Looked for a handful of key parameters pertaining to the overall effectiveness of the program and prevalence of the issue being addressed Traced those parameter values back to the original data, and located the statistical sampling uncertainty provided

we see that there is a substantial bias in the programs that we select. Programs that we select have a large positive bias on average.

n the first tab (“True vs false rejections”), we see the distribution of programs we accept and reject compared with whether or not they were truly better or worse than cash transfers. As expected, we generate many false rejections, due largely to the decision threshold of 3 being a “hedge” of sorts. More importantly, we observe false positives (i.e. programs that got “lucky”).

nce this set of matched communities has been generated generalised linear mixed models (e.g., multilevel models) will be used to assess changes in outcomes before and after the intervention, at different time periods, while controlling for other variables including whether the area is a control or intervention area.

All we need to do is change the units of the calculation and see if the result changes because of it. If it does, the calculation violates scale invariance, and for some reason the result depends on the units of measure that are used to calculate it.

Major Future Additions​

Static / sensitivity analysis Guesstimate has Sensitivity analysis that's pretty useful. This could be quite feasible to add, though it will likely require some thinking.

Right now Squiggle mostly works with probability distributions only, but it should also work smoothly with probabilities.

Gallery

Some distribution operations (like horizontal shift) return an unnormalized distriibution.

distriibution

Second argument to SampleSet.fromDist must be a number.

Recall the three formats of distributions. We can force any distribution into SampleSet format

For every point on the x-axis, operate the corresponding points in the y axis of the pdf.

Pointwise operations are done with PointSetDist internals rather than SampleSetDist internals.

TODO: this isn't in the new interpreter/parser yet.

A projection over a stretched x-axis.

Samples are converted into PDF shapes automatically using kernel density estimation and an approximated bandwidth. Eventually Squiggle will allow for more specificity.

mixture(1,2,normal(5,2)), the first two arguments will get converted into point mass distributions with values at 1 and 2.

mixture(pointMass(1),pointMass(2),pointMass(5,2)).

Array of Distributions Input

Most functions are namespaced under their respective types to keep functionality distinct. Certain popular functions are usable without their namespaces.

For example,

Squiggle dictionaries work similarly to Python dictionaries. API.

Example

mixture​

If both values are above zero, a lognormal distribution is used. If not, a normal distribution is used.

[5].

Say that $2B to $20B, or 10x to 100x the amount that Open Philanthropy has already spent, would have a 1 to 10% chance of succeeding at that goal [5].

[0]. This number is $138.8 different than the $138.8M given in Open Philanthropy's website, which is probably not up to date with their grants database.

For completeness, I do estimate the impacts of a standout intervention as well.

cost = 2B to 20B

probabilityOfSuccess = 0.01 to 0.1 # 1% to 10%.

counterfactualAccelerationInYears = 5 to 50

We guess that implementation challenges would limit effectiveness and funding opportunities. As a result, we do not anticipate doing further research on this program in the near future.

we estimate that this net distribution will reduce the number of deaths each year within this population from 12 to about 11.4

Step Four: 12 of those people are expected to die every year of any cause In order to estimate how many lives these nets might save, we first need to know how many people in this population would have died without the protection of the nets. The mortality rates and population demographics in Guinea suggest that about twelve out of 1,431 people would have died per year of any cause (including malaria).8

skeptical that a 4- to 8-week program like StrongMinds would have benefits that persist far beyond a year.

t conclusions.

they seem unintuitive to us and further influence our belief that StrongMinds is less cost-effective than HLI’s estimates.

This post provides an overview and analysis of the Doing Good Better  book giveaway through Effective Altruism New Zealand (EANZ). The analysis covers data collected from survey responses between 05-Jan-17 and 17-Dec-19, for which there were a total of 298 responses, with appreciable variance in the amount of the survey which was completed. This analysis was initially completed around Jan 2020 so any reference to "to date" refers to then.

