Comment author: stijnbruers 15 April 2018 07:26:40PM 1 point [-]

This project seems to be a bit similar to an idea that I have. I start with a population ethical view of variable critical level utilitarianism https://stijnbruers.wordpress.com/2018/02/24/variable-critical-level-utilitarianism-as-the-solution-to-population-ethics/ So everyone can choose his or her own preferred critical level utility. Most people seem to agreggate around two values: 1) the totalists prefer a critical level of 0, which corresponds with total utilitarianism (the totalist view) and 2) the personalists or negativists prefer a conditionally maximum critical level (for example the utility of the most prefered state), which is close to negative utilitarianism and the person-affecting view. (I will not go into the conditionality part here) When we create new people, they can be either totalists or personalists (or something else, but that seems to be a minority. Or they can be in a morally uncertain, undecided superposition between totalists and personalists, but then we are allowed to choose for them their critical levels. If we make a choice for a situation where a totalist with a positive utility (well-being) is created, that positive utility counts as a benefit or a gratitude regarding our choice. If we caused the existence of a personalist (or negativist), we did not create a benefit. And if that personalist complains against our choice because it prefers another situation, we actually harmed that person. Now we have to add all benefits and harms (all gratitudes and complaints) for everyone who will exist in the choice that we will make. Concerning the far future and existential risks, we need to know how many totalists and personalists there will be in the future. Studying the current distribution of totalists and personalists can give us a good estimate. This might be related to the N-ratios of people. Totalists have low N-ratios, personalists/negativists have high N-ratios

Comment author: David_Althaus 16 April 2018 11:54:47AM 0 points [-]

Interesting, yeah, thx for the pointer!

Comment author: kbog  (EA Profile) 04 April 2018 03:12:10PM 2 points [-]

First, consider the individual level. Imagine a participant answered with “infinite” in twenty dilemmas. Further assume that the average equivalence number of this participant in the remaining ten dilemmas was also extremely high, say, one trillion. Unless this person has an unreasonably high E-ratio (i.e. is unreasonably optimistic about the future), this person should, ceteris paribus, prioritize interventions that reduce s-risks over, say, interventions that primarily reduce risks of extinction but which might also increase s-risks (such as, perhaps, building disaster shelters11); especially so if they learn that most respondents with lower average equivalence numbers do the same.

That's not descriptive ethics though, that's regular moral philosophy.

For the 2nd point, moral compromise on a movement level makes sense but not in any unique way for population ethics. It's no more or less true than it is for other moral issues relevant to cause prioritization.

Comment author: David_Althaus 05 April 2018 09:04:29AM 2 points [-]

That's not descriptive ethics though, that's regular moral philosophy.

Fair enough. I was trying to express the following point: One of the advantages of descriptive ethics, especially if done via a well-designed questionnaire/survey, is that participants will engage in some moral reflection/philosophy, potentially illuminating their ethical views and their implications for cause prioritization.

For the 2nd point, moral compromise on a movement level makes sense but not in any unique way for population ethics. It's no more or less true than it is for other moral issues relevant to cause prioritization.

I agree that there are other issues, including moral ones, besides views on population ethics (one’s N-ratios and E-ratios, specifically) that are relevant for cause prioritization. It seems to me, however, that the latter are comparatively important and worth reflecting on, at least for people who spent at most a very limited amount of time doing so.

17

Descriptive Population Ethics and Its Relevance for Cause Prioritization

Summary Descriptive ethics is the empirical study of people's values and ethical views, e.g. via a survey or questionnaire. This overview focuses on beliefs about population ethics and exchange rates between goods (e.g. happiness) and bads (e.g. suffering). Two variables seem particularly important and action-guiding in this context, especially when trying... Read More
Comment author: MichaelPlant 24 September 2017 04:10:32PM 1 point [-]

Some nitpicks in turn!

I’d guess that almost as much as 20% of all EQ-5D health states are psychologically impossible. This indicates that the whole system is suboptimal.

I don't think this follows. If these states are impossible (I don't disagree) then they'll never come in real life, so it won't matter what people say in the TTOs. As long as people make sensible judgements about the health states that actually occur, it doesn't matter what they say in impossible ones. I think you should push the fact they don't make sensible judgements in general - affective forecasting stuff, etc.

IMHO, another big problem is the evaluation of states worse than death (SWD) (and states of severe mental illness such as depression arguably belong in this category). For example, most studies don't even allow for SWD assessments. Furthermore, most researchers transform negative evaluations, limiting them to a lower bound of -1. Assuming that people with a history of mental illness more often evaluate health states indicating severe mental illness as highly negative (i.e. give utilities as lower than -1), then this ex-post transformation causes their judgments to have less influence than the judgments of uninformed people who underestimate the severity of mental illness.

