Complexity Thoughts

Complexity Thoughts delves into the intricacies of complex systems, networks, and artificial intelligence impacting public health and societal sustainability. It bridges disciplines to explore connectivity, system dynamics, and interventions from microscopic to societal levels, integrating insights from research on ecosystems, brain networks, generative AI, and more for comprehensive understanding and actionable solutions.

Complex Systems Network Science Artificial Intelligence Public Health Sustainable Society Ecological Complexity Evolutionary Science Societal Issues Neuroscience Climate Change Generative AI Stochastic Thermodynamics Social Interactions Human Behavior

The hottest Substack posts of Complexity Thoughts

And their main takeaways

Complexity Thoughts: Issue #46

259 implied HN points • 28 Oct 24
🔬 Science Ecology Evolution Neuroscience Human behavior Complex Systems
  1. Biodiversity is important for the stability of ecosystems, as different species contribute to their health and resilience. Losing biodiversity can harm not just the species we see, but also the tiny organisms that support them.
  2. Ecosystem synchrony is a concept that helps us understand how different ecosystems respond to changes in their environment. It looks at how similar patterns in ecosystem functions can reveal important information about their health.
  3. Belief dynamics show how people's beliefs change over time, influenced by their social networks. Understanding these dynamics can help tackle issues like misinformation and social conflict.

AI, Cybernetics, and Complexity: unpacking the 2024 Nobel Prizes

319 implied HN points • 14 Oct 24
🔬 Science Complexity Cybernetics Artificial Intelligence Physics Biology
  1. The 2024 Nobel Prizes recognized important advances in AI, but these discoveries are also deeply connected to complex systems. This shows that complexity science is becoming a more accepted area in high-level research.
  2. Understanding complex systems requires looking beyond traditional boundaries of science. The future of breakthroughs may rely on merging different scientific fields and using interdisciplinary approaches.
  3. Success in tackling complex challenges, like climate change and health issues, will need both detailed analysis of parts and a broader view of systems. Researchers must balance reductionist methods with insights from complexity science.

Nobel Prize to the Statistical Physics of artificial neural networks

379 implied HN points • 08 Oct 24
🔬 Science Physics Machine Learning Artificial Intelligence Computational Models Statistical Methods
  1. John J. Hopfield and Geoffrey E. Hinton won the Nobel Prize for their work on artificial neural networks. Their research helps us understand how machines can learn from data using ideas from physics.
  2. Hopfield's networks use energy minimization to recall memories, similar to how physical systems find stable states. This shows a connection between physics and how machines learn.
  3. Boltzmann machines, developed by Hinton, introduce randomness to help networks explore different configurations. This randomness allows for better learning from data, making these models more effective.

Complexity Thoughts: Issue #45

139 implied HN points • 11 Oct 24
🔬 Science Evolution Artificial Intelligence Neuroscience Complex Systems
  1. New ideas in network science can help understand complex systems better. This approach looks at how systems behave over time, rather than just focusing on stable points.
  2. The evolution of multicellular organisms has led to many new species and ecosystems. Key innovations in multicellularity help organisms adapt and thrive in different environments.
  3. Research shows that convolutional neural networks (CNNs) face limits in recognizing patterns. This limitation is linked to the complexity of the data they're trained on, raising questions about their reliability.

Less is more, more is different and sparse is better: but why?

199 implied HN points • 02 Feb 24
  1. Less is more and sparse is better when it comes to complex systems' connectivity, balancing efficiency and diversity of responses.
  2. Thermodynamics and information theory play crucial roles in understanding the emergence of specific topological features in complex systems.
  3. The variational principle offers a powerful framework to describe a variety of physical and artificial systems, including complex networks.
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One year of Complexity Thoughts

259 implied HN points • 03 Nov 23
  1. The author shared their one-year journey of exploring complex systems through digital spaces.
  2. The goal was to build a community where knowledge could be shared one paper at a time.
  3. The community has grown to hundreds and the author will continue to unravel complexity through knowledge-building.

Complexity Thought: 2023 Influential Papers Collection

159 implied HN points • 30 Dec 23
  1. Research on ecological complexity suggests integration with complex systems science for more progress
  2. Modern evolutionary science can provide insights for addressing major global issues
  3. Applying complexity science to societal issues can provide actionable insights for interventions

Complexity Thoughts: Issue #24

139 implied HN points • 12 Jan 24
  1. Complex networks can amplify disruptions, needing robustness measures and early-warning signals.
  2. Understanding how brains work as networks involves examining network properties' influence on brain dynamics.
  3. Epidemic control models can benefit from utilizing high-resolution data and new diagram methods.

Complexity Thoughts: Issue #25

99 implied HN points • 26 Jan 24
  1. Feedback is crucial for free services like Complexity Thoughts to thrive and improve.
  2. Real-world networks balance response diversity and information flow for optimal structure.
  3. Neural circuits can be synchronized through specific circuit topologies, aiding in information processing.

