These People are Not Well

Posted on by
PNAS Logo

Nasonline.org

https://www.nasonline.org/publications/pnas

..publications of the (USA) National Association of Science

“Near-term climate prediction is one of the Grand Challenges of the World Climate Research Program (14). There have also been other significant efforts in this domain, for instance, with the subseasonal to seasonal (S2S) prediction project (15, 16). But, in many cases, numerical modeling still does, and also might continue to, leave vulnerable societies with insufficient warning time ahead of climate phenomena.”

Josef Ludescher josef.ludescher@pik-potsdam.de, Maria Martin https://orcid.org/0000-0002-1443-0891 josef.ludescher@pik-potsdam.de, NiklasBoers https://orcid.org/0000-0002-1239-9034, +9, and Hans JoachimSchellnhuberAuthors Info & Affiliations

Edited by Michael E. Mann, The Pennsylvania State University, University Park, PA, and approved October 6, 2021 (received for review February 27, 2020)

November 15, 2021

118 (47) e1922872118

https://doi.org/10.1073/pnas.1922872118

4,96611

Metrics

Total views4,966

Abstract

“Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of high-impact phenomena: 1) El Niño events, 2) droughts in the central Amazon, 3) extreme rainfall in the eastern Central Andes, 4) the Indian summer monsoon, and 5) extreme stratospheric polar vortex states that influence the occurrence of wintertime cold spells in northern Eurasia. In this perspective, we argue that network-based approaches can gainfully complement numerical modeling.

“If societies are able to anticipate disruptive events, they can take measures to save thousands of lives and to avoid billions of economic costs (1–5). A most evident, globally disruptive event is certainly the current COVID-19 pandemic. Even though it seems impossible to accurately predict the emergence of such a virus itself, the pandemic bears several characteristics that are also shared by other disruptions: The general risk of something like this happening was known before, but economic and societal preparations to limit harmful impacts are strongly dependent on a credible, science-based warning, preferably with significant time before the event or at least before its full unfolding (the spreading, in the case of a virus) and with specifications of foreseeable impacts. Such a warning is not always possible, but there are promising new avenues. Here, we describe our perspective on this research challenge from the point of view of network theory and its usefulness for better understanding and for forecasting specific climate phenomena.

“Relevant climate phenomena that have the potential to produce major disruptions in societies are, for instance, the El Niño phenomenon, the Indian summer monsoon, and extreme weather patterns like persistent heat waves, cold spells, or rainstorms as associated with stalling planetary Rossby waves (6). For instance, a popular saying in India—that the “true finance minister” is the monsoon—is based on the fact that water resources are vital for India, where the rural economy accounts for about 45% of GDP (7). El Niño occurrences are well known for their global impacts on weather patterns and therefore societies. Floods and heatwaves, especially concurring with droughts, directly affect humans and nature, and can wreak havoc in agriculture. Beyond the climate system, highly challenging events of a disruptive nature are large-magnitude earthquakes, outbreaks of epidemics, and, on the individual level, physiological disasters like heart attacks. These phenomena often emerge with little precursory signal or no warning time at all, making effective adaptation challenging, if not impossible. The pertinent lack of predictive power, however, is not surprising, since most of those high-impact events are generated by complex systems composed of many nonlinearly interacting entities.

“In the case of weather and climate, forecasting relies predominantly on numerical models (8). Starting with Richardson (9) in the 1920s, it has been a long way to the first successful prediction (10) in 1950 and, eventually, to the highly sophisticated general circulation and Earth system models of today (11). These simulators rely on initial conditions (especially for weather forecasts, i.e., the prediction of atmospheric dynamics for up to 2 wk) and boundary conditions (which are more relevant for seasonal and longer-ranging forecasts, involving slower climate components like the oceans) and deliver very good forecasts for a broad range of physical quantities. However, their predictive power for certain climate phenomena beyond the weather timescale can be rather limited: The dependence on precise initial and boundary conditions and the necessity to simplify, inherent to any modeling approach, as well as the chaotic nature of the system under study will hit hard limits to further improvement (12, 13).

“Despite multiple efforts toward seamless prediction, a gap remains in prediction skill between the subseasonal weather forecast and seasonal and longer climate predictions. Near-term climate prediction is one of the Grand Challenges of the World Climate Research Program (14). There have also been other significant efforts in this domain, for instance, with the subseasonal to seasonal (S2S) prediction project (15, 16). But, in many cases, numerical modeling still does, and also might continue to, leave vulnerable societies with insufficient warning time ahead of climate phenomena, within as well as outside of the above mentioned gap: There are types of climate phenomena that still notoriously elude reliable long-term forecasting through numerical modeling. For five specific climate phenomena examples discussed below, network theory has led to (in some cases) considerably earlier forecasts compared to state-of-the-art operational forecasts (SI Appendix, Table S1).

Here we argue that the predictability limitations of existing operational forecasts are partly due to the basic intention of numerical models: the goal of faithfully mirroring the local nature of direct interactions in the physical world. However, the models are not perfect mimicries of nature. Processes, for example, turbulence, are not resolved at all or only at a possibly insufficient resolution, and tuned parametrizations have to be employed (17). In particular, teleconnections present in observational data may be not well represented or even absent within numerical models. Thus, identifying and then analyzing the evolution of teleconnections with time can provide an additional avenue to predicting large-scale climate phenomena. The beginnings of this promising avenue can be traced back to Sir Gilbert Walker (18) in the early 20th century, when he first noticed teleconnections, and has now gained a new and much broader perspective through the advent of complex network analyses.

