In both cases the estimated effects are very small and not statistically distinguishable from zero

we focus on pre-trends

Vote Switching Before Area B

Hence, in this case, Diesel-Euro5 car owners constitute a “fake” treatment group

The null result on the Democratic Party is particularly interesting, as it suggests that voters did not penalize the party of the incumbent mayor, who was directly accountable for the introduction of the traffic ban.

27 Our findings are robust to restricting the control group to Diesel-Euro5 owners only, and to controlling for the number of kilometers driven per year, as well as for the frequency of car use (see Supplementary Tables SI-8–SI-10, respectively).

substantively sizable shift considering that the baseline rate of support for Lega in the sample was 24.4%. Put differently, owning a car affected by the vehicle ban raised the probability of voting for Lega in the subsequent elections by 55% ab

Table 2. Voting for Lega in EU Elections of 2019

Our main dependent variable is an indicator that takes the value 1 if the respondent reports voting for Lega, and 0 otherwise. We also investigate potential treatment effects on support for other parties. In particular, to assess potential anti-incumbent effects, we examine support for the Democratic Party, the party of the city’s mayor.

we do not know the exact timing of the disbursement, that is, whether the compensation was actually received by the time the EU elections took place.Footnote 25 In other words, our indicator variable equals 1 if the respondent has received compensation at any point in time prior to the survey (January 2021), and zero otherwise

eassuringly, earlier studies (e.g., Colantone and Stanig Reference Colantone and Stanig2018) show that individuals with these characteristics tend to be less likely to support a radical-right party such as Lega. Hence, the composition of the treatment group should in fact work against finding a pro-Lega effect of the policy.Footnote

Table 1 compares the characteristics of the different groups of car owners in terms of their age, gender, education, and income.

specifications of the following form:(1)

As another type of control group, we also interviewed 303 owners of new cars in the Euro6 category (both Diesel and Petrol). These car owners serve as a useful placebo test, for reasons we detail below.

Starting in April 2019, city residents affected by the ban could apply for compensation from the Municipality of Milan.Footnote 12 The initial 2019 call for compensation was open only to low-income car owners (i.e., with an adjusted household income below €25,000 per year, or €28,000 if aged 65+). In the next year, the income criterion was dropped, and hence the call was effectively open to all residents

aim is to compare owners of affected cars to owners of relatively similar-yet-unaffected cars.Footnote 10 Specifically, our treatment group will consist of owners of Diesel-Euro4 cars, while the control group will consist of Petrol-Euro4, Diesel-Euro5, and Petrol-Euro5 car owners.Footnote

The policy identifies the most polluting categories of vehicles and bans them from accessing and circulating within the area.

seem to

If anything, affected car owners exhibited slightly more environment-friendly attitudes.

was even larger

owners of banned cars were 13.5 percentage points more likely to vote for Lega in the European Parliament elections of 2019

close to zero and well below statistical significance.

owners of banned vehicles—who incurred a median loss of €3,750—were significantly more likely to vote for Lega in the subsequent elections

we exploit arbitrary discontinuities in the rules dictating the car models that would be covered by the ban and employ a difference-in-differences estimation to identify the policy’s effect on voting behavior.

the electoral impact of the introduction of the Area B ban.

original survey with a targeted sampling design that we conducted among residents of Milan. The survey collected detailed information about respondents’ car ownership, environmental views, and political behavior.

In line with this pattern, recipients of compensation from the local government were not more likely to switch to Lega.

indicates that this electoral change did not stem from a broader shift against environmentalism, but rather from disaffection with the policy’s uneven pocketbook implications.

