Firm competition and collusion involve complex dynamics, particularly when
considering communication among firms. Such issues can be modeled as problems
of complex systems, traditionally approached through experiments involving
human subjects or agent-based modeling methods. We propose an innovative
framework called Smart Agent-Based Modeling (SABM), wherein smart agents,
supported by GPT-4 technologies, represent firms, and interact with one
another. We conducted a controlled experiment to study firm price competition
and collusion behaviors under various conditions. SABM is more cost-effective
and flexible compared to conducting experiments with human subjects. Smart
agents possess an extensive knowledge base for decision-making and exhibit
human-like strategic abilities, surpassing traditional ABM agents. Furthermore,
smart agents can simulate human conversation and be personalized, making them
ideal for studying complex situations involving communication. Our results
demonstrate that, in the absence of communication, smart agents consistently
reach tacit collusion, leading to prices converging at levels higher than the
Bertrand equilibrium price but lower than monopoly or cartel prices. When
communication is allowed, smart agents achieve a higher-level collusion with
prices close to cartel prices. Collusion forms more quickly with communication,
while price convergence is smoother without it. These results indicate that
communication enhances trust between firms, encouraging frequent small price
deviations to explore opportunities for a higher-level win-win situation and
reducing the likelihood of triggering a price war. We also assigned different
personas to firms to analyze behavioral differences and tested variant models
under diverse market structures. The findings showcase the effectiveness and
robustness of SABM and provide intriguing insights into competition and
collusion.

通过使用智能代理建模方法，模拟了企业间的竞争、勾结，以及通信对价格收敛的影响。结果表明，没有通信时智能代理往往达到默契勾结，价格收敛在柏特朗均衡价格之上但低于垄断或卡特尔价格；而允许通信时，智能代理能够实现更高程度的勾结，价格接近卡特尔价格，勾结形成更快，价格收敛更平稳。这些结果表明，通信可以增强企业之间的信任，鼓励频繁小幅度的价格偏差，以探索更高级别的双赢机会，减少引发价格战的可能性。

运用 GPT 的 “豚鼠试验”：一种研究企业竞争与勾结的新型智能代理建模方法

"Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling  Approach for Studying Firm Competition and Collusion

We develop a theoretical model to study strategic interactions between
adaptive learning algorithms. Applying continuous-time techniques, we uncover
the mechanism responsible for collusion between Artificial Intelligence
algorithms documented by recent experimental evidence. We show that spontaneous
coupling between the algorithms' estimates leads to periodic coordination on
actions that are more profitable than static Nash equilibria. We provide a
sufficient condition under which this coupling is guaranteed to disappear, and
algorithms learn to play undominated strategies. We apply our results to
interpret and complement experimental findings in the literature and to the
design of learning-robust strategy-proof mechanisms. We show that ex-post
feedback provision guarantees robustness to the presence of learning agents. We
fully characterize the optimal learning-robust mechanisms: they are menu
mechanisms.

该研究基于连续时间技术提出了一个理论模型，旨在研究适应性学习算法之间的策略互动。通过揭示算法之间的自发耦合机制，该研究证明了人工智能算法之间存在的勾结现象，并提出了一种足以消除算法间自发耦合的充分条件，以及设计学习鲁棒性策略的机制所必要的回馈机制。

自适应算法和耦合协作

Adaptive Algorithms and Collusion via Coupling

Cloud vendors are increasingly offering machine learning services as part of
their platform and services portfolios. These services enable the deployment of
machine learning models on the cloud that are offered on a pay-per-query basis
to application developers and end users. However recent work has shown that the
hosted models are susceptible to extraction attacks. Adversaries may launch
queries to steal the model and compromise future query payments or privacy of
the training data. In this work, we present a cloud-based extraction monitor
that can quantify the extraction status of models by observing the query and
response streams of both individual and colluding adversarial users. We present
a novel technique that uses information gain to measure the model learning rate
by users with increasing number of queries. Additionally, we present an
alternate technique that maintains intelligent query summaries to measure the
learning rate relative to the coverage of the input feature space in the
presence of collusion. Both these approaches have low computational overhead
and can easily be offered as services to model owners to warn them of possible
extraction attacks from adversaries. We present performance results for these
approaches for decision tree models deployed on BigML MLaaS platform, using
open source datasets and different adversarial attack strategies.

该研究提出了一种基于云的提取监视器，通过观察单个和串通的对手用户的查询和响应流来量化模型的提取状态，从而使用信息增益来测量具有不断增加查询数量的用户的模型学习速率，并维护智能查询摘要以在串通存在的情况下测量与输入特征空间覆盖度相关的学习速率，以提醒模型所有者可能存在侵犯攻击。