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Big data, machine learning and
uncertainty in foresight studies
Vinicius Muraro and Sergio Salles-Filho
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
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.
Keywords Uncertainty, Futures studies, Foresight, Big data, Machine learning, Artificial intelligence
Paper type Research paper
1. Introduction
Across decades, futures studies have adapted approaches and tools to cope with
uncertain and complex contexts at sectoral, regional and national scales. Over the past 40
years, the term “foresight” has gained widespread usage, denoting studies facilitating
evidence-based policies and strategies, both at governmental and corporate levels.
Foresight seeks a collective future vision among stakeholders, using diverse qualitative and
quantitative methodologies, including artificial intelligence tools such as big data and
machine learning (BDML) (Miles et al.,2008,2016).
BDML has been changing the way that many industries and sectors think about the future
(Xu et al.,2018). Big data refers to massive data sets and their tools to support acquiring,
managing and processing information. Machine learning algorithms, including large
language models (LLM) such as ChatGPT and other chatbots, provide a refined analytical
mechanism that looks for structure, classification and hidden patterns in data sets, based on
historical (big) data (von der Gracht et al.,2015). However, limitations, such as data quality,
data reliability, data interpretability and ethical issues, could impact foresight activities and
results (Reimsbach-kounatze, 2015).
Vinicius Muraro is based at
the Department of Business
Administration, Lund
University, Lund, Sweden.
Sergio Salles-Filho is based
at the Department of
Science and Technology
Policy, Universidade
Estadual de Campinas,
Campinas, Brazil.
Received 27 December 2022
Revised 19 August 2023
21 February 2024
Accepted 21 April 2024
©Vinicius Muraro and Sergio
Salles-Filho. Publishedby
Emerald Publishing Limited. This
article is published under the
CreativeCommons Attribution
(CC BY 4.0) licence. Anyone
may reproduce, distribute,
translate and create derivative
works of this article (for both
commercial and non-commercial
purposes), subject to full
attribution to the original
publication and authors. The full
terms of this licence may be seen
at http://creativecommons.org/
licences/by/4.0/legalcode
To DCPT/IG/Unicamp, InSySPo
(www.ige.unicamp.br/insyspo/)
and LabGEOPI (www.ige.
unicamp.br/geopi/en/home).
This research was financed by
CAPES –Coordination for the
Improvement of Higher
Education Personnel (Finance
Code 001) and FAPESP –Sa
˜o
Paulo Research Foundation
within the framework of the
program Sa
˜o Paulo Excellence
Chair under the name
“InSySPo –Innovation Systems,
Strategies and Policy” (grant
number 2018/05144–4) in the
years 2016–2020.
DOI 10.1108/FS-12-2022-0187 Emerald Publishing Limited, ISSN 1463-6689 jFORESIGHT j
There are few studies about the effect on foresight approaches and decision-making in
complex environments by using BDML. Some questions remain when discussing the role of
BDML for foresight. Given that BDML has important effects on the availability and
processing of large amounts of data, how will it affect capabilities and methodological
approaches for foresight? Would BDML be able to reshape the concept of uncertainty and
the practice of managing uncertainty in decision-making processes?
The objective of this study is to explore the effects of BDML in foresight practice, focusing
on understanding how these techniques are changing conceptual and methodological
approaches for foresight. The structure of this study is as follows: Section 2 introduces the
theoretical background, followed by Section 3 outlining the research design and methods.
In Section 4, the results are presented, followed by a discussion in Section 5. The
concluding remarks are provided in Section 6.
2. Conceptual background
2.1 Uncertainty and the future
The Second World War highlighted the value of good planning, strategizing and
management of complex situations, which led to the formalization of futures studies as an
important decision support tool for government and business sectors (Georghiou et al.,
2008;Miles, 2010). Because we do not have a historical series of figures about the future,
we intuitively base our prospective on the information we have (past and present), try to
unfold it and imagine what might happen. Uncertainty is undoubtedly the underlying
condition in all kinds of methods and approaches.
There is a vast literature on uncertainty and how it has been considered and managed for
prospecting –even predicting –the future and making decisions (Dosi and Egidi, 1991;
Knight, 1921;Marchau et al.,2019;Shackle, 1969,2010). In this study, we adopt a
combination of well-known concepts of uncertainty to contribute to one of the objectives
presented above: to what extent does BDML affect the perception and nature of uncertainty
itself in foresight activities.
The first concept is Frank Knight’s classic definition of uncertainty, which is the inevitable
condition of the partial knowledge that underlies all decisions, ‘‘neither total ignorance nor
complete and perfect information, but partial knowledge”(
Knight, 1921). A second concept,
which complements Knight’s definition, is that of George Shackle, for whom a decision is
always made under bounded uncertainty, meaning “neither perfect foresight nor chaos”
(Shackle, 1969).