Curious. Hmm. IIRC, DALYs and QALYs don't have a neutral point: 1 is healthy, 0 is dead, but it's not specified where between 0 and 1 is neutral. Is neutral 0.5? 0? Unless you know where neutral is you can't specify the minimum point on the scale, because it doesn't make sense.

Assuming that people with a history of mental illness more often evaluate health states indicating severe mental illness as highly negative (i.e. give utilities as lower than -1)

What would -1 mean here? DALYs and QALYs aren't well-being scales and can't straightforwardly be interpreted as such.

Comment author: David_Althaus 28 September 2017 06:46:13PM *  3 points [-]

As long as people make sensible judgements about the health states that actually occur, it doesn't matter what they say in impossible ones.

Good point. But I wonder whether they reinterpret the meanings of some of the dimensions of the ED-Q5 in order to make sense of some of the health states they are asked to rate.

Unless you know where neutral is you can't specify the minimum point on the scale, because it doesn't make sense.

Agree.

What would -1 mean here? DALYs and QALYs aren't well-being scales and can't straightforwardly be interpreted as such.

This depends on the study. I'm afraid it will take me a couple of paragraphs to explain the methodology, but I hope you'll bear with me :)

The literature review by Tilling et al. (2010) concluded that only 8% of all TTO studies even allow for subjects to rate health states as worse than death (i.e. as below 0), so for the vast majority of studies, the minimum point on the scale is indeed 0. I think this is problematic since e.g. health states like 33333 (if they are permanent) are probably worse than death for many, maybe even most people.

Of the few TTO studies that allow for negative values, the protocols by Torrence et al. (1982) and Dolan (1997) are used by almost all of them. Below a quote by Tilling et al. (2010), describing these two methods:

The method developed by Torrance et al. (1982) gives respondents a choice between a scenario of living in full health for ti years followed by the state to be valued for tj years (ti + tj= T), followed by death, and an alternative scenario, which is to die immediately. The value T is fixed (e.g., 10 y). The value of ti (and therefore also the value of tj) is varied until a point of indifference is found between the 2 scenarios. The utility value for that health state is then given by – ti/tj. [... Dolan (1997)] used a method similar to this, but the 1st scenario is to live in the health state to be valued for tj years followed by full health for ti years (i.e., the ordering of the 2 states is reversed).”

These two TTO protocols, in theory, would allow for extremely negative (and even infinite) negative values. Tilling et al. (2010) explain:

“[...] negative values can be extremely negative. A participant who would not accept any amount of time, however short, in a poor state of health is implying that such a state is infinitely bad.”

How do researchers respond? Again, I’ll quote Tilling et al. (2010, emphasis mine):

“Given the mathematical intractability of dealing with negative infinity (a single value of negative infinity in a sample of respondents would give a mean value of negative infinity), researchers usually censor such responses. Under such censoring, the lower bound is determined by the (relatively arbitrary) choice of the smallest unit of time the TTO procedure will iterate toward.”

In the two most commonly used TTO protocols, the smallest unit of time the TTO procedure iterates toward for SWD is 1 year. Consequently, the lower bound is -9. (Sometimes, the smallest united of time is 3 months, so the lowest possible value is -39.)

To give a concrete example: The subject is indifferent between A) living for 2 years in full health and for 8 years in health state 33333 and B) dying immediately. Thus, the value for health state 33333, for this subject, is - 8/2 = - 4.

Now almost all researchers then transform these values, such that the lowest possible value is -1. In my view, this is somewhat arbitrary.

Below some quotes by Devlin et al. (2011) on the matter:

“Because the elicitation procedure produces such extreme negative values, researchers have responded by doing ex post transformations to bound negative valuations to - 1 in various ways (Lamers, 2007). Crucially, once transformed, the negative numbers for SWD can no longer be interpreted as ‘utility’ scores, measured on the same scale as those for SBD (Patrick et al., 1994). Yet standard practice in calculating QALYs is to treat all values reported in value sets as commensurable. For example, an improvement from - 0.2 (an SWD) to 0, experienced over one year is interpreted as, producing a gain of 0.2 QALYs, and this is treated [...] as identical to an improvement from 0 to 0.2 experienced for one year, whereas the underlying ‘untransformed value’ for the SWD might suggest these two improvements in health are valued quite differently.”

...

