Building the Black Box: Cyberneticians and Complex Systems

2019 ◽  
Vol 45 (4) ◽  
pp. 575-595
Author(s):  
Elizabeth R. Petrick
Keyword(s):  
Mathematical Model ◽  
Complex Systems ◽  
Human Brain ◽  
Black Box ◽  
Initial Shape ◽  
Digital Representations ◽  
Black Boxes ◽  
Machine Systems ◽  
Scientific Fields ◽  
Inputs And Outputs

In the 1950s and 1960s, cyberneticians defined and utilized a concept previously described by electronic engineers: the black box. They were interested in how it might aid them, as both a metaphor and as a physical or mathematical model, in their analysis of complex human-machine systems. The black box evolved as they applied it in new ways, across a range of scientific fields, from an unnamed concept involving inputs and outputs, to digital representations of the human brain, to white boxes that might be used to replicate black boxes. The diversity of understandings of the black box reflected the diversity of scientific perspectives and goals brought under the label of cybernetics. In this paper, I examine how cyberneticians drew upon the black box in their personal writings and publications. My goal is to unpack what the black box meant to these theorists as a starting framework from which we may understand the initial shape of the black box.

Before the “Black Box”: The Inputs and Outputs of Nineteenth-century Deep-sea Science

2019 ◽  
Vol 45 (4) ◽  
pp. 596-617
Author(s):  
Rodolfo John Alaniz
Keyword(s):  
Nineteenth Century ◽  
Deep Sea ◽  
Black Box ◽  
Scientific Practices ◽  
Black Boxes ◽  
Inputs And Outputs

Nineteenth-century investigations of the deep sea provide a case study for black box science. Naturalists were forced to theorize about a space for which they had no direct sensory observations. This study traces the emergence of bathymetry and deep-sea biology and then analyzes how men of science dealt with the uncertainty associated with their black box practices. I argue that these investigators created multiple types of black boxes based on their uncertainties and that these black boxes did not operate equivalently. Consequently, scholars should be aware of the different categories of black boxes when they describe scientific practices.

Symptoms as Black Boxes: Time to Look Inside the Box

2021 ◽  
pp. 216770262198972
Author(s):  
Carolyn E. Wilshire ◽  
Tony Ward ◽  
Samuel Clack
Keyword(s):  
Black Box ◽  
Classification Systems ◽  
Subsequent Research ◽  
Black Boxes ◽  
Core Questions

In our original article (this issue, p. ♦♦♦), we argued that focusing research on individual symptoms of psychopathology might provide valuable information about their underlying nature and result in better classification systems, explanations, and treatment. To this end, we formulated five core questions that were intended to guide subsequent research and symptom conceptualizations in the psychopathology domain. In this article, we respond to two commentaries on our article. We conclude that it is time to open the black box of symptoms and to take seriously their status as complex constructs.

Rough Tolerance Equality and Tolerance Black Boxes

1988 ◽  
Vol 11 (3) ◽  
pp. 289-296
Author(s):  
Juhani Nieminen
Keyword(s):  
Black Box ◽  
Black Boxes

The rough equality concept of Z. Pawlak is modified and the rough top and the rough bottom tolerance equalities are given and characterized. The same tolerance idea is applied also to black box notion introduced by Novotný and Pawlak; the concept thus obtained is called tolerance black box. Tolerance black boxes are characterized and their properties are described.

scholarly journals Dimensional Changes in Lipid Rafts from Human Brain Cortex Associated to Development of Alzheimer’s Disease. Predictions from an Agent-Based Mathematical Model

2021 ◽  
Vol 22 (22) ◽  
pp. 12181
Author(s):  
Guido Santos ◽  
Mario Díaz
Keyword(s):  
Alzheimer’S Disease ◽  
Mathematical Model ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Lipid Rafts ◽  
Brain Cortex ◽  
Clinical Symptoms ◽  
Senile Plaques ◽  
Amyloid Β ◽  
Human Brain Cortex

