15 papers · ranked by Valyu relevance
Peter Bossaerts, Nitin Yadav, Carsten Murawski
Modern theories of decision-making typically model uncertainty about decision options using the tools of probability theory. This is exemplified by the Savage framework, the most popular framework in decision-making research. There, decision-makers are assumed to choose from among available decision options as if they…
Andrew Currin, Konstantin Korovin, Maria Ababi, Katherine Roper + 3 more
'Douglas B. Kell' 'Philip J. Day' 'Ross D. King'] The theory of computer science is based around universal Turing machines (UTMs): abstract machines able to execute all possible algorithms. Modern digital computers are physical embodiments of classical UTMs. For the most important class of problem in computer science…
Pier Luigi Gentili
The goals and targets included in the 2030 Agenda compiled by the United Nations want to stimulate action in areas of critical importance for humanity and the Earth. These goals and targets regard everyone on Earth from both the health and economic and social perspectives. Reaching these goals means to deal with…
Tao Hong, William R. Stauffer
Economic deliberations are slow, effortful and intentional searches for solutions to difficult economic problems. Although such deliberations are critical for making sound decisions, the underlying reasoning strategies and neurobiological substrates remain poorly understood. Here two nonhuman primates performed a…
Christopher P. Kempes, Michael Lachmann, Andrew Iannaccone, G. Matthew Fricke + 3 more
'G. Matthew Fricke' 'M. Redwan Chowdhury' 'Sara I. Walker' 'Leroy Cronin'] Assembly theory (AT) quantifies selection using the assembly equation, identifying complex objects through the assembly index, the minimal steps required to build an object from basic parts, and copy number, the observed instances of the object.…
Hector Zenil
Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and…
Hector Zenil, Narsis A. Kiani, Jesper Tegnér
The principle of maximum entropy (Maxent) is often used to obtain prior probability distributions as a method to obtain a Gibbs measure under some restriction giving the probability that a system will be in a certain state compared to the rest of the elements in the distribution. Because classical entropy-based Maxent…
Hector Zenil, Narsis A. Kiani, Jesper Tegnér
Information-theoretic-based measures have been useful in quantifying network complexity. Here we briefly survey and contrast (algorithmic) information-theoretic methods which have been used to characterize graphs and networks. We illustrate the strengths and limitations of Shannon’s entropy, lossless compressibility…
Murat Erkurt
We investigate chaoticity and complexity of a binary general network automata of finite size with external input which we call a computron. As a generalization of cellular automata, computrons can have non-uniform cell rules, non-regular cell connectivity and an external input. We show that any finite-state machine can…
Hector Zenil, Santiago Hernández-Orozco, Narsis A. Kiani, Fernando Soler-Toscano + 2 more
'Fernando Soler-Toscano' 'Antonio Rueda-Toicen' 'Jesper Tegnér'] We investigate the properties of a Block Decomposition Method (BDM), which extends the power of a Coding Theorem Method (CTM) that approximates local estimations of algorithmic complexity based on Solomonoff-Levin’s theory of algorithmic probability…
Jorge M. Silva, Eduardo Pinho, Sérgio Matos, Diogo Pratas
Sources that generate symbolic sequences with algorithmic nature may differ in statistical complexity because they create structures that follow algorithmic schemes, rather than generating symbols from a probabilistic function assuming independence. In the case of Turing machines, this means that machines with the same…
Tiasa Mondol, Daniel G. Brown, Ercan Kuruoglu
We build an analysis based on the Algorithmic Information Theory of computational creativity and extend it to revisit computational aesthetics, thereby, improving on the existing efforts of its formulation. We discuss Kolmogorov complexity, models and randomness deficiency (which is a measure of how much a model falls…
Fernando Soler-Toscano, Hector Zenil, Jean-Paul Delahaye, Nicolas Gauvrit + 1 more
'Nicolas Gauvrit' 'Matthias Dehmer'] Drawing on various notions from theoretical computer science, we present a novel numerical approach, motivated by the notion of algorithmic probability, to the problem of approximating the Kolmogorov-Chaitin complexity of short strings. The method is an alternative to the…
Amirmohammad Farzaneh, Justin P. Coon, Mihai-Alin Badiu, Narsis A. Kiani + 2 more
'Narsis A. Kiani' 'Hector Zenil' 'Jesper Tegnér'] Throughout the years, measuring the complexity of networks and graphs has been of great interest to scientists. The Kolmogorov complexity is known as one of the most important tools to measure the complexity of an object. We formalized a method to calculate an upper…
Clare Horsman, Susan Stepney, Rob C. Wagner, Viv Kendon
Computing is a high-level process of a physical system. Recent interest in non-standard computing systems, including quantum and biological computers, has brought this physical basis of computing to the forefront. There has been, however, no consensus on how to tell if a given physical system is acting as a computer or…