So, our priors result in a normal distribution of heights

model_height_prior <- brm(

- brms’ default and my own

sample_prior = "only",

file = "models/model_height_prior.rds"

family = gaussian,

we can simulate what we believe the data to be

The sigma prior

We’re not super uncertain about people’s heights, though, so let’s use a normal distribution.

But this is the default prior. brms determines this automatic prior by peeking at the data, which is not what we want to do. Instead, we should create our own.

parameter refers to, well, sigma

we should start with defining our beliefs, rather than immediately jumping into running an analysis.

a null effect is

predicts costly intergroup behavior

he time we can expect to wait between events is a decaying exponential.

This is what “5 expected events” means! The most likely number of meteors is 5, the rate parameter of the distribution.

Include a field to add data from the “How did you hear about EA UNI NAME” question

Attrition rate = % of fellows who do not complete the fellowship, assuming that completion of a fellowship is defined as attending at least 6 of 8 meetings

The form will also include some formal definition for each category. What should this be?

Completed Fellowship, highly engaged

This form will consist of a list of all Fellows who filled out the “How you heard about us” question. Each organizer will be prompted to label each fellow as one of the following:

Post-Fellowship Engagement Form:

using email automation

It may not be worth tracking at all.

maybe) Other variables which we might be interested in (Group age, # of organizers, existing group size, etc.).

Fellowship application, and regularly track fellowship attendance for every Fellow.

to be sent 8/30

our base

Participating groups will fill

outreach data

mtcars) integrate(function(x) sin(x) ^ 2, 0, pi)

We Can community

Apply the quadratic approximation to the globe tossing data with rethinking::map().

Why hasn’t such a movement for something like Guided Consumption happened already? Because Guiding Companies, by definition, generate profit for charities instead of traditional investors, a major issue they face is that Guiding Companies cannot access the same investment pool of private equity and angel investors for seed money. One solution to this would be to seek financing from philanthropists, particularly those who are looking to spend their money to advance the same cause area as the Guiding Company. However, the question remains: if Guided Consumption is a more effective means of funding charities than direct donation, why has this not been more fully explored already?   I suspect that the reason stems from a deep-seated psychological separation between the way that people think about the business world, essentially a rather competitive, dog-eat-dog mindset and the kinder, more magnanimous mindset involved in charity work. The notion also seems to violate intuitions about sacrifice involved in charitable contributions, although these intuitions do not hold with the deliberate substitution of traditional stakeholders for charities. I would also note that some further red-teaming can be found in the comments of the longer paper.

But even if Guiding Companies engage in activities that consumers take issue with regarding traditional firms, such as competitive (i.e., princely) compensation for CEOs, it is not clear why this would cause a consumer to choose a company that enriches shareholder over a company that helps fight global poverty.

What if selfish motivations make for the best founders/investors/etc.? The efforts of philanthropic investors are cap

the public

Which Market Sectors?

low-differentiation sectors, it may be easier to construct a “no-brainer” where a consumer is genuinely ambivalent as to two product

Another approach is to capitalize on virtue-signaling, perhaps through products that could enable a consumer to conspicuously show that they bought through a Guiding Company.

A movement that enables everyday people to help charities without sacrificing anything personally should be much easier than one that demands people give significant things up or even mildly inconveniences people.

I am optimistic about the prospects for a movement developing because of what it allows for consumers: they get the same product, at the same price, but profits benefit charities rather than shareholders.

to be the most powerful, would likely require a social movement

although a Guiding Company would likely enjoy a degree of advantage correspondent with a Guiding Company being able to communicate this feature with its customer base.

the identity of the entities that benefit from your purchase, often, owners in some form.

. This is because charities are more popular than normal investors and

would have a competitive advantage

+ (1|reader)

Thinking by analogy to a Bayesian approach, what does it mean that we assume the intercept is a “random deviations drawn from a distribution”? Isn’t that what we always assume, for each parameter in a Bayesian model … so then, what would it mean for a Bayesian model to have a fixed (vs random) coefficient?

Why wouldn’t we want all our parameters to be random effects? Why include any fixed effects … considering general ideas of overfitting and effects as draws from larger distributions?

How distinct is this from the ‘regularization with cross-validation’ that we see in Machine learning approaches? E.g., I could do a ridge model where I allow only the coefficient on reader to be regularized; this also leads to the same sort of ‘shrinkage’ … so what’s the difference?

## Correlation of Fixed Effects:

Groups Name Variance Std.Dev. Corr ## Chick (Intercept) 103.61 10.179 ## Time 10.01 3.165 -0.99 ## Residual 163.36 12.781 ## Number of obs: 578, groups: Chick, 50

prior_covariance

higher confidence region.

“randomly varying” or “random effects”.

