Effective Altruism: Research Priorities and Opportunities: Public hosted slides, presented at LIS 9 Jun 2021

David Reinstein

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2021-06-13

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Preamble

EA principles, some key hubs

Grants, fellowships, jobs

Agendas and research questions

Message

We can now do better than this vague hope!

We can now do better than this vague hope!

The founders of Effective Altruism took ideas from Philosophy, Economics, and other parts of academia to build a rigorous approach to ‘doing the most good in the world’, and to exploring and measuring this.

  • Miraculously, “EA happened”

Miraculously, EA also has a passionate and influential group of supporters, and a substantial pool of funds for research, interventions, and advocacy!

Big opportunity for academic researchers to have impact, inspiration, funding

  • Partnering within academia, EA research audience

  • Grants

  • Helping students

  • Leave academia for greener (?) pastures at an EA-aligned org

EA and global priorities research offers a huge opportunity for academic researchers to have an positive impact (on the allocation of funds, and on the market of ideas).

There are opportunities for funding to support your research within academia, to promote the impact of your research (and gain valuable feedback), to help students find meaningful careers/research

…and/or you may want to leave academia work directly for an EA-aligned organization (like I did).

Things I’m not going to cover:

  • “Things I’m not going to cover” (no time)

What/who is EA? What research/funding is there?

What is EA?

Doing the ‘most good’ given limited resources (some relationship to utilitarianism)…

but how do we define ‘the most good’?

Question: What do EA’s prioritize most?

  1. Global Poverty
  2. Artificial Intelligence Risk
  3. Climate Change
  4. Cause prioritization
  5. Animal welfare

Results of impromptu survey at LIS conference: What they think EA’s prioritize

.kable_styling <- hijack(kableExtra::kable_styling, full_width=FALSE)
.kable <- hijack(knitr::kable, format.args = list(big.mark = ",", scientific = FALSE))
lis_ea_priority_guess <- factor(c(
"Cause prioritization",
"Global Poverty",
"Cause prioritization",
"Climate Change",
"Global Poverty",
"Climate Change",
"Global Poverty",
"Global Poverty",
"Cause prioritization",
"Cause prioritization",
"Climate Change",
"Cause prioritization",
"Climate Change",
"Artificial Intelligence Risk",
"Climate Change",
"Global Poverty",
"Cause prioritization",
"Cause prioritization",
"Climate Change",
"Global Poverty",
"Cause prioritization",
"Cause prioritization",
"Cause prioritization",
"Global Poverty",
"Global Poverty",
"Global Poverty",
"Cause prioritization",
"Climate Change",
"Climate Change",
"Global Poverty",
"Global Poverty",
"Climate Change",
"Global Poverty",
"Global Poverty",
"Cause prioritization",
"Climate Change",
"Global Poverty",
"Global Poverty"
)
)




lis_ea_priority_guess <- tibble(
  lis_ea_priority_guess = lis_ea_priority_guess
)

lis_ea_priority_guess %>% tabg(lis_ea_priority_guess) %>%
  .kable(caption = "What LIS respondents *think* EAs prioritize most:" ) %>%
  .kable_styling()
What LIS respondents think EAs prioritize most:
lis_ea_priority_guess n percent
Global Poverty 15 0.39
Cause prioritization 12 0.32
Climate Change 10 0.26
Artificial Intelligence Risk 1 0.03

2020 EA survey: all responses

(
priority_ordered_bar <- lik_priority_eas20 %>%
      as.data.frame() %>%
    select(-engagement_num) %>%
    likert() %>%
  plot(.,
     type="bar") +
  ggtitle(title)
)

All responses

… ‘Highly engaged’ (self-rated)

(
priority_ordered_bar_eng <- lik_priority_eas20 %>%
  filter(engagement_num==5) %>%
        select(-engagement_num) %>%
   as.data.frame() %>%
    likert() %>%
  plot(type="bar") +
  ggtitle(title)
)

Highly engaged only (rated 5/5)

How much? …

How much effective altruism/global priorities research funding is there?