AI-concerned think the risk that a genetically engineered pathogen will killmore than 1% of people within a 5-year period before 2100 is 12.38%, while the AIskeptics forecast a 2% chance of that event, with 96% of the AI-concerned abovethe AI skeptics’ median forecast

hose who did best on reciprocal scoring had lower forecasts ofextinction risk.72 We separately compare each forecaster’s forecast of others’ forecasts on ten key questions, for both expertsand superforecasters. We rank each forecaster’s accuracy on those 20 quantities relative to other participants,and then we compute each forecaster’s average rank to calculate an overall measure of intersubjective accuracy.73 This may be because superforecasters are a more homogenous group, who regularly interact with eachother outside of forecasting tournaments like this.74 Pavel Atanasov et al., “Full Accuracy Scoring Accelerates the Discovery of Skilled Forecasters,” SSRN WorkingPaper, (February 14, 2023), http://dx.doi.org/10.2139/ssrn.4357367.

ithin both groups—experts and superforecasters—more accurate reciprocalscores were correlated with lower estimates of catastrophic and extinction risk. Inother words, the better experts were at discerning what other people would predict,the less concerned they were about extinction

otal Catastrophic Risk

First, we can rule out the possibility that experts can’t persuade others of the severityof existential risks simply because of a complete lack of sophistication, motivation,or intelligence on the part of their audience. The superforecasters have all thosecharacteristics, and they continue to assign much lower chances than do experts.

Question and resolution details

general x-riskexperts

The median participant who completedthe tournament earned $2,500 in incentives, but this figure is expected to rise asquestions resolve in the coming years.

with 111completing all stages of the tournament

Participants made individual forecasts2. Teams comprised entirely of either superforecasters or experts deliberated andupdated their forecasts3. Blended teams from the second stage, consisting of one superforecaster team andone expert team, deliberated and updated their forecasts4. Each team saw one wiki summarizing the thinking of another team and againupdated their forecasts

We also advertised broadly, reaching participants withrelevant experience via blogs and Twitter. We received hundreds of expressions ofinterest in participating in the tournament, and we screened these respondents forexpertise, offering slots to respondents with the most expertise after a review of theirbackgrounds.1

We explained that after the tournament we would show the highest-qualityanonymized rationales (curated by independent readers) to panels of online surveyparticipants who would make forecasts before and after reading the rationale. Prizesgo to those whose rationales helped citizens update their forecasts toward greateraccuracy, using both proper scoring rules for resolvable questions and intersubjectiveaccuracy for unresolvable questions.21

One common challenge in forecasting tournaments is to uncover the reasoningbehind predictions.

scoring ground rules: questions resolving by 2030were scored using traditional forecasting metrics where the goal was to minimize thegap between probability judgments and reality (coded as zero or one as a function ofthe outcome). However, for the longer-run questions, participants learned that theywould be scored based on the accuracy of their reciprocal forecasts: the better theypredicted what experts and superforecasters would predict for each question, thebetter their score.

When we report probabilities of long-run catastrophic andexistential risk in this report, we report forecasters’ own (unincentivized) beliefs. But,we rely on the incentivized forecasts to calculate measures of intersubjective accuracy

the XPT:• What will be the global surface temperature change as compared to 1850–1900, indegrees Celsius? (By 2030, 2050, 2100)• By what year will fusion reactors deliver 1% of all utility-scale power consumed inthe U.S.?• How much will be spent on compute [computational resources] in the largest AIexperiment? (By 2024, 2030, 2050)• What is the probability that artificial intelligence will be the cause of death, within a5-year period, for more than 10% of humans alive at the beginning of that period?(By 2030, 2050, 2100)• What is the overall probability of human extinction or a reduction in the globalpopulation below 5,000? (By 2030, 2050, 2100)18 Participants also consented to participate in this study, via the University of Pennsylvania’s InstitutionalReview Board. The consent form detailed the format of the study.19 We define a catastrophic event as one causing the death of at least 10% of humans alive at the beginning ofa five-year period. We define extinction as reduction of the global population to less than 5,000.

feed these motivations through several potential mechanism

conflict environments can also exacerbate existing gender-basedinequalities or challenge traditional gender roles in a society

establishment of peace in the public space has substantial positive spillover effects in enhancingwomen’s well-being in the private space

Therefore, we identify the population average treatment effect (PATE) ofarmed combat exposure

Probing the mechanisms, our analysis first renders the use of violence as an instrumental behavior in intrahousehold bargaining as an unlikely mechanism by eliminating labor market outcomes and economic- and social-controlling behaviors from the list of usual suspects.

