Adam B. Barrett

Dylan C. Terry-Doyle, Adam B. Barrett

The breaching of planetary boundaries and the potentially catastrophic consequences of climate change are leading researchers to question the endless pursuit of economic growth. Several macroeconomic modelling studies have now examined whether a zero-growth trajectory in a capitalist system with interest-bearing debt can be economically stable, with mixed results. However, stability has not previously been explored at the microeconomic level, where it is important to know the consequences of zero-growth on e.g., distribution of firm sizes, market instability and risk of individual firm bankruptcy. Here we address this by developing an agent-based model incorporating Minskyan financial dynamics, the Post-Growth DYNamic Agent-based MINskyan (PG-DYNAMIN) model, and carrying out simultaneous macro- and microeconomic analyses. Accounting for the fact that growing capitalist economies are unstable and produce crises, we compare the relative stability of growth and zero-growth scenarios. This is achieved by tweaking an exogenous productivity parameter. We find zero-growth scenarios are viable yet exhibit distinct dynamics from growth scenarios. Under zero-growth, GDP was less volatile, there was reduced systemic risk in the credit network, lower unemployment rates, a higher wages share of GDP for workers, lower corporate debt to GDP ratio, and a reduction in market instability. Additionally, there was a higher rate of inflation, lower profit share of GDP for firms, increased market concentration, more economic crises with higher severity, and increased default probabilities for firms during periods of crises.

Romy Beauté, David J. Schwartzman, Guillaume Dumas et al.

Stroboscopic light stimulation (SLS) on closed eyes typically induces simple visual hallucinations (VHs), characterised by vivid, geometric and colourful patterns. A dataset of 862 sentences, extracted from 422 open subjective reports, was recently compiled as part of the Dreamachine programme (Collective Act, 2022), an immersive multisensory experience that combines SLS and spatial sound in a collective setting. Although open reports extend the range of reportable phenomenology, their analysis presents significant challenges, particularly in systematically identifying patterns. To address this challenge, we implemented a data-driven approach leveraging Large Language Models and Topic Modelling to uncover and interpret latent experiential topics directly from the Dreamachine's text-based reports. Our analysis confirmed the presence of simple VHs typically documented in scientific studies of SLS, while also revealing experiences of altered states of consciousness and complex hallucinations. Building on these findings, our computational approach expands the systematic study of subjective experience by enabling data-driven analyses of open-ended phenomenological reports, capturing experiences not readily identified through standard questionnaires. By revealing rich and multifaceted aspects of experiences, our study broadens our understanding of stroboscopically-induced phenomena while highlighting the potential of Natural Language Processing and Large Language Models in the emerging field of computational (neuro)phenomenology. More generally, this approach provides a practically applicable methodology for uncovering subtle hidden patterns of subjective experience across diverse research domains.