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Wong ML, Cleland CE, Arend D, Bartlett S, Cleaves HJ, Demarest H, Prabhu A, Lunine JI, Hazen RM. On the roles of function and selection in evolving systems. Proc Natl Acad Sci U S A 2023; 120:e2310223120. [PMID: 37844243 PMCID: PMC10614609 DOI: 10.1073/pnas.2310223120] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 09/10/2023] [Indexed: 10/18/2023] Open
Abstract
Physical laws-such as the laws of motion, gravity, electromagnetism, and thermodynamics-codify the general behavior of varied macroscopic natural systems across space and time. We propose that an additional, hitherto-unarticulated law is required to characterize familiar macroscopic phenomena of our complex, evolving universe. An important feature of the classical laws of physics is the conceptual equivalence of specific characteristics shared by an extensive, seemingly diverse body of natural phenomena. Identifying potential equivalencies among disparate phenomena-for example, falling apples and orbiting moons or hot objects and compressed springs-has been instrumental in advancing the scientific understanding of our world through the articulation of laws of nature. A pervasive wonder of the natural world is the evolution of varied systems, including stars, minerals, atmospheres, and life. These evolving systems appear to be conceptually equivalent in that they display three notable attributes: 1) They form from numerous components that have the potential to adopt combinatorially vast numbers of different configurations; 2) processes exist that generate numerous different configurations; and 3) configurations are preferentially selected based on function. We identify universal concepts of selection-static persistence, dynamic persistence, and novelty generation-that underpin function and drive systems to evolve through the exchange of information between the environment and the system. Accordingly, we propose a "law of increasing functional information": The functional information of a system will increase (i.e., the system will evolve) if many different configurations of the system undergo selection for one or more functions.
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Affiliation(s)
- Michael L. Wong
- Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC20015
- Sagan Fellow, NASA Hubble Fellowship Program, Space Telescope Science Institute, Baltimore, MD21218
| | - Carol E. Cleland
- Department of Philosophy, University of Colorado, Boulder, CO80309
| | - Daniel Arend
- Department of Philosophy, University of Colorado, Boulder, CO80309
| | - Stuart Bartlett
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA91125
| | - H. James Cleaves
- Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC20015
- Earth Life Science Institute, Tokyo Institute of Technology, Tokyo152-8550, Japan
- Blue Marble Space Institute for Science, Seattle, WA98104
| | - Heather Demarest
- Department of Philosophy, University of Colorado, Boulder, CO80309
| | - Anirudh Prabhu
- Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC20015
| | | | - Robert M. Hazen
- Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC20015
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Gershenson C. Emergence in Artificial Life. ARTIFICIAL LIFE 2023; 29:153-167. [PMID: 36787448 DOI: 10.1162/artl_a_00397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement "life is complex." Thus understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. But how useful is this to understanding living systems? Artificial Life (ALife) has been developed in recent decades to study life using a synthetic approach: Build it to understand it. ALife systems are not so complex, be they soft (simulations), hard (robots), or wet(protocells). Thus, we can aim at first understanding emergence in ALife, to then use this knowledge in biology. I argue that to understand emergence and life, it becomes useful to use information as a framework. In a general sense, I define emergence as information that is not present at one scale but present at another. This perspective avoids problems of studying emergence from a materialist framework and can also be useful in the study of self-organization and complexity.
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Affiliation(s)
- Carlos Gershenson
- Universidad Nacional, Autánoma de México.
