In the EU there’s some recent regulation about bottle caps being attached to bottles, to prevent littering. (this-is-fine.jpg)
Can you let the app come up with a good way to estimate the cost benefit ratio of this piece of regulation? E.g. (environmental?) benefit vs (economic? QALY?) cost/drawbacks, or something like that. I think coming up with good metrics to quantify here is almost as interesting as the estimate itself.
Or as a possible more concrete prompt if preferred: “Create a cost benefit analysis for EU directive 2019⁄904, which demands that bottle caps of all plastic bottles are to remain attached to the bottles, with the intention of reducing littering and protecting sea life.
Output:
key costs and benefits table
economic cost for the beverage industry to make the transition
expected change in littering, total over first 5 years
QALYs lost or gained for consumers throughout the first 5 years”
I tried two variations. In the first, I just copied and pasted the text you provided. In the second, i first asked Perplexity to find relevant information about your prompt—then I pasted this information into Squiggle AI, with the prompt.
Of the ones with data, they agree that the costs are roughly €5.1B, the QALYs gained are around 30 to 3000, leading to a net loss of around €−5.1B. (They both estimate figures of around €50k to €150k of willingness to pay for a QALY, in which case 30 to 3000 QALYs is very little compared to the cost).
My personal hunch is that this is reasonable as a first pass. That said, I think the estimate of the QALYs gained seems the most suspect to me. These models estimated this directly—they didn’t make sub-models of this, and their estimates seem wildly overconfident if this is meant to include sea life.
I think it could make sense to make further models delving more into this specific parameter.
i first asked Perplexity to find relevant information about your prompt—then I pasted this information into Squiggle AI, with the prompt.
It’d be cool if you could add your perplexity api key and have it do this for you. a lot of the things i thought of would require a bit of background research for accuracy
Yep, this is definitely one of the top things we’re considering for the future. (Not sure about Perplexity specifically, but some related API system).
I think there are a bunch of interesting additional steps to add, it’s just a bit of a question of developer time. If there’s demand for improvements, I’d be excited to make them.
In the EU there’s some recent regulation about bottle caps being attached to bottles, to prevent littering. (this-is-fine.jpg)
Can you let the app come up with a good way to estimate the cost benefit ratio of this piece of regulation? E.g. (environmental?) benefit vs (economic? QALY?) cost/drawbacks, or something like that. I think coming up with good metrics to quantify here is almost as interesting as the estimate itself.
Or as a possible more concrete prompt if preferred: “Create a cost benefit analysis for EU directive 2019⁄904, which demands that bottle caps of all plastic bottles are to remain attached to the bottles, with the intention of reducing littering and protecting sea life.
Output:
key costs and benefits table
economic cost for the beverage industry to make the transition
expected change in littering, total over first 5 years
QALYs lost or gained for consumers throughout the first 5 years”
Okay, I ran this a few times.
I tried two variations. In the first, I just copied and pasted the text you provided. In the second, i first asked Perplexity to find relevant information about your prompt—then I pasted this information into Squiggle AI, with the prompt.
Here are the outputs without the Perplexity data:
https://squigglehub.org/models/ai-generated-examples/eu-bottle-directive-172
https://squigglehub.org/models/ai-generated-examples/eu-bottle-directive-166
And with that data:
https://squigglehub.org/models/ai-generated-examples/eu-bottle-directive-with-data-113
https://squigglehub.org/models/ai-generated-examples/eu-bottle-directive-with-data-120
Of the ones with data, they agree that the costs are roughly €5.1B, the QALYs gained are around 30 to 3000, leading to a net loss of around €−5.1B. (They both estimate figures of around €50k to €150k of willingness to pay for a QALY, in which case 30 to 3000 QALYs is very little compared to the cost).
My personal hunch is that this is reasonable as a first pass. That said, I think the estimate of the QALYs gained seems the most suspect to me. These models estimated this directly—they didn’t make sub-models of this, and their estimates seem wildly overconfident if this is meant to include sea life.
I think it could make sense to make further models delving more into this specific parameter.
It’d be cool if you could add your perplexity api key and have it do this for you. a lot of the things i thought of would require a bit of background research for accuracy
Yep, this is definitely one of the top things we’re considering for the future. (Not sure about Perplexity specifically, but some related API system).
I think there are a bunch of interesting additional steps to add, it’s just a bit of a question of developer time. If there’s demand for improvements, I’d be excited to make them.