Principal Trade-off Analysis

TL;DR

Principal Trade-off Analysis (PTA) is a novel decomposition method that embeds two-player zero-sum games into a low-dimensional space, revealing strategic trade-offs and symmetries more effectively than previous approaches, demonstrated on various example games.

Contribution

PTA introduces a PCA-like embedding for games, enabling insightful analysis of strategic trade-offs and symmetries in complex game structures.

Findings

PTA identifies key strategic trade-offs in Kuhn poker.

PTA reveals symmetries and win conditions in Blotto.

PTA uncovers natural clusters and cycles in Pokemon game data.

Abstract

How are the advantage relations between a set of agents playing a game organized and how do they reflect the structure of the game? In this paper, we illustrate "Principal Trade-off Analysis" (PTA), a decomposition method that embeds games into a low-dimensional feature space. We argue that the embeddings are more revealing than previously demonstrated by developing an analogy to Principal Component Analysis (PCA). PTA represents an arbitrary two-player zero-sum game as the weighted sum of pairs of orthogonal 2D feature planes. We show that the feature planes represent unique strategic trade-offs and truncation of the sequence provides insightful model reduction. We demonstrate the validity of PTA on a quartet of games (Kuhn poker, RPS+2, Blotto, and Pokemon). In Kuhn poker, PTA clearly identifies the trade-off between bluffing and calling. In Blotto, PTA identifies game symmetries, and specifies strategic trade-offs associated with distinct win conditions. These symmetries reveal limitations of PTA unaddressed in previous work. For Pokemon, PTA recovers clusters that naturally correspond to Pokemon types, correctly identifies the designed trade-off between those types, and discovers a rock-paper-scissor (RPS) cycle in the Pokemon generation type - all absent any specific information except game outcomes.