Partial knowledge refers to situations in which information remains incomplete regardless of
the contextual factors or available means of inquiry. The information cannot be obtained
simply because it does not exist in the present, only in the future. Knight’s proposal is a
seminal one, as it attempts to distinguish uncertainty from risk. In his proposition,
uncertainty refers to the impossibility to know all possible outcomes of a certain event in
advance. On the contrary, when all possibilities can be known beforehand, it is not
uncertainty, but rather risk –or ambiguity, as argued by several authors later on (Dequech,
2000;Marchau et al., 2019). Risk is calculable, because the variables are fully known ex
ante and subject to an objective probabilistic distribution (e.g. a roulette wheel). Ambiguity,
on the other hand, is the situation where information about the variables is hidden and does
not vary over time (a classic example is a box with nblack balls and mred balls) (Dequech,
2000;Ellsberg, 1961).
Under the condition of uncertainty, variables change over time and cannot be known ex ante,
either because new variables may emerge and change the system, and the methods of
calculation and interpretation may also be altered. This is a characteristic of open systems, as
pointed out by Prigogine and Stengers (1984). Uncertainty, as defined here, is then a
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condition of open systems (variables and possible outputs vary over time). Risk and
ambiguity are conditions of closed systems (variables and possible outputs do not vary over
time). Human and social affairs are, by definition, open systems (Metcalfe et al., 2021;
Prigogine and Stengers, 1984;Shackle, 1969).
Partial knowledge enables us to define an “event horizon,” which frames a bounded
uncertainty at a given moment in a given context for a given set of eyes. This means that the
conditions of uncertainty will be, in practice, bounded by the state of art (available data)
and by the imagination (unavailable data) of those involved in the process of framing what is
relevant to take into consideration when looking at the future. George Shackle’s concept of
bounded uncertainty complements Frank Knight’s concept of partial knowledge by
suggesting that when considering the future, agents form potential scenarios by using
known information and envisioning “unknowns” to define the range of possible or
subjectively probable outcomes. It remains uncertain in the same sense as Knight’s, and it
is bounded by the perspectives of the agents. Futures must be envisioned, and decisions
be made, that is why “unknowns” must be framed and bounded [1].
Marchau et al. (2019) propose a slightly different way of defining uncertainty, ambiguity and
risk by introducing “levels of uncertainty.” The authors suggest classifying uncertain
conditions into 4 þ1 levels between total determinism and complete ignorance (similar to
Shackle’s bounded uncertainty). Level 1 corresponds to the context of “a clear enough
future,” where only a few deterministic outputs can be predicted (corresponding, in our
perspective, to the concept of risk); Level 2 occurs in situations with identifiable alternative
futures, treatable by stochastic systems (in our view, similar to ambiguity); Level 3 would
arise in contexts with some plausible, non-deterministic futures; Level 4 is divided into two
sub-levels: 4a many plausible futures, and 4b unknown futures. Levels 3 and 4 fit in the
definition of uncertainty we use here (Metcalfe et al., 2021). This practical way to split
uncertainty may help understanding the impacts of BDML over futures studies as we will
explore further later in this article.
2.2 Foresight to manage uncertainty
Foresight is defined as a systematic and structured process of thinking, imagining and
creating assumptions about the future, exploring trends and potential scenarios that could
emerge from a varied source of data and opinions. Different from forecast analysis, which
looks for predictions, foresight proposes to reach possible views of the future because it
considers that social systems are open, influenced by known and unknown variables.
Therefore, foresight has become a popular tool for science, technology and innovation
precisely because it proposes a logic for managing uncertainty and building a common
perception of the future instead of trying to predict it (Irvine and Martin, 1984;Loveridge,
2009;Miles et al.,2017).
Foresight always operates under conditions of partial knowledge and bounded uncertainty. By
definition, being partial and bounded means being context- and time-dependent. The parallel
with the concept of event horizon seems to be helpful. An event horizon is the visible limit of an
event, beyond which nothing can be seen or detected, except if you trespass it and look back.
The only possibility to really see the future is going there. Even with time travel, the future, much
like a black hole, remains an unidirectional path; once reached, returning is not possible [2].
This is how human beings deal with social phenomena, which are open and complex systems
whose variables, interactions and outcomes cannot be fully known ex ante. Resultingly, in
foresight studies, agents are always working over data and their interpretation. If we embrace
this perspective, in theoretical terms, the application of BDML to futures studies might –very
likely –reshape agents’ perceptions of how events will unfold, consequently influencing their
decisions, not necessarily improving accuracy. The advent of BDML and artificial intelligence
introduces the prospect of augmenting the existing informational landscape through an
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enormous volume of data, bolstering computational capacity and generating uncountable
scenarios of potential outcomes. These new capabilities pose the potential to question
the essence of uncertainty itself –pondering whether uncertainty might become risk or
ambiguity –but also to recalibrate the methodologies used in futures studies.
