Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good?
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/35394 https://doi.org/10.1080/15326349.2021.2006066 |
Resumo: | We can describe the size evolution of a harvested population in a randomly varying environment using stochastic differential equations. Previously, we have compared the profit performance of four harvesting policies: (i) optimal variable effort policy, based on variable effort; (ii) optimal penalized variable effort policies, penalized versions based on including an artificial running energy cost on the effort; (iii) stepwise policies, staircase versions where the harvesting effort is determined at the beginning of each year (or of each biennium) and kept constant throughout that year (or biennium); (iv) constant harvesting effort sustainable policy, based on constant effort. They have different properties, so it is also worth looking at combinations of such policies and studying the single and cross-effects of the amount of penalization, the absence or presence and type of steps, and the restraints on minimum and maximum allowed efforts. Using data based on a real harvested population and considering a logistic growth model, we perform such a comparison study of pure and mixed policies in terms of profit, applicability, and other relevant properties. We end up answering the question: is the optimal enemy of the good? |
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Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good?logistic growthmixed policiesoptimal controlpenalized policyprofit optimizationstepwise effortstochastic differential equationsWe can describe the size evolution of a harvested population in a randomly varying environment using stochastic differential equations. Previously, we have compared the profit performance of four harvesting policies: (i) optimal variable effort policy, based on variable effort; (ii) optimal penalized variable effort policies, penalized versions based on including an artificial running energy cost on the effort; (iii) stepwise policies, staircase versions where the harvesting effort is determined at the beginning of each year (or of each biennium) and kept constant throughout that year (or biennium); (iv) constant harvesting effort sustainable policy, based on constant effort. They have different properties, so it is also worth looking at combinations of such policies and studying the single and cross-effects of the amount of penalization, the absence or presence and type of steps, and the restraints on minimum and maximum allowed efforts. Using data based on a real harvested population and considering a logistic growth model, we perform such a comparison study of pure and mixed policies in terms of profit, applicability, and other relevant properties. We end up answering the question: is the optimal enemy of the good?CEMAPRE/REM; FCT - Project UIDB/05069/2020; CIMA; FCT - Project UID/04674/2020Taylor & Francis2023-08-03T10:42:21Z2023-08-032023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/35394https://doi.org/10.1080/15326349.2021.2006066http://hdl.handle.net/10174/35394https://doi.org/10.1080/15326349.2021.2006066engBrites, Nuno M.; Braumann, Carlos A. (2023). Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? Stochastic Models 39(1): 41-59.1532-63491532-4214https://www.tandfonline.com/doi/full/10.1080/15326349.2021.2006066MAT- Publicações- Artigos em Revistas Internacionais Com Arbitragem Científicanbrites@iseg.ulisboa.ptbraumann@uevora.pt340Brites, Nuno M.Braumann, Carlos A.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:38:15Zoai:dspace.uevora.pt:10174/35394Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:23:32.708588Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? |
title |
Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? |
spellingShingle |
Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? Brites, Nuno M. logistic growth mixed policies optimal control penalized policy profit optimization stepwise effort stochastic differential equations |
title_short |
Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? |
title_full |
Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? |
title_fullStr |
Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? |
title_full_unstemmed |
Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? |
title_sort |
Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? |
author |
Brites, Nuno M. |
author_facet |
Brites, Nuno M. Braumann, Carlos A. |
author_role |
author |
author2 |
Braumann, Carlos A. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Brites, Nuno M. Braumann, Carlos A. |
dc.subject.por.fl_str_mv |
logistic growth mixed policies optimal control penalized policy profit optimization stepwise effort stochastic differential equations |
topic |
logistic growth mixed policies optimal control penalized policy profit optimization stepwise effort stochastic differential equations |
description |
We can describe the size evolution of a harvested population in a randomly varying environment using stochastic differential equations. Previously, we have compared the profit performance of four harvesting policies: (i) optimal variable effort policy, based on variable effort; (ii) optimal penalized variable effort policies, penalized versions based on including an artificial running energy cost on the effort; (iii) stepwise policies, staircase versions where the harvesting effort is determined at the beginning of each year (or of each biennium) and kept constant throughout that year (or biennium); (iv) constant harvesting effort sustainable policy, based on constant effort. They have different properties, so it is also worth looking at combinations of such policies and studying the single and cross-effects of the amount of penalization, the absence or presence and type of steps, and the restraints on minimum and maximum allowed efforts. Using data based on a real harvested population and considering a logistic growth model, we perform such a comparison study of pure and mixed policies in terms of profit, applicability, and other relevant properties. We end up answering the question: is the optimal enemy of the good? |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-08-03T10:42:21Z 2023-08-03 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/35394 https://doi.org/10.1080/15326349.2021.2006066 http://hdl.handle.net/10174/35394 https://doi.org/10.1080/15326349.2021.2006066 |
url |
http://hdl.handle.net/10174/35394 https://doi.org/10.1080/15326349.2021.2006066 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Brites, Nuno M.; Braumann, Carlos A. (2023). Harvesting optimization with stochastic differential equations models: is the optimal enemy of the good? Stochastic Models 39(1): 41-59. 1532-6349 1532-4214 https://www.tandfonline.com/doi/full/10.1080/15326349.2021.2006066 MAT- Publicações- Artigos em Revistas Internacionais Com Arbitragem Científica nbrites@iseg.ulisboa.pt braumann@uevora.pt 340 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Taylor & Francis |
publisher.none.fl_str_mv |
Taylor & Francis |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136717186269184 |