Usage

Create a suggestions file from the difference between old.txt and new.txt: suggs diff old.txt new.txt

To review suggestions:

The original text, ++[your suggested addition,]++

The handle must start with @ and must be the last word:

You can sign the comment with a @handle as the last word.

To make a comment, enclose it in %%[ and [%%:

ke this: The original text, ++[your suggested addition,]++ and more text.

3. How the XPT works

1.33% [0.17,

The median is straightforward tocalculate, transparent, robust to extreme outlying observations, and understandableto people with a basic knowledge of statistics. Also, reassuringly, it is never thehighest nor the lowest of the five methods we considered as potential aggregationmethods. For these reasons, we think the median provides an ideal middle ground foraggregating forecasts in this project.

otal Extinction Risk

bold claims that attract audiences and funding—and to keep their predictions vagueenough so they can never be proven wrong.

Some have argued more broadl

Do ‘broad’ approaches to improving effective governance, and ultimately serving the farfuture, tend to be more or less effective in expectation than ‘narrow’ approaches (such asworking on reducing the risk of bioengineered pandemics)?

How can evidence be disseminated mosteffectively?

Under what conditions should a social planner preserve ‘option value’ bydelaying an important, irreversible decision to acquire more information, thereby delegatingdecision-making authority to future agents with potentially different values and preferences(cf. Bishop 1982; Dixit and Pindyck 1994)?

hen et al 2023; Toma andBell 2023

Intergenerational governance and policy-making

Vivalt, Coville, KC 2023

Gonzalez-Ricoy and Gosseries 2017

Can we design mechanisms to ensure that AI systems exhibit desirable behavioursuch as truth-telling or a lack of deception?

govern powerful non-state entities

inequalities,

Read and Toma, 2023

Song et al 2012)

Healy and Malhorta 2009

economic models predict the impact of advancedAI systems on political institutions and inequalities

(Acemoglu, 2023

Bersiroglu andErdil, 2023

Shulman, C., & Thornley, E. (2023). How Much Should Governments Pay to Prevent Catastrophes? Longtermism'sLimited Role. In Barratt, Greaves, Thorstad (eds.) Essays on Longtermism.

Alexandrie and Eden, forthcoming

E.g. Jordà et al. 2022

ased on the historical record of such events, what is the tail distributionof harmful impacts (e.g., fatalities) from pandemics, asteroids, wars, and other potentialdisasters? (E.g. Marani 2021;

Aschenbrenner2019

2023

Klenow et al. 2023

To what extent are forecasting methods informative for assessing the probability of globalcatastrophic risks and other future events of special importance for social welfare? (Karger

Karger, E., Rosenberg, J., Jacobs, Z., Hickman, M., Hadshar, R., Gamin, K., ... & Tetlock, P. E. (2023). ForecastingExistential Risks Evidence from a Long-Run Forecasting Tournament. FRI Working Paper No. 1.

Kalai & Kalai 2001)

Andreoni 2018

1.1 Strategic issues in altruistic decision

(cf.Andreoni & Payne 2003)

Whatdetermines the optimal spending schedule for altruistic decision-makers?

n altruistic decision-maker that funds a charitable intervention may crowd out fundingfrom other actors (e.g., governments or philanthropists)

Research agenda draft for GPI Economics

single chat requests: chatgpt_single()

ecause military shocks generate plausibly exogenous variationin economic production, the findings improve upon the correlational evidence in Section 3.Second, the implied biodiversity-GDP elasticities from the quasi-experiment are largerthan their OLS counterparts. By leveraging shocks in a 2SLS setting, the quasi-experiment-based elasticity estimates alleviate classic measurement error and endogeneity problems.However, the fact that the two methods produce elasticities of similar order of magnitudeadds confidence to the overall credibility of the estimates.