This model assumes that each participant’s individual intercept and slope parameters are deviations from this average, and these random deviations drawn from a distribution of possible intercept and slope parameters.

It’s the fixed effects estimate, the center of gravity in the last plot.

and can give rise to subtle biases that require considerable sophistication to avoid.)

I’ll introduce ranks in a minute. For now, notice that the correlation coefficient of the linear model is identical to a “real” Pearson correlation, but p-values are an approximation which is is appropriate for samples greater than N=10 and almost perfect when N > 20.

correlation coefficient of the linear model

t-tests, lm, etc., is simply to find the numbers that best predict yyy.

under the assumption that ℋ1H1{\mathcal {H}}_1 is true, the associated credible interval for the test-relevant parameter provides a range that contains 95% of the posterior mass.

he Bayes factor (e.g., Etz and Wagenmakers 2017; Haldane 1932; Jeffreys 1939; Kass et al. 1995; Wrinch and Jeffreys 1921) reflects the relative predictive adequacy of two competing models or hypotheses, say ℋ0H0{\mathcal {H}}_0 (which postulates the absence of the test-relevant parameter) and ℋ1H1{\mathcal {H}}_1 (which postulates the presence of the test-relevant parameter).

a [100×(1−𝛼)][100×(1−α)][100 \times (1-\alpha )]% confidence interval contains only those parameter values that would not be rejected if they were subjected to a null-hypothesis test with level 𝛼α\alpha.

They found that only 70% of their large (20:1) samples produced correct solutions, leading them to conclude that a 20:1 participant to item ratio produces error rates well above the field standard alpha = .05 level.

f course, the participant to item ratio is not a good benchmark for the appropriate sample size, so this is not enough to demonstrate that the sample size is insufficient. They did find support that this is not enough by sampling data of various sample sizes from a large data s

thumb only involve

participants to item ratios of 10:1

Costello & Osborne (2005).

Costello & Osborne (2005).

the number of underlying factors and which items load on which factor

In other words, the goal is to explore the data and reduce the number of variables.

is to study latent factors that underlie responses to a larger number of items

The module also comes with a reviewer reputation system based on the assessment of reviews themselves, both by the community of users and by other peer reviewers. This allows a sophisticated scaling of the importance of each review on the overall assessment of a research work, based on the reputation of the reviewer.

By transparent we mean that the identity of the reviewers is disclosed to the authors and to the public

Digital research works hosted in these repositories can thus be evaluated by an unlimited number of peers that offer not only a qualitative assessment in the form of text, but also quantitative measures that are used to build the work’s reputation.

One important element still missing from open access repositories, however, is a quantitative assessment of the hosted research items that will facilitate the process of selecting the most relevant and distinguished content.

Einstein scooped Hilbert by a few days at most in producing general relativity. In that sense

EA focuses on two kinds of moral issue. The first is effective action in the here and now — maximising the bang for your charitable buck. The second is the very long run: controlling artificial general intelligence (AGI), or colonizing other planets so that humanity doesn’t keep all its eggs in one basket.

this issue in acute form

duty

(You barge past me, about my lawful business, on your mission of mercy. “Out of the way! Your utility has already been included in my decision calculus!” Oh yeah, pal? Can I see your working?)

Another reason is just that other people’s concerns, right or wrong, deserve listening to.

Utilitarianism is an outgrowth of Christianity.1

Faced with questions of the infinite future, it swiftly devolves into fun with maths.

What will motivate you if you don’t change the world? Can you be satisfied doing a little? Cultivating the mental independence to work without appreciation, and the willpower to keep pedalling your bike, might be a valuable investment. Ambition is like an oxidizer. It gets things going, and can create loud explosions, but without fuel to consume, it burns out. It also helps to know what you actually want. “To maximize the welfare of all future generations” may not be the true answer.

Just by arithmetic, only few will succeed.

Here’s a more general claim: the more local the issue, the less substitutable people are. Many people are working on the great needs of the world.

net contribution

Department’s

My marginal contribution would be small.

But enough other people are thinking about it already. I trust them to work it out. My marginal contribution would be small.

After a visit to Lesswrong, someone will probably associate EA more with preventing bad AIs than with expanding access to clean drinking water.

which makes them a clear badge of identity

Contributing to the first topic requires discipline.

More might come out of it than from all your earnest strivings.

It’s questionable whether you even have the right.

and admirable practices like steelmanning.

They contain contradictions. That makes them rich enough to cope with human life, which is contradictory.

g and pushed it to be more than this

the Axial religions

moral demands

Either someone is maximizing utility, or they’re in the way.

it imposes extreme moral demands on people. Sell all your possessions and give them to the poor.

