EdoArad
Bio
Participation1
Hey! I'm Edo, married + 2 cats, I live in Tel-Aviv, Israel, and I feel weird writing about myself so I go meta.
I'm a mathematician, I love solving problems and helping people. My LinkedIn profile has some more stuff.
I'm a forum moderator, which mostly means that I care about this forum and about you! So let me know if there's anything I can do to help.
I'm currently working full-time at EA Israel, doing independent research and project management. Currently mostly working on evaluating the impact of for-profit tech companies, but I have many projects and this changes rapidly.
Posts 49
Comments796
Topic Contributions32
I have a very limited understanding of this field, so the following is probably a bit wrong but hopefully leading in the right direction.
In the US (and maybe some other countries) there is a regulated energy demand & supply prediction market. This is set up at various points in the electric grid, which is connected to many different suppliers of both renewable and non-renwable sources of electricity.
The coal power plants are required to be able to deliver enough electricity so that there is a low chance of power outage. The bound on that chance is defined by regulations; I'm not sure whether it is measured retroactively (say, % of days without power per household) or directly from the prediction market (say, prepare to supply at least 4 std above the mean demand). Prediction is key here, as plants need to prepare in advance (say, 12 hours, not sure) for some technical reason and are committed to some degree of energy generation. The better the prediction, the less they need to generate to be safe from edge cases.
The addition of highly-variable/unpredictable renewable sources of energy to the grid means that they can take some of the load off from coal plants. But again, increasing the predictive accuracy means that the coal plants can generate less excess energy.
I think there are some estimates of exactly how much near-term CO2 emissions are being reduced based on better predictions. I'm not sure how to think about the long-term effects on the industry.
Also, there may be other solutions in the longer term. Say, better energy storage mechanisms may replace the need to have highly accurate predictions, as suppliers may save excess energy with less waste.
As a career, my main guess is that the potential for E2G here is greater than the direct impact one has. That is, these are generally high-paying roles and I suspect that donating, say, 10% of the income to Clean Air Task Force would be much more effective for the energy transition. If that's the case, it should be compared to other possible jobs with even higher salaries.
However, if you are interested in learning more about the industry in general, I think that working in such a company could be a great way for people with strong quantitative background to do so. Generally, when doing any type of data science you are going to learn a lot on the subject matter, and in this case it may even be more so.
Again, these are rough guesses. I'd be interested if you or anyone else here would find relevant literature on the topic or write some stronger opinions and analyses.
Thanks! Yeah, I totally agree - the topic is surprisingly delicate and nonintuitive, and my examples above are too technical.
By the way, I'd love it if other people would write posts that make the exact same point but better! (or for different audiences).
I think in this thread there are two ways of defining costs:
- Michael considers the cost as the total amount spent
- Stan suggests a case where the cost is the amount needed to be spent per unit of intervention.
I think this is the major source of disagreement here, right?
This discussion resembles the observation that the cost-effectiveness ratio should mostly be used in the margin. That is, in the end we care about something like and when we decide where to spend the next dollar we should compute the derivatives with respect to that extra resource and choose the intervention which maximizes that increased value.
Things to add:
- Graphical example
- Explicit discussion of GiveWell's use of cost/effect
- Real-world examples where this causes a significant error
- Expand on the lognormal example, discuss cases where the variance is in the same order of magnitude as the expected value
- What this says about how we should model uncertainty
I like the distinction of the various kinds of risk aversions :) Economists seem too often to conflate various types of risk aversion with concave utility.
I feel a bit uneasy with REV as a decision model for risk aversion about outcomes. Mainly, it seems awkward to adjust the probabilities instead of the values, if we care more about some kinds of outcomes. It doesn't feel like it should be dependent on the probability for these possible outcomes.
Why use that instead of applying some function to the outcome's value?
If I understand correctly, all the variables are simulated freshly for each model. In particular, that applies to some underlying assumptions that are logically shared or correlated between models (say, sentience probabilities or x-risks).
I think this may cause some issues when comparing between different causes. At the very least, it seems likely to understate the certainty by which one intervention may be better than another. I think it may also affect the ordering, particularly if we take some kind of risk aversion or other non-linear utility into account.
To be clear, this would be a problem in any uncertainty-based CEA modeling, and clearly the situation in non-randomized models is usually much worse. It may also be very minor, not sure.