Month: May 2023

End Times by Peter Turchin

From the pioneering co-founder of cliodynamics, the groundbreaking new interdisciplinary science of history, a big-picture explanation for America’s civil strife and its possible endgames

Peter Turchin, one of the most interesting social scientists of our age, has infused the study of history with approaches and insights from other fields for more than a quarter century. End Times is the culmination of his work to understand what causes political communities to cohere and what causes them to fall apart, as applied to the current turmoil within the United States. 

Back in 2010, when Nature magazine asked leading scientists to provide a ten-year forecast, Turchin used his models to predict that America was in a spiral of social disintegration that would lead to a breakdown in the political order circa 2020. The years since have proved his prediction more and more accurate, and End Times reveals why.

The lessons of world history are clear, Turchin argues: When the equilibrium between ruling elites and the majority tips too far in favor of elites, political instability is all but inevitable. As income inequality surges and prosperity flows disproportionately into the hands of the elites, the common people suffer, and society-wide efforts to become an elite grow ever more frenzied. He calls this process the wealth pump; it’s a world of the damned and the saved. And since the number of such positions remains relatively fixed, the overproduction of elites inevitably leads to frustrated elite aspirants, who harness popular resentment to turn against the established order. Turchin’s models show that when this state has been reached, societies become locked in a death spiral it’s very hard to exit.

In America, the wealth pump has been operating full blast for two generations. As cliodynamics shows us, our current cycle of elite overproduction and popular immiseration is far along the path to violent political rupture.  That is only one possible end time, and the choice is up to us, but the hour grows late.

More at: www.penguinrandomhouse.com

Neuromorphic learning, working memory, and metaplasticity in nanowire networks

Alon Loeffler,  Adrian Diaz-Alvarez,  Ruomin Zhu,  Natesh Ganesh, James M. Shine, Tomonobu Nakayama, Zdenka Kuncic

SCIENCE ADVANCES 21 Apr 2023 Vol 9, Issue 1

Nanowire networks (NWNs) mimic the brain’s neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed “seven plus or minus two” rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to “synaptic metaplasticity” in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.

Read the full article at: www.science.org

Call for Abstracts: Conference on Complex Systems 2023

Abstract submission is now open. The closing date is June 10, 2023. Successful presenters will be notified by the end of June. Presenters must register by the end of July to have their presentations confirmed. Each presenter can register for a maximum of two presentations at Parallel Sessions

More at: ccs2023.org

Computational capability of ecological dynamics

Masayuki Ushio, Kazufumi Watanabe, Yasuhiro Fukuda, Yuji Tokudome and Kohei Nakajima

Royal Society Open Science April 2023 Volume 10 Issue 4

Ecological dynamics is driven by complex ecological networks. Computational capabilities of artificial networks have been exploited for machine learning purposes, yet whether an ecological network possesses a computational capability and whether/how we can use it remain unclear. Here, we developed two new computational/empirical frameworks based on reservoir computing and show that ecological dynamics can be used as a computational resource. In silico ecological reservoir computing (ERC) reconstructs ecological dynamics from empirical time series and uses simulated system responses for information processing, which can predict near future of chaotic dynamics and emulate nonlinear dynamics. The real-time ERC uses real population dynamics of a unicellular organism, Tetrahymena thermophila. The temperature of the medium is an input signal and population dynamics is used as a computational resource. Intriguingly, the real-time ecological reservoir has necessary conditions for computing (e.g. synchronized dynamics in response to the same input sequences) and can make near-future predictions of empirical time series, showing the first empirical evidence that population-level phenomenon is capable of real-time computations. Our finding that ecological dynamics possess computational capability poses new research questions for computational science and ecology: how can we efficiently use it and how is it actually used, evolved and maintained in an ecosystem?

Read the full article at: royalsocietypublishing.org

Heterogeneity extends criticality

Fernanda Sánchez-Puig, Octavio Zapata, Omar K. Pineda, Gerardo Iñiguez, and Carlos Gershenson

Front. Complex Syst., 03 May 2023

Criticality has been proposed as a mechanism for the emergence of complexity, life, and computation, as it exhibits a balance between order and chaos. In classic models of complex systems where structure and dynamics are considered homogeneous, criticality is restricted to phase transitions, leading either to robust (ordered) or fragile (chaotic) phases for most of the parameter space. Many real-world complex systems, however, are not homogeneous. Some elements change in time faster than others, with slower elements (usually the most relevant) providing robustness, and faster ones being adaptive. Structural patterns of connectivity are also typically heterogeneous, characterized by few elements with many interactions and most elements with only a few. Here we take a few traditionally homogeneous dynamical models and explore their heterogeneous versions, finding evidence that heterogeneity extends criticality. Thus, parameter fine-tuning is not necessary to reach a phase transition and obtain the benefits of (homogeneous) criticality. Simply adding heterogeneity can extend criticality, making the search/evolution of complex systems faster and more reliable. Our results add theoretical support for the ubiquitous presence of heterogeneity in physical, biological, social, and technological systems, as natural selection can exploit heterogeneity to evolve complexity “for free”. In artificial systems and biological design, heterogeneity may also be used to extend the parameter range that allows for criticality.

Read the full article at: www.frontiersin.org