The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs), and specifically GPT-4. We find that (1) LLM-based agents are adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in oligopoly settings to the detriment of consumers, and (3) variation in seemingly innocuous phrases in LLM instructions ("prompts") may increase collusion. These results extend to auction settings. Our findings underscore the need for antitrust regulation regarding algorithmic pricing, and uncover regulatory challenges unique to LLM-based pricing agents.

These applications have high, ever-growing requirements in terms of security, reliability and performance. Currently, the development of these applications is heavily dependent on cloud, the abundant resources of which are a necessity for the computationally intensive Artificial Intelligence (AI) methods. However, cloud-native processing requires transmitting data between the end users and the cloud, which increases the latency, burdens the core network and raises privacy concerns. Hence, several computing paradigms, such as edge and fog computing, Multi-access Edge Computing (MEC) and cloudlets (Ren et al. (2020)), have emerged to bring the computing and storage resources from the cloud to the edge, closer to the end users. Even though these paradigms have differences in their architectural considerations and driving forces, they all have the same essence: placing and using computational resources between the end user and the distant cloud in order to reduce latency and energy consumption, as well as increase security and privacy by keeping the application data local. Bringing the intelligent applications onto the edge between the end users and the cloud is not a simple task. Traditional AI is inherently centralized and resource consuming, while the edge is inherently distributed and limited in resources. Further, the edge nodes are highly heterogeneous in terms of their capabilities, while the edge environment as a whole is characterized by intermittent connectivity, distributed and non-IID data, as well as geographically distributed, opportunistic computing resources (Kokkonen et al. (2022)). Research on developing and adapting AI methods to the edge environment has been coined as AI on Edge (Lovén et al. (2019); Deng et al. (2020)), which is an active research area with an ample amount of research (Deng et al. (2020); Xu et al. (2021); Park et al. (2021)).

As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation. We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions.