Big data, machine learning and uncertainty in foresight studies
(2024) In Foresight- Abstract
- Purpose: Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.
Design/methodology/approach: The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current... (More) - Purpose: Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.
Design/methodology/approach: The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.
Findings: It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.
Originality/value: This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/98810e50-ca4b-40b3-adc4-6d78f16619e6
- author
- Muraro, Vinicius LU and Salles-Filho, Sergio Luiz Monteiro
- organization
- publishing date
- 2024-05-21
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- Uncertainty, Futures studies, Foresight, Big data, Machine learning, Artificial intelligence
- in
- Foresight
- article number
- FS-12-2022-0187
- pages
- 17 pages
- publisher
- Emerald Group Publishing Limited
- external identifiers
-
- scopus:85193471795
- ISSN
- 1463-6689
- DOI
- 10.1108/FS-12-2022-0187
- language
- English
- LU publication?
- yes
- id
- 98810e50-ca4b-40b3-adc4-6d78f16619e6
- date added to LUP
- 2024-05-20 10:50:51
- date last changed
- 2024-06-17 15:08:49
@article{98810e50-ca4b-40b3-adc4-6d78f16619e6,
abstract = {{Purpose: Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.<br/><br/>Design/methodology/approach: The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.<br/><br/>Findings: It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.<br/><br/>Originality/value: This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.}},
author = {{Muraro, Vinicius and Salles-Filho, Sergio Luiz Monteiro}},
issn = {{1463-6689}},
keywords = {{Uncertainty; Futures studies; Foresight; Big data; Machine learning; Artificial intelligence}},
language = {{eng}},
month = {{05}},
publisher = {{Emerald Group Publishing Limited}},
series = {{Foresight}},
title = {{Big data, machine learning and uncertainty in foresight studies}},
url = {{http://dx.doi.org/10.1108/FS-12-2022-0187}},
doi = {{10.1108/FS-12-2022-0187}},
year = {{2024}},
}