英文标题：
《Inferring agent objectives at different scales of a complex adaptive
  system》
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作者：
Dieter Hendricks, Adam Cobb, Richard Everett, Jonathan Downing and
  Stephen J. Roberts
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最新提交年份：
2017
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英文摘要：
  We introduce a framework to study the effective objectives at different time scales of financial market microstructure. The financial market can be regarded as a complex adaptive system, where purposeful agents collectively and simultaneously create and perceive their environment as they interact with it. It has been suggested that multiple agent classes operate in this system, with a non-trivial hierarchy of top-down and bottom-up causation classes with different effective models governing each level. We conjecture that agent classes may in fact operate at different time scales and thus act differently in response to the same perceived market state. Given scale-specific temporal state trajectories and action sequences estimated from aggregate market behaviour, we use Inverse Reinforcement Learning to compute the effective reward function for the aggregate agent class at each scale, allowing us to assess the relative attractiveness of feature vectors across different scales. Differences in reward functions for feature vectors may indicate different objectives of market participants, which could assist in finding the scale boundary for agent classes. This has implications for learning algorithms operating in this domain.
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中文摘要：
我们引入了一个框架来研究金融市场微观结构在不同时间尺度上的有效目标。金融市场可以被视为一个复杂的适应系统，在这个系统中，有目的的主体集体地、同时地创造和感知与其交互的环境。有人建议，多个代理类在此系统中运行，具有自上而下和自下而上因果关系类的非平凡层次结构，每个层次具有不同的有效模型。我们推测，代理类实际上可能在不同的时间尺度上运行，从而对相同的感知市场状态做出不同的反应。给定特定于规模的时间状态轨迹和根据总体市场行为估计的行动序列，我们使用反向强化学习来计算每个规模上的聚合代理类的有效奖励函数，从而评估不同规模上特征向量的相对吸引力。特征向量奖励函数的差异可能表明市场参与者的目标不同，这有助于找到代理类的规模边界。这对在该领域中运行的学习算法有影响。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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