Scenario Planning Research Articles

Anassessment of the state of conservation planning in Europe.

Expanding and managing current habitat and species protection measures is at the heart of the European biodiversity strategy. A structured approachis needed to gain insights into such issues is systematic conservation planning, which uses techniques from decision theory to identify places and actions that contribute most effectively to policy objectives given a set of constraints. Yet culturally and historically determined European landscapes make the implementation of any conservation plans challenging, requiring an analysis of synergies and trade-offs before implementation. In this work, we review the scientific literature for evidence of previous conservation planning approaches, highlighting recent advances and success stories. We find that the conceptual characteristics of European conservation planning studies likely reduced their potential in contributing to better-informed decisions. We outline pathways towards improving the uptake of decision theory and multi-criteria conservation planning at various scales, particularly highlighting the need for (a) open data and intuitive tools, (b) the integration of biodiversity-focused conservation planning with multiple objectives, (c)accounting of dynamic ecological processes and functions, and (d)better facilitation of entry-points and co-design practices of conservation planning scenarios with stakeholders. By adopting and improving these practices, European conservation planning might become more actionable and adaptable towards implementable policy outcomes. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

MSSPN: Automatic first-arrival picking using a multistage segmentation picking network

Picking the first arrival of prestack gathers is an indispensable step in seismic data processing. To enhance the efficiency of seismic data processing, some deep-learning-based methods for first-arrival picking have been developed. However, when applying currently trained models to data that significantly differ from the training set, the results are often suboptimal. We refer to this predictive scenario as cross-survey picking. Therefore, further improving model generalization for accurate cross-survey picking has become an urgent problem. To overcome the problem, we develop a multistage picking method called multistage segmentation picking network (MSSPN), which breaks down the complex picking task into four stages. In the first stage, we develop a coarse segmentation network to recognize a rough trend of first arrivals. Second, a robust trend estimation method is developed in the second stage to further obtain a tighter range of first arrivals. Third, a refined segmentation network is conducted in the third stage to pick high-precision first arrivals. Finally, we develop a velocity constraint-based postprocessing strategy to remove the outliers of network pickings. Extensive experiments indicate that MSSPN outperforms current state-of-the-art methods under the cross-survey test situation in terms of the metrics of accuracy and stability. Particularly, MSSPN achieves 94.64% and 89.74% accuracy under the cross-survey field cases of the median and low signal-to-noise ratio data, respectively.

The rationality of explanation or human capacity? Understanding the impact of explainable artificial intelligence on human-AI trust and decision performance

Artificial intelligence models can process massive amounts of data and surpass human experts in predictions. However, the lack of trust in algorithms sealed in the “black box” is one of the most challenging barriers to taking advantage of AI in human decision-making. Improving algorithm transparency by presenting explanations is one of the most common approaches to curing this. Explainable artificial intelligence (XAI) has been a recent research focus, but most concentrate on explainable algorithm development rather than human factors. Thus, the objective of this study is twofold: (1) to explore whether or not XAI can improve human performance and trust in AI in the competitive tasks of sales prediction, and (2) to reveal the different impact routines XAI on individuals with different task-related capacities. Based on a quasi-experimental study, our results indicate that XAI can improve human decision accuracy in the scenario of sales prediction in cross-border e-commerce. XAI cannot improve self-report trust to AI but can improve behavioral trust. We also found the placebo effect of explanation for relatively low task-related capacity.

LEET: stock market forecast with long-term emotional change enhanced temporal model.

The stock market serves as a macroeconomic indicator, and stock price forecasting aids investors in analysing market trends and industry dynamics. Several deep learning network models have been proposed and extensively applied for stock price prediction and trading scenarios in recent times. Although numerous studies have indicated a significant correlation between market sentiment and stock prices, the majority of stock price predictions rely solely on historical indicator data, with minimal effort to incorporate sentiment analysis into stock price forecasting. Additionally, many deep learning models struggle with handling the long-distance dependencies of large datasets. This can cause them to overlook unexpected stock price fluctuations that may arise from long-term market sentiment, making it challenging to effectively utilise long-term market sentiment information. To address the aforementioned issues, this investigation suggests implementing a new technique called Long-term Sentiment Change Enhanced Temporal Analysis (LEET) which effectively incorporates long-term market sentiment and enhances the precision of stock price forecasts. The LEET method proposes two market sentiment index estimation methods: Exponential Weighted Sentiment Analysis (EWSA) and Weighted Average Sentiment Analysis (WASA). These methods are utilized to extract the market sentiment index. Additionally, the study proposes a Transformer architecture based on ProbAttention with rotational position encoding for enhanced positional information capture of long-term emotions. The LEET methodology underwent validation using the Standard & Poor's 500 (SP500) and FTSE 100 indices. These indices accurately reflect the state of the US and UK equity markets, respectively. The experimental results obtained from a genuine dataset demonstrate that this method is superior to the majority of deep learning network architectures when it comes to predicting stock prices.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

