Evidence map›Paper›PMID 36111832›Full record

ReviewWiley interdisciplinary reviews. Cognitive science2022

From evolutionary ecosystem simulations to computational models of human behavior.

Peter J Bentley, Soo Ling Lim

Open access · greenAbstract readReview
In one paragraph

Review in Wiley interdisciplinary reviews. Cognitive science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.4field-weighted citation impact, top 10% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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  4. From evolutionary ecosystem simulations to computational models of human behavior.Wiley interdisciplinary reviews. Cognitive science · 2022
    Review
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 at 1 institution in 1 country.

Peter J BentleyDepartment of Computer Science, University College London (UCL), London, UK.ORCID https://orcid.org/0000-0001-6744-9302
Soo Ling LimDepartment of Computer Science, University College London (UCL), London, UK.ORCID https://orcid.org/0000-0003-1325-6542
The London College · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence.

Indexed as

Artificial IntelligenceRoboticsAlgorithmsCognitionComputer SimulationEcosystemHumansagent-based modelingcognitionevolutionary computationevolutionary ecosystemshuman behavior

Identifiers

PMID36111832
PMCPMC9786238
OpenAlexW4296036869

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.