The Nonlinear Library EA - Exploring Key Cases with the Portfolio Builder by Hayley Clatterbuck
Jul 10, 2024
12:15
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Exploring Key Cases with the Portfolio Builder, published by Hayley Clatterbuck on July 10, 2024 on The Effective Altruism Forum.
Key Links:
Link to the tool
Link to post introducing the tool
2-min Intro Video
5-min Basics Video
Introduction
Here, we show how the Portfolio Builder can be used to explore several scenarios and controversies of interest to the EA community. We are far from certain that the parameter values we have chosen below are accurate; we have not rigorously vetted them. Instead, we have used intuitive parameter values for illustrative purposes. Our goal here is to show users how they can put the Portfolio Tool to work, not to settle first-order questions.
Portfolio allocations across cause areas are strongly influenced by one's views about the potential scale of successes in those areas and the probability of those successes. For example, if you assign little or no value to the lives of animals, you will think that even "successful" projects in that area achieve very little. The expected value of x-risk actions varies by many orders of magnitude depending on one's estimate of the value of the future (and future risk trajectories).
In the tool, scale is primarily captured by the value of the first $1,000, which users can specify via their answer to quiz question 3 or by manually adjusting the 'Positive Payoff Magnitude' in the custom settings. This sets the initial scale of the payoff curve, telling you how much value is achieved for $1,000. Then, the rate of diminishing returns tells you how much the curve's slope changes as more money is spent.
For this reason, the rate of diminishing returns also captures aspects of overall scale. If you think that a cause area is very cost-effective at small amounts of money but that returns fall off rapidly, the overall amount of good that can be achieved in that area will be quite small. We can use these two strategies to characterize several key scenarios.
Focusing on the next few generations
Consider the "common sense case" for x-risk reduction, which only assesses x-risk actions by their effects on the next several generations. Here, we draw on Laura Duffy's analysis of the cost-effectiveness of x-risk actions under a wide range of assumptions and decision theories.
To evaluate the relatively short-term effects of x-risk reduction, we assume that the world's average population size and quality of life per person will remain similar to their current levels (generating about 8 billion DALYs/year) and only consider value over the next 120 years. Therefore, we estimate that there are about 1 trillion DALYs in the span of the future that we care about here.
One way to characterize this scenario is to start with an estimate of the basis points of risk reduced per billion dollars spent (basis points per billion or bp/bn). Then, we use the estimate of the available value in the short-term future to calculate the expected value of a basis point reduction in risk. The naive way to do this is as follows.[1] Suppose we estimate that a successful x-risk project would reduce 3 bp/bn.
If the future contains 1 trillion DALYs, 3 bp has an expected value of 300 million DALYs. A cost-effectiveness of 300 million DALYs per 1 billion dollars spent is the same as 300 DALYs per $1000.[2]
Suppose we take that 300 DALYs/$1000 estimate to be the all-things-considered estimate of the cost-effectiveness of x-risk, which already incorporates both the possibilities of futility and backfire. Then we would assume that x-risk has a 100% chance of success and adjust the scale and diminishing returns until the expected DALYs/$1000 is correct (for the budget we're going to spend):
At these levels of cost-effectiveness, the tool tends to assign all of the portfolio to x-risk causes under EV maximization.[3]
However, we don't think that the 3 ...
Key Links:
Link to the tool
Link to post introducing the tool
2-min Intro Video
5-min Basics Video
Introduction
Here, we show how the Portfolio Builder can be used to explore several scenarios and controversies of interest to the EA community. We are far from certain that the parameter values we have chosen below are accurate; we have not rigorously vetted them. Instead, we have used intuitive parameter values for illustrative purposes. Our goal here is to show users how they can put the Portfolio Tool to work, not to settle first-order questions.
Portfolio allocations across cause areas are strongly influenced by one's views about the potential scale of successes in those areas and the probability of those successes. For example, if you assign little or no value to the lives of animals, you will think that even "successful" projects in that area achieve very little. The expected value of x-risk actions varies by many orders of magnitude depending on one's estimate of the value of the future (and future risk trajectories).
In the tool, scale is primarily captured by the value of the first $1,000, which users can specify via their answer to quiz question 3 or by manually adjusting the 'Positive Payoff Magnitude' in the custom settings. This sets the initial scale of the payoff curve, telling you how much value is achieved for $1,000. Then, the rate of diminishing returns tells you how much the curve's slope changes as more money is spent.
For this reason, the rate of diminishing returns also captures aspects of overall scale. If you think that a cause area is very cost-effective at small amounts of money but that returns fall off rapidly, the overall amount of good that can be achieved in that area will be quite small. We can use these two strategies to characterize several key scenarios.
Focusing on the next few generations
Consider the "common sense case" for x-risk reduction, which only assesses x-risk actions by their effects on the next several generations. Here, we draw on Laura Duffy's analysis of the cost-effectiveness of x-risk actions under a wide range of assumptions and decision theories.
To evaluate the relatively short-term effects of x-risk reduction, we assume that the world's average population size and quality of life per person will remain similar to their current levels (generating about 8 billion DALYs/year) and only consider value over the next 120 years. Therefore, we estimate that there are about 1 trillion DALYs in the span of the future that we care about here.
One way to characterize this scenario is to start with an estimate of the basis points of risk reduced per billion dollars spent (basis points per billion or bp/bn). Then, we use the estimate of the available value in the short-term future to calculate the expected value of a basis point reduction in risk. The naive way to do this is as follows.[1] Suppose we estimate that a successful x-risk project would reduce 3 bp/bn.
If the future contains 1 trillion DALYs, 3 bp has an expected value of 300 million DALYs. A cost-effectiveness of 300 million DALYs per 1 billion dollars spent is the same as 300 DALYs per $1000.[2]
Suppose we take that 300 DALYs/$1000 estimate to be the all-things-considered estimate of the cost-effectiveness of x-risk, which already incorporates both the possibilities of futility and backfire. Then we would assume that x-risk has a 100% chance of success and adjust the scale and diminishing returns until the expected DALYs/$1000 is correct (for the budget we're going to spend):
At these levels of cost-effectiveness, the tool tends to assign all of the portfolio to x-risk causes under EV maximization.[3]
However, we don't think that the 3 ...