Falk Lieder

Research Group Leader @ Max Planck Institute for Intelligent Systems, Tübingen
Working (6-15 years of experience)

Bio

I lead the Rationality Enhancement Group at the MPI for Intelligent Systems in Tübingen and will join the psychology department of UCLA as an Assistant Professor in July 2023.

I completed my PhD 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 from the University of Osnabrück.

Comments
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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.

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?

Thank you so much for pointing that out, Vael! I had completely overlooked that information. That's really helpful to know.

How quickly should grantees impacted by recent events apply to this call? Is there a hard or soft deadline for these applications? I have to decide how much time I should invest in adapting, updating, and improving the previous application. I assume you want applicants to attach a proposal detailing the planned projects, the project's pathway to impact, and evidence of its chances to succeed.

I made a typo. I meant to ask you about integrating the type-checking functionality of Pedant with the probabilistic modeling functionality of Squiggle. I think a version of Squiggle where each value has units that are propagated through the calculations would be very useful. This would allow the user to see whether the  final result has the right units.

When do you think a tool that combines the strengths of Squiggle and Guesstimate will become available? Given where you are at now, what do you think would be the fastest way to integrate the dimensional analysis capabilities of Pedant with the probabilistic modelling capabilities of Squiggle? How long would it take?

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