Internal Decision-making Processes Research Articles

Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data

ABSTRACT The Global Human Settlement Layer (GHSL) project fosters an enhanced, public understanding of the human presence on Earth. A decade after its inception in the Digital Earth 2020 vision, GHSL is an established project of the European Commission’s Joint Research Centre and an integral part of the Copernicus Emergency Management Service. The 2023 GHSL edition, a result of rigorous research on Earth Observation data and population censuses, contributes significantly to understanding worldwide human settlements. It introduces new elements like 10-m-resolution, sub-pixel estimation of built-up surfaces, global building height and volume estimates, and a classification of residential and non-residential areas, improving population density grids. This paper evaluates the key components of the GHSL, including the Symbolic Machine Learning approach, using novel reference data. These data enable a comparative assessment of GHSL model predictions on the evolution of built-up surface, building heights, and resident population. Empirical evidence suggests that GHSL estimates are the most accurate in the public domain today, e.g. achieving an IoU of 0.98 for the water class, 0.92 for the built-up class, and 0.8 for the non-residential class at 10 m resolution. At 100 m resolution, we find that the MAE of built-up surface estimates corresponds to 6% of the grid cell area, the MAE for the building height estimates is 2.27 m, and we find a total allocation accuracy of 83% for resident population. This paper consolidates the theoretical foundation of the GHSL and highlights its innovative features for transparent Artificial Intelligence, facilitating international decision-making processes.

Leveraging artificial intelligence and machine learning analytics for development effectiveness: a comprehensive approach to extract insights from project data

ABSTRACT In the normal course of business, international development institutions investing in development projects generate thousands of project-related documents summarising and analysing each aspect of their policymaking processes and policy lifecycles from design and financing through implementation performance and ultimately project/policy outputs, outcomes and effectiveness-related data. Moreover, institutions’ internal processes for quality assurance, monitoring and results measurement systems generate quantitative and qualitative primary and secondary data that can be employed to extract insights about lessons learned, internal decision-making processes, business model changes and project and portfolio performance. Resource and time constraints, however, result in the underutilisation of these valuable data sources for knowledge management, planning, evaluation and analysis. Specifically, qualitative insights housed in project documents remain largely inaccessible and cannot be employed systematically in combination with other quantitative data, in a unified framework. This article explores how machine learning can accelerate knowledge generation and management and aid results-based decision-making in line with development institutions’ existing development effectiveness frameworks through two main channels. The first leverages project documentation for historical portfolio systematisation to extract key strategic topics as well as lessons learned through the application of techniques such as text mining and topic modelling. The second proposes an agency-based system for predictive analytics, which analyses current and historical data to make predictions about future outcomes or trends to strengthen data-driven decision-making.

Synergizing ChatGPT and experiential learning: unravelling TOC based production planning and control variants through the dice game

Amidst debates around the impact of Artificial Intelligence (AI) technologies like ChatGPT in education, our study explores their role in enhancing the ‘theory of experiential learning’, particularly in Production and Operations Management (POM). We demonstrate how Goldratt’s Dice Game, as an experiential learning aid, allows undergraduate students in a senior-level production planning and control (PPC) course to apply knowledge and skills in a dynamic, interactive setting. This study presents how these students, supported by ChatGPT's insights, gain a deeper understanding of the DBR system, focusing on buffer management, internal (i.e. a dominant capacity constraint), external (i.e. market demand constraint), and interactive decision-making processes. We detail manual and Excel-based simulation models for Drum-Buffer-Rope (DBR) variants, reflecting on experiential learning outcomes. Concluding with managerial implications, our research advocates for the synergy of ChatGPT-aided theoretical learning with experiential models, presenting a comprehensive approach for understanding POM fundamentals such as Production Planning & Control (PPC) systems.

