Human behavioral complexity peaks at age 25

Gauvrit, Nicolas; Zenil, Héctor; Toscano, Fernando Soler; Delahaye, Jean‐Paul; Brugger, Peter

doi:10.1371/journal.pcbi.1005408

Cited by 57 publications

(54 citation statements)

References 54 publications

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“…We have introduced a new approach to the study of the behaviour of animals and humans based on algorithmic information theory. We have shown that humans best outsmart computers when they are tested on randomness generation at the age of 25 [4]. When competing against all possible algorithms, we have found that human behaviour is at its most algorithmically random at 25 years of age, thereby introducing a new measure of cognitive complexity possibly related to other biological and cultural phenomena, such as creativity.…”

Section: Algorithmic Intelligencementioning

confidence: 99%

Compression is Comprehension and the Unreasonable Effectiveness of Digital Computation in the Natural World

Zenil¹

2020

Unravelling Complexity

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Chaitin's work, in its depth and breadth, encompasses many areas of scientific and philosophical interest. It helped establish the accepted mathematical concept of randomness, which in turn is the basis of tools that I have developed to justify and quantify what I think is clear evidence of the algorithmic nature of the world. To illustrate the concept I will establish novel upper bounds of algorithmic randomness for elementary cellular automata. I will discuss how the practice of science consists in conceiving a model that starts from certain initial values, running a computable instantiation, and awaiting a result in order to determine where the system may be in a future state-in a shorter time than the time taken by the actual unfolding of the phenomenon in question. If a model does not comply with all or some of these requirements it is traditionally considered useless or even unscientific, so the more precise and faster the better. A model is thus better if it can explain more with less, which is at the core of Chaitin's "compression is comprehension". I will pursue these questions related to the random versus possibly algorithmic nature of the world in two directions, drawing heavily on the work of Chaitin. I will also discuss how the algorithmic approach is related to the success of science at producing models of the world, allowing computer simulations to better understand it and make more accurate predictions and interventions.

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Section: Algorithmic Intelligencementioning

confidence: 99%

Compression is Comprehension and the Unreasonable Effectiveness of Digital Computation in the Natural World

Zenil¹

2020

Unravelling Complexity

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“…So we can reasonably hope that similar tools will approximate logical depth (see e.g. Zenil, Delahaye, and Gaucherel 2012;Gauvrit et al 2017).…”

Section: Organized Complexity and Traditional Ethical Issuesmentioning

confidence: 99%

Universal Ethics: Organized Complexity as an Intrinsic Value

Vidal

Delahaye

2019

Evolution, Development and Complexity

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How can we think about a universal ethics that could be adopted by any intelligent being, including the rising population of cyborgs, intelligent machines, intelligent algorithms or even potential extraterrestrial life? We generally give value to complex structures, to objects resulting from a long work, to systems with many elements and with many links finely adjusted. These include living beings, books, works of art or scientific theories. Intuitively, we want to keep, multiply, and share such structures, as well as prevent their destruction. Such objects have value not because more information (in bits) would simply mean more value. Instead, they have value because they require a long computational history, numerous interactions for their construction that we can assimilate to a computation, and they display what we call organized complexity. To propose the foundations of a universal ethics based on the intrinsic value of organized complexity, we first discuss conceptions of complexity, and argue that Charles Bennett's logical depth is certainly a first approximation of what we are looking for. We then put forward three fundamental imperatives: to preserve, augment and recursively promote organized complexity. We show a broad range of applications with human, non-human and non-living examples. Finally, we discuss some specific issues of our framework such as the distribution of complexity, of managing copies and erasures, and how our universal ethics tackles classical ethical issues. In sum, we propose a clear, homogenous and consistent ethical foundation that can integrate many universal ethics desiderata.

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“…An example illustrating how to achieve this in the context of, e.g., a Bayesian approach, has been provided in [45] and consists in replacing the uninformative prior by the uninformative algorithmic probability distribution, the so-called Universal Distribution, as introduced by Levin [20]. The general approach has already delivered some important results [46] by, e.g., quantifying the degree of human cognitive randomness that previous statistical approaches and measures such as Entropy made it impossible to quantify. Animated videos have been made available explaining applications to graph complexity (https://youtu.be/E238zKsPCgk) and to cognition in the context of random generation tasks (https://youtu.be/E-YjBE5qm7c).…”

Section: An Algorithmic Maximum Entropy Modelmentioning

confidence: 99%

Low-algorithmic-complexity entropy-deceiving graphs

2017

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In estimating the complexity of objects, in particular, of graphs, it is common practice to rely on graph- and information-theoretic measures. Here, using integer sequences with properties such as Borel normality, we explain how these measures are not independent of the way in which an object, such as a graph, can be described or observed. From observations that can reconstruct the same graph and are therefore essentially translations of the same description, we see that when applying a computable measure such as the Shannon entropy, not only is it necessary to preselect a feature of interest where there is one, and to make an arbitrary selection where there is not, but also more general properties, such as the causal likelihood of a graph as a measure (opposed to randomness), can be largely misrepresented by computable measures such as the entropy and entropy rate. We introduce recursive and nonrecursive (uncomputable) graphs and graph constructions based on these integer sequences, whose different lossless descriptions have disparate entropy values, thereby enabling the study and exploration of a measure's range of applications and demonstrating the weaknesses of computable measures of complexity.

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Human behavioral complexity peaks at age 25

Cited by 57 publications

References 54 publications

Compression is Comprehension and the Unreasonable Effectiveness of Digital Computation in the Natural World

Compression is Comprehension and the Unreasonable Effectiveness of Digital Computation in the Natural World

Universal Ethics: Organized Complexity as an Intrinsic Value

Low-algorithmic-complexity entropy-deceiving graphs

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