Breaking Status-Quo Inertia in Living Temporal Games: Dynamic Intervention, Implementation, and Structural Design

TL;DR

This paper explores how dynamic interventions can overcome status-quo inertia in living temporal games, introducing new structural and informational strategies within a continuous-time stochastic framework.

Contribution

It formalizes intervention classes, proves a threshold theorem for inertia, and demonstrates the effectiveness of structural modifications over price-based transfers.

Findings

A threshold theorem for inertia survival under transfer perturbations.

Edge replacements can succeed where price interventions fail.

A dynamic pivot mechanism achieves second-best efficiency under private information.

Abstract

Westudy how a planner can design dynamic interventions to overcome status-quo inertia in living temporal games, where strategic agents control their state (active, sleep, partially dead) on a temporal network. Building on the continuous-time stochastic game framework of our companion paper, we introduce three intervention classes: bounded transfers (price based), structural modifications (edge deletion, addition, or replacement), and information signals. We formalize the notion of inertia depth and prove a threshold theorem: the status quo equilibrium survives all transfer perturbations whose magnitude is below a critical bound that depends on the remaining horizon. A central structural dominance result shows that for any finite transfer budget there exists a family of games where no bounded price intervention can eliminate the inefficient equilibrium, yet a single edge replacement (continuous-flow to discrete-transport) succeeds. We then study private-information subclasses with static types. Using a uniformization reduction, we prove an impossibility result: no direct mechanism can simultaneously satisfy ex post incentive compatibility, ex post budget balance, and history privacy while always implementing an efficient equilibrium. In the same subclass we construct a dynamic pivot mechanism that achieves second-best efficiency with bounded deficit. Finally, we show that replacing continuous-flow edges by discrete-transport edges weakly expands the set of implementable outcomes, highlighting the importance of temporal semantics for mechanism design. Our results extend the static analysis of [5] to continuous time strategic networks and provide a rigorous foundation for subsequent papers on learning and mean-field design.