LCA-based environmental benefit allocation between steel and cement industries in steel byproduct recycling

Among the steel byproducts, more than 80% of blast furnace slag is recycled in the cement industry. However, the criteria for allocating environmental benefits between the steel and cement industries need to be more transparent. This study aims to allocate the environmental benefits between the steel and cement industries when granulated blast furnace slag (GBFS) is recycled to replace Portland cement in the production of slag cement (SC). Specifically, this research proposes how the recycling of GBFS, a byproduct of the steel production process, can quantify environmental burdens and benefits from both attributional life cycle assessment (ALCA) and consequential life cycle assessment (CLCA) perspectives. It also suggests methods for allocating these environmental benefits between the steel and cement industries. Following the final agreement on the EU Carbon Border Adjustment Mechanism (CBAM), this study emphasizes the growing importance of interconnected allocation of environmental benefits through recycling. Various allocation methods using ALCA and CLCA approaches have been assessed, significantly impacting the analysis results. Furthermore, these findings of the study are expected to provide guidance for potential policy decisions and internal decision-making processes, highlighting the environmental benefits that GBFS recycling offers to both steel and cement industries.

Response-locked theta dissociations reveal potential feedback signal following successful retrieval

Abstract Successful memory retrieval relies on memory processes to access an internal representation and decision processes to evaluate and respond to the accessed representation, both of which are supported by fluctuations in theta (4-8Hz) activity. However, the extent to which decision making processes are engaged following a memory response is unclear. Here, we recorded scalp electroencephalography (EEG) while human participants performed a recognition memory task. We focused on response-locked data, allowing us to investigate the processes that occur prior to and following a memory response. We replicate previous work and find that prior to a memory response theta power is greater for identification of previously studied items (hits) relative to rejection of novel lures (correct rejections; CRs). Following the memory response, the theta power dissociation ‘flips’ whereby theta power is greater for CRs relative to hits. We find that the post-response ‘flip’ is more robust for hits that are committed quickly, potentially reflecting a positive feedback signal for strongly remembered experiences. Our findings suggest that there are potentially distinct processes occurring before and after a memory response that are modulated by successful memory retrieval.

Navigating Turbulence: Analyzing the Crisis Response of Sriwijaya Air following the SJ182 Plane Crash using Situational Crisis Communication Theory (SCCT)

Aircraft accidents show that the industry is vulnerable to crisis and capture the complexity of crisis management and communication. This study examines the crisis response strategies employed by Sriwijaya Air in the aftermath of the SJ-182 plane crash on January 9, 2021. Utilizing the Situational Crisis Communication Theory (SCCT) framework, the research categorizes and analyzes the airline's crisis communication actions based on primary and secondary strategies. The content analysis of news articles from Kompas.com, Detik.com, CNN Indonesia, and Reuters, as well as posts from Sriwijaya Air's social media accounts, reveals the implementation of excuse, compensation, partial apology, and ingratiation strategies. The findings indicate that while Sriwijaya Air avoided denial strategies, its delayed response negatively impacted public perception. The study highlights the importance of prompt and transparent communication in crisis management. It suggests areas for future research, including examining internal decision-making processes and the long-term impacts on organizational reputation and resilience.

Semi-supervised learning model synergistically utilizing labeled and unlabeled data for failure detection in optical networks

In optical networks, reliable failure detection is essential for maintaining quality of service. The methodology has evolved from traditional performance threshold-driven approaches to contemporary data-driven AI algorithms, predominantly employing supervised and unsupervised learning. However, with the advent of second-level telemetry, optical transport networks have amassed a wealth of unlabeled performance data, while labeled data remains limited due to the intensive effort required for annotation. In this scenario, to address the challenges of scarce labeled data in supervised learning and the accuracy issues in unsupervised methods, we propose an OpenFE-VIME semi-supervised model. This model synergizes the robustness of supervised approaches with the flexibility of unsupervised approaches. It not only leverages the abundant reservoir of unlabeled data but also addresses the challenges posed by the limited availability of labeled data, enabling reliable and efficient failure detection. Upon evaluation using performance data from OTN node devices in the operator’s optical backbone network, the OpenFE-VIME model demonstrates remarkable performance, achieving an F1-score of 0.947 and accuracy of 0.946, while significantly reducing false negative and false positive rates to 0.073 and 0.035, respectively. Moreover, our research explores the model’s capabilities in utilizing both labeled and unlabeled data and investigates the threshold for training convergence across various data ratios. Additionally, the model’s internal mechanisms and decision-making processes are interpreted using t-SNE visualization, offering enhanced insights into its operational efficacy.

