# Strategic Prediction with Latent Aggregative Games

## TL;DR

This paper presents a novel class of structured prediction models based on latent aggregative games that capture strategic interactions and can recover meaningful behaviors from real voting data.

## Contribution

It introduces a new game-based modeling framework with convergent dynamics and identifiability conditions, enabling transferability across different sets of players.

## Key findings

- Successfully recover strategic interactions from voting data
- Propose two new aggregator paradigms with convergence guarantees
- Model demonstrates transferability across different scenarios

## Abstract

We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions. Our games map the input context to outcomes by first condensing the input into private player types that specify the utilities, weighted interactions, as well as the initial strategies for the players. The game is played over multiple rounds where players respond to weighted aggregates of their neighbors' strategies. The predicted output from the model is a mixed strategy profile (a near-Nash equilibrium) and each observation is thought of as a sample from this strategy profile. We introduce two new aggregator paradigms with provably convergent game dynamics, and characterize the conditions under which our games are identifiable from data. Our games can be parameterized in a transferable manner so that the sets of players can change from one game to another. We demonstrate empirically that our games as models can recover meaningful strategic interactions from real voting data.