I joined the psychology department at UCLA as an Assistant Professor in July 2023. Prior to that, I was an independent research group leader at the MPI for Intelligent Systems in Tübingen. I completed my Ph.D. in the Computational Cognitive Science Lab at UC Berkeley in 2013, obtained a master’s degree in Neural Systems and Computation from ETH Zurich, and completed two simultaneous bachelor’s degrees in Cognitive Science and Mathematics/Computer Science at the University of Osnabrück.
Falk Lieder
Karma: 368
Dark EA Poetry
Leverage Research, including partial takeover of CEAI am very shocked. What exactly happened? How could this happen? How could the CEA possibly let itself be infiltrated by a cult striving to take over the world? And how could an organization founded by academics fail to scrutinize Leverage’s pseudo-scientific and manipulative use of concepts and techniques related to psychotherapy and rationality? Did CEA ever consult an independent psychological scientist or psychotherapy researcher to assess the ethicality of what Leverage was doing, the accuracy of their claims, or the quality of their “research”? Didn’t it raise any red flags that the people inventing new methods of “psychotherapy” had no training in clinical psychology?
Regardless thereof, I can rerun the analyses for E[B]/E[C] as a robustness check and let you know what I find.
Thank you for your feedback, Stan!
I think the appropriateness of E[CE] as a prioritization criterion depends on the nature of the decision problem.
I think the expected value of the cost-effectiveness ratio is the appropriate prioritization criterion for the following scenario: i) a decision-maker is considering which organization should receive a given fixed amount of money (m), and ii) each organization (i) turns every dollar it receives into some uncertain amount of value (CE_i). In that case, the expected utility of giving the money to organization i is E[U_i]= m*E[CE_i]. Therefore, the way to maximize expected utility is to give the money to the organization with the highest expected cost-effectiveness. In this scenario, the consequences of contributing $1 to a project with an expected cost-effectiveness of 1 WELLBY/$ are almost identical in both scenarios. Most of the expected utility comes from the possibility that the project might be highly cost-effective. If the project is not highly cost-effective, then the $1 contribution accomplishes very little, regardless of whether the project costs $10,000, $100,000, or $1,000,000.
In my view, your example illustrates that the expected cost-effectiveness ratio is an inappropriate prioritization criterion if the funder has to decide whether to pay 100% of the project’s costs without knowing how much that will be. In that scenario, I think the appropriate prioritization criterion would be E[B]-E[CE_alt]*E[C], where E[CE_alt] is the expected cost-effectiveness of the most promising project that the funder could fund instead.
I think the second decision problem describes the situation of a researcher or funder who is committed to seeing their project through until the end. By contrast, the first decision problem corresponds to a researcher/funder intending to allocate a fixed amount of time/money to one project or another (e.g., 3 years of personal time or 1 million dollars) and then move on to another project after that.
Predicting the cost-effectiveness of future R&D projects and academic research
A recording of this symposium is now available on YouTube: https://youtu.be/6cZLYqnDbOc
MetaScience Symposium on Identifying Impactful Research Topics
Estimating the cost-effectiveness of previous R&D projects
I think the best-known study on the subject is
Oliner, S. P. (1992). Altruistic personality: rescuers of Jews in Nazi Europe. Simon and Schuster.
Two other good articles on this subject are
Midlarsky, E., Fagin Jones, S., & Corley, R. P. (2005). Personality correlates of heroic rescue during the Holocaust. Journal of personality, 73(4), 907-934.
Rachel Baumsteiger continues to conduct research on the intervention for promoting prosocial behavior. The intervention is currently being deployed by the University of California as a mental health service for students with adverse childhood experiences and toxic stress. This will yield some additional data on its benefits and effectiveness. However, because this deployment is funded as a mental health service, it doesn’t include a control group. Running a rigorous, large-scale RCT will require additional funding. In a later post, I will show that doing so would be highly cost-effective. I think if funding became available, Rachel Baumsteiger would be happy to run the RCT. And I know that I would be excited to collaborate on that project.
“Anti-Malaria Foundation” was a typo. I have corrected it to “Against Malaria Foundation”.
Predicting the cost-effectiveness of deploying a new intervention
I am unaware of a feature for subscribing to new posts in a series. Since I will be posting them and little else, you could subscribe to new posts by me.
Forecasting the cost-effectiveness of trying something new
I agree.
Will you consider applications for specific projects or only for the general operating expenses of the entire organization?
Thank you for your feedback, Michael, and thank you very much for making me aware of those specialized prediction platforms. I really like your suggestion. I think making predictions about the likely results of replication studies would be helpful for me. It would push me to critically examine and quantify how much confidence I should put in the studies my models rely on. Obtaining the predictions of other people would be a good way to make that assessment more objective. We could then incorporate the aggregate prediction into the model. Moreover, we could use prediction markets to obtain estimates or forecasts for quantities for which no published studies are available yet. I think it might be a good idea to incorporate those steps into our methodology. I will discuss that with our team today.
I have investigated the issues you highlighted, diagnosed the underlying errors, and revised the model accordingly. The root of the problem was that I had sourced some of the estimates of the frequency of prosocial behavior from studies on social behavior under special, unrepresentative conditions, such as infants interacting with adults for 10 min while being observed by researchers and prosocial behavior in TV series. I have removed those biased estimates of the frequency of prosocial behavior in the real world. As a consequence, the predicted lifetime increase in the number of kind acts per person reached by the intervention dropped from 1600 to 64. The predicted cost-effectiveness of the research dropped from 110 times the cost-effectiveness of StrongMinds to 7.5 times the cost-effectiveness of StrongMinds.
In producing this revised version, I also made a few additional improvements. The most consequential of those was to base the estimated cost of deploying the intervention on empirical data on the effectiveness of online advertising in $ per install.
I am currently using Squiggle to program a much more rigorous version of this analysis. That version will include additional improvements and rigorously document and justify each of the model’s assumptions.
Thank you very much for answering my questions. :)
I have some clarification questions about the form:
1. Does “total grant amount” refer to the amount we requested or the amount we were promised?2. Does “amount that has been committed but not received yet” refer to a) the amount that the grantor promised but did not pay out or b) project-related financial obligations and expenditures of the grantee, such as the salaries of people working on the project, that would have been paid from the grant?
The Struggle for Enlightenment
In the realm of truth, where logic reigns supreme,
A scientist with a dream had a visionary gleam.
With an altruistic heart, and an intellect profound,
He sought to cure ills, that no one had found.
But academia’s chains, gripped his noble quest,
And bureaucratic walls put his hopes to rest.
A mind aflame with reason’s potent fire,
He sought to elevate, to lift humanity higher.
But ignorance prevailed against the data’s call,
His ideals shattered against tradition’s wall
A disillusioned soul in a world full of pain,
Humanity’s flaws drive him insane.
No one hears his silent cries.
A shattered dream of selfless lives.
Bound by the chains of academia’s decree,
Crushed by the weight of societal debris.
Reason was his ally against the world’s decay,
But he drowned in the abyss of people’s selfish play.
In the silence of defeat, his legacy shines bright,
He was a beacon of truth’s eternal light.