Month: May 2024

Non-Spatial Hash Chemistry as a Minimalistic Open-Ended Evolutionary System

Hiroki Sayama

There is an increasing level of interest in open-endedness in the recent literature of Artificial Life and Artificial Intelligence. We previously proposed the cardinality leap of possibility spaces as a promising mechanism to facilitate open-endedness in artificial evolutionary systems, and demonstrated its effectiveness using Hash Chemistry, an artificial chemistry model that used a hash function as a universal fitness evaluator. However, the spatial nature of Hash Chemistry came with extensive computational costs involved in its simulation, and the particle density limit imposed to prevent explosion of computational costs prevented unbounded growth in complexity of higher-order entities. To address these limitations, here we propose a simpler non-spatial variant of Hash Chemistry in which spatial proximity of particles are represented explicitly in the form of multisets. This model modification achieved a significant reduction of computational costs in simulating the model. Results of numerical simulations showed much more significant unbounded growth in both maximal and average sizes of replicating higher-order entities than the original model, demonstrating the effectiveness of this non-spatial model as a minimalistic example of open-ended evolutionary systems.

Read the full article at: arxiv.org

Making Sense of Chaos: A Better Economics for a Better World, by J. Doyne Farmer

We live in an age of increasing complexity, where accelerating technology and global interconnection hold more promise – and more peril – than any other time in human history. As well as financial crises, issues around climate change, automation, growing inequality and polarization are all rooted in the economy, yet standard economic predictions fail us.

Many books have been written about Doyne Farmer and his pioneering work in chaos and complexity theory. Making Sense of Chaos is the first in his own words, presenting a manifesto for doing economics better. In a tale of science and ideas, Farmer fuses his profound knowledge with stories from his life to explain how to harness a scientific revolution to address the economic conundrums facing society.

Using big data and ever more powerful computers, we can for the first time apply complex systems science to economic activity, building realistic models of the global economy. The resulting simulations and the emergent behaviour we observe form the cornerstone of complexity economics. This new science, Farmer shows, will allow us to test ideas and make significantly better economic predictions – and, ultimately, create a better world.

More at: www.penguin.co.uk

Workshop: Demystifying machine learning for population researchers. November 5-6, Rostock, Germany

Advances in computational power and statistical algorithms, in conjunction with the increasing availability of large datasets, have led to a Cambrian explosion of machine learning (ML) methods. For population researchers, these methods are useful not only for predicting population dynamics but also as tools to improve causal inference tasks. However, the rapid evolution of this literature, coupled with terminological disparities from conventional approaches, renders these methods enigmatic and arduous for many population researchers to grasp.

This workshop on November 5 to 6, 2024 at the Max Planck Intsitute for Demographic Research (MPIDR) in Rostock, Germany, clarifies the goals, techniques, and applications of machine learning methods for population research. The workshop covers

  • an introduction to ML methods for population researchers,
  • showcases of ML applications to answer causal questions,
  • discussions of the current developments of ML for population health, fertility and family dynamics, and
  • fosters critical discussions about the shortfalls of these techniques.

The main focus of this workshop is on ML techniques using quantitative population data and research questions, not on ML language models. The workshop consists of keynotes, contributed sessions, and a tutorial.

More at: www.demogr.mpg.de