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The equations of evolutionary change by natural selection are commonly expressed in statistical terms. Fisher's fundamental theorem emphasizes the variance in fitness. Quantitative genetics expresses selection with covariances and…

Populations and Evolution · Quantitative Biology 2012-11-20 Steven A. Frank

Evolution is the process of optimal adaptation of biological populations to their living environments. This is expressed via the concept of fitness, defined as relative reproductive success. However, it has been pointed out that this…

Populations and Evolution · Quantitative Biology 2025-04-17 Luís MA Bettencourt , Brandon J Grandison , Jordan T Kemp

Evolution is a fundamental process that shapes the biological world we inhabit, and reinforcement learning is a powerful tool used in artificial intelligence to develop intelligent agents that learn from their environment. In recent years,…

Neural and Evolutionary Computing · Computer Science 2023-06-19 Taboubi Ahmed

This introductory article presents the special Discussion and Debate volume "From black swans to dragon-kings, is there life beyond power laws?" published in Eur. Phys. J. Special Topics in May 2012. We summarize and put in perspective the…

Adaptation and Self-Organizing Systems · Physics 2012-05-07 D. Sornette , G. Ouillon

This paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms…

Information Theory · Computer Science 2013-04-05 Carlos Gershenson

Life emerged on the Earth within the first quintile of its habitable window, but a technological civilization did not blossom until its last. Efforts to infer the rate of abiogenesis, based on its early emergence, are frustrated by the…

Earth and Planetary Astrophysics · Physics 2022-06-08 David Kipping

Model Selection is a key part of many ecological studies, with Akaike's Information Criterion the most commonly used technique. Typically, a number of candidate models are defined a priori and ranked according to their expected…

Applications · Statistics 2020-04-17 Edward Wheatcroft

A common assumption in evolutionary thought is that adaptation drives an increase in biological complexity. However, the rules governing evolution of complexity appear more nuanced. Evolution is deeply connected to learning, where…

Populations and Evolution · Quantitative Biology 2025-08-06 Hagai Rappeport , Mor Nitzan

We apply the theory of learning to physically renormalizable systems in an attempt to develop a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution…

Populations and Evolution · Quantitative Biology 2022-10-12 Vitaly Vanchurin , Yuri I. Wolf , Mikhail I. Katsnelson , Eugene V. Koonin

Instructive influence of environment on heredity has been a debated topic for centuries. Darwin's identification of natural selection coupled to chance variation as the driving force for evolution, against a formal interpretation proposed…

Genomics · Quantitative Biology 2007-05-23 Antoine Danchin

Concomitant with the evolution of biological diversity must have been the evolution of mechanisms that facilitate evolution, due to the essentially infinite complexity of protein sequence space. We describe how evolvability can be an object…

Populations and Evolution · Quantitative Biology 2009-11-10 David J. Earl , Michael W. Deem

The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level…

Methodology · Statistics 2008-12-18 Andreas Buja , David Mease , Abraham J. Wyner

Causal inference is central to scientific discovery, yet choosing appropriate methods remains challenging because of the complexity of both statistical methodology and real-world data. Inspired by the success of artificial intelligence in…

Artificial Intelligence · Computer Science 2026-04-07 Can Wang , Hongyu Zhao , Yiqun Chen

According to the "hard-steps" model, the origin of humanity required "successful passage through a number of intermediate steps" (so-called "hard" or "critical" steps) that were intrinsically improbable with respect to the total time…

Populations and Evolution · Quantitative Biology 2025-02-18 Daniel B. Mills , Jennifer L. Macalady , Adam Frank , Jason T. Wright

Mathematical proofs are both paradigms of certainty and some of the most explicitly-justified arguments that we have in the cultural record. Their very explicitness, however, leads to a paradox, because the probability of error grows…

Symbolic Computation · Computer Science 2022-04-13 Scott Viteri , Simon DeDeo

The main topic of this thesis is the analysis of evolution equations reflecting issues in ecology and population dynamics. In mathematical modelling, the impact of environmental elements and the interaction between species is read into the…

Analysis of PDEs · Mathematics 2021-03-08 Elisa Affili

While evolution has inspired algorithmic methods of heuristic optimisation, little has been done in the way of using concepts of computation to advance our understanding of salient aspects of biological phenomena. We argue that under…

Computational Complexity · Computer Science 2012-06-05 Hector Zenil , James A. R. Marshall

The question as to why most higher organisms reproduce sexually has remained open despite extensive research, and has been called "the queen of problems in evolutionary biology". Theories dating back to Weismann have suggested that the key…

Populations and Evolution · Quantitative Biology 2015-09-04 Andrew Lewis-Pye , Antonio Montalban

We begin with the premise that the law of entropy could prove to be fundamental for the evolution of intelligent life and the advent of technological civilization. Building on recent theoretical results, we combine a modern approach to…

Popular Physics · Physics 2015-05-18 Vladimir Bozhilov , Duncan H. Forgan

A central challenge in statistical inference is the presence of confounding variables that may distort observed associations between treatment and outcome. Conventional "causal" methods, grounded in assumptions such as ignorability, exclude…

Methodology · Statistics 2025-09-09 Ellis Scharfenaker , Duncan K. Foley