Faithful Counterfactual Visual Explanations (FCVE)

Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations that non-experts easily understand, and they cannot accurately identify models’ intrinsic decision-making processes. To address these challenges, we propose to develop a counterfactual explanation (CE) model that balances plausibility and faithfulness. This model generates easy-to-understand visual explanations by making minimum changes necessary in images without altering the pixel data. Instead, the proposed method identifies internal concepts and filters learned by models and leverages them to produce plausible counterfactual explanations. The provided explanations reflect the internal decision-making process of the model, thus ensuring faithfulness to the model.

The European Union and the Treaty on the Prohibition of Nuclear Weapons: Let’s Agree that we Disagree

The European Union has traditionally been an efficient actor on nonproliferation and peaceful uses of nuclear energy, but a poor actor on disarmament, mainly due to internal decisions within the Union on this issue. However, due to the growing crisis in the global arms control architecture and the entry into force of the Treaty on the Prohibition of Nuclear Weapons (TPNW), increased pressure is being exerted on the EU not only to adopt concrete positions on this issue, but also to act. This, it is argued, will be complicated, if not impossible, due to the internal structure and decision-making process of the Union, which cannot be asked to do more than it can do.

Parent firm dividend payouts and subsidiary earnings management: Evidence from mandatory dividend policy

Despite the increasing prevalence of business group structures, earnings management within these groups has not been thoroughly explored, particularly concerning the impact of internal decision-making processes. This study fills this gap by investigating the effect of parent firm dividends on the earnings management of subsidiaries within a group. Using the establishment of China’s State-owned Capital Operating Budget System as an exogenous shock, we find that parent firm dividends significantly reduce the earnings management of subsidiaries. A mechanism analysis reveals that the improved earnings quality is primarily driven by the increased financing demand due to net capital outflow at subsidiaries rather than the agency problem mechanism based on dividend agency cost theory. Parent firm dividends primarily reduce downward earnings management and have a stronger influence on subsidiaries with shorter control chains. Heterogeneity analysis shows that this mitigating effect is more pronounced in firms with weaker external financial support and higher information asymmetry. Overall, we analyze how dividends paid by state-owned parent firms affect earnings management among their subsidiaries, offering valuable insights to business groups and state-owned entity managers involved in corporate governance.

Cities and More Effective Multi-level Governance

This article has been inspired by the vision set out in the UN Secretary’s report General Our Common Agenda (2021) and the report of High-Level Advisory Board on Effective Multilateralism entitled A Breakthrough for People and Planet. Effective and Inclusive Global Governance for Today and the Future (2023). Given the growing role of cities in international relations, the research aim of this article is to answer the question: how to make multi-level governance more effective? The author argues that more effective multi-level governance has to be more inclusive and that cities should be formally included in international decision-making processes. The author also makes the case for the growing role of cities in international relations, cooperation and addressing global problems, including migration, climate change and pandemics such as the recent Covid-19.

공적개발원조(ODA) 수원체계에 대한 연구

Purpose: This paper analyzes the ODA management system, including the internal communication system, partnership structure, and policy decision-making and implementation process, of Lao PDR, Indonesia and Vietnam, which are Korea’s key partner countries in Asia. Based on the analysis, the paper aims to draw policy recommendations for donors to enhance aid effectiveness. Originality: This study analyzes recipient countries’ ODA management system from aid effectiveness perspective, which is relatively understudied compared to donors’ aid management system. It specifies the scope of aid management system by developing a new analytical framework based on the four elements consisting of legalization and legislation, key implementing agencies, communciation and partnership, and monitoring and evaluation process. Methodology: The study conducts literature review on the relationship between aid effectiveness and recipient countries’ governance. Based on the analytical framework, case studies on Korea’s three key aid recipient countries, i.e. Lao PDR, Indonesia, and Vietnam are conducted. The study utilizes official government documents and academic literature, and incorporates feedback from experts. Result: Although the three countries had different aid management systems, following similarities were found: mid-long term national development strategies having close linkage to the aid management system; recipient countries strengthening governance and institutional basis for enhanced ownership and transparency; while the role and capacity of key implementing agencies leading to different types of aid inefficiency issues. This implies that ODA management system has direct impact on aid efficiency and effectiveness. Conclusion and Implication: The study shows the importance of donors’ approach to recipient countries based on the latter’s context. Also, in-depth understanding of recipient countries’ aid management system can help donors better identify potential areas of cooperation and design new projects through ODA. The same applies to Korea, which is a key donor in all three country cases.