We found that Fundraising Pages which received the £5 donation raised £118 more (on average) than the pages in the control group.

ngle claim published in a paper to evaluating the credibility of published papers more holistically. In phase 2, which began in 2021, 200 "bushel" papers were evaluated holistically. Participants working in IDEA groups evaluated the seven credibility signals:

drop_na(ends_with("_s"))

‘BOTEC’: Back of the envelope calculations are central to RP’s work

A NATIONWIDE TWITTER EXPERIMENT PROMOTING VACCINATION IN INDONESIA

SECTOR

somemustuseIDEAprotocol,butmostcanuseasingleroundofelicitation.Whatthey allhaveincommonisthatthey mathematically aggregatejudgments abouttheprobability ofsomeeventorsubjectivedegrees ofbelief,intoasingle,value.

Interestingly, this also means that the prior for σ is now dependent on the prior for the slope, because

This means that the estimate for sigma is the square root of 1 minus the variance of the slope estimate (0.75²). I

prior(normal(0, 0.5), class = "b", lb = -1, ub = 1)

add_predicted_draws(model_height_weight) %>%

add_epred_draws(model_height_weight) %>%

CEARCH discovering a Cause X every three years and significantly increasing support for it.

sample_prior = TRUE,

The output shows us that we need to set two priors, one for the Intercept and one for sigma. brms also already determined a default prior for each, but we’ll ignore that for now.

The prior for the slope is a lot easier now. We can simply specify a normal distribution with a mean of 0 and a standard deviation equal to the size of the effect we deem likely, together with a lower bound of 0 and upper bound of 1.

Apparently our prior was still very uninformed because the posterior shows we can be a confident in a much narrower range of slopes!

I increased the adapt_delta, as suggested in the documentation, from .8 to .9.

he Rhat values did not show this was problematic but

egression model into a simple correlation analysis. That way we can specify a prior on what we think the correlation should be

Please note, not all rigor criteria are appropriate for all manuscripts.

ScreenIT Sep 27, 2021 SciScore for 10.1101/2021.09.22.461342: (What is this?)Please note, not all rigor criteria are appropriate for all manuscripts.

Is the study design appropriate and are the methods used valid? Yes

Some details of the methods are lacking. For example, the MUpro provides two methods, it is necessary to specify which method was used in the analysis. The confidence score of each prediction should also be provided. Besides, some results from I-Mutant and MUpro were conflicting, the authors may want to discuss the discrepancy.

Discussion, revision and decision Discussion and Revision Author response We would like to thank the reviewers for their valuable comments. Below we provide pointwise response and the changes made in the revised manuscript. To Dr. Jyotsnamayee Sabat

PeerRef Dec 15, 2021 Discussion, revision and decision

Author response

Nov 26, 2021 Peer review report Reviewer: Hurng-Yi Wang Institution: Institute of Ecology and Evolutionary Biology, National Taiwan University email: hurngyi@gmail.com Section 1 – Serious concerns Do you have any serious concerns about the manuscript such as fraud, plagiarism, unethical or unsafe practices? No Have authors’ provided the necessary ethics approval (from authors’ institution or an ethics committee)? not applicable Section 2 – Language quality How would you rate the English language quality? Medium quality Section 3 – validity and reproducibility Does the work cite relevant and sufficient literature? No Is the study design appropriate and are the methods used valid? No Are the methods documented and analysis provided so that the study can be replicated? Yes Is the source data that underlies the result available so that the study can be … More Peer review report Reviewer: Hurng-Yi Wang Institution: Institute of Ecology and Evolutionary Biology, National Taiwan University email: hurngyi@gmail.com

Read the original source

I agree to change to Verified manuscript.

and are shown below.

We would like to thank the reviewers for their valuable comments. Below we provide pointwise response and the changes made in the revised manuscript.

Pt-12:

The “Analysis of the Mutational Profile of Indian Isolates” should be moved to Materials and Methods.