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas
- Centro de Ciencias de la Complejidad
- Lakeside Labs GmbH
- Santa Fe Institute
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Abrahão FS, Zenil H. Emergence and algorithmic information dynamics of systems and observers. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20200429. [PMID: 35599568 PMCID: PMC9125223 DOI: 10.1098/rsta.2020.0429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
One of the challenges of defining emergence is that one observer's prior knowledge may cause a phenomenon to present itself as emergent that to another observer appears reducible. By formalizing the act of observing as mutual perturbations between dynamical systems, we demonstrate that the emergence of algorithmic information does depend on the observer's formal knowledge, while being robust vis-a-vis other subjective factors, particularly: the choice of programming language and method of measurement; errors or distortions during the observation; and the informational cost of processing. This is called observer-dependent emergence (ODE). In addition, we demonstrate that the unbounded and rapid increase of emergent algorithmic information implies asymptotically observer-independent emergence (AOIE). Unlike ODE, AOIE is a type of emergence for which emergent phenomena will be considered emergent no matter what formal theory an observer might bring to bear. We demonstrate the existence of an evolutionary model that displays the diachronic variant of AOIE and a network model that displays the holistic variant of AOIE. Our results show that, restricted to the context of finite discrete deterministic dynamical systems, computable systems and irreducible information content measures, AOIE is the strongest form of emergence that formal theories can attain. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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Affiliation(s)
- Felipe S. Abrahão
- National Laboratory for Scientific Computing (LNCC), 25651-075 Petropolis, Rio de Janeiro, Brazil
- Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, 75005 Paris, France
| | - Hector Zenil
- Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, 75005 Paris, France
- Oxford Immune Algorithmics, RG1 3EU Reading, UK
- The Alan Turing Institute, British Library 2QR, 96 Euston Rd, London NW1 2DB, UK
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Karolinska Institutet, 171 77 Stockholm, Sweden
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Abrahão FS, Zenil H. Emergence and algorithmic information dynamics of systems and observers. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022. [PMID: 35599568 DOI: 10.6084/m9.figshare.c.5901204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
One of the challenges of defining emergence is that one observer's prior knowledge may cause a phenomenon to present itself as emergent that to another observer appears reducible. By formalizing the act of observing as mutual perturbations between dynamical systems, we demonstrate that the emergence of algorithmic information does depend on the observer's formal knowledge, while being robust vis-a-vis other subjective factors, particularly: the choice of programming language and method of measurement; errors or distortions during the observation; and the informational cost of processing. This is called observer-dependent emergence (ODE). In addition, we demonstrate that the unbounded and rapid increase of emergent algorithmic information implies asymptotically observer-independent emergence (AOIE). Unlike ODE, AOIE is a type of emergence for which emergent phenomena will be considered emergent no matter what formal theory an observer might bring to bear. We demonstrate the existence of an evolutionary model that displays the diachronic variant of AOIE and a network model that displays the holistic variant of AOIE. Our results show that, restricted to the context of finite discrete deterministic dynamical systems, computable systems and irreducible information content measures, AOIE is the strongest form of emergence that formal theories can attain. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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Affiliation(s)
- Felipe S Abrahão
- National Laboratory for Scientific Computing (LNCC), 25651-075 Petropolis, Rio de Janeiro, Brazil
- Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, 75005 Paris, France
| | - Hector Zenil
- Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, 75005 Paris, France
- Oxford Immune Algorithmics, RG1 3EU Reading, UK
- The Alan Turing Institute, British Library 2QR, 96 Euston Rd, London NW1 2DB, UK
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Karolinska Institute, 171 77 Stockholm, Sweden
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Zenil H, Minary P. Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences. Nucleic Acids Res 2019; 47:e129. [PMID: 31511887 PMCID: PMC6846163 DOI: 10.1093/nar/gkz750] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 07/10/2019] [Accepted: 08/27/2019] [Indexed: 01/01/2023] Open
Abstract
We introduce and study a set of training-free methods of an information-theoretic and algorithmic complexity nature that we apply to DNA sequences to identify their potential to identify nucleosomal binding sites. We test the measures on well-studied genomic sequences of different sizes drawn from different sources. The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint high and low nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that the complexity indices are informative of nucleosome occupancy. We found that, while it is clear that the gold standard Kaplan model is driven by GC content (by design) and by k-mer training; for high occupancy, entropy and complexity-based scores are also informative and can complement the Kaplan model.
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Affiliation(s)
- Hector Zenil
- Oxford Immune Algorithmics, Oxford University Innovation, Oxford, UK
- Algorithmic Dynamics Lab, Unit of Computational Medicine, SciLifeLab, Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
- Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris, France
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Peter Minary
- Department of Computer Science, University of Oxford, Oxford, UK
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Corominas-Murtra B, Seoane LF, Solé R. Zipf's Law, unbounded complexity and open-ended evolution. J R Soc Interface 2018; 15:20180395. [PMID: 30958235 PMCID: PMC6303796 DOI: 10.1098/rsif.2018.0395] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 11/19/2018] [Indexed: 11/12/2022] Open
Abstract
A major problem for evolutionary theory is understanding the so-called open-ended nature of evolutionary change, from its definition to its origins. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterize evolution on multiple scales. This property seems to be a characteristic feature of biological and technological evolution and is strongly tied to the generative potential associated with combinatorics, which allows the system to grow and expand their available state spaces. Interestingly, many complex systems presumably displaying OEE, from language to proteins, share a common statistical property: the presence of Zipf's Law. Given an inventory of basic items (such as words or protein domains) required to build more complex structures (sentences or proteins) Zipf's Law tells us that most of these elements are rare whereas a few of them are extremely common. Using algorithmic information theory, in this paper we provide a fundamental definition for open-endedness, which can be understood as postulates. Its statistical counterpart, based on standard Shannon information theory, has the structure of a variational problem which is shown to lead to Zipf's Law as the expected consequence of an evolutionary process displaying OEE. We further explore the problem of information conservation through an OEE process and we conclude that statistical information (standard Shannon information) is not conserved, resulting in the paradoxical situation in which the increase of information content has the effect of erasing itself. We prove that this paradox is solved if we consider non-statistical forms of information. This last result implies that standard information theory may not be a suitable theoretical framework to explore the persistence and increase of the information content in OEE systems.
