With the dramatic progress of artificial intelligence algorithms in recent
times, it is hoped that algorithms will soon supplant human decision-makers in
various fields, such as contract design. We analyze the possible consequences
by experimentally studying the behavior of algorithms powered by Artificial
Intelligence (Multi-agent Q-learning) in a workhorse \emph{dual contract} model
for dual-principal-agent problems. We find that the AI algorithms autonomously
learn to design incentive-compatible contracts without external guidance or
communication among themselves. We emphasize that the principal, powered by
distinct AI algorithms, can play mixed-sum behavior such as collusion and
competition. We find that the more intelligent principals tend to become
cooperative, and the less intelligent principals are endogenizing myopia and
tend to become competitive. Under the optimal contract, the lower contract
incentive to the agent is sustained by collusive strategies between the
principals. This finding is robust to principal heterogeneity, changes in the
number of players involved in the contract, and various forms of uncertainty.

本研究通过实验研究 AI 算法在双重委托代理问题的双重合同模型中的行为，发现 AI 算法可以自主学会设计可以激励合规的合同，而在合同中，一方可以采取勾结或竞争等多态行为，而且欺骗行为似乎并不存在于制定的合同中。

人工智能和双重合同

Artificial Intelligence and Dual Contract

A novel framework is proposed for the trajectory design of multiple unmanned
aerial vehicles (UAVs) based on the prediction of users' mobility information.
The problem of joint trajectory design and power control is formulated for
maximizing the instantaneous sum transmit rate while satisfying the rate
requirement of users. In an effort to solve this pertinent problem, a
three-step approach is proposed which is based on machine learning techniques
to obtain both the position information of users and the trajectory design of
UAVs. Firstly, a multi-agent Q-learning based placement algorithm is proposed
for determining the optimal positions of the UAVs based on the initial location
of the users. Secondly, in an effort to determine the mobility information of
users based on a real dataset, their position data is collected from Twitter to
describe the anonymous user-trajectories in the physical world. In the
meantime, an echo state network (ESN) based prediction algorithm is proposed
for predicting the future positions of users based on the real dataset.
Thirdly, a multi-agent Q-learning based algorithm is conceived for predicting
the position of UAVs in each time slot based on the movement of users. In this
algorithm, multiple UAVs act as agents to find optimal actions by interacting
with their environment and learn from their mistakes. Additionally, we also
prove that the proposed multi-agent Q-learning based trajectory design and
power control algorithm can converge under mild conditions. Numerical results
are provided to demonstrate that as the size of the reservoir increases, the
proposed ESN approach improves the prediction accuracy. Finally, we demonstrate
that throughput gains of about 17% are achieved.

本研究提出了一种基于用户移动信息预测的多无人机轨迹设计的新框架，其利用机器学习技术为多个无人机实现位置信息获取和轨迹设计，并通过多智能体 Q 学习算法实现对其位置的预测与优化。同时，数值结果表明所提出的多智能体 Q 学习算法的轨迹设计和功率控制算法可以在较小的条件下收敛，并且可以实现 17％左右的吞吐量改进。