Three-dimensional unmanned aerial vehicle path planning utilizing artificial gorilla troops optimizer incorporating combined mutation and quadratic interpolation operators

In real terrain and dynamic obstacle scenarios, the complexity of the 3D UAV path planning problem greatly increases. Thus, to procure the optimal flight path for UAVs in such scenarios, an augmented Artificial Gorilla Troops Optimizer, denoted as OQMGTO, is proposed. The proposed OQMGTO algorithm introduces three strategies: combination mutation, quadratic interpolation, and random opposition-based learning, aiming to enhance the ability to timely escape from local optimal path areas and rapidly converge to the global optimal path. Given the flight distance, smoothness, terrain collision, and other five realistic factors of UAVs, specific constraint conditions are proposed to address complex scenarios, aiming to construct a path planning model. By optimizing this model, OQMGTO algorithm solves the path planning problem in complex scenarios. The extensive validation of OQMGTO algorithm on CEC2017 test suite enhances its credibility as a powerful optimization tool. Comparison experiments are conducted in simulated terrain scenarios, including six multi-obstacle terrain scenarios and three dynamic obstacle scenarios. The experimental findings validate OOMGTO algorithm can assist UAV in searching for excellent flight paths, featuring high safety and reliability characteristics, which confirms the superiority of OOMGTO algorithm for path planning in simulated terrain scenarios. Furthermore, in four flight missions carried out in real terrains, OQMGTO algorithm demonstrates superior search performance, planning smooth trajectories without mountain collision.

Reimagining Pathways Into Culture and Creative Industries in the City of Seattle

In view that Culture and Creative Industries (CCI) are multi-sector, emergent, and complex, this paper couples the theoretical perspective of Margaret Archer’s Morphogenetic Approach with Adam Kahane’s Tansformative Scenario Planning (TSP) as its strategic framework to discover, articulate, and connect cross-sector (community-based organizations, industry, government, and education) CCI efforts. This study has sought to understand how and in what ways the actions found in the creative sector can be leveraged to enhance participation and ameliorate the outputs of socio-economic efforts across the full spectrum of the greater Seattle region. Thus, though this example it contributes to the creation of vital places of public welfare, where public frameworks and policies nurture accessibility and participate in connected culture and creative ecology.

Scenario’s for the hydrogen market and strategic options for MNEs

Aim: The purpose of this article is to investigate how hydrogen-based energy will be implemented in the future economy and to find strategic options in the future hydrogen market for multinational enterprises (MNEs) active in the energy sector. Design/Research methods: The current literature on hydrogen production tends to focus on the technical and cost aspects, while there is limited attention on how one could implement hydrogen in the future. In this explorative research, interviews with experts formed a basis and the scenario planning methodology was used. Conclusions/findings: The three scenarios that were developed are based on the potential strategies of the European Union, which are Hydrogen Independence, Cost Optimization, and Energy Security. Based on a literature review, five key characteristics that will impact the functioning of the future hydrogen economy were selected. By using notions from the environmental- and positioning schools of Mintzberg, strategic options for MNEs were identified in each scenario. In all three scenarios, the implementation of hydrogen-based alternatives will differ. Besides this, the strategic options that MNEs can obtain will also change. Originality; value of the article: This article helps us understand how hydrogen-based energy sources will be implemented into the economy, taking on board the priorities of the European Union. Furthermore, it gives managers of MNEs guidance to identify strategic options that fit alongside their firms’ strategy. Finally, this research gives an incentive for future research as, on several topics, the interviewees could not find consensus. Keywords: Hydrogen Market, Scenario Planning, Renewable Energy Sources (RES), Strategy, Multinational Enterprises (MNEs). JEL: L22 L25 Q40 Q47.

Reducing Model Uncertainty: Optimized Ensemble Methods for High-Stakes Prediction Tasks

Reliable forecasts are critical in high-risk application areas, including healthcare, finance, and autonomous systems. In these critical applications, this research investigates the application of optimized ensemble techniques to minimize model uncertainties. To address such issues, we describe an approach that integrates multiple learning models to enhance real-time data processing performance and overcome the computational hurdles imposed by such methods. Using real-time working examples to generate simulation reports, we see how ensemble methods can help build more accurate predictions. Additionally, this paper presents typical issues in high-stake prediction scenarios and provides actionable advice on tactfully resolving them by model fine-tuning and uncertainty minimization. The findings, therefore, stress the necessity of applying effective AI solutions to increase the reliability of advanced decisions, thus enhancing performance in extended application domains.

