Summary: The most dangerous existential risks appear to be the ones that we only became aware of recently. As technology advances, new existential risks appear. Extrapolating this trend, there might exist even worse risks that we haven’t discovered yet.

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Cross-posted to the Effective Altruism Forum.

Summary

• How we should spend our philanthropic resources over time depends on how much we discount the future. A higher discount rate means we should spend more now; a lower discount rate tells us to spend less now and more later.
• We (probably) should not assign less moral value to future beings, but we should still discount the future based on the possibility of extinction, expropriation, value drift, or changes in philanthropic opportunities.
• According to the Ramsey model, if we estimate the discount rate based on those four factors, that tells us how quickly we should consume our resources¹.
• We can decrease the discount rate, most notably by reducing existential risk and guarding against value drift. We still have a lot to learn about the best ways to do this.
• According to a simple model, improving our estimate of the discount rate might be the top effective altruist priority.

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Summary

Previously, we talked about how much leverage altruists should use. We looked at estimates of optimal leverage based on future projected returns, but this required making certain assumptions about how asset prices behave.

In many ways, theoretical asset pricing models do not reflect how investments behave in practice. These models may overestimate how much leverage to use. We can learn something about the extent of this overestimation by backtesting leveraged portfolios on historical price data.

In the backtests I performed, theoretically-optimal leverage according to the Samuelson share usually did not differ much from empirically optimal leverage according to backtests. However, the Samuelson share overestimated optimal leverage more often than it underestimated, and following the Samuelson share would have occasionally resulted in bankruptcy.

After performing this analysis, I am now somewhat more confident that it makes sense for altruists to apply substantial leverage to their altruistic portfolios, although probably less than the Samuelson share. However, investors should ensure they understand what that entails—in backtests, optimally-leveraged portfolios usually encountered >90% drawdowns at some points.

Disclaimer: I am not an investment advisor and this should not be taken as investment advice. This content is for informational purposes only. Please do your own research or seek professional advice and otherwise take reasonable precautions before making any significant investment decisions. Any given portfolio results are hypothetical and do not represent returns achieved by an actual investor.

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Cross-posted to the Effective Altruism Forum.

Last updated 2020-05-17.

Summary

Philanthropic investors probably have greater risk tolerance than self-interested ones. Altruists can use leverage—borrowing money to invest—to increase the expected utility of their portfolios. They may wish to lever their portfolios at much higher ratios than self-interested investors—likely 2:1 to 3:1, and perhaps much higher (practical concerns notwithstanding).

Unlike normal investors, altruists care about reducing their correlations with other investors, so they should heavily tilt their portfolios toward uncorrelated assets.

This essay will discuss:

1. Traditional vs. altruistic investing
2. Basic arguments for using leverage
3. Appropriate levels of risk for altruists
4. The importance of uncorrelated assets, and where investors might be able to find them
5. Potential changes for philanthropic behavior

Disclaimer: I am not an investment advisor and this should not be taken as investment advice. This content is for informational purposes only. Please do your own research or seek professional advice and otherwise take reasonable precautions before making any significant investment decisions. Any given portfolio results are hypothetical and do not represent returns achieved by an actual investor.

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In an early draft of a paper, Philip Trammell points out two mistakes in my essay on giving now vs. later. And since writing it, I have identified a third mistake.

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Epistemic status: I don’t really understand Kantian deontology.

Summary: According to the rules of Kantian deontology, an action must be impermissible if it has any probability of resulting in an impermissible outcome. But all actions have some probability of resulting in such an outcome. Therefore, all actions are impermissible.

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https://mdickens.me/credence-bet/

In response to a Facebook post, I created a page to make it easy to make bets with people. If two people disagree about a claim and they want to bet on it, they can use this form to calculate how much money each person should bet. Each person should input their best estimate of the probability of the claim being true, and the form will tell them how much to bet. The form ensures that the bet will be fair for both participants–they both expect to win the same amount of money.

Recently, I was browsing IMDb’s list of top-rated TV shows:

According to IMDb ratings, Planet Earth II is the second-best TV show of all time, with 9.5 stars out of 10. But if you look at the ratings of each individual episode, they range from 6.8 to 7.9¹:

In general, the rating of a TV show usually differs from the average rating of that show’s episodes. What does the list of top TV shows look like if we sort by average episode rating instead of show rating? Perhaps voters have different motivations when they’re rating shows than when they’re rating individual episodes, and it could be interesting to see how the ratings differ.

So I downloaded the IMDb public database to find out².

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Experiments are a critical part of science—perhaps even the central feature. But middle school and high school science experiments don’t teach students how experiments are supposed to work.

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Altruists often would like to get good predictions on questions that don’t necessarily have great market significance. For example:

• Will a replication of a study of cash transfers show similar results?
• How much money will GiveWell move in the next five years?
• If cultured meat were price-competitive, what percent of consumers would prefer to buy it over conventional meat?

If a donor would like to give money to help make better predictions, how can they do that?

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