liked the paper and I enjoyed writing it, I thought I’d share some details about our writing process.
Why have a process?
Why should you even use a writing process? Shouldn’t writing be pure creativity, pulled by the force of inspiration? That would be ideal. But in real life, it rarely works. Creation is 5 percent creativity and inspiration. The remaining 95 percent is hard work and discipline. Working hard without some kind of process is not only time-consuming, it's flat-out painful.
Aumann’s agreement theorem
Aumann’s agreement theorem, roughly speaking, says that two agents acting rationally (in a certain precise sense) and with common knowledge of each other’s beliefs cannot agree to disagree.
In other words, when two truth-seeking individuals share information, they approach the same beliefs.
Sometimes, when I’m in a discussion with others, I remember Aumann's agreement theorem. To me, it’s an ideal: Whenever we discuss facts about the world, we should approach agreement. Otherwise, we're failing. This ideal assumes that we are:
Scientific improvement of decision-making and risk management
At Empiricast, we're trying to help organizations make better decisions by working scientifically with forecasting.
At the request of a potential client, we wrote a white paper where we describe our approach and the scientific foundations we’re building upon.