Features of managing the international strategic partnership of a manufacturing enterprise in the conditions of global changes

In the dynamic landscape of global business, this paper investigates the intricacies involved in managing international strategic partnerships for manufacturing enterprises amid ongoing global changes. The introduction highlights the broader problematics, contextualizing it within recent research and publications, and emphasizing the relevance to significant scientific and practical challenges. The core of the paper delves into the nuances of effective management strategies for manufacturing enterprises engaged in international strategic partnerships. It places a particular emphasis on the impact of global transformations, scrutinizing how these changes influence management practices and the optimization of international collaborations during periods of instability. Economic globalization emerges as another pivotal factor, shaping the market dynamics and strategic decisions of manufacturing enterprises. The paper discusses adaptive financial management and agile business models as essential components for success in an interconnected global economy. It navigates the opportunities presented by international collaboration while acknowledging the risks associated with economic interdependence. Strategic management amid global uncertainties becomes a focal point, advocating for flexibility, scenario planning, and continuous learning. The paper underscores the importance of proactive risk management strategies and adaptive leadership to navigate the complexities introduced by global transformations. In conclusion, the paper provides a comprehensive examination of the features involved in managing the international strategic partnership of manufacturing enterprises amidst global changes. It offers insights into navigating the challenges and leveraging opportunities, providing a roadmap for effective management in an evolving global business environment.

Creative principles for managing innovation projects in BANI environment

In the dynamic landscape of innovation, where environments are increasingly characterized as Brittle, Accelerating, Nonlinear, and Incomprehensible (BANI), traditional project management approaches fall short of addressing the complexities and uncertainties that arise. This abstract explores a set of creative principles tailored for the effective management of innovation projects within BANI environments. Drawing inspiration from agile frameworks, design thinking, and open innovation, these principles aim to navigate the brittleness, acceleration, nonlinearity, and incomprehensibility inherent in such contexts. Adaptive leadership, rapid prototyping, and scenario planning emerge as key components, fostering a culture of continuous learning and cross-functional collaboration. Emphasizing innovation metrics beyond traditional KPIs, the abstract advocates for an ecosystem thinking approach to holistically comprehend the intricate interplay of factors within BANI environments. By integrating these creative principles, organizations can position themselves to not only adapt to the challenges presented by BANI but to thrive amid uncertainty, turning it into a catalyst for innovative growth. The world of innovation is facing unprecedented challenges. The acronym BANI – Brittle, Anxious, Nonlinear, and Incomprehensible – aptly captures the current environment characterized by fragile systems and interconnectedness. A single event can trigger cascading disruptions across industries and continents. Heightened uncertainty and anxiety. Rapid change and unforeseen events create constant pressure and require agility in decisionmaking. Non-linear trajectories and emergent phenomena. Traditional planning becomes obsolete in a world where cause and effect are blurry and unexpected outcomes arise. An incomprehensible information landscape. The sheer volume and complexity of data make it difficult to discern truth and make informed decisions. By embracing these creative principles, we can transform innovation projects from rigid endeavours into dynamic journeys of exploration and discovery. In the face of the BANI environment, embracing creativity, learning from failures, and pivoting quickly are the keys to unlocking ground breaking ideas and shaping a better future.

Is the Young Precariat a Problem of Modern Democracy? A Case Study of Poland

This article addresses the issue of the role and importance of the young precariat for the functioning of the democratic system. Based on scenario planning, it presents three possible directions for the development of democracy in the context of meeting the needs of the young precariat. The first scenario assumes a continuation of the measures applied so far by democratic governments towards young precarious people related to social policy and the low representation of the young generation in politics. The second involves a move away from democracy towards non-democratic systems, where the needs of precarious people are irrelevant. The third assumes a new approach among democratic governments to the needs of young precarious workers and the shaping of new social policies, as well as the creation of incentives for young precarious workers to be more widely involved in these policies. The empirical context for these considerations is an attempt to determine the possibility of the occurrence of each of these scenarios in Polish conditions, based on the results of qualitative studies conducted via asynchronous interviews with representatives of the young Polish precariat. The research relates to Poland and takes into account the characteristics of the Polish precariat. The article uses a mixed research methodology, combining different methods for solving research problems, including collecting, analyzing, interpreting, and presenting quantitative and qualitative data.