An example of a continuous foresight process is presented in Figure 1 and includes five
basic steps: pre-foresight, recruitment, generation, action and renewal (Miles, 2002;
Popper, 2008) On pre-foresight, the main activities are defining rationales, objectives,
project team and the prospective methodologies. Recruiting people (i.e. facilitators, experts
and other stakeholders) and collecting data for generating future insights are part of the
recruitment phase. Generation is considered the heart of the foresight process, which
focuses on the prospective effort of the exercise. In the action phase, the foresight process
is up to its primary objective, informing decision-making. Finally, the renewal phase consists
of monitoring and evaluating processes to verify if foresight has achieved its goals and
prepare foresight’s new cycle. Several foresight methods can be included in this process,
ranging from more qualitative (e.g. brainstorming, expert panels, workshops and focus
groups) to more quantitative approaches (e.g. bibliometrics, trend extrapolation and
statistical and economic models). In most of cases, foresight uses multi-method
approaches, combining opinion-driven with data-driven techniques and tools to develop
informed insight about what the future might hold.
2.3 New data-driven prospective approaches
Data-driven approaches using BDML are receiving increasing attention in literature (Kayser
and Blind, 2017;Trappey et al., 2019;Zhou et al.,2020). Big data is defined as a set of data
whose size becomes a problem, and the usual collection, storage, management and
analysis tools do not fit correctly. It is also often characterized by the 5Vs –volume, variety,
velocity, value and veracity (Loukides, 2010). Precisely, machine learning algorithms
analyze historical (big) data by acting as adaptative systems that could perform tasks more
efficiently through experience. They can continuously learn the patterns from historical data
and can make projections about the future. Several methods are currently used in machine
learning, such as linear regression, logistic regression, decision trees, support vector
machines (SVM), artificial neural networks (ANN) and deep learning (Hastie et al.,2009;
Lecun et al.,2015;Shalev-Shwartz and Ben-David, 2014).
Various sources provide valuable data for foresight activities, such as scientific
publications, patents, news, social media, websites and other structured and unstructured
databases. Regarding algorithms, text mining techniques like natural language processing
(NLP) and LLM are used for text analysis, enabling pattern recognition, concept
summarization, relationship identification and document classification (Kehl et al.,2020),
possibly providing conceptual maps, technological trends and offering insight of social
behavior (Amanatidou et al.,2012;Geurts et al.,2022;Glassey, 2012;Pang, 2010). As an
Figure 1 Continuous foresight process
jFORESIGHT j
example, Spaniol and Rowland (2023) demonstrated that ChatGPT could generate
preliminary scenarios, reducing costs and fostering new insights in scenario planning. This
approach enhances participants’ “futures literacy” and encourages discussing diverse
future scenarios. Additionally, Albert et al. (2015) proposed a method to gauge technology
maturity through sentiment analysis of blog texts, offering a unique viewpoint compared to
patents or scientific literature, as it reflects public opinions. This approach involves
collecting blog data, applying sentiment analysis to identify terms related to different
technology maturity levels and integrating expert surveys for validation. The literature
suggests that foresight methods are reshaped to introduce massive data to support
prospective outputs and decision-making. This is made either by adopting it as a slot in
consolidated methodologies or by combining methods that use massive data in their
processes (Yufei et al., 2016).
To leverage the potential of BDML, Geurts et al. (2022) propose a hybrid AI-expert
approach, a reevaluation of the role of experts in foresight, involving a dynamic
collaboration between the collective knowledge of human experts and the data-driven
capabilities of BDML. They highlight several reasons for this approach: first, as foresight
often deals with emerging phenomena, relying only on historical data can confine thinking to
past patterns, impeding the identification of novel possibilities (open system, new
variables); second, this integration reinforces the validity of findings by encouraging the
exploration of new alternative futures and fostering diverse mental models for prospective
scenarios; third, the dynamic interplay between human experts and AI mechanisms aids in
the identification and rectification of biased outcomes arising from data, by ensuring the
contextualization of results within a comprehensive framework. The integration of AI has
also the potential to automate aspects of information gathering, analysis and scenario
development, fostering a semi-automated continuous foresight process that liberates
human capacity for intuition, creativity and imagination (Brandtner and Mates, 2021;
Roz
ˇanec et al.,2023;Spaniol and Rowland, 2023). As a new generation of foresight studies,
Saritas et al. (2022) define “Foresight-on-site,” suggesting that it should be carried out
closer to the sites of its application, in a continuous and wide participatory model. In this
sense, BDML and AI automation in foresight could enhance not only policy formulation but
also the operational decisions and actions in response to real-time challenges.
