# Collaborative Optimization for Collective Decision-making in Continuous Spaces

## Garg, Nikhil and Kamble, Vijay and Goel, Ashish and Marn, David and Munagala, Kamesh

Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These problems are typically discretized before running an election or decided upon through negotiation by representatives. We propose a meta-algorithm called \emphIterative Local Voting for collective decision-making in this setting, in which voters are sequentially sampled and asked to modify a candidate solution within some local neighborhood of its current value, as defined by a ball in some chosen norm. In general, such schemes do not converge, or, when they do, the resulting solution does not have a natural description. We first prove the convergence of this algorithm under appropriate choices of neighborhoods to plausible solutions in certain natural settings: when the voters’ utilities can be expressed in terms of some form of distance from their ideal solution, and when these utilities are additively decomposable across dimensions. In many of these cases, we obtain convergence to the societal welfare maximizing solution. We then describe an experiment in which we test our algorithm for the decision of the U.S. Federal Budget on Mechanical Turk with over 4,000 workers, employing neighborhoods defined by \mathcalL^1, \mathcalL^2 and \mathcalL^∞balls. We make several observations that inform future implementations of such a procedure.