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Affiliation(s)
| | - Luís F. Seoane
- Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- UPF-PRBB, ICREA-Complex Systems Lab, Dr Aiguader 88, 08003 Barcelona, Spain
- Institute Evolutionary Biology, UPF-CSIC, Pg Maritim Barceloneta 37, 08003 Barcelona, Spain
| | - Ricard Solé
- UPF-PRBB, ICREA-Complex Systems Lab, Dr Aiguader 88, 08003 Barcelona, Spain
- Institute Evolutionary Biology, UPF-CSIC, Pg Maritim Barceloneta 37, 08003 Barcelona, Spain
- Santa Fe Institute, 1399 Hyde Park Road, 87501 Santa Fe, NM, USA
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Hernández-Orozco S, Kiani NA, Zenil H. Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180399. [PMID: 30225028 PMCID: PMC6124114 DOI: 10.1098/rsos.180399] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 07/20/2018] [Indexed: 05/07/2023]
Abstract
Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations. Here, we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations (no recombination) in various examples, particularly binary matrices, in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistically uniform but algorithmically uniform. We show that algorithmic distributions can evolve modularity and genetic memory by preservation of structures when they first occur sometimes leading to an accelerated production of diversity but also to population extinctions, possibly explaining naturally occurring phenomena such as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the End Triassic) whose causes are currently a cause for debate. The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results. These results validate some suggestions in the direction that computation may be an equally important driver of evolution. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to accelerate convergence of artificial evolutionary algorithms.
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Affiliation(s)
- Santiago Hernández-Orozco
- Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México (UNAM), Mexico
- Algorithmic Dynamics Lab, Unit of Computational Medicine, SciLifeLab, Department of Medicine Solna, Centre for Molecular Medicine, Stockholm, Sweden
- Algorithmic Nature Group, LABORES, Paris, France
| | - Narsis A. Kiani
- Algorithmic Dynamics Lab, Unit of Computational Medicine, SciLifeLab, Department of Medicine Solna, Centre for Molecular Medicine, Stockholm, Sweden
- Algorithmic Nature Group, LABORES, Paris, France
| | - Hector Zenil
- Algorithmic Dynamics Lab, Unit of Computational Medicine, SciLifeLab, Department of Medicine Solna, Centre for Molecular Medicine, Stockholm, Sweden
- Algorithmic Nature Group, LABORES, Paris, France
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Siqueiros-García JM, Froese T, Gershenson C, Aguilar W, Sayama H, Izquierdo E. ALife and Society: Editorial Introduction to the Artificial Life Conference 2016 Special Issue. ARTIFICIAL LIFE 2018; 24:1-4. [PMID: 29369713 DOI: 10.1162/artl_e_00256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Affiliation(s)
- Jesús M Siqueiros-García
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, and Laboratorio Nacional de Ciencias de la Sostenibilidad, Universidad Nacional Autónoma de México
| | - Tom Froese
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, and Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México
| | - Carlos Gershenson
- * Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas and Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México; and SENSEable City Lab, Massachusetts Institute of Technology
| | - Wendy Aguilar
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México
| | - Hiroki Sayama
- Center for Collective Dynamics of Complex Systems and Department of Systems Science and Industrial Engineering, Binghamton University
| | - Eduardo Izquierdo
- Cognitive Science Program, Indiana University, Bloomington, Indiana, USA
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Elastic Multi-scale Mechanisms: Computation and Biological Evolution. J Mol Evol 2017; 86:47-57. [PMID: 29248946 DOI: 10.1007/s00239-017-9823-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Accepted: 12/09/2017] [Indexed: 10/18/2022]
Abstract
Explanations based on low-level interacting elements are valuable and powerful since they contribute to identify the key mechanisms of biological functions. However, many dynamic systems based on low-level interacting elements with unambiguous, finite, and complete information of initial states generate future states that cannot be predicted, implying an increase of complexity and open-ended evolution. Such systems are like Turing machines, that overlap with dynamical systems that cannot halt. We argue that organisms find halting conditions by distorting these mechanisms, creating conditions for a constant creativity that drives evolution. We introduce a modulus of elasticity to measure the changes in these mechanisms in response to changes in the computed environment. We test this concept in a population of predators and predated cells with chemotactic mechanisms and demonstrate how the selection of a given mechanism depends on the entire population. We finally explore this concept in different frameworks and postulate that the identification of predictive mechanisms is only successful with small elasticity modulus.
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