Implementing BDML in foresight studies, while promising, is not without some challenges.
Issues such as poor data quality –due to issues of relevance, inaccuracy or inconsistency –
coupled with the potential for data manipulation during processes like dimensionality
reduction or data cleaning, can bias analyses and distort foresight outcomes (Hagen et al.,
2019;L’Heureux et al.,2017). Furthermore, ethical considerations around data privacy and
ownership rights compound these challenges (Stahl and Wright, 2018). To effectively
navigate these complexities, a comprehensive ethical framework is essential, promoting a
responsible application of BDML in foresight initiatives (Mittelstadt and Floridi, 2016).
Besides some limitations, the potential of BDML to impact prospective and futures studies is
not negligible. As argued in this study, BDML changes the methodological toolbox of
futures studies; in doing so, it may also have effects on the outputs and outcomes of any
prospective exercise and consequently, the perceptions about the future.
3. Research design and methods
The methodological approach to explore the effects of the use of BDML tools in foresight is
twofold: bibliometric analysis (Hess, 1997), to obtain a current overview of foresight studies
supported by BDML and deepen in newly developed methodological approaches, and
survey analysis, to raise the perceptions and opinions from foresight experts regarding
BDML. Both approaches have been largely used to analyze trends and approach experts’
opinions (Cabral et al., 2019;Karaca and O
¨ner, 2015;Keller and von der Gracht, 2014), and
combined, they can provide adequate knowledge to discuss current and future
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methodological changes and the role of uncertainty in foresight. Detailed methodological
steps are presented in Table 1.
3.1 Bibliometrics –current overview of foresight and big data and machine learning
To capture the current state of foresight studies integrated with BDML, we constructed three
complementary data sets. The first, “Foresight in Science, Technology, and Innovation” (FSTI
database), encompasses papers focused on foresight studies in these domains. The second,
“Big Data and Machine Learning” (BDML database), includes publications on the
advancements and applications of those technologies. The third, “Foresight in STI with Big
Data and Machine Learning” [3] (FSTIþBDML), represents an intersection of the two
aforementioned data sets, comprising articles and reviews that embody the combination
between foresight studies and BDML technologies, all sourced from Scopus up to the year
2021. Scopus, an Elsevier product, indexes more than 75,000 items from more than 5,000
publishers and 16,000 authors profiles, comprehending a substantial part of academic
research in the field. We used the software Vantage Point [4] for uploading, deduplicating and
cleaning the data –tasks that included removing incomplete records, correcting errors and
standardizing data formats to ensure a robust analysis foundation. NLP techniques, including
tokenization (the process of breaking down text or a sequence of characters into smaller units
called tokens) and n-grams (sequential combinations of tokens), were used to classify the
papers based on their methodological approaches by analyzing the co-occurrence of terms in
the abstract related to various foresight methodologies (Popper, 2008) and BDML techniques.
This analysis pinpointed the relationships between various foresight methodologies and BDML
techniques. To augment the accuracy of our classification, manual reading of selected papers
supplemented the NLP-driven process. Furthermore, we compiled a list of corresponding
authors along with their email addresses from the FSTI database, which served to conduct a
survey analysis, engaging foresight practitioners and experts directly in our study. Tables and
charts were created in Microsoft Excel, and Gephi supported network analysis and
visualization. Due to the lack of a consolidated foresight studies database, we limited to
analyze scientific publications on the topic.
3.2 Experts survey –trends of foresight and big data and machine learning
3.2.1 Projections about effects of big data and machine learning on foresight process.
Based on the literature review and inspired by Keller and von der Gracht (2014),we
formulated 12 future projections about the implications of BDML in 2025. The set of
projections in Table 2 was categorized based on the five steps of foresight process to
explore the impact of BDML in different stages of foresight development.
Table 1 Methodological approach: bibliometrics and survey
# Step Tool/method
Bibliometrics
(phase 1)
1 Definition of keywords for searching documents regarding foresight supported by BDML Literature review
2 Searching and collecting peer review documents Scopus
3 Data cleaning, remove inconsistent and duplicate records Vantage Point
4 Classification of methodological approaches by NLP of abstract fields and manual reading Vantage Point
5 Data visualization: creation of tables, charts and network graphs MS Excel/Gephi
Survey (phase 2) 6 Development of projections regarding the impact of BDML in foresight Literature review
7 Online survey design Survey Monkey
8 Defining target respondents: experts’ e-mail address collection Vantage Point/Python
9 Send, follow and monitor survey answering Survey Monkey
10 Analyze results and data visualization Vantage Point/Excel
Source: Authors’ own creation
jFORESIGHT j
The initiation of the foresight process, particularly the definition of objectives and
methodological choices, is increasingly influenced by access to advanced databases and
data analysis tools like BDML. The first two projections (P1, P2), termed pre-foresight
projections, address this initial influence. BDML techniques are expected to significantly
affect the recruitment step, demanding analytical skills from the team to acquire future-
relevant data, assure data quality and identify experts that could contribute to the foresight
exercise, leading to projections P3, P4 and P5. Projections related to the generation step
(P6, P7, P8) were based on the notion that high-quality data and improved analysis would
enhance prospective activities, fostering imagination and complementing qualitative
methods. Consequently, various BDML techniques could support the action step (P9, P10)
by facilitating the transfer of prospective outputs to decision-making processes and
enhancing decision quality. The renewal projections (P11, P12) aim to assess the impact of
BDML in monitoring implementation and evaluating the achievement of foresight objectives.