An Intelligent Web-based Energy Management System for Distributed Energy Resources Integration and Optimization

The integration of renewable energy sources into power distribution systems frequently presents challenges for conventional energy management systems (EMS) due to the unpredictable and unstable characteristics of such energy sources. As a result, novel and cutting-edge solutions are required. This paper presents an intelligent web-based energy management system (iW-EMS) specifically designed to address the integration and optimization of distributed energy resources, as outlined in the proposed approach. The system incorporates a hybrid novel optimization approach that integrates simulated annealing and cone programming to effectively manage the distribution of energy resources and attain optimal outcomes from the proposed EMS. Additionally, it leverages generative AI services to create optimal scenarios based on historical data and real-time information, ensuring adaptability to the dynamic nature of renewable energy generation, providing a user-friendly and flexible web environment for scenario planning. The proposed framework facilitates seamless communication and collaboration among stakeholders involved in renewable energy integration, while also enabling the incorporation of real-world data sources such as weather forecasts and energy consumption patterns into the planning process.

Genomic prediction within and across maize landrace derived populations using haplotypes.

Genomic prediction (GP) using haplotypes is considered advantageous compared to GP solely reliant on single nucleotide polymorphisms (SNPs), owing to haplotypes' enhanced ability to capture ancestral information and their higher linkage disequilibrium with quantitative trait loci (QTL). Many empirical studies supported the advantages of haplotype-based GP over SNP-based approaches. Nevertheless, the performance of haplotype-based GP can vary significantly depending on multiple factors, including the traits being studied, the genetic structure of the population under investigation, and the particular method employed for haplotype construction. In this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces. Populations comprised either doubled haploid lines (DH) derived directly from landraces, or gamete capture lines (GC) derived from crosses of the landraces with an inbred line. For two different landraces, both types of populations were generated, genotyped with 600k SNPs and phenotyped as lines per se for five traits. Our study explores three prediction scenarios: (i) within each of the four populations, (ii) across DH and GC populations from the same landrace, and (iii) across landraces using either DH or GC populations. Three haplotype construction methods were evaluated: 1. fixed-window blocks (FixedHB), 2. LD-based blocks (HaploView), and 3. IBD-based blocks (HaploBlocker). In within population predictions, FixedHB and HaploView methods performed as well as or slightly better than SNPs for all traits. HaploBlocker improved accuracy for certain traits but exhibited inferior performance for others. In prediction across populations, the parameter setting from HaploBlocker which controls the construction of shared haplotypes between populations played a crucial role for obtaining optimal results. When predicting across landraces, accuracies were low for both, SNP and haplotype approaches, but for specific traits substantial improvement was observed with HaploBlocker. This study provides recommendations for optimal haplotype construction and identifies relevant parameters for constructing haplotypes in the context of genomic prediction.

Facing Economic Uncertainty: Adaptive Audit Strategies

: This qualitative study delves into adaptive audit strategies amid economic uncertainty to investigate auditors' navigation of uncertain economic landscapes. Through a systematic review of relevant literature, employing purposive sampling and thematic analysis, the research aims to uncover key insights. Findings reveal that auditors deploy various adaptive practices, including integrating data analytics and advanced technologies, adopting risk-based auditing approaches, and utilizing scenario planning techniques. These strategies enhance audit quality, efficiency, and effectiveness by identifying patterns, prioritizing audit procedures, and assessing financial resilience. Challenges such as data privacy concerns and the need for technical expertise are identified. Future research opportunities include exploring the impact of emerging technologies on audit practices and assessing auditors' role in environmental, social, and governance factors. Overall, the study underscores the importance of agility, innovation, and adaptability in contemporary audit practices, offering valuable insights for auditors and organizations confronting economic uncertainty.

Exploring urban planning as a lever for emission and exposure control: Analysis of master plan actions over greater Paris

In this paper we set up a modeling chain to study the impact of different urban planning scenarios on air quality and ultimately the exposure of the population. The analysis relates to the intensity of the polluting activities associated with each scenario, as well as their environmental and health impact. The implementation of a 2030 prospective scenario on Ile-de-France allows us to assess the magnitude of the leverage effect of the actions recommended in the regional master plan. The objective is to quantify the importance of emission reductions, but also the gain in terms of exposure to pollutants, which can be obtained when we transcribe into the model the implementation of regulatory texts on the metropolis of Greater Paris. The results allow us to debate the paradox between reducing emissions and increasing the exposure created by situations of high urban densification.

