<div class="csl-bib-body">
<div class="csl-entry">Feichtinger, G., Grass, D., & Winkler-Dworak, M. (2020). The Mathematics of Ageing. <i>Central European Journal of Operations Research</i>, <i>28</i>(2), 371–399. https://doi.org/10.1007/s10100-019-00661-w</div>
</div>
-
dc.identifier.issn
1435-246X
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/143309
-
dc.description.abstract
Age is a crucial variable in social sciences and particularly in population dynamics. In the first part of this paper, a two-state optimal control model is proposed to explain the substantial variations of scientific production over the life cycle of researchers. We identify conditions under which typical hump-shaped age-specific patterns of scientific production turn out to be optimal for individual researchers. The second part of the paper deals with the ageing of learned societies. In a nutshell, the dilemma of a learned society is that keeping young, i.e. electing young entrants, has the drawback of reducing the replacement rate of members. It turns out that electing a mix of young and old members delivers the optimal solution of the problem, i.e. guaranteeing a young age structure, while ensuring a high recruitment rate.
en
dc.language.iso
en
-
dc.publisher
SPRINGER
-
dc.relation.ispartof
Central European Journal of Operations Research
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
optimal control
en
dc.subject
Management Science and Operations Research
en
dc.subject
scientific production over the life cycle
en
dc.subject
Age-structured models
en
dc.subject
optimal recruitment of learned societies.
en
dc.title
The Mathematics of Ageing
en
dc.title.alternative
Linking demography and operations research to study the greying of academia
