Associate Professor in Machine Learning

Group Decision Making via Differentiable Programming

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Differentiable programming (also known as software 2.0) offers a novel approach to coding, focused on defining parametrized differentiable model to solve a problem instead of coding a precise algorithm. In this post we explore the use of this coding paradigm to solve the problem of consensus reaching in group-decision making.

We first define a model for aggregating preferences, and then we use differentiable programming to compute an optimal change to the original preferences that would lead to a consensus. We analyze the results and evaluate some altervative models. In our simulations we will use models, but we will underline how these can be easily extended.

Notebook here

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