Causal Network Model III: Findings

post by Alex_Barry · 2017-11-22T15:43:14.476Z · score: 7 (7 votes) · EA · GW · Legacy · 4 comments

Contents

    DisclaimerThe model is both very simplified, and many of the results depend to a large extent on particular variables with values about which we have very little information. Because of this, and because of the general limitations of the model, any findings should be taken as at most invitations to further research, rather than as concrete pronouncements of effectiveness. (In the past we have found a fair number of mistakes involving numbers being wrong by a few orders of magnitude!)
    For the sake of space and readability we largely omit these qualifiers throughout the rest of the post, although will bring it up when results seem particularly uncertain.
    We strongly recommend you read part I (particularly sections 2 and 5) to get appropriate background for the model before this post. 
  1. Summary of important findings
      1.1 Steakless Salvation
      1.2 Climate Catastrophe:
      1.3 Cagefree Costs
      1.4 Existential Effectiveness
      1.5 Morality Matters
      1.6 Larder Logic
  2. Highlighted areas for future research
  3. Effects of specific inputs
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3 comments

This is a writeup of the findings from the Causal Networks Model, created by CEA summer research fellows Alex Barry and Denise Melchin. Owen Cotton-Barratt provided the original idea, which was further developed by Max Dalton. Both, along with Stefan Schubert, provided comments and feedback throughout the process.

This is of a multipart series of posts explaining what the model is, how it works and our findings. We recommend you read the ‘Introduction & user guide’ post first before this post to give the correct background to our model. The structure of the series is as follows:

  1. Introduction & user guide (Recommended before reading this post)

  2. Technical guide (optional reading, a description of the technical details of how the model works)

  3. Findings (this post)

  4. Climate catastrophe (one particularly major finding)

 

The structure of this post is as follows:

 

  1. Summary of important findings

  2. Highlighted areas for further research

  3. Effects of specific inputs

 

Some of the results listed in the last section are fairly minor, so readers may wish to focus only on the first two sections.

 

Disclaimer
The model is both very simplified, and many of the results depend to a large extent on particular variables with values about which we have very little information. Because of this, and because of the general limitations of the model, any findings should be taken as at most invitations to further research, rather than as concrete pronouncements of effectiveness. (In the past we have found a fair number of mistakes involving numbers being wrong by a few orders of magnitude!)

 

For the sake of space and readability we largely omit these qualifiers throughout the rest of the post, although will bring it up when results seem particularly uncertain.

 

We strongly recommend you read part I (particularly sections 2 and 5) to get appropriate background for the model before this post.

 

1. Summary of important findings

1.1 Steakless Salvation

Even with very small probabilities of success, research into developing cost-effective farmed meat alternatives (‘clean meat’) can be cost-competitive compared with other animal welfare alternatives.

 

This is due to the potential for ‘clean meat’ to gain a large proportion of the market share very quickly once it is lower in price but equal in quality to conventional meat. This means that it will scale far better than most animal interventions. Additionally this seems to be potentially a very effective way to reduce climate change, which has many other beneficial effects, as explained below.

 

This is true even for relatively limited forms of ‘clean meat’, as in our model we only consider the possibility of developing cost-competitive ‘clean’ ground meat, which seems much more attainable in the short term; this is still effective enough to seem plausibly better than conventional animal outreach.

 

This all assumes that the market is not actively hostile to clean meat, and all else equal will simply chose the cheaper option. This seems particularly applicable in the ground meat case.

 

1.2 Climate Catastrophe:

Climate change seems to be a much bigger problem than most people normally consider, both due to the potential damage caused by the Earth’s temperature rising 2-3 degrees, as well as the tail risk of runaway warming being a global catastrophic risk.

 

Unfortunately, many typical EA activities (e.g. giving more resources to the global poor, improving farmed animal conditions) probably cause increases in CO2 emissions, and so could potentially be negative overall due to the effects of climate change. This argument is particularly worrying if you think the potential of the far future morally dominates decision-making. For more discussion and elaboration on the climate x-risk connection see Part IV.

 

1.3 Cagefree Costs

One example of how considering climate change could cause an apparently positive intervention to be negative is corporate outreach focused on animal welfare. In particular this affects Mercy For Animals’s success in 2016 in getting large businesses to pledge to change from battery to cage-free eggs, affecting a total of 80 million laying hens a year. While this is a large win for animal welfare, cage-free hens are somewhat less efficient, causing more CO2e emissions per egg. This effect is large enough that for every year earlier MFA caused this to happen compared to when it would have happened otherwise, it will cost (very, very approximately) 500 QALYs due to death and disease from climate change before 2050.

 

This means that if you value a chicken-QALY at less than 1/20,000 of a human QALY, our model outputs this intervention as neutral, or even negative. [1]

 

1.4 Existential Effectiveness

Using our default estimates of the chance of existential risk and researchers’ ability to reduce it (or even estimates orders of magnitude lower), existential and global catastrophic risk research and policy work dominates other categories in terms of value. This is true even when comparing to other interventions in terms of QALYs saved before 2050. [2]


Therefore, you could justify giving to existential risk research charities even if you took a person-affecting view or strongly discounted future lives, as long as you put enough chance on research being able to reduce the risks. (That said, if you value guaranteed impact over high expected impact, then e.g. global poverty charities might still be more attractive).

