Agent Group Chat: An Interactive Group Chat Simulacra For Better Eliciting Collective Emergent Behavior

To investigate the role of language in human collective behaviors, we developed the Agent Group Chat simulation to simulate linguistic interactions among multi-agent in different settings. Agents are asked to free chat in this simulation for their own purposes based on their character setting, aiming to see agents exhibit emergent behaviours that are both unforeseen and significant. Four narrative scenarios, Inheritance Disputes, Law Court Debates, Philosophical Discourses, Movie Casting Contention, are integrated into Agent Group Chat to evaluate its support for diverse storylines. By configuring specific environmental settings within Agent Group Chat, we are able to assess whether agents exhibit behaviors that align with human expectations. We evaluate the disorder within the environment by computing the n-gram Shannon entropy of all the content speak by characters. Our findings reveal that under the premise of agents possessing substantial alignment with human expectations, facilitating more extensive information exchange within the simulation ensures greater orderliness amidst diversity, which leads to the emergence of more unexpected and meaningful emergent behaviors. The code is open source in https://github.com/MikeGu721/AgentGroup, and online platform will be open soon.

CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data

Hyperbolic space can embed tree metric with little distortion, a desirable property for modeling hierarchical structures of real-world data and semantics. While high-dimensional embeddings often lead to better representations, most hyperbolic models utilize low-dimensional embeddings, due to non-trivial optimization as well as the lack of a visualization for high-dimensional hyperbolic data. We propose CO-SNE, extending the Euclidean space visualization tool, t-SNE, to hyperbolic space. Like t-SNE, it converts distances between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of high-dimensional data $X$ and low-dimensional embeddings $Y$. However, unlike Euclidean space, hyperbolic space is inhomogeneous: a volume could contain a lot more points at a location far from the origin. CO-SNE thus uses hyperbolic normal distributions for $X$ and hyberbolic \underline{C}auchy instead of t-SNE's Student's t-distribution for $Y$, and it additionally attempts to preserve $X$'s individual distances to the \underline{O}rigin in $Y$. We apply CO-SNE to high-dimensional hyperbolic biological data as well as unsupervisedly learned hyperbolic representations. Our results demonstrate that CO-SNE deflates high-dimensional hyperbolic data into a low-dimensional space without losing their hyperbolic characteristics, significantly outperforming popular visualization tools such as PCA, t-SNE, UMAP, and HoroPCA, the last of which is specifically designed for hyperbolic data.