  In multi-agent environments in which coordination is desirable, the history
of play often causes lock-in at sub-optimal outcomes. Notoriously, technologies
with a significant environmental footprint or high social cost persist despite
the successful development of more environmentally friendly and/or socially
efficient alternatives. The displacement of the status quo is hindered by
entrenched economic interests and network effects. To exacerbate matters, the
standard mechanism design approaches based on centralized authorities with the
capacity to use preferential subsidies to effectively dictate system outcomes
are not always applicable to modern decentralized economies. What other types
of mechanisms are feasible? In this paper, we develop and analyze a mechanism
that induces transitions from inefficient lock-ins to superior alternatives.
This mechanism does not exogenously favor one option over another -- instead,
the phase transition emerges endogenously via a standard evolutionary learning
model, Q-learning, where agents trade-off exploration and exploitation.
Exerting the same transient influence to both the efficient and inefficient
technologies encourages exploration and results in irreversible phase
transitions and permanent stabilization of the efficient one. On a technical
level, our work is based on bifurcation and catastrophe theory, a branch of
mathematics that deals with changes in the number and stability properties of
equilibria. Critically, our analysis is shown to be structurally robust to
significant and even adversarially chosen perturbations to the parameters of
both our game and our behavioral model.
