Cultural evolution in populations of Large Language Models

Research in cultural evolution aims at providing causal explanations for the change of culture over time. Over the past decades, this field has generated an important body of knowledge, using experimental, historical, and computational methods. While computational models have been very successful at generating testable hypotheses about the effects of several factors, such as population structure or transmission biases, some phenomena have so far been more complex to capture using agent-based and formal models. This is in particular the case for the effect of the transformations of social information induced by evolved cognitive mechanisms. We here propose that leveraging the capacity of Large Language Models (LLMs) to mimic human behavior may be fruitful to address this gap. On top of being an useful approximation of human cultural dynamics, multi-agents models featuring generative agents are also important to study for their own sake. Indeed, as artificial agents are bound to participate more and more to the evolution of culture, it is crucial to better understand the dynamics of machine-generated cultural evolution. We here present a framework for simulating cultural evolution in populations of LLMs, allowing the manipulation of variables known to be important in cultural evolution, such as network structure, personality, and the way social information is aggregated and transformed. The software we developed for conducting these simulations is open-source and features an intuitive user-interface, which we hope will help to build bridges between the fields of cultural evolution and generative artificial intelligence.Comment: 17 pages, 20 figures. Open-source code available at https://github.com/jeremyperez2/LLM-Cultur

Mise en place d'un système informatique de la surveillance de la résistance aux antibiotiques à l'Hôpital Avicenne

PARIS-BIUP (751062107) / SudocSudocFranceF

Exploring Platinum Speciation with X-ray Absorption Spectroscopy under High-Energy Resolution Fluorescence Detection Mode

International audienceCritical to interpreting platinum chemical speciation using X-ray absorption spectroscopy (XAS) is the availability of reference spectra of compounds with known Pt redox and coordination. Here we compare different techniques for Pt LIII-edge X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectral regions for a large set of Pt-O-Cl-S reference compounds of known structures. The measurements were conducted in HERFD (high-energy resolution fluorescence detection, high-resolution or HR) mode, as well as in two conventional modes such as transmission (TR) and nominal-resolution total fluorescence yield (TFY or NR). Samples analyzed here included Pt0 (TR), PtIIS (HR), PtIVS2 (TR), K2PtIICl4 (HR + TR), K2PtIVCl6 (HR + TR), PtIVO2 (HR + TR), C6H12N2O4PtII (HR + TR), and aqueous solutions of K2PtIICl4 and H2PtIVCl6 (NR + TR), as well as (NH4)2PtIV(S5)3 (HR + TR). XANES spectra in HERFD mode offer a better energy resolution than in conventional modes, allowing a more accurate identification of Pt redox state and coordination geometry. EXAFS spectra in all three modes for a given compound yield identical within errors values of Pt-neighbor interatomic distances and mean square relative displacement (MSRD, σ2) parameters. In contrast, both TR and NR spectra on the one hand and HR spectra on the other hand yield distinct amplitude reduction factor (S02) values, 0.76 ± 0.04 and 0.99 ± 0.07 (1 standard error), respectively. This study contributes to the development of an open-access XAS database SSHADE

Flax shives-PBAT processing into 3D printed fluorescent materials with potential sensor functionalities

International audienceIncorporation of unused agricultural by-products into materials is a relevant strategy in developing biosourced and economically competitive products that limits the environmental impacts of plastics. Development of 3D printing techniques offers the possibility to design such biomaterials while bringing new functionalities, however, it is critical to characterize and control both the plant material properties and the interactions between the plant material and the polymeric matrix during the whole process, from filament production to 3D printing. In this study, flax shives were selectively milled and then used as a starting material to be grafted to a fluorophore whose fluorescence varies under pH. The resulting fluorescent shives were processed with poly-(butylene-terephthalate) (PBAT) by extrusion to produce a filament reinforced with 10 %-wt of flax shives, which was the subsequently 3D printed. Extensive microstructural characterization (particle size and shape analysis, X-ray microtomography) demonstrated that the flax particles were homogeneously distributed into the 3D printed material. Despite the relatively low content of fluorescent flax shives in the final 3D printed material (1%-wt) and successive heating stages (during extrusion and 3D printing), a strong fluorescent emission could still be measured. This work paves the way for using fluorescent flax shives as reinforcements into composites, thus making 4D materials with potential applications as sensors depending on the fluorophore used. 50 μm). In particular, a greater reactivity can be obtained throug