 

1.5 Morality Matters

Many of the actions considered in the model end up being positive under some moral theories and negative under others. Examples include the farmed animal welfare case set out above, or actions that could negatively affect the far future while providing value today. This suggests that charity recommendations should be more dependant on the particulars of people's moral theories.

 

Despite seeming obvious when stated, this seems to be somewhat at odds with how the EA community actually operates, where charity evaluators etc. don’t really talk much about morality when giving recommendations.

 

1.6 Larder Logic

Whether one expects interventions that reduce the number of farmed animals to be positive or negative often depends on whether one thinks factory farmed cows have lives worth living. This is also true for other animals, but cows seem to the biggest case for disagreement.

 

2. Highlighted areas for future research

As discussed in the first post, many of the model’s results depend to a large extent on values we know very little about, and there are many important areas of the world we were not able to include in the model due to complexity or time constraints.

 

In particular, the model suggests that it would be very useful to learn more about the following areas:

 

Some things that we were unable to capture in the model that also seem important to investigate include:

 

3. Effects of specific inputs

In this section we go into detail about the effects of changing the different funding inputs on the user tool, how these effects depend on the ‘important variables’ and the impact of different moral interpretations of the results.

 

(In this section ‘good for climate change’ means reducing CO2 emissions etc.)

 

Veg*n outreach

 

Government / corporate animal welfare reform

 

 Animal Product Alternatives

 

 GiveDirectly

 

Deworming

 

Against Malaria Foundation

 

EA Outreach

 

Cause prioritisation

 

Global catastrophic and x-risk policy and strategy

 

 Global catastrophic and x-risk research

 

 Far future (global catastrophic and x-risk) outreach

 

General animal outreach / far future / global poverty

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Our next post will be a writeup of one particular finding of the model, the climate change catastrophic risk connection findings of the model which constitutes Part IV of the series.

 

Feel free to ask questions in the comment section, or email us (denisemelchin@gmail.com or alexbarry40@gmail.com).

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[1]: Non-caged hens seem to emit about 15-25% more greenhouses gases per egg (due to increased food, heating and land requirements and a higher proportion of eggs being lost), which results in around 500,000 tonnes more CO2 emitted per year. Our very rough estimates lead to the conclusion that approximately each additional 1,000 Tonnes of CO2e emitted will cause the loss of one QALY before 2050.

 

[2]: Default assumptions used in our model were 7% x-risk chance by 2050, and 10,000 researches working for a decade could half x-risk to 3.5%.Hence 1 researcher year reduces risk by 0.000035% (percentage points) and say one researcher year costs $50,000, so 7*10^-10 percentage points reduction in x-risk per $. If extinction costs 25*7 billion = 175 billion QALYS, multiplying out gives just over 1 QALY saved per $.

 

4 comments

Comments sorted by top scores.

comment by Owen_Cotton-Barratt · 2017-11-23T23:15:36.136Z · score: 2 (2 votes) · EA(p) · GW(p)

Thanks for the write-up!

I found the figures for existential-risk-reduced-per-$ with your default values a bit suspiciously high. I wonder if the reason for this is in endnote [2], where you say:

say one researcher year costs $50,000

I think this is too low as the figure to use in this calculation, perhaps by around an order of magnitude.

Firstly, that is a very cheap researcher-year even just paying costs. Many researcher salaries are straight-up higher, and costs should include overheads.

A second factor is that having twice as much money doesn't come close to buying you twice as much (quality-adjusted) research. In general it is hard to simply pay money to produce more of some of these specialised forms of labour. For instance see the recent 80k survey of willingness to pay of EA orgs to bring forward recent hires, where the average willingness to forgo donations to move a senior hire forward by three years was around $4 million.

comment by Alex_Barry · 2017-11-24T17:58:45.829Z · score: 1 (1 votes) · EA(p) · GW(p)

Ah good point on the researcher salary, it was definitely just eyeballed and should be higher.

I think a reason I was happy to leave it low was as a fudge to take into account that the marginal impact of a researcher now is likely to be far greater than the average impact if there were 10,000 working on x-risk, but I should have clarified that as a separate factor.

In any case, even adjusting the cost of a researcher up to $500,000 a year and leaving the rest unchanged does not significantly change the conclusion, with the very rough calculation still giving ~$10 per QALY (but obviously leaves less wiggle room for skepticism about the efficacy of research etc.)

comment by Denkenberger · 2017-11-24T19:06:15.861Z · score: 2 (2 votes) · EA(p) · GW(p)

Indeed, the Oxford Prioritisation Project found cost-effectiveness about an order of magnitude lower than yours for AI. But still it was more cost-effective than global poverty interventions even in the present generation. And alternate foods for agricultural catastrophes are even more cost effective for the present generation.