Alzheimer’s disease (AD) is a neurodegenerative disease caused by abnormal functioning of critical physiological processes in nerve cells and aberrant accumulation of protein aggregates in the brain. The initial cause remains elusive—the only unquestionable risk factor for the most frequent variant of the disease is age. Lipid rafts are microdomains present in nerve cell membranes and they are known to play a significant role in the generation of hallmark proteinopathies associated to AD, namely senile plaques, formed by aggregates of amyloid β peptides. Recent studies have demonstrated that human brain cortex lipid rafts are altered during early neuropathological phases of AD as defined by Braak and Braak staging. The lipid composition and physical properties of these domains appear altered even before clinical symptoms are detected. Here, we use a coarse grain molecular dynamics mathematical model to predict the dimensional evolution of these domains using the experimental data reported by our group in human frontal cortex. The model predicts significant size and frequency changes which are detectable at the earliest neuropathological stage (ADI/II) of Alzheimer’s disease. Simulations reveal a lower number and a larger size in lipid rafts from ADV/VI, the most advanced stage of AD. Paralleling these changes, the predictions also indicate that non-rafts domains undergo simultaneous alterations in membrane peroxidability, which support a link between oxidative stress and AD progression. These synergistic changes in lipid rafts dimensions and non-rafts peroxidability are likely to become part of a positive feedback loop linked to an irreversible amyloid burden and neuronal death during the evolution of AD neuropathology.

scholarly journals Artificial Cognition: How Experimental Psychology Can Help Generate Explainable Artificial Intelligence

2021 ◽  
Author(s):  
J. Eric T. Taylor ◽  
Graham Taylor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Neural Networks ◽  
Experimental Approach ◽  
Well Being ◽  
Human Mind ◽  
Black Box ◽  
Psychological Science ◽  
Black Boxes ◽  
Explainable Artificial Intelligence

Artificial intelligence powered by deep neural networks has reached a levelof complexity where it can be difficult or impossible to express how a modelmakes its decisions. This black-box problem is especially concerning when themodel makes decisions with consequences for human well-being. In response,an emerging field called explainable artificial intelligence (XAI) aims to increasethe interpretability, fairness, and transparency of machine learning. In thispaper, we describe how cognitive psychologists can make contributions to XAI.The human mind is also a black box, and cognitive psychologists have overone hundred and fifty years of experience modeling it through experimentation.We ought to translate the methods and rigour of cognitive psychology to thestudy of artificial black boxes in the service of explainability. We provide areview of XAI for psychologists, arguing that current methods possess a blindspot that can be complemented by the experimental cognitive tradition. Wealso provide a framework for research in XAI, highlight exemplary cases ofexperimentation within XAI inspired by psychological science, and provide atutorial on experimenting with machines. We end by noting the advantages ofan experimental approach and invite other psychologists to conduct research inthis exciting new field.

Mind and Matter

Mind Shift ◽  
2021 ◽  
pp. 19-31
Author(s):  
John Parrington
Keyword(s):  
Human Brain ◽  
Rene Descartes ◽  
Brain Structures ◽  
Human Mind ◽  
Black Box ◽  
Human Consciousness ◽  
Common Thread ◽  
René Descartes

This chapter discusses different views on the basis of human consciousness. A major problem with much popular speculation about the biological roots of consciousness is that those who advocate a gene-based view of consciousness often appear to have little understanding of modern genetics, while speculation about how brain structures shape that consciousness often bear little resemblance to emerging knowledge about the complexity of an actual human brain. There is a common thread here, which is that idealised genes and brains have been substituted for real ones. Unfortunately, because of this tendency, it is not clear how much we have really advanced forwards from René Descartes and his belief that the human mind was an unknowable entity, or for that matter, the behaviourists with their view that the human mind could be treated as a black box. In contrast, to understand human consciousness, there is a need to understand real genes, real brains, and how these have evolved in humans compared to other species.

Summary, Conclusions, and Future Trends

2012 ◽  
Author(s):  
Lionel Raff ◽  
Ranga Komanduri ◽  
Martin Hagan ◽  
Satish Bukkapatnam
Keyword(s):  
Electronic Structure ◽  
Complex Systems ◽  
Human Brain ◽  
Ab Initio ◽  
Large Scale ◽  
Dimensional Space ◽  
Electronic Structure Calculations ◽  
Potential Surfaces ◽  
The 1960S ◽  
Structure Calculations