states’relative differences in response to aggregate military buildups (which are themselves largelydriven by geopolitical factors) – are unlikely to be correlated with unobservable determinantsof local biodiversity. That is, we assume the United States will not increase national mili-tary spending because states that receive larger military procurement contracts have worsebiodiversity

First, we producecausal estimates of the elasticities between biodiversity outcomes and air pollution. We use aresearch design that isolates variation in local pollution driven by transported pollution fromdistant, upwind cities (e.g., Deryugina et al., 2019; Anderson, 2020). We show that “upwindpollution” coming from areas over 300 km away generates substantial variation in local airquality, and these imported pollution shocks cause reductions in local biodiversity outcomes.Second, we estimate the impact of the military spending shocks on air pollution, and multiplythese estimates by the biodiversity-pollution elasticities we obtain from step one. Together,these exercises give us the expected impact of the military shocks on biodiversity throughair pollution. We find that pollution accounts for 20-60 percent of the reduced form effect ofmilitary shocks, suggesting air pollution is a first-order pathway underlying the production-biodiversity link

Second, there is substantial distributional heterogeneity, where the negativeassociation at the lowest decile of biodiversity is almost twice as large as the average.

FC = /opt/homebrew/bin/gfortran-11

CXX = /opt/homebrew/bin/g++-11

CC = /opt/homebrew/bin/gcc-11

book

We are now planning a further update in response to additional comments (e.g., from James Snowden and GiveWell). We expect this will include updating our analysis with recently completed studies  and refining some technical aspects of the analysis, including:Our systematic review, and the weight we place on different sources of evidenceEstimated spillover benefits for household membersCost estimatesTechnical details, such as:How long do the effects of psychotherapy last?How important is the expertise of the deliverer or number of sessions?Are the effects of psychotherapy affected by publication bias?

but longtermists often claim priorities such as AI alignment and preventing pandemics are important, even if we solely consider present wellbeing, so we shouldn’t dismiss the possibility.

we have a full list of research ideas that we hope to explore

If such views are true, that would count against longtermism

credible cause for longtermists.

We’ve published two working papers on moral uncertainty: The property rights approach to moral uncertainty and Wheeling and dealing: An internal bargaining approach to moral uncertainty, which both explore a novel, bargaining-based approach to acting when you’re uncertain what’s morally right. (This is very roughly akin to the ‘moral parliament’ approach.) We’re currently working with two external  co-authors  on a new paper that combines these ideas, which we plan to publish in an academic journal.

5.1 Using WELLBYs to compare the value of extending lives against improving lives

Although unlikely, we may also do some work relating to animal welfare; a challenge is that we prefer to rely on self-reports, which animals can’t give.

Assess the social desirability bias and other self-reporting biases in SWB data (for example: Do people give answers surveyors want? Is it a problem? If so, can anything be done?)Explore whether the measure of SWB matters (for example, if the key outcome is happiness rather than life satisfaction, do we get different priorities?)

Our working paper A Happy Possibility about Happiness (and other) Scales, a working paper attempts to provide the first overview of both the theory and evidence of the comparability of subjective wellbeing scales (e.g., is your 7/10 the same as my 7/10?). We plan to revise this for publication in an academic journal.

Our article To WELLBY or not to WELLBY? sets out the WELLBY method, its strengths, weaknesses, and areas for future work. To expand on this, we are:

3. The nature of wellbeing

existing work (where a public document is available)

Some work is both existing and current (where we have extant research we are updating).

which means we have a number of ongoing projects.

The notable ones are:

We also have a long list of organisations we would like to explore, including the Shamiri Institute, Action for Happiness, and Koko.

e expect others will provide different types of mental health interventions, such as social-emotional learning. We expect to examine Friendship Bench, Sangath, and CorStone unless we find something more promising.

Based on our cause area report on mental health and our cost-effectiveness analysis of psychotherapy, we think mental health is a promising area in which to find cost-effective interventions to improve wellbeing.

so we will also update our assessment of StrongMinds after we update our psychotherapy evaluation.

