Evidence map›Paper›PMID 34738510›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2022

A Game Theoretic Analysis of Competition Between Vaccine and Drug Companies during Disease Contraction and Recovery.

Kjell Hausken, Mthuli Ncube

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Kjell HauskenFaculty of Science and Technology, University of Stavanger, Stavanger, Norway.ORCID 0000-0001-7319-3876
Mthuli NcubeSaid Business School, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfectious diseases such as COVID-19 and HIV/AIDS are behaviorally challenging for persons, vaccine and drug companies, and donors.

methodsIn 3 linked games in which a disease may or may not be contracted,

resultsEach person chooses risky behavior if its utility outweighs safe behavior, accounting for nature's probability of disease contraction which depends on how many are vaccinated. Each person buys a vaccine or drug if the companies produce them and if their utilities (accounting for side effects and virus mutation) outweigh the costs, which may be subsidized by a sponsor. DISCUSSION: Drug purchases depend on nature's recovery probability exceeding the probability in the absence of a drug. Each company develops and produces a vaccine or drug if nature's probability of successful development is high, if sufficiently many persons buy the vaccine or drug at a sales price that sufficiently exceeds the production price, and if the donor sponsors.

conclusionAccounting for all players' interlinked decisions allowing 14 outcomes, which is challenging without a game theoretic analysis, the donor maximizes all persons' expected utilities at the societal level to adjust how persons' purchases and the companies' development and production are subsidized. HIGHLIGHTS: A game theoretic approach can help explain the production decisions of vaccine and drug companies, and the decisions of persons and a donor, impacted by Nature.In 3 linked games,

Indexed as

COVID-19COVID-19 VaccinesGame TheoryVaccinesHumansProbabilityRisk-TakingCOVID-19 VaccinesVaccinesCOVID-19deathdisease contractiondonorsdrug companiesdrug developmentgame theorypatientsrecoverysafe versus risky behaviorsubsidiesvaccine companiesvaccine development

Identifiers

PMID34738510
PMCPMC9189729

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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.