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2026-01-21

Some heuristics to understand a new scientific field quickly

Disclaimer

• Quick Note

Summary

• Ask yourself what is the most important question in this scientific field, assuming the goal is maximising human happiness (not satisfying your curiosity).
• Ask yourself what are the main instruments used to collect data in this field. Actually go look at the raw data collected by each instrument.
• Figure out how much experiments cost. Figure out how much money can good work make (from the market or from academic grantmakers), and who gets this money. Figure out how much is the cost associated (in dollars or lives) with bad work.

Main

• Hamming question
  • Ask yourself: What is the most important question in this field, assuming the goal is maximising human happiness or minimising human suffering or similar? Do not assume satisfying curiosity is the goal, when you ask yourself the Hamming question.
  • For example, in the field of biotech, genetic engineering of humans is obviously more important to pay attention to, than genetic engineering of plants.
  • You can also ask yourself what is the hamming question across multiple scientific fields. You'll end up realising most scientific fields are a waste of time, like I did. That is a whole separate discussion.
• Instruments of data acquisition
  • Make a list of all scientific instruments used to acquire new data in your scientific field. Actually go look up photos and videos of the data each of these intruments acquired, in some important experiments.
  • Many low quality papers refuse to share the raw data their work is based on, ignore these papers.
  • For example, in the field of biotech, main instruments include DNA sequencing, protein assays, electron microscope. So you should actually go look at the raw data in the file formats outputted by a DNA sequencer, an electron microscope and a protein assay.
  • Your entire field of research is a bunch of humans doing input, processing and output. Input is costly (paying for experimentalists and their equipment is costly) and processing is costly (paying for theoreticians is costly) so your field needs to intelligently decide which inputs are worth capturing and which inputs are worth thinking more deeply about.
  • If input is bad or too small, good processing won't fix it.
• Social and financial incentives
  • Ask yourself: How much does an experiment cost?
    • If experiments cost more, probably theoreticians do a lot of work, even in deciding which experiments are worth running.
    • If experiments are cheap, probably there's a huge amount of data actively being collected by the field right now. (Or, maybe the field is already mostly solved, and nobody has important new questions in it.)
  • Ask yourself: What is the cost of doing an experiment poorly? Has someone already suffered this cost before?
    • In fields like medicine and food, cost of bad experiments is deaths of people. Expect there to be a lot of bureacratic safety boards that would have prevented a lot of experiments from happening (including some useful ones). "Regulations are written in blood" is a common maxim. It's not enough for there to be some hypothetical cost, but someone should have actually observed it in practice, for the regulations and bureaucracy to enter the field.
  • Ask yourself: What is the reward of doing good work?
    • Ask yourself: Is the reward for solving this problem big or small? Would a successful product in this field make a million dollars, or a billion, or a trillion? Just multiply theoretical maximum number of users with your guess of their willingness-to-pay in dollars, to get a dollar estimate.
    • For example, building strong materials makes you trillions of dollars if the material is cheaper than steel, billions of dollars if the material is cheaper than platinum, and only millions of dollars if the material is more expensive than diamond.
    • Ask yourself: Which company makes money when good work happens? Which academic institutes get to use this work to extract more funds out of the grantmaker? Does any of the money flow back to the person who actually does the good work?
    • The more intermediaries are sitting in between the work and the profit, the less you should expect good work to be happening.
  • Ask yourself: What is the cultural attitude of the people in the field towards those who solve hard problems? What is the cultural attitude of the people in the field towards those who make money from the solutions?
    • A lot of people's cultural attitudes are probably downstream of the incentives above. There might be many people in the field who do not like the current financial incentive structure for various reasons, and they might be judgmental of people who aim to make money in response. This is basically them applying social incentives in the opposite direction, to reduce the total incentive for you.
    • Usually the people making money win regardless, and people judging them lose. Sometimes the field as a whole succeeds in bringing in some regulation at national or international level, to block the people making money.

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