These projections underwent internal validation by experts and foresight practitioners.
Given the exponential growth in digital technologies, a short time horizon was chosen to
capture both the immediate and near-future impacts of BDML on foresight practices
already adapting to technological advancements. All projections were assessed in three
aspects: expected probability of projection’s occurrence (EP), desirability of projection’s
occurrence (DE) and impact on foresight industry if the projection occurs (IF). The aspects
were measured on a five-point Likert scale, with the categories: 1) very low; 2) low; 3)
neither low nor high; 4) high; 5) very high (Likert, 1932).
3.2.2 Online survey design. Supported by Survey Monkey platform, the online survey was
structured in four parts: introduction; instructions; projections; and demographic questions.
The introduction presented a succinct abstract, the research’s objectives, the participant’s
contribution and confidentiality and privacy terms. The main concepts were explained in the
instructions part (foresight, uncertainty, big data and machine learning), as well as the
structure of the questions and projections. The next section presented the set of projections,
Table 2 Description of 12 projections about influence of BDML
# Foresight step Short title Projection
1 Pre-foresight Support objectives definition Future-oriented activities’ objectives will be easily defined using BDML tools for
its development in 2025
2 Pre-foresight Guide methodological choice The possibility of using BDML tools will make the methodological choice for
futures studies easier in 2025
3 Recruitment Require analytical skills Data scientists or coding, analyzing and data visualization skills will be
required to develop consistent futures studies in 2025
4 Recruitment Support data collection The relevant data for future-oriented activities will be less time-consuming and
easily accessed using big data tools in 2025
5 Recruitment Improve data quality The quality of future-relevant data will be significantly enhanced by the
application of BDML tools or techniques in foresight studies in 2025
6 Generation Enhance data analysis The quality of future-relevant data analysis will be significantly enhanced by
the adoption of BDML tools and techniques in foresight studies in 2025
7 Generation Drive qualitative methods BDML tools and techniques will be critical to support qualitative analysis in
foresight activity in 2025
8 Generation Increase data manipulation The use of big BDML solutions for futures studies will increase the frequency of
manipulated (or biased) data in 2025
9 Action Support results transfer BDML tools and techniques will increase futures studies’ embeddedness to
strategic planning or decision-making in 2025
10 Action Increase decision accuracy BDML tools and techniques will increase the accuracy of decision-making
based on foresight in 2025
11 Renewal Support foresight evaluation Evaluating and monitoring future-oriented activities will be easily reached
when using BDML tools in 2025
12 Renewal Support objectives’ reach Futures studies’ objectives will be easily reached when BDML tools are used
for its development in 2025
Source: Authors’ own creation
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including dedicated open comment boxes for the projections in each stage of the foresight
process. The last part of the survey was composed of demographic questions. To define the
target audience, we follow the survey guidelines of Mota et al. (2020). The experts were
invited according to the list of the corresponding authors’ names and e-mails identified on
the FSTI database. To increase the response rate, author’s name, title of the paper and
publication year were included in the invitation mail (Sauermann and Roach, 2013). The
survey was also sent to the World Futures Studies Federation (WFSF) [5] and Millennium
Project (MP) [6] members, through group mail. In total, 7,753 foresight experts were directly
invited through e-mail via Survey Monkey platform. Descriptive statistics were used to
analyze the variables related to the projections. Subsequently, the projections were grouped
based on their average EP and IF scores, allowing us to identify patterns and prioritize
implications for the foresight field. Finally, the software Microsoft Excel and Vantage Point
were used to create tables and charts for data visualization.
4. Results
4.1 Bibliometric analysis of foresight studies supported by big data and machine
learning
The publications were collected in November 2022. As is shown in Figure 2, the primary
data corpus used to perform the analysis is based on foresight studies supported by BDML
(FSTIþBDML). Figure 3 shows the annual distribution and the accumulated number of
publications for the past 20years, with an average of 14 publications per year. Although the
number of publications has increased year upon year, research regarding the application of
BDML in foresight is still incipient.