Since the introduction of classical and semiclassical molecular dynamics (MD) methods in the 1960s and Gaussian procedures to conduct electronic structure calculations in the 1970s, a principal objective of theoretical chemistry has been to combine the two methods so that MD and quantum mechanical studies can be conducted on ab initio potential surfaces. Although numerous procedures have been attempted, the goal of first principles, ab initio dynamics calculations has proven to be elusive when the system contains five or more atoms moving in unrestricted three-dimensional space. For many years, the conventional wisdom has been that ab initio MD calculations for complex systems containing five or more atoms with several open reaction channels are presently beyond our computational capabilities. The rationale for this view are (a) the inherent difficulty of high level ab initio quantum calculations on complex systems that may take numerous, large-scale computations impossible, (b) the large dimensionality of the configuration space for such systems that makes it necessary to examine prohibitively large numbers of nuclear configurations, and (c) the extreme difficulty associated with obtaining sufficiently converged results to permit accurate interpolation of numerical data obtained from electronic structure calculations when the dimensionality of the system is nine or greater. Neural networks (NN) derive their name from the fact that their interlocking structure superficially resembles the neural network of a human brain and from the fact that NNs can sense the underlying correlations that exist in a database and properly map them in a manner analogous to the way a human brain can execute pattern recognition. Artificial neurons were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, an MIT logician. NNs have been employed by engineers for decades to assist in the solution of a multitude of problems. Nevertheless, the power of NNs to assist in the solution of numerous problems that occur in chemical reaction dynamics is just now being realized by the chemistry community.

Validating Drug Court Collaboration Instrument: Differences in Model Adherence Between Adult and Juvenile Drug Courts

2019 ◽  
Vol 63 (11) ◽  
pp. 1990-2017 ◽  
Author(s):  
Xiaohan Mei ◽  
Jacqueline G. van Wormer ◽  
Ruibin Lu ◽  
Mia J. Abboud ◽  
Faith E. Lutze
Keyword(s):  
Drug Abuse ◽  
Drug Court ◽  
National Sample ◽  
Black Box ◽  
Drug Courts ◽  
Juvenile Courts ◽  
Black Boxes ◽  
Current Article ◽  
Guiding Principles ◽  
Better Than

Drug courts aim to significantly address drug abuse and drug-related criminality. However, the effectiveness of drug courts varies from court to court. The variation of success demands insights regarding what is going on inside the “black box” of drug court practices. Therefore, it is necessary to evaluate to what extent drug courts are operated in adherence with guiding principles and strategies. Using a national sample and validated measures, the current article examines the “black boxes” of adult and juvenile drug courts across the country. We found that, in general, adult drug courts face less model adherence challenges in comparison with juvenile courts, which may, in part, explain why adult drug courts perform better than juvenile drug courts overall.

scholarly journals The Quantitative Comparison Between the Neuronal Network and the Cosmic Web

2020 ◽  
Vol 8 ◽  
Author(s):  
F. Vazza ◽  
A. Feletti
Keyword(s):  
Complex Systems ◽  
Human Brain ◽  
Neuronal Network ◽  
Memory Capacity ◽  
Quantitative Comparison ◽  
The Self ◽  
Self Organization ◽  
Neural Connectivity ◽  
Network Properties ◽  
Degree Of Similarity

We investigate the similarities between two of the most challenging and complex systems in Nature: the network of neuronal cells in the human brain, and the cosmic network of galaxies. We explore the structural, morphological, network properties and the memory capacity of these two fascinating systems, with a quantitative approach. In order to have an homogeneous analysis of both systems, our procedure does not consider the true neural connectivity but an approximation of it, based on simple proximity. The tantalizing degree of similarity that our analysis exposes seems to suggest that the self-organization of both complex systems is likely being shaped by similar principles of network dynamics, despite the radically different scales and processes at play.

Of Mice and Men: What Rodent Populations Can Teach Us about Complex Spatial Dynamics

1988 ◽  
Vol 20 (1) ◽  
pp. 99-109 ◽  
Author(s):  
H Couclelis
Keyword(s):  
Population Dynamics ◽  
Complex Systems ◽  
Cellular Automaton ◽  
Chaotic System ◽  
Spatial Dynamics ◽  
Human Geography ◽  
System Behavior ◽  
Rodent Population ◽  
Rodent Populations ◽  
Scientific Fields

Models of complex systems need not be themselves complex, let alone complicated. To illustrate this important point, a very simple cellular automaton model of rodent population dynamics is used to generate a wide variety of different spatiotemporal structures corresponding to different forms of equilibrium, cyclical, quasi-cyclical, and chaotic system behavior. The issue of complexity as it pertains to a number of different contemporary scientific fields is then discussed, and in particular its implications for prediction. The discussion ends with some general reflexions about modeling in human geography.

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