人口游戏中的设计灾难：破坏稳定的浪费性锁定技术
Catastrophe by Design in Population Games: Destabilizing Wasteful Locked-in Technologies
摘要：
在需要协调的多智能体环境中，游戏的历史往往会导致锁定在次优结果。众所周知，尽管成功开发了更环保和/或更有社会效益的替代品，但具有重大环境足迹或高社会成本的技术仍然存在。根深蒂固的经济利益和网络效应阻碍了现状的改变。使问题更加严重的是，基于中央权力机构的标准机制设计办法，这些权力机构有能力利用优惠补贴来有效地支配系统结果，但这种办法并不总是适用于现代分权经济。还有哪些其他类型的机制是可行的？在本文中，我们开发和分析了一种机制，诱导从低效的锁定过渡到优越的替代品。这种机制不会外生地支持一个选项而不是另一个选项-相反，相变通过标准的进化学习模型Q学习内生地出现，其中代理人权衡探索和利用。对高效和低效技术的相同瞬态影响的消除鼓励了探索，并导致不可逆的相变和高效技术的永久稳定。在技术层面上，我们的工作是基于分叉和突变理论，这是一个数学分支，处理平衡点的数量和稳定性的变化。至关重要的是，我们的分析表明，在结构上是强大的显着的，甚至adversarially选择的扰动我们的游戏和我们的行为模型的参数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