“Here we suggest that the evolving interactions (manifesting, e.g., via correlations) between different and often rather distant locations can provide new insights and serve as predictors for a large variety of climate phenomena. The philosophy behind this approach is that, even in a simple system, composed, for instance, of two coupled nonlinear oscillators, one will observe aleatoric behavior providing very limited information when measuring the motion of each oscillator individually. However, when evaluating the coupling between them, for example, via synchronization [as already detected in the 17th century by Christiaan Huygens (19)], one will obtain new and valuable information about the system (20). Analogously, while one might not necessarily extract useful information from measurements of single locations on the globe, the links, for example, the interactions between the sites and their evolution in time, can provide, as in the examples below, critical novel information for forecasting.”//

My thanks to @wired4weldWordPress – that my old, 2014 to 2021, moniker for this now extraordinary global leadership blog greeneconomyaction.com – for enabling us to take an old document November 15 2021 and put it out fully referenced, indexed and ‘identifiered’ for readers’ own study purposes.

As a serious scholar and not a goggle-eyed consumer of the memes, tat and bog-splurt of narcissist narrative from populists, demagogues, schoolboys (nothing against them: I once was one), shills, grifters, the sub-optimally socially (non fiscal non sugar-high non oxytocin-2 soaked transactional relationship-builder) intelligent and above all (or in the vertical dimension below-all as sentient ADULT life-forms go) the raging undiagnosed and untreated sociopath “influencers” (many evidently psychopathic BUT ALL OVERWHELMINGLY TEDIOUS & IRKSOME to the well-loved and self-respecting teenager or adult) in a neurocogitive state of arrested at the pre-formal operations,1 or the pre Critical Thinking, psycho-emotional development stage of all of our precious lives catastrophically been persuaded, even, convinced in their hundreds of millions around the world that we want their putrid self-obsessive scrafed-up junk information to flood into our day-to-day internet working apps – mine’s of course @X – you knew that – when NOT ONE OF THESE PEOPLE LET ALONE THE NON-PERSON TROLLBOTS OF COURSE ever reads our careful and indeed loving replies or pleas for sanity, let alone human dignity and decency – and lately I’ve had to burn TWENTY MINUTES a day on this toxic sludge – most of it pro “green” – oh yarda yarda see mean ‘n nasty John’s flying fist – oh you missed it.

If any of us is seriously preening ourselves about membership of the publishing industry – any branch, form or format – at this moment in the history of the human race, then he. she and indeed they if there’s more than one of them are set to have their faces collide with any number of fists, at the head of the queue of lively bidders and punters their own teenaged or adult kids.

I’ll leave you with the Michael Mann’s3 weather forecasting stuff. Mother. Be Prepared, quoth 1st Baronet Lord Baden Powell and his sister Agnes in 1910. What the f… is wrong with you people laughing at me?

John Blundell

Instantaneously Conjucted R & LCHS Neurolinguistics, 2-5 Set Quantum Relations Logic, Integral Sociology Political Theory, Weather-systems, Atmospheric-science after Robert Boyle, Sucessfully Growing Stuff, Philosophy of Science, Human Futures

1 Menu – Piaget’s theory of cognitive development is a comprehensive theory about the nature and development of human intelligence. It was originated by the Swiss developmental psychologist Jean Piaget (1896–1980). The theory deals with the nature of knowledge itself and how humans gradually come to acquire, construct, and use it.[1] Piaget’s theory is [catastrophically only] known as a developmental stage theory [amongst the allopath medical grandees, chieftains, godfathers and their plaything inherently corrupt politicians of the entire United States of America commercial-for-gross-profit medical collapsing world but I’ve set myself a goal to totally fix this by mid 2024].

Look and learn – in your environs. You’re not what you eat – there’s a nutrition-hungry digestive system that takes care there but you are (or rather were) what you shit out. And oh, your doing and pompous dignified obedient being are so NOT it. There’s becoming, right? Like the 12 second 100 metres, miracle goals, oh shut up, Kevin.. These gags just keep coming. I have to stop now. Clunck.

Simply Psychology

Psychology » Child Psychology

Piaget’s Theory And Stages Of Cognitive Development

By

Saul Mcleod, PhD

Updated on

November 5, 2023

Reviewed by

Olivia Guy-Evans, MSc

On This Page:

Key Takeaways

  • Jean Piaget is famous for his theories regarding changes in cognitive development that occur as we move from infancy to adulthood.
  • Cognitive development results from the interplay between innate capabilities (nature) and environmental influences (nurture).
  • Children progress through four distinct stages, each representing varying cognitive abilities and world comprehension: the sensorimotor stage (birth to 2 years), the preoperational stage (2 to 7 years), the concrete operational stage (7 to 11 years), and the formal operational stage (11 years and beyond).
  • A child’s cognitive development is not just about acquiring knowledge, the child has to develop or construct a mental model of the world, which is referred to as a schema.
  • Piaget emphasized the role of active exploration and interaction with the environment in shaping cognitive development, highlighting the importance of assimilation and accommodation in constructing mental schemas.

2 the socio-sexual emotional fulfilment and empowerment-in-the-other adult or teen enzyme, a beauty that one

3 Dr. Michael Mann is Presidential Distinguished Professor in the Department of Earth and Environmental Science at the University of Pennsylvania

Leave a comment