2.1 Updated evaluation of psychotherapy

Are the effects of psychotherapy affected by publication bias?

e’ve found that psychotherapy for depression is several times more cost-effective than cash transfers for improving happiness, deworming has an unclear long-term effect,

From this work, we’ve found that psychotherapy is several times more cost-effective than cash transfers or deworming for improving happiness. We concluded that comparing psychotherapy to antimalarial bednets, a life-saving intervention, depends heavily on various philosophical assumptions: treating depression ranges from about as good as to several times better than antimalarial bednets, depending on the assumptions.

evaluated the cost-effectiveness of organisations that provide  psychotherap

e, including psychedelics, opioids, poverty, loneliness, sleep, and air pollution.

Longlist of future cause areas to explore

1.2 Child development effects (e.g., abuse, trauma, nutrition)

that may have large impacts on wellbeing as well. So far we have completed shallow reviews on pain, lead exposure, and immigration.

Research agenda

A working paper exploring a bargaining-based approach to moral uncertainty

A revised paper on the theory and current evidence on scale cardinality (e.g., is your 7/10 the same as my 7/10?)

An academic paper setting out our method for measuring impact using wellbeing

We will conduct new research on how to measure and interpret subjective wellbeing measures:

2. Organisation evaluations

Non-mood-related mental health issues (e.g., psychotic and trauma-related disorders)Child development effects (e.g., abuse, trauma, nutrition)Fistula repair surgeryBasic housing improvements (e.g., concrete floors)

1. Cause area explorations

Conduct further theoretical work:

asurement of wellbeing. This has included evaluating philosophical views of wellbeing and life satisfaction, pioneering methods to conduct cost-effectiveness analyses using wellbeing, and conducting novel research on wellbeing measurement. We

Where relevant, we hone in and compare the top organisations implementing those interventions.

picking new cause areas to investigate, then narrowing down to the specific organisations – which will enable us to look broadly and deeply at the same time.

large, solvable, and unduly neglected.

broad analyses of different causes.

An ultimate goa

We will explore whether we should improve the wellbeing of people alive now or in future generations:

An academic journal book review of Will MacAskill’s What We Owe The Future

An experimental survey to test assumptions about subjective wellbeing measures, including comparability, linearity, and the neutral point

An academic paper on life satisfaction theories of wellbeing

the cost-effectiveness of several organisations (partially informed by our cause exploration work)

Applied research to maximise global wellbeing

Non-mental health organisations (organisations TBD)

To find new promising solutions to the biggest problems,

From this work, we’ve found that psychotherapy is several times more cost-effective than cash transfers or deworming for improving happiness.

engineering a paradigm shift towards a wellbeing approach among decision-makers

Ultimately, we measure impact in WELLBYs (wellbeing-adjusted life years), a method born in academia

For the first time in human history

The idea that the quality of a society should be judged by the happiness of its people is an old idea, stretching back at least to the Enlightenment, if not Aristotle.

[This post contains the Happier Lives Institute's research agenda for next 18 month. After a foreword, we give a brief summary of our plans, then go into more depth]

Making referee payments or charity donations: Three-quarters of our respondents said that referees would do a better job if they were better rewarded for their effort. Among them, about 75% indicated that referees should be paid for timely completion of the report. This payment could take many forms e.g., a donation to a charity or research fund.

OftW pre-giving-tuesday-email upselling split test (considering ‘impact vs emotion’) c

Phase 2: EAMTT – Bringing together and engaging Academics, EA orgs, and marketers

The output shows we need to set a prior on sigma, the Intercept, and on the male coefficient.

Post-event (complete) response rates, ratings (1-6), by school

we can change the question to “What is the probability that this intervention is better than 1x (i.e. cash transfers)?” We can set a critical value for that threshold (e.g. we accept programs that we are 90% sure are better than cash transfers). As above, that value comes straight out of the distribution from our PSA: it’s simply the proportion of outcome results from our PSA-generated distribution which are ≥1.

as a preview of what might happen with a full accounting of uncertainty. Code, data, and modified workbook are available.

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].