Project prospection: investing and learning
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | https://hdl.handle.net/10438/34411 |
Resumo: | Consideramos um modelo no qual uma firma realiza investimentos em um projeto de inovação, objetivando sua conclusão. O término de tal projeto depende de uma ruptura que ocorre a depender de sua factibilidade (“bom”) ou infactibilidade (“ruim”), nunca ocorrendo em caso de um projeto não factível. Enquanto essa ruptura não é atingida, a firma atualiza suas crenças a respeito da qualidade do projeto de maneira pessimista e reconsidera suas decisões de investimento. Nosso principal objetivo é avaliar o efeito do aprendizado em suas decisões. Apesar de aumentos no investimento acarretarem atualizações mais rápidas de crença, as percepções acerca da qualidade do projeto são prejudicadas, o que pode levar a redução no fluxo de investimentos no futuro. Como resultado, concluímos que uma política mais parcimoniosa é ótima, isto é, atualizações bayesianas reduzem o investimento em projetos desse tipo. |
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Figueiredo Neto, José EdilsonEscolas::EPGECosta, Carlos Eugênio Ellery Lustosa da Faro, José HelenoGorno, Leandro2023-10-24T17:25:44Z2023-10-24T17:25:44Z2023-03-31https://hdl.handle.net/10438/34411Consideramos um modelo no qual uma firma realiza investimentos em um projeto de inovação, objetivando sua conclusão. O término de tal projeto depende de uma ruptura que ocorre a depender de sua factibilidade (“bom”) ou infactibilidade (“ruim”), nunca ocorrendo em caso de um projeto não factível. Enquanto essa ruptura não é atingida, a firma atualiza suas crenças a respeito da qualidade do projeto de maneira pessimista e reconsidera suas decisões de investimento. Nosso principal objetivo é avaliar o efeito do aprendizado em suas decisões. Apesar de aumentos no investimento acarretarem atualizações mais rápidas de crença, as percepções acerca da qualidade do projeto são prejudicadas, o que pode levar a redução no fluxo de investimentos no futuro. Como resultado, concluímos que uma política mais parcimoniosa é ótima, isto é, atualizações bayesianas reduzem o investimento em projetos desse tipo.We consider the problem of a firm continuously investing in a innovation project. The conclusion of the project relies on the occurrence of a breakthrough, which may never happen depending whether the idea is feasible (“good”) or unfeasible (“bad”). While such breakthrough is not achieved, the company updates its beliefs about the project quality and adjusts the amount of resources allocated to the project’s development. Our main goal is to evaluate the effect of learning in the investment decision. Although increasing investment leads to a quicker update in beliefs, it also damages the perception about the project quality, which may end up reducing future funding. As a result, we find that a more conservative policy is optimal, that is, in the present model, learning induces a lower investment rate in such projects.engBayesian learningProject conclusionR&DNon-homogeneous Poisson processInvestimentosTeoria bayesiana de decisão estatísticaProcesso de PoissonPesquisa e desenvolvimentoProject prospection: investing and learninginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVTEXTDissertação_final_organized_organized (2).pdf.txtDissertação_final_organized_organized (2).pdf.txtExtracted texttext/plain49077https://repositorio.fgv.br/bitstreams/1d4c8350-4ec1-4d0c-9a5c-c6163c68083e/download54b5a5729afffe2ba4c17e9a0ba10be0MD53PDF.txtPDF.txtExtracted 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dc.title.por.fl_str_mv |
Project prospection: investing and learning |
title |
Project prospection: investing and learning |
spellingShingle |
Project prospection: investing and learning Figueiredo Neto, José Edilson Bayesian learning Project conclusion R&D Non-homogeneous Poisson process Investimentos Teoria bayesiana de decisão estatística Processo de Poisson Pesquisa e desenvolvimento |
title_short |
Project prospection: investing and learning |
title_full |
Project prospection: investing and learning |
title_fullStr |
Project prospection: investing and learning |
title_full_unstemmed |
Project prospection: investing and learning |
title_sort |
Project prospection: investing and learning |
author |
Figueiredo Neto, José Edilson |
author_facet |
Figueiredo Neto, José Edilson |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EPGE |
dc.contributor.member.none.fl_str_mv |
Costa, Carlos Eugênio Ellery Lustosa da Faro, José Heleno |
dc.contributor.author.fl_str_mv |
Figueiredo Neto, José Edilson |
dc.contributor.advisor1.fl_str_mv |
Gorno, Leandro |
contributor_str_mv |
Gorno, Leandro |
dc.subject.eng.fl_str_mv |
Bayesian learning Project conclusion R&D Non-homogeneous Poisson process |
topic |
Bayesian learning Project conclusion R&D Non-homogeneous Poisson process Investimentos Teoria bayesiana de decisão estatística Processo de Poisson Pesquisa e desenvolvimento |
dc.subject.bibliodata.por.fl_str_mv |
Investimentos Teoria bayesiana de decisão estatística Processo de Poisson Pesquisa e desenvolvimento |
description |
Consideramos um modelo no qual uma firma realiza investimentos em um projeto de inovação, objetivando sua conclusão. O término de tal projeto depende de uma ruptura que ocorre a depender de sua factibilidade (“bom”) ou infactibilidade (“ruim”), nunca ocorrendo em caso de um projeto não factível. Enquanto essa ruptura não é atingida, a firma atualiza suas crenças a respeito da qualidade do projeto de maneira pessimista e reconsidera suas decisões de investimento. Nosso principal objetivo é avaliar o efeito do aprendizado em suas decisões. Apesar de aumentos no investimento acarretarem atualizações mais rápidas de crença, as percepções acerca da qualidade do projeto são prejudicadas, o que pode levar a redução no fluxo de investimentos no futuro. Como resultado, concluímos que uma política mais parcimoniosa é ótima, isto é, atualizações bayesianas reduzem o investimento em projetos desse tipo. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-10-24T17:25:44Z |
dc.date.available.fl_str_mv |
2023-10-24T17:25:44Z |
dc.date.issued.fl_str_mv |
2023-03-31 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
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https://hdl.handle.net/10438/34411 |
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https://hdl.handle.net/10438/34411 |
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eng |
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eng |
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openAccess |
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