How much effective altruism/global priorities research funding is there?

Evidence

(
op_research_grants_tab <-
open_phil_grants %>%
  filter(possible_research==TRUE) %>%
  group_by(year) %>%
  dplyr::summarise(total =  format(sum(amount, na.rm = TRUE), big.mark=",", scientific=FALSE), grants = n()) %>%
  arrange(-year) %>%
    mutate(year=as.character(year)) %>%
    .kable(caption = "Open Phil (likely) research funding by year")  %>%
 .kable_styling()
)
Open Phil (likely) research funding by year
year total grants
2021 19,464,223 14
2020 77,681,029 76
2019 122,463,958 63
2018 38,589,084 52
2017 57,666,403 45
2016 22,386,936 27
2015 2,591,000 9
2014 1,437,720 3
2013 445,000 2

By year and focus area:

(
  op_res_grants_tab_yr_area <-
open_phil_grants %>%
    filter(possible_research==TRUE) %>%
    dplyr::group_by(year, focus_area) %>% # drop_na(!!yvar, !!treatvar) %>%
    summarise(total = sum(amount_usd_k,  na.rm = TRUE)) %>%
    spread(year, total, fill=0) %>%
    arrange(-`2020`) %>%
    .kable(caption = "OpenPhil (likely research) grants by year and area, in $1000 USD") %>%
    .kable_styling()
)
## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
OpenPhil (likely research) grants by year and area, in $1000 USD
focus_area 2013 2014 2015 2016 2017 2018 2019 2020 2021
Scient. Res. 0 0 0 7,085 27,500 19,720 43,770 43,751 672
Biosec. 0 0 300 1,943 7,747 8,704 1,625 12,792 1,000
AI risk 0 0 1,186 6,333 10,798 3,128 61,288 10,891 15,450
Glob. Catastr. 0 0 0 3,070 3,758 0 1,703 4,586 1,500
Farm Animal 0 0 0 820 2,022 4,580 3,296 3,261 841
Other 0 0 10 500 1,550 47 1,050 806 0
Glob. Health/Dev. 0 0 0 0 2,864 210 3,176 678 0
Macro-econ 0 0 0 700 0 700 1,150 600 0
Immig. Pol. 0 1,185 390 0 0 400 0 200 0
Crime/Justice 445 0 180 1,636 1,427 1,101 5,066 115 0
Land Ref. 0 0 275 300 0 0 340 0 0
US pol. 0 253 250 0 0 0 0 0 0 
(
  op_res_grants_line <-
open_phil_grants %>%
  group_by(year, focus_area) %>%
    mutate(total = sum(amount_usd_k, na.rm = TRUE)) %>%
    ggplot() +
    aes(x = year, y = amount_usd_k, colour = focus_area) +
    geom_jitter(width = 0.5, height = 0.2, size=0.8) +
  scale_colour_discrete(labels = function(x) str_wrap(x, width = 15)) +
    geom_line(aes(x=year, y=total)) +
  ylab("Grant amounts in $1k")
)

(
open_phil_grants %>%
  filter(possible_research==TRUE) %>%
  filter(year==2020) %>%
  group_by(Focus.Area) %>%
  summarise(total = format(sum(amount, na.rm = TRUE), big.mark=",", scientific=FALSE), grants = n()) %>%
  dplyr::arrange(-grants) %>%
  .kable(caption = "Open Phil (likely) research funding, 2020") %>%
  .kable_styling()
)
Open Phil (likely) research funding, 2020
Focus.Area total grants
Scientific Research 43,750,718 33
Farm Animal Welfare 3,261,351 13
Potential Risks from Advanced Artificial Intelligence 10,891,345 8
Biosecurity and Pandemic Preparedness 12,792,330 7
Global Health & Development 678,358 5
Criminal Justice Reform 115,000 3
Global Catastrophic Risks 4,586,224 2
Macroeconomic Stabilization Policy 600,000 2
Other areas 805,703 2
Immigration Policy 200,000 1