The most frequently used methodology (Figure 4) is patent analysis –a quantitative method
that uses statistical methods to analyze patent data –which is mentioned in 21% of total
papers. Text mining corresponds to 20%, followed by machine learning algorithms, which
includes techniques such as linear and logistic regression, K-means clusterization, SVM,
naı
¨ve Bayes, decision trees and random forest algorithms.
A foresight study can combine an average of six different methods (Popper, 2008);
therefore, Figure 5 shows the co-occurrence network of foresight methodologies and the
BDML techniques. Each node in this network represents a different approach (a foresight
method or BDML technique), the size of the node represents the number of papers that use
this method or technique and the colors of the nodes distinguish the type of the approach.
According to the network analysis, patent analysis is often used along with text and data
mining techniques, machine learning algorithms and statistical methods. The network also
Figure 2 Model of data collection results for FSTI, BDML and FSTIþBDML (1968–2021)
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shows that foresight methodologies such as time series analysis, trend extrapolation analysis,
modeling, roadmap and scenarios are often complemented with BDML techniques.
4.2 Survey assessment of future projections
The survey was available to receive answers from October 19th to October 26th, 2020. A
total of 479 researchers and foresight experts (mostly authors from the collected
bibliometric data) participated in this study. Most of participants were affiliated to
universities (66,2%), followed by research institutions, consultancies and governmental
institutions. Almost 60% of experts have declared more than ten years of experience in the
futures field. Regarding the geographic region, half of consulted experts were from Europe
(49.9%), followed by Asia (15.9%), Northern America (14.2%) and Latin America and the
Figure 3 Publications by year in FSTIþBDML
Figure 4 Publications per methodological approach in FSTIþBDML papers
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Caribbean (13.9%). Figure 6 shows the distribution of answers and the mean value for each
question and projection.
The projections P3 (require analytical skills) and P9 (support results transfer) were
considered to have a higher EP. On the other hand, P1 (support objective definition) and
P12 (support objectives’ reach) were those with the lowest aggregate EP values. Some
respondents expressed skepticism about the outcomes of BDML in foresight, citing
variations in data quality and potential for introducing more bias. This is in line with low value
of DE on P8 (increase data manipulation). Experts recognize data manipulation as a risk to
data-driven foresight, potentially producing biased outcomes. The projections with the
highest IF values were P3 (require analytical skill) and P4 (support data collection). The
respondents expressed concerns on developing new competences to leverage BDML
outputs. The general perception was that having dealt with the potential risks, BDML would
improve data analysis in foresight, not necessarily reducing uncertainty.
In Figure 7, the projections were plotted with the mean value of IF on the y-axis, the
mean value of EP on the x-axisandthemeanvalueofDEasaproportionofthe
bubbles’ size.
Projections were split into four different groups based on EP and FI. Group 1 is composed
of projections with high EP and high IF, and includes the P3, P9 and P8. Group 2 has
Figure 5 Methodological approach’s co-occurrence network in FSTIþBDML papers
jFORESIGHT j
medium EP but still high IF, including data-related projections P4, P5, P6 and P7. With
medium EP and relatively low IF, Group 3 includes P2, P10 and P11. Group 4 presents low
EP and IF and includes P1 and P12.
Some methodological limitations of the survey analysis must be mentioned. First, we could
not run a complete comparison between the characteristics of the original sample (7,753
invited foresight experts) to those who gave complete and valid answers to the survey (479
respondents). However, the minimum sample size was achieved for 95% of confidence
level and 5% of margin of error.
Figure 7 Group of projections based on EP and IF
Figure 6 Distribution of answers in EP, DE and IF dimensions
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5. Discussion
Bibliometric data and experts’ opinions demonstrate that foresight will be increasingly
implemented and supported by BDML. This development will have two key impacts, over how
foresight methodologies are developed and implemented and over the way practitioners will
perceive and face uncertainty in decision-making and unfold foresight results to policymaking.
5.1 Effects on foresight methodologies and practice
The integration of BDML with prospective methodologies has emerged as a promising avenue,
with the potential to attract new researchers, increase futures literacy and change the landscape
of future studies (Spaniol and Rowland, 2023). Bibliometric results indicate a significant increase
of BDML techniques within foresight methodologies, especially text and data mining (for pattern
recognition and relationship analysis among concepts) and varied machine learning algorithms
(for classification, predictive analytics and the recent generation of text and images through
LLM). However, to be able to leverage the potential and mitigate risks of BDML in foresight, it is
indispensable analytical competencies within foresight teams, as derived from high EP in P3.