Read the full article

Article activity feed Version 2 published on bioRxiv

AgarwalNita Parekh

Abstract

In this study we carried out the early distribution of clades and subclades state-wise based on shared mutations in Indian SARS-CoV-2 isolates collected (27 th Jan – 27 th May 2020). Phylogenetic analysis of these isolates indicates multiple independent sources of introduction of the virus in the country, while principal component analysis revealed some state-specific clusters. It is observed that clade 20A defining mutations C241T (ORF1ab: 5’ UTR), C3037T (ORF1ab: F924F), C14408T (ORF1ab: P4715L), and A23403G (S: D614G) are predominant in Indian isolates during this period. Higher number of coronavirus cases were observed in certain states, viz ., Delhi, Tamil Nadu, and Telangana. Genetic analysis of isolates from these states revealed a cluster with shared mutations, C6312A (ORF1ab: T2016K), C13730T (ORF1ab: A4489V), C23929T, and C28311T (N: P13L). Analysis of region-specific shared mutations carried out to understand the large number of deaths in Gujarat and Maharashtra identified shared mutations defining subclade, I/GJ-20A (C18877T, C22444T, G25563T (ORF3a: H57Q), C26735T, C28854T (N: S194L), C2836T) in Gujarat and two sets of co-occurring mutations C313T, C5700A (ORF1ab: A1812D) and A29827T, G29830T in Maharashtra. From the genetic analysis of mutation spectra of Indian isolates, the insights gained in its transmission, geographic distribution, containment, and impact are discussed.

Pt-13: I want to know how the representative sequences were selected for different states. Is it based on no. of sequences submitted or positivity rate of a particular region? All the Indian isolates available in GISAID for the period 27th Jan – 27th May 2020 were download and considered for analysis. NO state-wise selection was done.

Demographic Analysis of Mutations in Indian SARS-CoV-2 Isolates

Demographic Analysis of Mutations in Indian SARS-CoV-2 Isolates

What cause area(s) is/are you interested in working in if there was a role or project that was a good fit? (select all that apply)

What type of role(s) would you be interested in working in? (select all that apply)

What obstacles are holding you back from changing roles or co-founding a new project? (select all that apply)

Add the SurveyMonkey account’s OAuth token to your .Rprofile file. To open and edit that file, run usethis::edit_r_profile(),

15desire to “pay it forward” for other donors by supporting the matching fund after receiving matching funds. This possibility may be explored infuture research. About a third ofdonors werewilling to support the matching fund with some or all of their donation. This provided enough matching funds to cover the matching funds received by donors, making the micro-matching system self-sustaining.Despite a long history of altruism,including centuries of organized philanthropy, humans have only recently attempted to systematically measure the cost-effectiveness of altruistic endeavors with the goal of doing as much good as possible10,11. The effective altruism movement is growing and has been notably successful in securing large commitments from relatively few people32,33. Effective altruism’s potential for more widespread adoptionis unknown. The sevenstudiesandproof-of-conceptdemonstration presented here are cause for optimism, grounded in a more detailed understanding of altruistic motivation. Today, relatively few donors prioritize effectiveness. But our results suggest that effective giving can be a satisfying complement to giving based on personal feelings, adding a “competence glow”27to the proverbial “warm glow” of giving. Some donors are willing to incentivize bundle donations in others, promoting a chain of giving that is both personally meaningful and effective. The stakes are high, as ordinary people have the power to do enormous good. The limited proof-of-concept demonstrationreported onhere raised funds sufficient to provide 100,700deworming treatments and 17,500 malaria nets, among other benefits. (See Supplementary Materials.) A better understanding of moral motivation, and how to channel it, could dramatically increase the impact of human altruism.MethodsAll reported studies, including the final proofofconcept,were pre-registered, except forStudy 7 (which was a pre-test for the proof of concept). Formore detailed descriptions of the methodsand results, please refer to our Supplementary Materialsavailable at https://osf.io/zu6j8/?view_only=28050663bd6b4b5cae0a73ad8068bc34. Across Studies 1-7w

add_predicted_draws

howed us the posterior distributions of the two parameters

Apparently our posterior estimate for the Intercept is 154.63

Here we see that the posterior distribution

Notice that we sample from the prior so we can not only visualize our posterior later, but also the priors we have just defined.

whether the chains look good.

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.