The hottest Decision Theory Substack posts right now

Newcomb's problem involves two boxes: one potentially holds more money than the other based on what an oracle predicts you will choose. Choosing both boxes might seem smart since it guarantees some amount, but there's a deeper philosophical debate about the best strategy.
Some people strongly believe that just picking the blue box (the one with the most potential money) is obviously the best choice, but it's unfair to call those who pick both boxes 'stupid' since their reasoning also makes sense.
Ultimately, this problem raises important questions about decision-making and what it means to want something, suggesting that understanding our desires and decision strategies is complex and varies from person to person.

Threats and warnings are not the same. A threat implies a promise of harm, while a warning offers a caution about potential danger.
Decision-making can be influenced by understanding these differences. Knowing how people respond to threats and warnings helps in planning actions.
Real-life examples can illustrate the impact of threats versus warnings. Recognizing these concepts can improve communication and strategy in various situations.

Bayesian reasoning is something we all use, even if we don't realize it. It's more about how we naturally think than some complex math.
There are two types of uncertainty: aleatoric (random) and epistemic (based on knowledge). Mixing them helps us make better decisions.
Arguing over which type of probability is 'real' is silly. It's better to recognize that life involves many messy decisions where formal reasoning can help, but is often complicated.

We should measure the value of scientific papers to understand their real impact. If a paper doesn't change how people act or think, then it may not be worth much.
To figure out the value of a paper, we can use a formula that compares what outcomes we expect with the information from the paper versus without it. This helps us see if the research is actually useful.
It's important to have good estimates and decisions tied to the research to see its true worth. By doing this, we can better judge which scientific papers are really making a difference.

A famous bet involving coin flips shows how people's risk preferences can be inconsistent. People might reject a single gamble but accept multiple repeats because they think it lowers their risk.
The original advice about investing suggests buying stocks when young and bonds as you age. However, Samuelson's argument raises doubts about this common belief, challenging how we think about risk.
The idea of loss aversion helps explain why people might choose to repeat risky bets. People tend to feel the pain of losing money more than the joy of gaining, which can lead to seemingly irrational decisions.

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The Sleeping Beauty paradox involves a coin flip that affects how often she wakes up, which raises questions about probability. People have different opinions on how she should assess the chance of heads when she wakes up.
One group, called 'halfers', believes the chance of heads remains 50/50 since she doesn't gain new information about the coin when waking up.
Another group, 'thirders', argues she should think there's a one in three chance it's heads because of how many times she might wake up, depending on the coin flip.

There is still no reliable way to detect lies in large language models.
Probing the beliefs of language models is challenging due to limited behavioral evidence and an opaque internal structure.
The debate on whether language models have beliefs is still ongoing, with contrasting views on the necessity of beliefs for these models.

There are two common reactions to uncertainty: one is to act like everything is knowable and try to control it, which can lead to poor decisions. The other is to give up and think that nothing can be done about the unknown, which doesn't help either.
Instead of sticking to those two extremes, there's a better approach. It's important to recognize that not-knowing can lead to new ideas and actions.
We can break down uncertainties into different types. Understanding these helps us figure out how to deal with situations where we don't have all the answers.

Life is full of tradeoffs. When you want something, like a stable job or a big house, you may have to give up other freedoms, like moving easily or having more free time.
It’s important to clearly define what tradeoffs you are okay with. This helps you make better decisions, whether it’s about what to eat or where to live.
Instead of just asking what you want, think about what you are willing to sacrifice. This question helps you understand your priorities and can lead to clearer choices.

Total ordering means you can rank all your options from best to worst. For example, you can see which forex provider offers the best deal for your money.
Partial ordering is when you can compare some options, but not others. Like, you might know one car is better than another, but you can't say how a car compares to a scooter.
In total ordering, you end up with one best choice, while in partial ordering, there can be multiple good choices without a clear best.