ArticleRoyal Society open science2018
Information theory, predictability and the emergence of complex life.
Article in Royal Society open science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Fundamental constraints to the logic of living systems.Interface focus · 2024Review
- Plausible pathway for a host-parasite molecular replication network to increase its complexity through Darwinian evolution.PLoS computational biology · 2022Article
- From the origin of life to pandemics: emergent phenomena in complex systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Evolution of Brains and Computers: The Roads Not Taken.Entropy (Basel, Switzerland) · 2022Article
- A Mutation Threshold for Cooperative Takeover.Life (Basel, Switzerland) · 2022Article
- Towards the Idea of Molecular Brains.International journal of molecular sciences · 2021Review
- Fate of Duplicated Neural Structures.Entropy (Basel, Switzerland) · 2020Review
- Decoding collective communications using information theory tools.Journal of the Royal Society, Interface · 2020Article
- Ion-Based Cellular Signal Transmission, Principles of Minimum Information Loss, and Evolution by Natural Selection.International journal of molecular sciences · 2019Article
- Probing complexity: thermodynamics and computational mechanics approaches to origins studies.Interface focus · 2019Article
- The plant body as a network of semi-autonomous agents: a review.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- Evolutionary aspects of reservoir computing.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- The Computational Boundary of a "Self": Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition.Frontiers in psychology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated with detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated with maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.