The role of cities in the reconstruction of Ukraine

Throughout human history, cities have been targeted in wars due to their significance for politics, economy, communication and population. Today such Ukrainian cities as Kyiv, Mariupol, Kharkiv and Irpin can be added to this list. This commentary focuses on the long-term challenge of the reconstruction of Ukraine and the role cities may play in this process given their growing role in international decision-making processes. These roles include through the bilateral channels of the twin or sister cities system and international and national city networks. Furthermore, such reconstruction efforts are already evident in Ukraine and highlight how city support activities are not limited only to the post-conflict phase.

Analysis of budgetary decision-making in a Federal Institute of Education

This article examines the organizational model of decision-making on discretionary spending and its perceptions in a Federal Institute of Education, describing dysfunctionalities resulting from it. The research uses a secondary data analysis of budget execution, norms, minutes and other decision-making sources, as well as perceptions collected at different levels of management through a survey. Dysfunctions were identified through quantitative indicators, and predictive variables were raised through regression. Perceptions about the expenditure management model were reduced to two constructs through factor analysis: budgetary appropriatons protection and generalized apathy. These perceptions about spending management are in line with a political decision-making model characterized by anomalies and contractual failures, which manifest themselves in budgetary dysfunctions, such as rushed spending at the end of the year and excessive flexibility of expenses. Aspects of the prevailing political decision-making model result in unrealized action plans, hasty spending at the end of the fiscal year, and disproportionate use of flexibility in budget execution. The research contributed to the understanding and improvement of the internal decision-making process in bodies endowed with budgetary autonomy.

Adaptive active Brownian particles searching for targets of unknown positions

Developing behavioral policies designed to efficiently solve target-search problems is a crucial issue both in nature and in the nanotechnology of the 21st century. Here, we characterize the target-search strategies of simple microswimmers in a homogeneous environment containing sparse targets of unknown positions. The microswimmers are capable of controlling their dynamics by switching between Brownian motion and an active Brownian particle and by selecting the time duration of each of the two phases. The specific conduct of a single microswimmer depends on an internal decision-making process determined by a simple neural network associated with the agent itself. Starting from a population of individuals with random behavior, we exploit the genetic algorithm NeuroEvolution of augmenting topologies to show how an evolutionary pressure based on the target-search performances of single individuals helps to find the optimal duration of the two different phases. Our findings reveal that the optimal policy strongly depends on the magnitude of the particle’s self-propulsion during the active phase and that a broad spectrum of network topology solutions exists, differing in the number of connections and hidden nodes.

The Journal of Consumer Behaviour's first 20 years

AbstractWe analyzed and compared the Journal of Consumer Behaviour content in its first 20 years of publication. Articles were categorized into seven topics (Internal Factors, External Factors, Decision Process, Social Issues, Internet, Managerial Issues, and Research Methods) with subtopics. Authorship, citations based on Google Scholar, geographic representation of authors, and methodologies employed across the first 20 years were also explored. The results are discussed in terms of their implications for the journal and consumer behaviour. This analysis provides suggestions for future research to be published by the Journal of Consumer Behaviour.