Utilitarians reduce all concerns to maximising utility. They can’t be swayed by argument, except about how to maximise utility. This makes them slightly like paperclip maximisers themselves.

hey recycle humans into paperclips

AIs which

The Effective Altruism

and turns them into numbers.

as preferences,

err on

TheRodentBirthContr

Demonstrate the potential for impact via thought experiments like The Drowning Child (although use this sparingly and cautiously, as people can be turned off by obligation framing).

13.1.1 Other lists and categorizations of tools

Approaches to overcoming the barriers and biases discussed in previous chapters.

The first intervention, surgical treatment, can’t even be seen on this scale, because it has such a small impact relative to other interventions. And the best strategy, educating high-risk groups, is estimated to be 1,400 times better than that. (It’s possible that these estimates might be inaccurate, or might not capture all of the relevant effects. But it seems likely that there are still big differences between interventions.)

I created R functions for TOST for independent t-tests, paired samples t-tests, and correlation

So my functions don’t require access to the raw data

while we are more used to think in terms of standardized effect sizes (correlations or Cohen’s d).

After choosing the SESOI, you can even design your study to have sufficient power to reject the presence of a meaningful effect.

your data set (e.g., means, standard deviations, correlations

think they should power their study, rather than the set of analyses they will conduct

This section was written by David Reinstein and Luke Arundel.

An example of this is presented in the study by Lichenstein et al., (1978).

Experiential distance describes one’s proximity to a particular situation or feeling as a result of having seen something or been a part of it. In particular, a greater experiential distance is seen to make it more difficult to imagine a particular situation or feeling, therefore making it harder to empathize with someone going through it. This could create a barrier to giving effectively as most charitable giving is motivated by empathy and sympathy for victims (Lowenstein & Small, 2007). Experiential distance explains why it is easier for an individual to feel empathy for a victim if they have personally experienced the ailment or someone close to them has. As a result, they do not have to imagine the suffering it may have caused because they have directly or indirectly physically experienced it. In other words, the experiential gap is smaller. For example, people living in wealthy nations are more likely to be affected by cancer than malaria, leading to a greater support for that cause.

predict the precisionof an iROAS estimate that a proposed experimental design will deliver.

design process featuring a statistical power analysis is a crucial step in planning of aneffective experiment.

If it is obvious from this plot that the incrementaleffects have ceased to increase beyond the end of the intervention period, there is no needto add a cooldown period, since doing so only adds more uncertainty to the estimate ofiROAS

Due to the marketing intervention, incremental cost of clicks increasedduring the test period.

shows a similar TBR Causal Effect analysis for incremental ad spend (cost ofclicks) ∆cost(t).

nd the 90%middle posterior intervals of the predicted counterfactual time series, y∗t .

Bayesian inference is usedto estimate the unknowns and to derive the posterior predictive distribution of y∗t foreach t.

The simplest possible relationship is given by the regression model,yt = α + βxt + t, t in pretest period

or TBR, data are aggregated across geos to provide one observation ofresponse metric volume at every time interval for the control and treatment groups:

matched market tests, which may compare the behavior of users in a singlecontrol region with the behavior of users in a single test region.

ime-Based Regression Framework

This appendix provides a brief introduction to the several types of software and processes used to creating websites such as Increasing Effective Charitable Giving and Researching and writing for Economics students. We aim to encourage others to participate in this collaborative work, and to spin off their own projects. If you would like to provide feedback or ask a question about these projects then using ‘hypothes.is’ is an easy way to do so (please write directly in the html and contact me at daaronr at gmail dot com to let me know you’ve done so).

10 Effect of analytical (effectiveness) information on generosity

Much of this project is being openly presented in the (in-progress) “Impact of impact treatments on giving: field experiments and synthesis” bookdown, a project organised in the dualprocess repo.

We consider a range published work (from a non-systematised literature review) including laboratory and hypothetical experiments, and non-experimental analysis of related questions (such as ‘the impact of general charitable ratings).

After participants had completed the initial survey, which took most of them at least 10 minutes, we measured their volunteering behaviour by asking them whether they would be willing to fill in a ‘a few extra questions’ for charity (59.3% responded yes) rather than skipping directly to the final questions. The participants were informed that their choice would be completely anonymous and that 5 SEK (around 0.50 euro or 50 US cents) would be donated to a charity organization of their choosing if they would fill out the additional questions.

Given the conjugacy of the beta for the binomial,

The statistical hypothesis we wish to investigate is whether the proportion of left-handed men is greater than, equal to, or less than the proportion of left-handed women.