Furthermore, it is crucial to equip practitioners with a diverse skill set encompassing data
literacy, data analytics, qualitative approaches and domain expertise, to deal with the increasing
complexity of the future (Gary and von der Gracht, 2015;Tetlock and Gardner, 2015). Such
blended competencies also facilitate the communication between stakeholders, enhancing
participation, improving futures literacy and ensuring the quality of processes and results in
foresight (Keller and von der Gracht, 2014). To this end, data scientists can contribute
significantly by handling data collection, storage and cleaning, while providing accurate
interpretations of analyses, in collaboration with practitioners and domain experts (Pankratova
and Savastiyanov, 2014;Thu et al., 2022).
The high EP in P9 suggests that BDML tools could be relevant in the implementation of
foresight results by continuously monitoring trends, setting priorities, making decisions and
supporting policy making (van Belkom, 2020;Keller and von der Gracht, 2014). Short-term
automated decision-making is already a reality in some forecasting exercises (Chaboud et al.,
2014;Roozbehani et al., 2010), but for foresight and long-term decisions (open and complex
systems), human participation will still be required (Geurts et al.,2022;Ivanov, 2022). In this
context, Keenan et al. (2020) describe how digital tools can strengthen foresight and support
the development and improvement of STI policies. As an example, Japan’s National Graduate
Institute for Policy Studies developed the SciREX Policymaking Intelligent Assistance System
(SPIAS). SPIAS uses big data and semantic technologies to analyze socio-economic impacts
of research, evaluate scientists’ performance pre- and post-grants and map emerging
technologies. By providing up-to-date data analysis, based on national and international
databases, the system supports evidence-based policymaking and policy analysis in Japan.
The results on projections in Group 2 (P4, P5, P6, P7) highlighted the potential of data-driven
analyses to complement and enhance existing foresight methodologies, as demonstrated in
bibliometrics, including patent analysis, time-series analysis, scenarios and roadmaps. The
mix of methods used in foresight studies, as illustrated in Figure 5, clearly demonstrates how
BDML techniques are integrated with both quantitative and qualitative methodologies. Many
authors (Denter et al.,2022;Kayser et al.,2014;Kayser and Shala, 2020;Nazarenko et al.,
2021;Santo et al.,2006;Yufei et al.,2016) describe examples of how BDML could support
foresight practice and decision-making, by providing up-to-date information, guiding
creative processes and potentially avoiding expert biases.
Despite the potential benefits and the increasing use of BDML in foresight, manipulation of
data and biased algorithms emerge as challenges to practitioners and data scientists (P8)
(L’Heureux et al., 2017). Efforts to mitigate bias involve data management processes and the
collaborative integration of human expertise with data analysis outcomes, to critically examine
and contextualize data, as the hybrid AI-expert approach proposes (Geurts et al., 2022).
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BDML’s influence on the initial (pre-foresight) and final (renewal) stages of the foresight
process is expected to be limited, according to the results on P1, P2, P11 and P12. In these
stages, tasks are predominantly shaped by other factors such as the sponsor’s strategy,
scope, costs, available competences and time for conducting the foresight study.
5.2 Effects on uncertainty and decision making
The results of P10, P12 and projections on Group 2 suggest that the use of BDML is neither
expected to simplify the complexity of foresight nor the accuracy of decision-making under
uncertainty but may modify the “partiality” of knowledge. For the time being, knowledge will remain
partial when referring to the future and will certainly change the bases on which uncertainty will be
delimited by agents, in the sense proposed by Shackle (1969) and Metcalfe et al. (2021).
The premise that “the more data and information on a given subject, the easier it will be for a
better decision” depends on how the subject is framed. For well-defined subjects that are less
affected by the emergence of new variables –a condition of risk and ambiguity or uncertainty
levels 1 and 2 –more data can narrow the possibilities that lie ahead. There is evidence that
short-term, data-driven forecast studies will be the most impacted by the adoption of BDML
tools, in line with the empirical findings of Tetlock and Gardner (2015). On the other hand, BDML
can also open more prospecting fronts in open systems (possibly revealing new variables) and
even increasing the complexity of decision-making, by modifying the partiality of knowledge.
BDML applied to futures studies may only interfere over contexts of uncertainty if they are
able to convert uncertainty into ambiguity or risk. In other words, converting open systems
into closed ones. This would require encompassing all potential future possibilities for a
specific context within a certain timeframe. For it to be observable, this situation would have
to endure for at least the time it was predicted for. In this condition, either time would not act
to bring about change or BDML tools would be able to anticipate any changes, making that
system a closed one for a certain timeframe.
The approach that Marchau et al. (2019) put forth is particularly valuable at this juncture.