Closer to Citizens or Ticking Boxes? Political Parties and Participatory Practices in Hungary

Political parties use participatory practices to connect with their members and with the broader electorate. While substantial research indicates how people get involved, very little is known about why political parties use participatory practices. The present article addresses this gap in the literature and seeks to identify the reasons parties do so. We elaborate an analytical framework that includes three main categories of explanations: party characteristics, internal dynamics, and external dynamics. Our analysis focuses on Hungary, explores party statutes and manifestos, and relies on 26 semi-structured interviews with party elites from ten political parties. The findings indicate that parties have several purposes in mind, which vary across parties, that are often linked to broader concerns about citizen engagement and to attempts to give members a voice in internal and external decision-making processes.

Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation for Explainable Fake News Detection

Fake news detection has become a significant topic based on the fast-spreading and detrimental effects of such news. Many methods based on deep neural networks learn clues from claim content and message propagation structure or temporal information, which have been widely recognized. However, firstly, such models ignore the fact that information quality is uneven in propagation, which makes semantic representations unreliable. Additionally, most models do not fully leverage spatial and temporal structures in combination. Finally, internal decision-making processes and results are non-transparent and unexplained. In this study, we developed a trust-aware evidence reasoning and spatiotemporal feature aggregation model for more interpretable and accurate fake news detection. Specifically, we first designed a trust-aware evidence reasoning module to calculate the credibility of posts based on a random walk model to discover high-quality evidence. Next, from the perspective of spatiotemporal structure, we designed an evidence-representation module to capture the semantic interactions granularly and enhance the reliable representation of evidence. Finally, a two-layer capsule network was designed to aggregate the implicit bias in evidence while capturing the false portions of source information in a transparent and interpretable manner. Extensive experiments on two benchmark datasets indicate that the proposed model can provide explanations for fake news detection results, and can also achieve better performance, boosting the F1-score 3.5% on average.

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput.

Two Oscillatory Correlates of Attention Control in the Alpha-Band with Distinct Consequences on Perceptual Gain and Metacognition.

Behavioral consequences and neural underpinnings of visuospatial attention have long been investigated. Classical studies using the Posner paradigm have found that visual perception systematically benefits from the use of a spatially informative cue pointing to the to-be-attended spatial location, compared with a noninformative cue. Lateralized α amplitude modulation during visuospatial attention shifts has been suggested to account for such perceptual gain. However, recent studies on spontaneous fluctuations of prestimulus α amplitude have challenged this notion. These studies showed that spontaneous fluctuations of prestimulus α amplitude were associated with the subjective appreciation of stimulus occurrence, while objective accuracy was instead best predicted by the frequency of α oscillations, with faster prestimulus α frequency accounting for better perceptual performance. Here, in male and female humans, by using an informative cue in anticipation of lateralized stimulus presentation, we found that the predictive cue not only modulates preparatory α amplitude but also α frequency in a retinotopic manner. Behaviorally, the cue significantly impacted subjective performance measures (metacognitive abilities [meta-d']) and objective performance gain (d'). Importantly, α amplitude directly accounted for confidence levels, with ipsilateral synchronization and contralateral desynchronization coding for high-confidence responses. Crucially, the contralateral α amplitude selectively predicted interindividual differences in metacognitive abilities (meta-d'), thus anticipating decision strategy and not perceptual sensitivity, probably via excitability modulations. Instead, higher perceptual accuracy both within and across participants (d') was associated with faster contralateral α frequency, likely by implementing higher sampling at the attended location. These findings provide critical new insights into the neural mechanisms of attention control and its perceptual consequences.SIGNIFICANCE STATEMENT Prior knowledge serves the anticipation of sensory input to reduce sensory ambiguity. The growing interest in the neural mechanisms governing the integration of sensory input into our internal representations has highlighted a pivotal role of brain oscillations. Here we show that distinct but interacting oscillatory mechanisms are engaged during attentional deployment: one relying on α amplitude modulations and reflecting internal decision processes, associated with subjective perceptual experience and metacognitive abilities; the other relying on α frequency modulations and enabling mechanistic sampling of the sensory input at the attended location to influence objective performance. These insights are crucial for understanding how we reduce sensory ambiguity to maximize the efficiency of our conscious experience, but also in interpreting the mechanisms of atypical perceptual experiences.