Large language models (LLMs) have demonstrated impressive capabilities as autonomous agents with rapidly expanding applications in various domains. As these agents increasingly engage in socioeconomic interactions, identifying their potential for undesirable behavior becomes essential. In this work, we examine scenarios where they can choose to collude, defined as secretive cooperation that harms another party. To systematically study this, we investigate the behavior of LLM agents acting as sellers in simulated continuous double auction markets. Through a series of controlled experiments, we analyze how parameters such as the ability to communicate, choice of model, and presence of environmental pressures affect the stability and emergence of seller collusion. We find that direct seller communication increases collusive tendencies, the propensity to collude varies across models, and environmental pressures, such as oversight and urgency from authority figures, influence collusive behavior. Our findings highlight important economic and ethical considerations for the deployment of LLM-based market agents.

I've been fortunate enough to attend the first meeting of what then became the Trusted IoT consortium, held in Berkeley in late 2016. The key idea was, combining properties of blockchain such as identity and immutable ledger with sensor data where provenance, authenticity, location, and governance are crucial for actions taken on the basis of the sensor data stream. It still takes an effort to wrap your mind around the way the two areas, Blockchain and IoT, interoperate. Various objects need to be ascertained as being something, belonging to someone, and being somewhere. One example is Supply Chain -- when a container is shipped from say Shanghai to Oakland, the buyer, the seller and the shipper all need to know, and to be able to prove, that it follows a certain state machine -- with loading, sailing, arrival, customs, and unloading, sequenced one after the other in time.

In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with ``zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile.