A class of distributed adaptive pricing mechanisms for societal systems with limited information
A class of distributed adaptive pricing mechanisms for societal systems with limited information
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一类信息有限社会系统的分布式自适应定价机制
DOI：
10.1109/cdc.2017.8263863
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
A. Teel
中科院分区：
文献类型：
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作者：
J. Poveda;Philip N. Brown;Jason R. Marden;A. Teel
针对仿射拥堵对策中的分布式自适应定价问题，提出了一类学习动力学模型。我们考虑这样的设置，其中大量用户面临在有限数量的可用资源之间进行选择的问题，每个资源具有仅依赖于使用该特定资源的用户份额的特定成本函数。由于用户的数量是恒定的，他们的个人决策会影响所有可用资源的性能，从而产生一个种群博弈，其中每个资源都可以被视为博弈中的一个特定策略。鉴于人口博弈中的纳什均衡可能不是社会最优的这一众所周知的事实，社会规划者面临着设计激励机制以诱导社会最优纳什均衡的挑战。为了实现这一点，我们提出了一类无模型的分布式定价算法，它保证收敛到诱导社会最优纳什均衡的最优通行费集合。我们的结果使我们能够考虑对通行费做出即时反应的用户群体，以及具有社会动态的人群。由于算法是分布式的和数据驱动的，它们可以在游戏的完整信息不可用的情况下实现。结合博弈论、鲁棒集值动态系统和自适应控制的工具，建立了一个收敛结果。
In this paper, we present a class of learning dynamics for distributed adaptive pricing in affine congestion games. We consider the setting where a large population of users is faced with the problem of choosing between a finite number of available resources, each resource having a particular cost function that depends only on the share of users using that particular resource. Since the mass of users is constant, their individual decisions affect the performance of all the available resources, thus generating a population game where each resource can be seen as a particular strategy in the game. Given the well-known fact that Nash equilibria in population games may not be socially optimal, a social planner is faced with the challenge of designing incentive mechanisms that induce a socially optimal Nash equilibrium. To achieve this, we present in this paper a class of model-free distributed pricing algorithms that guarantee convergence to the set of optimal tolls that induce a socially optimal Nash equilibrium. Our results allow us to consider populations of users that react instantaneously to tolls, as well as populations with social dynamics. Since the algorithms are distributed and data-driven, they can be implemented in settings where full information of the game is not available. By combining tools from game theory, robust set-valued dynamical systems, and adaptive control, a convergence result is established.