A Survey of Evolutionary Games in Biology.

Kalliopi Kastampolidou, Theodore Andronikos

Abstract readReview

In one paragraph

Review in Advances in experimental medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kalliopi KastampolidouDepartment of Informatics, Ionian University, Corfu, Greece.

Theodore AndronikosDepartment of Informatics, Ionian University, Corfu, Greece. andronikos@ionio.gr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The applications of game theory in biology are numerous and include the comparison and modeling situations between bacteria, viruses, etc. This work provides insights about the connection between biology and evolving populations with classical and quantum evolutionary game theory and explains the benefits of unconventional computing methods in the study of such phenomena. In particular, the introduction of automata brings new possibilities into the decision-making process.

Indexed as

Biological EvolutionGame TheoryModels, BiologicalAnimalsDecision MakingHumansQuantum TheorySurveys and QuestionnairesAutomata theoryEvolutionary biologyEvolutionary game theoryGame theoryQuantum biologyQuantum game theory

Identifiers

PMID32468541

DOI10.1007/978-3-030-32622-7_23

What OpenQuestion holds

Textmetadata

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Registered trials

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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.