Computational social choice seeks algorithmic answers to questions about preference aggregation, safety of elections, robustness of outcomes, stability, etc. It overwhelmingly models societies as composed of discrete agents. We propose to study computational social choice problems in a society continuum} setting, where a society is modeled as a distribution of infinitely many infinitesimal agents of different types. An analogous approach has been very useful in physics (it is the basis of statistical mechanics), economics (mean field games), and other fields. As an initial case study, we focus on election attacks (bribery and control), which have been extensively studied in the discrete setting. We show that a broad class of standard election attacks becomes polynomial-time solvable in the society continuum. The class contains problems that are NP-hard discretely, among them Borda- and Bucklin-CCDV and unit-cost Borda-SWAP BRIBERY. Furthermore, we give polynomial-time algorithms for $k$-Approval-SWAP BRIBERY when $k$ is constant for general costs, and when $k$ varies and the cost function is additively separable. The latter result contrasts with the discrete problem, which we prove NP-complete for additively separable costs and every fixed $k\ge 2$. In contrast, we prove that Borda-SWAP BRIBERY and $k$-Approval-SWAP BRIBERY, both with general costs, remain computationally hard in the society continuum. To obtain these results, we use both continuous and discrete optimization techniques, such as the Configuration LP framework and dynamic programming. Of particular note is the technique underlying our hardness proofs, which shows how to ''reverse the flow of hardness'' between LP formulations and pricing problems.

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