通过对抗优化方法高效推断多智能体博弈参数，提升市场预测精度。
Efficient Inverse Multiagent Learning
- 将逆博弈问题建模为最小最大优化，设计多项式时间算法求解。
- 在西班牙电力市场数据上，预测性能显著优于ARIMA方法。
- 适用于需要快速拟合行为数据的经济建模与市场分析场景。
本文研究逆博弈论（即逆多智能体学习），目标是找到博弈收益函数的参数，使得期望（或采样）行为构成均衡。我们将这些问题形式化为生成对抗（即极小极大）优化问题，并开发了多项式时间算法求解：前者依赖精确的一阶黑盒，后者依赖随机一阶黑盒。我们进一步将该方法扩展至多项式时间与样本数内求解逆多智能体模拟学习问题，在此问题中，我们寻求一个模拟体（simulacrum），即一组参数及其对应的均衡，使得其期望行为能复现给定观测。实验表明，该方法在基于时间序列数据预测西班牙电力市场价格时，性能显著优于广泛使用的ARIMA方法。
原文摘要 · Abstract (English)
In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. sampled) behavior is an equilibrium. We formulate these problems as generative-adversarial (i.e., min-max) optimization problems, for which we develop polynomial-time algorithms to solve, the former of which relies on an exact first-order oracle, and the latter, a stochastic one. We extend our approach to solve inverse multiagent simulacral learning in polynomial time and number of samples. In these problems, we seek a simulacrum, meaning parameters and an associated equilibrium that replicate the given observations in expectation. We find that our approach outperforms the widely-used ARIMA method in predicting prices in Spanish electricity markets based on time-series data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。