Complexity Thoughts: Issue #22

119 implied HN points • 20 Dec 23
  1. This issue explores stochastic thermodynamics and establishes thermodynamic bounds on time-reversal asymmetry.
  2. Cross-inhibition can lead to group consensus, even among strongly opinionated minorities, challenging traditional hypotheses.
  3. Real-world biological systems exhibit greater order and robustness against perturbations than previously thought, redefining our understanding of complex cognition.

Complexity Thoughts: Issue #18

139 implied HN points • 27 Oct 23
  1. State estimation of physical systems is possible even when the governing equations are unknown.
  2. Human groups show cooperative and deceptive behavior through stigmergic interactions.
  3. Systems evolve based on function and selection, leading to an increase in diversity and patterned behavior.

Complexity science meets ancient Japanese art

139 implied HN points • 25 Oct 23
  1. Exploring the combination of generative AI and ancient Japanese art in representing complex concepts.
  2. Using generative AI to replicate the style of Japanese paintings for drawing concepts in complexity science.
  3. Visual representations of complex concepts such as stigmergy, collective behavior, and self-organization using generative AI.

Complexity Thoughts: Issue #17

139 implied HN points • 20 Oct 23
  1. Complex and disordered systems exhibit logarithmic aging due to self-organization and instabilities.
  2. There is a need to unify different computational methods for understanding interactions in complex systems.
  3. Dynamic network structures can impact the spread of cooperation and prosocial behavior.

Complexity Thoughts: Issue #21

99 implied HN points • 15 Dec 23
  1. Social interactions are crucial for the frequency and quality of newsletters like Complexity Thoughts.
  2. A variety of topics from different disciplines are covered in Complexity Thoughts, highlighting the importance of interdisciplinary approaches.
  3. Research in complex systems and networks can provide insights into various fields like human behavior, neuroscience, and biological systems.

Complexity Thoughts: Issue #16

139 implied HN points • 07 Oct 23
  1. Complexity Thoughts newsletter explores intricate dynamics in various fields.
  2. Different studies highlight how indirect effects shape interactions in ecosystems.
  3. Research predicts potential mid-century collapse of the Atlantic meridional overturning circulation and warns of climate tipping points.

Complexity Thoughts: Issue #14

159 implied HN points • 01 Sep 23
  1. Ecologists have discovered an equation of state linking species diversity, productivity, abundance, and biomass using maximum entropy and metabolic theory
  2. A new approach in network community detection allows for performance comparison without relying on 'ground-truth' labels, revealing biases in some methods
  3. Recent studies in network neuroscience showcase experimental evidence for the free-energy principle, neural network formalization, and insights into neurodegenerative diseases

Complexity Thoughts: Issue #20

99 implied HN points • 01 Dec 23
  1. Complexity Thoughts newsletter relies on social interactions for content quality and frequency.
  2. Optimizing control in nonequilibrium systems is crucial for advancements in nanodevice design.
  3. Spatially embedded recurrent neural networks shed light on how brain networks function and optimize.

Complexity Thoughts: Issue #19

99 implied HN points • 17 Nov 23
  1. Complex systems can be understood by studying networks and collective behavior.
  2. Short-term dynamics can be a better predictor of outcomes than stability in complex ecosystems.
  3. Understanding the synchronization of systems with heterogeneity provides insights into stability in realistic settings.

Navigating the transformative potential of generative AI: a complex systems perspective

139 implied HN points • 30 Jun 23
  1. Different perspectives exist on the potential impact of AI, ranging from concerns about manipulation to optimism about enlightenment.
  2. The deployment of powerful AI systems can have positive impacts like enhancing education, but it also poses risks such as misuse for manipulation and invasion of privacy.
  3. There is ongoing debate about the emergent abilities of large AI models and the need to better understand their functioning and potential impact on society.

Complexity Thoughts: Issue #15

99 implied HN points • 15 Sep 23
  1. Monte Carlo simulations are super fast now, great for studying systems with long-range interactions.
  2. New discoveries show the dynamic nature of cellular responses to stress, with potential for innovative cancer therapies.
  3. Artificial intelligence is revolutionizing scientific discovery, from generating hypotheses to designing new proteins.

Phantom oscillations in your data?

19 implied HN points • 10 Feb 24
  1. Dimensionality reduction techniques like PCA can lead to misleading results if blindly applied without deep understanding of the data and its context
  2. Patterns observed using PCA may not always reflect actual data due to mathematical artifacts, especially in cases of continuous spatial variation or non-oscillatory data
  3. Using PCA with neuroscience data can result in detecting phantom oscillations that don't exist in the data due to the nature of the analysis method, emphasizing the need to interpret results carefully and consider the limitations of the algorithm

What's going on with assembly theory?

4 HN points • 16 Oct 23
  1. A new paper on assembly theory sparked online discussions and debates in academic communities
  2. Assembly theory proposes a framework to analyze molecular assembly and has shown experimental correlations with mass spectrometry data
  3. The academic arena emphasizes the importance of polite and constructive scientific exchanges while evaluating new theories and practicing good scientific standards