By organization

(
  op_res_grants_tab_orgs_area <-
open_phil_grants %>%
    filter(possible_research==TRUE) %>%
    dplyr::group_by(Organization.Name) %>% # drop_na(!!yvar, !!treatvar) %>%
  summarise(total = sum(amount_usd_k,  na.rm = TRUE), `number of grants` = n()) %>%
      arrange(-total) %>%
    filter(total>5000) %>%
    .kable(caption = "OpenPhil (likely research) grants by corganization  and area, in $1000 USD") %>%
  .kable_styling()
)
OpenPhil (likely research) grants by corganization and area, in $1000 USD
Organization.Name total number of grants
Georgetown University 55,250 2
UC Berkeley 29,552 18
Nuclear Threat Initiative 20,439 6
Sherlock Biosciences 17,500 1
Machine Intelligence Research Institute 14,756 5
University of Washington (Institute for Protein Design) 11,368 1
Stanford University 8,294 1
Open Phil AI Fellowship 6,760 4
Arizona State University 6,421 1
University of Southern California 6,238 3
Rutgers University 5,982 2
MIT Synthetic Neurobiology Group 5,970 2
Stanford University 5,752 10
Telethon Kids Institute 5,300 1
Foundation for Food and Agriculture Research 5,292 6
Harvard University 5,068 3

Other organizations funding or doing EA/GP research, or likely interested

knitr::include_url("https://www.longview.org/grantmaking",  height = "800px")

EA funding model

Maybe 500 million USD per year in EA/adjacent donations (+ about 250 million from OpenPhil)

  • GiveWell moving ~$80M per year

  • Founders Pledge

  • Longview

  • Effective Giving

  • EA Funds

  • “Gates Foundation seems to do ~$100M-500M/yr of grants in global economic development that seem to have cost-effectiveness on par with GiveWell work”

It’s not all research funding, but some of it is, and it is all interested in prioritization/effectiveness research.

Other research orgs

knitr::include_url("https://eaforum.issarice.com/posts/f6kg8T2Lp6rDqxWwG/list-of-ea-related-organisations", height = "700px")

link

Research orgs (partial list):

  • Future of Humanity Institute (about 70 including affiliates, fellows, affiliates)
  • Global Priorities Institute (about 20 researchers/affiliates)
  • Rethink Priorities (11 researchers, hiring more)
  • GiveWell (about 10 researchers/advisors)
  • Animal Charity Evaluators (5-ish)
  • Machine Intelligence Research Institute (about 20)

How do most ‘effective altruists’ engage; what is most strongly advocated? (Quiz to audience)

  1. Political action
  2. Effective charitable donations/Earning to give
  3. Pursuing a high-impact career
  4. Avoiding environmental damage through personal actions
lis_engage_priority <- factor(c(
"Pursuing a high-impact career",
"Effective charitable donations/Earning to give",
"Effective charitable donations/Earning to give",
"Effective charitable donations/Earning to give",
"Effective charitable donations/Earning to give",
"Pursuing a high-impact career",
 "Pursuing a high-impact career",
 "Pursuing a high-impact career",
 "Pursuing a high-impact career",
"Pursuing a high-impact career",
"Pursuing a high-impact career",
"Pursuing a high-impact career",
"Effective charitable donations/Earning to give",
"Effective charitable donations/Earning to give",
 "Pursuing a high-impact career",
"Pursuing a high-impact career",
"Effective charitable donations/Earning to give",
"Pursuing a high-impact career",
 "Pursuing a high-impact career",
"Effective charitable donations/Earning to give",
"Pursuing a high-impact career",
"Pursuing a high-impact career",
"Pursuing a high-impact career"