“A related issue is whether or not values of negative states should be bounded to 1. It is not obvious why there should be no states worse than 1. For example, the phrase ‘it would have been better if he had never been born’ could truly be applied to people who have undergone torture and other types of brief but extreme suffering. There is no theoretical basis for imposing a limit on the level of disutility associated with these extreme sufferings.”

And here another quote by Tilling et al (2010):

“[...] it is not obvious why there should be no states worse than –1. Although it makes data analysis easier to transform values in this fashion, arguably 1 y of extreme pain and discomfort might provide as much disutility as 2 y of full health provides in utility.”

I hope this explains my previous comment.

References:

Devlin, N. J., Tsuchiya, A., Buckingham, K., & Tilling, C. (2011, 02). A uniform time trade off method for states better and worse than dead: Feasibility study of the ‘lead time’ approach. Health Economics, 20(3), 348-361.

Dolan, P. (1997). Modeling Valuations for EuroQol Health States. Medical Care, 35(11), 1095-1108.

Tilling, C., Devlin, N., Tsuchiya, A., & Buckingham, K. (2010, 09). Protocols for Time Tradeoff Valuations of Health States Worse than Dead: A Literature Review. Medical Decision Making, 30(5), 610-619.

Torrance, G. W., Boyle, M. H., & Horwood, S. P. (1982, 12). Application of Multi-Attribute Utility Theory to Measure Social Preferences for Health States. Operations Research, 30(6), 1043-1069.

Comment author: David_Althaus 24 September 2017 10:50:12AM 4 points [-]

Great post!

Nitpick:

For instance, the worst possible health state would be represented by “11111”.

I think "11111" usually refers to full health. (cf. the "EQ-5D Value Sets: Inventory, Comparative Review and User Guide" by Szende, Oppe & Devlin, 2007).

As part of a bigger project on descriptive (population) ethics, I've been working on a literature review of health economics. It also contains a section on the EQ-5D and its weaknesses. Here some excerpts:

Problem II: Impossible health states Another problem is that many health states, such as e.g. 22123 are psychologically impossible or at least very implausible. E.g. if you have “no problems with performing your usual activities (work, study, housework, family or leisure activities, etc.) ”, you can’t, simultaneously, suffer from “extreme depression”. This is immediately obvious to anyone who ever suffered from severe depression.

I’d guess that almost as much as 20% of all EQ-5D health states are psychologically impossible. This indicates that the whole system is suboptimal.

Problem III: Using “immediate death” Another problem is that subjects are often asked to choose between “immediate death” vs. the alternative scenario. However, this means that the subject is unable to say goodbye to their loved ones, or get their affairs in order. Arguably, the difference between dying immediately and dying in e.g. 3 months can make an enormous difference."

(Incorporating the TTO lead-time approach can easily overcome this problem.)

Anway, you write:

First, DALYs are biased towards physical health. The instruments used for eliciting them and affective forecasting errors cause mental health to be underrepresented.

I couldn't agree more.

IMHO, another big problem is the evaluation of states worse than death (SWD) (and states of severe mental illness such as depression arguably belong in this category). For example, most studies don't even allow for SWD assessments. Furthermore, most researchers transform negative evaluations, limiting them to a lower bound of -1. Assuming that people with a history of mental illness more often evaluate health states indicating severe mental illness as highly negative (i.e. give utilities as lower than -1), then this ex-post transformation causes their judgments to have less influence than the judgments of uninformed people who underestimate the severity of mental illness.

I discuss this problem, as well as other problems, in much greater detail in my doc.

I plan on publishing the doc within the next months, but if you're interested I'm happy to send you a link to the current version.

Comment author: Julia_Wise  (EA Profile) 07 December 2016 04:22:40PM *  10 points [-]

There are lots of cases of correct models failing to take off for lack of good strategy. The doctor who realized that handwashing prevented infection let his students write up the idea instead of doing it himself, with the result that his colleagues didn't understand the idea properly and didn't take it seriously (even in the face of much lower mortality in his hospital ward). He got laid off, took to writing vitriolic letters to people who hadn't believed him, and died in disgrace in an insane asylum.

Comment author: David_Althaus 20 January 2017 05:53:16PM 0 points [-]

Otoh, a few decades later handwashing did become mainstream. So I'd think that correct and clearly useful models have a great advantage in becoming adopted eventually. Good strategy/movement building is more relevant for hastening the rate of adoption.

To take another example: Communism profited from extremely good strategy/movement building at the beginning (Engels being one of the first EtGlers ever). But it ultimately failed to become widely accepted because it brought about bad consequences. Admittedly, it's still pretty popular, probably because it appeals to human intuitions (such as anti-market bias, etc.)