mega michael cause prioritization madness

Quantitative models offer a superior approach in determining which interventions to support. However, naive cost-effectiveness estimates have big problems. In particular:

1. They don’t give stronger consideration to more robust estimates.
2. They don’t always account for all relevant factors.

This is an implementation of Michael Dickens' attempt to buid a quantitative model for cause selection which does not have these limitations.

The model makes estimates by using expected-value calculations to produce probability distributions of utility values. It then uses these estimates as evidence to update a prior over the effectiveness of different interventions. Treating estimates as evidence updating a prior means that interventions with more robust evidence of effectiveness have better posteriors.

This app is deployed at http://mdickens.me/causepri-app/.

You can add new models by editing this repo. We're very happy to help you out with that.

This version was implemented by Michael Dickens and Buck Shlegeris.

Installation and Usage

This needs Ruby and stuff. If you don't have a Ruby environment (with bundle and gems and stuff), get that. Then run:

bundle install

to install the dependencies.

You probably need to go to the quantitative_model directory and run make to compile the C++ program.

Then run it with

bundle exec ruby server.rb 8080

and it should be running on port 8080.

Using the Backend

You can run the C++ backend independently of the frontend by calling

./a.out inputs.txt

inputs.txt contains the inputs needed for the backend model. Right now it's missing a lot of inputs but you can add them if you want to use them.

How to Add a New Intervention

To add calculations for a new intervention, you'll need to modify quantitative_model/QuantitativeModel.cpp.

1. Add a function to estimate the expected value of the intervention. Look at targeted_values_spreading_estimate(Table& t) or any of the other estimate functions to see how expected value estimates work. They access Distribution objects stored in the table t. Distribution objects support addition, multiplication by other distributions and by scalars, and reciprocals (via .reciprocal()).
2. In main, call your new function and store the result in a Distribution variable. You may then call print_results on the variable (look at how print_results is used for the other estimates in main).