# Be less scared of overconfidence
source: https://ift.tt/ybHC0x9 tags: #literature #insights #selfgrowth #benkuhn uid: 202212101111 —
Ben Kuhn!
In both of these situations, I had some mental model of what was going on (“this epidemic is growing exponentially,” “this startup seems good”) based on the particulars of the situation, but instead of using my internal model to make a prediction, I threw away all my knowledge of the particulars and instead used a simple, easy-to-apply heuristic (“experts are usually right,” “markets are efficient”).
I frequently see people leaning heavily on this type of low-information heuristic to make important decisions for themselves, or to smack down overconfident-sounding ideas from other people.
This startup is growing incredibly fast and the founders are some of the most effective people I’ve ever met, but at their current VC valuation, the total comp is lower than my Big Tech job so I can’t justify the move.
I think I could have a big impact as an academic researcher, but most grad students end up depressed and don’t land a tenure-track position, so it’s not worth trying.
You’re going to start a company? Are you aware that 90% of startups fail? What makes you think you and your ragtag band of weirdos are the chosen ones?
Who are you to be sounding the alarm about a pandemic when every past alarm has been false and all the reputable, top-tier experts say not to worry?
These all place way too much weight on the low-info heuristic.
What’s worse, these low-info heuristics almost always push in the direction of being less ambitious, because the low-info view of any ambitious project is that it will fail (most projects run behind schedule, most startups fail, most investors underperform the market, etc.).
Why do people find low-info heuristics so compelling? A few potential reasons:
Many (most?) attempts to reason via specific details are wrong. Most people who think “I’m going to beat the market” don’t; most people who think “I know better than all the experts” are less Balaji Srinivasan and more Time Cube guy.
The reasoning and evidence backing up low-info heuristics is (relatively) legible and easily verifiable. If I claim “90% of startups fail,” I can often cite a study for support. Whereas if I claim “the markets aren’t freaking out enough about COVID,” I’d need to make a much more complicated argument to explain my reasoning.
It’s relatively straightforward to reason with low-info heuristics even when you’re not an expert in the domain. For something like a forecasting challenge, where forecasters need to make predictions across a wide range of topics and can’t possibly be an expert in all of them, this is very important.
Because it’s much more objective, reasoning via low-info heuristics gives you many fewer opportunities to fall prey to biases like optimism bias, motivated reasoning, the planning fallacy, etc.
Sometimes I see people use the low-info heuristic as a “baseline” and then apply some sort of “fudge factor” for the illegible information that isn’t incorporated into the baseline—something like “the baseline probability of this startup succeeding is 10%, but the founders seem really determined so I’ll guesstimate that gives them a 50% higher probability of success.” In principle I could imagine this working reasonably well, but in practice most people who do this aren’t willing to apply as large of a fudge factor as appropriate. Strong evidence is common 202212092211
For example, the efficient market hypothesis (“asset prices incorporate all available information, so it’s hard to beat the market” used in the above example to infer that “venture capitalists value companies correctly”) is justified by economic theory that relies on a few assumptions:
Low transaction costs: The cost of doing a trade in the market (in this case, an investment) must be near-zero so that people can use any mispricings to get rich.
Enough smart money: The well-informed and rational players in the market need to have enough capital to take advantage of any pricing inefficiencies that they notice.
No secrets: The “available information” must be available to enough of the smart money that it can be used to correct mispricings.
Ability to profit: There must be a way for a smart market participant to make money from a mispriced asset.
In the case of venture capital, many of these assumptions are super false. Fundraising takes a lot of time and money: transaction costs are high. Venture capitalists YOLO their valuations after a few meetings: they frequently miss important information. And it’s impossible to short-sell startups, so there’s no market mechanism to correct an overpriced company. You can see the outcome of this in the fact that there are venture capitalists that consistently beat “the market’s” returns.
Unfortunately, low-info heuristics tell you that outliers can’t exist. By definition, most members of any group are not outliers, so any generalized heuristic will predict that whatever you’re looking at isn’t an outlier either. If you index too heavily on what the average outcome is, you’re deliberately blinding yourself to the possibility of finding an outlier.
The problem is that the bad consequences of underconfidence and under-ambition are severe but subtle, whereas the bad consequences of overconfidence and wishful thinking are milder but more obvious. If you’re overconfident, you’ll try things that fail, and people will laugh at you. If you’re underconfident, you’ll avoid making risky bets, and miss out on the potential upside, but nobody will know for sure what you missed.
In fact, outperforming low-info heuristics isn’t just possible; it’s practically mandatory if you want to have an outsized impact on the world. That’s because leaning too heavily on low-info heuristics pushes people away from being ambitious or trying to search for outliers.
OK, so what should you do instead of relying on low-info heuristics? Here are my suggestions:
Build gears-level models of the decision you’re trying to make. If you’re deciding, e.g., where to work, try to understand what makes different jobs awesome or terrible for you.
Think really hard 202212101100 about the problem. Most inside views are wrong—to stand a fighting chance of beating the outside view, you’ll need to put a lot of effort in.
Don’t fool yourself with motivated reasoning. Stress-test your ideas; ask yourself what the best arguments against your inside view are and see if you can rebut them.
To the extent that you do use low-info heuristics, use them as a stress test rather than a default belief. “90% of startups fail” is useful to know as a warning to try to mitigate failure modes. It’s dangerous when you hear it and stop thinking there. Don’t be afraid to try ambitious things where the downside of failing is low, and the upside of succeeding is high!