Collapse of Coordination in Multiobjective Frontier Dynamics

ORAL

Abstract

Scientific and technological frontiers underpin economic growth, improved health, and national competitiveness. The frontier represents the Pareto boundary of jointly attainable performance across multiple objectives, and its evolution over time defines multiobjective frontier dynamics. Existing theories emphasize static Pareto trade-offs, offering a snapshot of feasible performance but little understanding of how frontiers coevolve across objectives. Analyzing three domains of rapid innovation, from large-language models to solar cells to computer hardware, we find a universal two-phase pattern. Early progress follows a coordinated phase, in which advances simultaneously improve all objectives and the frontier converges to a single point. Beyond a critical threshold, coordination collapses, giving rise to a fragmented phase with intensified trade-offs and objective-specific leaders. We explain this pattern with a minimal stochastic model of innovation. Two forces compete: (i) a steady pressure toward specialization from rare moves that help one objective while hurting the other, and (ii) a finite reservoir of “win–win” moves that advance all objectives but become exhausted as the system improves. As universally beneficial moves vanish, the system tips from coordination to fragmentation in a sharp dynamical phase transition whose timing scales with problem complexity—roughly the number of design dimensions times its logarithm—and whose location shifts with the number of inherent conflicts between objectives. The model yields a simple scaling collapse that unifies trajectories across technologies and matches the observed transitions. Coordination collapse thus emerges as a general organizing principle of multiobjective progress, offering testable forecasts for when frontiers will bifurcate, cautioning against over-extrapolating from early unified leaders, and informing the design of R&D portfolios, standards, and policy in domains where multiple goals must advance together.

*This work is supported by the National Science Foundation (award number 2404035).

Presenters

  • Siddharth Patwardhan

    • Northwestern University

Authors

  • Siddharth Patwardhan

    • Northwestern University
  • Chaoming Song

    • University of Miami
  • Dashun Wang

    • Northwestern University