The authors’ suggestion to “split” uncertainty into 4þ1 levels allows us to inquire whether
BDML will transform contexts characterized by Levels 3 (a few plausible futures) and 4a
(many plausible futures) into Level 2 (stochastic system model), or into Level 1 (single
deterministic system model). The prospect of BDML transforming a state of unknown
futures (Level 4b) into Level 2 or 1 appears even more challenging. This is because it would
necessitate an even greater reliance on its generative capability to imagine futures
autonomously. Theoretically speaking, all these transformations are possible. The extent to
which this will occur cannot yet be calculated nor proved. Again, it will depend on the
capacity of BDML to close systems, which can only be checked in the future.
6. Final remarks
This study aims to identify conceptual and methodological implications of integrating BDML into
the field of foresight. While the adoption of BDML in foresight remains in its nascent stages, its
presence is expanding, particularly in academic literature. Foresight experts and practitioners
anticipate some effects from BDML adoption. Primarily, it is growing the demand of novel
competencies among foresight team, such as data literacy and data analytics skills, to promote
a dynamic interaction between data outputs and human knowledge. These competencies,
associated with proper institutional control mechanisms, possess the potential to mitigate latent
risks associated with biased data and algorithms, thereby ensuring more reliability and
robustness of insights derived from BDML-supported foresight. Second, foresight methods will
be boosted by BDML techniques, which could enhance methodological capacity, automatically
monitor societal and technological trends and provide qualified knowledge for prospective
activities, opening path for imagining new and desirable futures. Consequently, foresight could
jFORESIGHT j
be extremely valuable for decision-making and evidence-based policy formulation, mainly for
addressing societal challenges, such as climate change.
Uncertainty will always prevail in open systems, which means that, whatever the evolution of
BDML (and other similar approaches as for LLM), any (big) data nor complex analysis will
ever foresee the future with fully accuracy. Data can improve qualified information, but it will
hardly replace imagination, creativity and expectation, which is essential for practitioners to
imagine futures (van Belkom, 2020;Boysen, 2020;Geurts et al.,2022) Rather, BDML’s most
important effect on prospective is precisely its influence on imagination, creativity and
expectation. If this is true, the perception of uncertainty may even increase because large
amounts of data and processing power will open new frontiers of knowledge and new
alternatives futures. The parallel with the concept of event horizon helps to understand this
point. The more details (data) are visible, the more one can know about what delimitates the
event horizon at a given point in time. However, as the events on the horizon change
continuously, the more you get, the more you will need to delimitate it. Time and context
always matter in future studies.
Notes
1. The ideas of partial knowledge and bounded uncertainty align with the well-established concept of
bounded rationality put forth by Herbert Simon (Simon, 1959). The concept that human rationality is
limited by the extent of available information and the various calculation methods used by
economic agents is rooted in similar notions as those of Knight and Shackle.
2. For the time being, the idea of reversing the time’s arrow (the symmetry of time) belongs to the
domain of quantum thermodynamics, and it is a matter of the microscopic-cum-atomic level. At the
macro level, the arrow of time is, to the best of our knowledge, one-way, and toward the future as
proposed by the famous British astrophysicist Arthur Eddington.
3. This query is a combination of FSTI and BDML databases’ queries: TITLE-ABS-KEY (“la
prospective” OR “foresight” OR “technologforecast” OR “technologanticipation” OR
“technologprediction” OR “future oriented stud” OR “future oriented analys”) AND TITLE-ABS-
KEY (“science” OR “technolog” OR “innovation”) AND TITLE-ABS-KEY (“big data” OR “text
mining” OR “data mining” OR “machine learning” OR “data analytics” OR “deep learning” OR
“artificial intelligence”) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “re”)).
4. Vantage Point is a text analytics software produced by Search Technology Inc., focused on
analyzing scientific, technical, market and patent information.
5. WFSF is a UNESCO and UN consultative partner with members in over 60 countries. It is a forum for
discussing ideas, visions and plans for alternative futures (for more information, access https://wfsf.org/).
6. Millennium Project is a global participatory think tank with 67 nodes around the world (for more
information, access www.millennium-project.org/).
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About the authors
Vinicius Muraro is a Postdoctoral Fellow and full member at CIRCLE –Center for Innovation
Research, at Lund University (Sweden). He holds a PhD in Science and Technology Policy
from the University of Campinas (Unicamp –Brazil) and has eight years of professional
experience in innovation consulting, focusing on impact evaluation and technology
foresight for large Brazilian companies and research institutes. His research interests are
innovation management science of science, foresight for STI, artificial intelligence and
quantitative methods. Vinicius Muraro is the corresponding author and can be contacted at:
murarosilva@gmail.com
Sergio Salles-Filho is a Full Professor in the Department of Science and Technology Policy
at the Universityof Campinas (Unicamp –Brazil). He was director of the Faculty of Applied
Sciences at Unicamp from 2010 to 2013 and director of the Institute of Geosciences at the
same university between 2017 and 2021. He was visiting researcher at the Manchester
Institute of Innovation Research between 2013 and 2014.
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