Inclusive Futures? A Systematic Review of Social Equity in Scenario Planning

Scenario planning is a valuable tool for dealing with uncertainty, but little is known about its consideration of social equity. We investigated if and how studies of scenario planning in the peer-reviewed literature address issues of social equity. After reviewing 157 peer-reviewed articles on scenario planning, we find that social equity is either ignored or underemphasized in scenario processes. Integrating social equity principles into the scenario planning method promises to help planners prepare for different social equity outcomes in the future. We suggest ways to incorporate social equity principles, such as recognition, representation, and redistribution, into the scenario planning method.

Mathematical programming-based methodology for the evaluation of supply chain collaborative planning scenarios

Nowadays, supply chain (SC) decentralised decision making is the most usual situation in SC operations planning. In this context, different companies can collaboratively plan to achieve a certain level of individual and SC performance. However in many cases, there is reluctance to collaborate because it is not known a priori which benefits will be reported. This paper aims to develop a mathematical programming-based methodology for the evaluation of different supply chain collaborative planning scenarios (MPM-SC-CP). It is assumed that different SC decision centres (DCs) make decisions based on mixed and integer linear programming models. Two main inputs feed the proposed MPM-SC-CP, a framework and associated methodology that support the integrated conceptual and analytical modeling of the SC-CP process in which several DCs make decisions according to spatio-temporal integration. Finally, an application to a real ceramic SC was conducted.

In search of better methods for the longitudinal assessment of tech-derived X-risks: How five leading scenario planning efforts can help

This mixed methods article discusses the role of scenario analysis as a tool to assess the broadest possible specter of positive and negative effects of technologies and social processes within current existential risk assessment and research. To do so, it compares and contrasts case studies of five ongoing, longitudinal scenario planning efforts of global nature– the Shell scenarios, the Intergovernmental Panel on Climate Change (IPCC), the CSIS Seven Revolutions Scenarios, The Millennium Project's State of The Future, and the NIC's Global Trends 2040, to derive lessons for whether and how the adjacent existential risk (X-risk) and tech assessment communities can better deploy scenario planning methodology. Observations are derived from interviews with practitioners involved in these efforts as well as an assessment of the publicly available material issued by each project, including the scenarios produced. The emphasis is on the methodologies used, the timelines considered, the forces each identifies as important scenario drivers, and the process of engagement once scenarios are created. Three practices identified include (1) constructing narratives based on strong data, (2) sticking to plausible scenarios (not wild cards), and (3) designing a process of engagement, which each of these does well albeit in different ways. The article concludes with a few brief recommendations for future scenario planning efforts, particularly relevant for x-risk studies that aim to be relevant for tech assessment and governance. Overall, this review shows global scenario efforts aimed at influencing governance would benefit from (a) a more uniformly shared vocabulary, (b) an underlying theory of cascading systemic change, (c) deeper methodological transparency, (d) increased transdisciplinary perspectives, yet (e) maintaining scientific rigor. These improvements would particularly benefit emerging tech-risk derived X-risk scenario efforts where the stakes are the highest they could be, safeguarding humanity's future.

NAVIGATING NON-TECHNICAL RISKS IN THE OIL & GAS INDUSTRY: INSIGHTS AND FRAMEWORKS - A REVIEW

Non-technical risks play a critical role in the oil and gas industry, influencing operational efficiency, financial performance, and stakeholder trust. This review explores key insights and frameworks for understanding and managing non-technical risks in the oil and gas sector. The review begins by defining non-technical risks and highlighting their significance in the industry. It then discusses various types of non-technical risks, including regulatory, environmental, social, and geopolitical risks, and their impact on oil and gas operations. The review also examines the interconnected nature of non-technical risks and how they can escalate into larger crises if not managed effectively. It emphasizes the importance of adopting a holistic approach to risk management that considers the interplay between different risk factors. Furthermore, the review identifies several key frameworks and methodologies for assessing and managing non-technical risks in the oil and gas industry. These include risk assessment tools, scenario planning, and stakeholder engagement strategies. The review concludes by highlighting the need for oil and gas companies to proactively identify, assess, and mitigate non-technical risks. It emphasizes the importance of integrating risk management into overall business strategies and decision-making processes. Overall, this review provides valuable insights and frameworks for navigating non-technical risks in the oil and gas industry. It offers practical recommendations for industry practitioners and policymakers to enhance risk management practices and ensure the long-term sustainability of the sector.
 Keywords: Oil and Gas, Insights, Frameworks, Navigating, Non- Technical Risks