What Is 30 Of 80000

What Is 30 Of 80000 – In order to compare the causes, we want to evaluate the value of solving different social problems. The problem is that even if we have an accurate idea of ​​what is important as “value” (for example, the amount of human aid created), it is not useful to evaluate the effect of solving many problems in these terms, even if you care about the long-term effects of our actions – it’s not an exercise to count how many people have benefited from a result or to measure how big those benefits are.

This means that we must instead use crude “bars” to compare the scale of various social problems. A measure is a factor that is expected to correlate with the positive effect of solving a social problem (what we really care about), but which is easy to measure.

What Is 30 Of 80000

People use measures (also called metrics or proxy goals) all the time in other areas when it is difficult to measure what we are really interested in. For example, school awards “grades”. It is a measure to compare the educational achievements of two students, although we know that grades do not capture everything that is important in education.

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We haven’t researched ourselves which metrics have the best positive impact. However, it is important research of our affiliates, the Institute for the Future of Humanity at the University of Oxford; The Global Priorities Project, which is part of the Center for Effective Altruism; and our supervisors. We try to align our views with those of these groups while giving some weight to the views of economists and our understanding of the so-called common sense among information professionals.

Any set of metrics will reflect certain value assumptions and therefore will vary from person to person. In the following text, we will outline the measures that are often used in 80,000 hours, while trying to highlight the most important assumptions behind them.

To measure the short-term impact of various actions, we often focus on “QALYs” or “quality-adjusted life years.” QALY is a metric for measuring health that is widely used in health economics. One QALY is one year of healthy life and can be gained by increasing the quality of health or prolonging life expectancy. You can read more about how QALYs are defined here.

Of course, health is not the only part of well-being. Ideally, we would be able to measure “well-adjusted life years” or “WALYs”. Unfortunately, such a metric has not yet been developed, so instead we must consider a patchwork of metrics to capture other aspects of well-being. One of the most important is income, but one can also look at life satisfaction, educational outcomes, satisfaction of basic human rights and others.

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Within income, we want to focus on the logarithm of income. It is generally accepted that there are diminishing returns to the utility of income, so economists often talk about the “law of diminishing utility”. This rule means that you get more help from a $100 bonus if your current wealth is $1,000 than when your wealth is $10,000. Also, the returns are assumed to be roughly logarithmic, in part because of the empirical data, as shown in the table below. There is some evidence that individual welfare cannot rise above around £50,000, although this has been disputed (see, for example, this review by Stevenson and Wolfers.

The law of diminishing marginal utility, combined with the belief that all people have a common moral need, is the main reason why many feel it is more important to focus on causes that help the developing world.

We think it is very possible that the suffering of animals in addition to humans also deserves moral weight (see the books of Peter Singer for further justification). This means that our measures should also include animal welfare. The main group aiming to compare animal welfare interventions are animal charity evaluators, who often use the “years spent on farms avoided” metric for different animals. We are not very sure how this metric compares to QALYs or income.

Previous measures have focused on short-term efficiency measurement. But we think that the well-being of future generations carries moral weight, so we still need metrics to measure long-term well-being.

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More important than the impact of our actions on short-term well-being, so the yardsticks for measuring the degree to which we are moving human civilization toward the long-term good may deserve more weight than those in the previous section.

Measures of improvement in the long term future must be distinguished from “future QALYs” or something similar, as that is very difficult to measure. Unfortunately, there are no universally accepted metrics that can be used as a proxy for our long-term impact, but here are our current ideas.

Economists often focus on GDP growth, or better yet, the GDP-adjusted measure of GDP as an Indicator of Sustainable Economic Well-Being. For example, Tyler Cowen proposed the “Growth Strategy”:

For a country, the choices that determine whether income doubles with each generation or instead with each successive generation are all other economic policy concerns.

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We give this view some weight because it seems to represent the view of economists, even if they do not find the arguments very convincing, especially for the reasons given in Chapter 3 of Nick Beckstead’s book . In short, because economic growth will eventually come to an end, increasing growth today will only bring the point of the day closer—a valuable result, but not the greatest way we can change the long-term future.

We are further convinced by Beckstead’s view that we should focus on what is possible to cause positive “changes in direction” to the future trajectory of civilization. See this thread for more discussion of the trade-off between progressive acceleration and lane changes.

We think that the most important category of these changes may be “emergent risks”—events that could completely limit the future of civilization, such as nuclear war. Nick Bostrom most famously argued that if the welfare of future generations is at stake, it is most important to “increase the probability of a good outcome” in the short term, ie. j. avoid existential danger.

The problem is that “permanent risk reduction” or “creating positive travel changes” are also difficult metrics to measure, so we want a set of metrics that are consistent with them but easy to track.

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We’re not sure what that should be, though we expect progress over the next few years. This means that keeping options open is very attractive, as is investing in further research.

Aside from keeping our options open and more research to be done, our best guess is that the most dangerous risks will be man-made rather than physical, and likely to be the result of technology. new. This position is widely shared by researchers in the field at the Institute for the Future of Humanity, including Nick Bostrom, and others such as the University of Cambridge’s Center for the Study of Our Future. This raises the possibility that technological progress in the most dangerous areas without a corresponding increase in our ability to use new technologies wisely could be detrimental overall.

What metrics are best for measuring the reduction probabilities of human-caused disaster? We’re not too sure, but suggest the following:

For more, see Chapter 14 of the book “Superintelligence” by Nick Bostrom, which deals mainly with the dangers of artificial intelligence, but also concerns the dangers of other new technologies. Toby Ord has also written about how to balance productivity and research with other activities.

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So far, we have focused on the longitudinal dimensions of human well-being. You may think that we should put more emphasis on factors such as “integrity” and “environmental diversity” on their long-term impact on human well-being, because these factors also have significant value.

However, if you want to add extra weight to the non-helpful amount, you can add them as an extra measure.

All of the above are high level measures, so it is always important to break them down further for practical reasons. For example, the first three dimensions suggest that “enhancing organizational quality” and “promoting effective altruism” are good subscales.

We think that the study of the best measures is still in its beginning, so we expect that in the medium term we will receive important new information that will allow our views to develop.

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This is a point in favor of supporting things that look good from multiple angles and keeping options open.

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