Proportion of Funding Available for Program

Transfers as a percentage of total costs. GiveDirectly has other costs! So how much of our money is going to people in need? The value is derived here: https://docs.google.com/spreadsheets/d/1L03SQuAeRRfjyuxy20QIJByOx6PEzyJ-x4edz2fSiQ4/edit#gid=537899494 This is calculated by finding the average proportion over the years. TODO: Create a predictive model, fitting a normal and a beta distribution to financials Cell: B5 Units: Unitless (percentage), 0-100%

Arbitrary Donation Size, chosen to be $100,000 by GiveWell

he current results are telling us more about the structure of the model than about the world. For real results, go try the Jupyter notebook!

minimally

Direct cash transfers

Probability distributions of value per dollar for GiveWell’s top charities

Instead, what I have done is uniformly taken GiveWell’s best guess and added and subtracted 20%. These upper and lower bounds then become the 90% confidence interval of a log-normal distribution2. For example, if GiveWell’s best guess for a parameter is 0.1, I used a log-normal with a 90% CI from 0.08 to 0.12.

But how do we usefully express probabilities over rankings

References

SettingsShow All Code

2.2 Multiple lines plot

2.1 Simple line plot

ggplotly(p) 197019801990200020104000800012000dateunemploy.cls-1 {fill: #3f4f75;} .cls-2 {fill: #80cfbe;} .cls-3 {fill: #fff;}plotly-logomark 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p <- ggplot(economics, aes(x = date, y = unemploy)) +

https://stats.idre.ucla.edu/spss/seminars/introduction-to-factor-analysis/a-practical-introduction-to-factor-analysis/

There are several rotation methods.

set number of participants

The reason for that is that these rules only involve one of two factors.

However, if you believe there is some latent construct that defines the interrelationship among items, then factor analysis may be more appropriate.

makes up common variance

your set of items perfectly.

assumes that there common variances takes up all of total variance, common factor analysis assumes that total variance can be partitioned into common and unique variance.

It conceptualizes constructs as causally determined by the observations (Edwards & Bagozzi, 2000). This is likely not what you want because this means that “principal component scores are”caused” by their indicators in much the same way that sumscores are “caused” by item scores

This means that the number of underlying factors is determined mostly empirically, rather than theoretically.

the latent factors

If the elasticity of substitution between capital and labor is bounded above 1, capital and labor are “gross substitutes”. In this case, any given level of output can be achieved with enough capital or enough labor; neither individual production factor is necessary. If we just have a high enough saving rate and pile up ever more capital—factories producing factories—output will rise indefinitely.

To enable them, you’ll need to first tell Git that your local branch has a remote equivalent: git push --set-upstream origin <branch-name>

subjective scales are best understood as cardinally comparable unless, and until, other evidence suggests otherwise.

how such tests could be used to ‘correct’ the data if people do not intuitively interpret subjective scales in the way hypothesised

In each case, there is evidence indicating the condition does hold and no strong evidence suggesting it does not.

(different individuals use the scale the same way and the scale end-points represent the real limits)

(each reported unit change represents the same change in magnitude)

The cardinality thesis could fail to be exactly true, but nevertheless be approximately true, such that it is unproblematic to treat it as true. 

However, all the other conditions can fail by degree and what’s important is by how much they deviate.

C1: phenomenal cardinality(the underlying subjective state, e.g. happiness, is felt in units)

Further, they interpret the scale as linear, so each point represents the same change in quantity.

scale interpretation should be understood as a search for a ‘focal point’ (or ‘Schelling point’), a default solution chosen in the absence of communication (Schelling, 1960).

too theoretical for social science.

eir decision (Kristoffersen, 2011). Thi

psychologists less so (Ferrer‐i‐Carbonell and Frijters, 2004). Despite

t would not be possible to use subjective scales to say what would increase overall happiness. 

Finally, we do not detect a significant spillover effect, that is an effect on non-recipients in other households in the same community. This result is important as a potential concern about cash transfers

A dollar to AMF is worth 10 times that of a dollar to GiveDirectly,

GWWC (already donated to top charities)fn-13 6x The Life You Can Savefn-14

this can be achieved by increasing η above and beyond the experimentally determined rate (e.g. by adding 1 to it).

Group Annual Consumption η = 1 η = 2 Median US incomefn-4 $21,000 1× 1× US poverty linefn-5 $6,000 3.5× 12× Mean income in Kenyafn-6 $1,400 15× 230× World Bank’s international poverty linefn-7 $230 91× 8,300x GiveDirectly’s average recipientsfn-8 $180 120× 14,000×         Table 1. Some key consumption levels.

how much more utility

To leverage parochialism, interventions should aim to broaden people's moral circle and

To leverage conformity, effective charities should be set as the default,

[40] or any benefits at all [34].

A series of studies by Montealegre and colleagues [36] showed that donors who are driven by effectiveness concerns (rather than emotional concerns) were rated as less moral and less desirable partners.

ate when they believe, or are explicitly told, that most others have also donated [48, 49, 50]

Altruism that is driven by emotions is perceived as more genuine [41].

Conformity