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- Muraro, Vinicius, et al.
(author)
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Big data, machine learning and uncertainty in foresight studies
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In: Foresight. - 1463-6689.
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Journal article (peer-reviewed)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.
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| 2. |
- Salles-Filho, Sergio Luiz Monteiro, et al.
(author)
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Monitoring and Evaluation as a Way to Complete and to Implement a Policy : The Case of Brazilian National Innovation Policy
- 2022
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Conference paper (other academic/artistic)abstract
- The present National Innovation Policy (henceforth called PNI) was instituted in Brazil by Decree No. 10.537, of October 28, 2020, within the federal public administration. The policy's objective is to guide, coordinate and articulate strategies, programs, and actions across several public entities to foster innovation in the productive sector. Thus, it aims to increase the productivity and competitiveness of companies and other institutions throughout the innovation process. The policy is structured around six axes: funding innovation, innovation culture, technological background, market development, educational systems, and intellectual property. Objectives and guidelines are defined for each of these Axes. However, neither a previous Theory of Change, a Logical Framework, nor a monitoring and evaluation system was established to guide the implementation. Operationally speaking, the policy was implemented by gathering ongoing actions from eighteen different federal ministries and agencies and then assigning them to the axes above. It was an ad hoc measure to put the policy into practice. As can be inferred, the selected actions did not emerge as a breakdown from axes. In 2021 the design and implementation of a monitoring and evaluation model (M&E) were commissioned to an independent evaluation group. The objective was to develop an original methodology and indicators for monitoring and evaluating outputs, outcomes and impacts of the PNI. The challenge of the model is twofold: to adjust the matching amongst actions, axes, and the policy's objectives, and to implement a common framework for M&E able to coordinate the involved agencies. A Theory of Change and a Logical Framework were ex-post designed and validated, and based on these references, the model was conceived. It defined a classical template for monitoring the ongoing actions (using flags and lights according to execution), adding indicators of outcomes and impacts. It is an integrated M&E model that, once implemented, will require that new actions be justified not only in terms of their outputs but also on the expected outcomes and impacts. Standard indicators of science, technology and innovation outcomes and impacts from different sources as for OECD, UNESCO, Global Innovation Index and Sustainable Development Goals (SDG/UN) have been gathered and organized to be employed in the M&E process. Non-standard indicators are also foreseen so that the model can measure internationally comparable and specific indicators. The whole model is now under development and validation among stakeholders. We expect this process to go beyond the domain of M&E and influences the way priorities are defined, and governance is established and run. By involving different actors, incorporating the SDGs into the policy's core, the evaluation process facilitated the policy's implementation and coordination. Furthermore, the articulation between the different stakeholders, promoted by the M&E, minimized fragmentation and uncoordinated actions within the scope of the policy's formulation. It is in this sense that the evaluation process is a way to transform the policy itself
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| 3. |
- Salles-Filho, Sergio Luiz Monteiro, et al.
(author)
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Perspectives for the Brazilian bioethanol sector : The innovation driver
- 2017
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In: Energy Policy. - : Elsevier BV. - 0301-4215. ; 108, s. 70-77
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Journal article (peer-reviewed)abstract
- This article addresses the future of Brazil's bioethanol sector, focusing on its capabilities to innovate, its efforts toward producing cellulosic ethanol, and on recently implemented policies. Contrary to what has been argued in the literature, Brazil may not be prepared to face the technological and market challenges now emerging in the biofuels domain worldwide. Important productive investments were made by oil, energy, and chemical companies in bioethanol production in Brazil in the first decade of the 2000s; however, that sector has not shown levels of investment in innovation—either for first- or second-generation bioethanol—compatible with the challenges of making bioethanol a global commodity. Even considering recent policies in Brazil toward cellulosic bioethanol, the results achieved have been far from sustainable. This situation is partially due to uncertainties surrounding biofuels worldwide and partially due to contradictory policies toward liquid fuels in Brazil. The Brazilian government has simultaneously promoted both fossil fuels and renewables, thereby creating ambiguity among decision makers. These and other findings were drawn from an in-depth survey on innovation in the bioethanol sector in Brazil conducted in 2014 among 35 productive units affiliated to 58 industrial groups.
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