Internal Decision-making Processes Research Articles

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.

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.

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 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.

The Mediation Effect of Business Environment on How Firm Characteristics Relate to Environmental Disclosure in Tanzania's Extractive Industry

This study determines the function of the business environment as a mediating factor on how firm characteristics relate to the environmental disclosure  in the Tanzanian extractive industry through legitimacy and stakeholder theories lens. The analysis makes use of panel data from the 2018 Tanzania  Extractive Industry Transparency Initiative (TEITI) report covering the years 2004 to 2018, following the adoption of an environmental management Act  and its implementing laws in Tanzania. To extract data from yearly reports, the manifest content analysis was employed. The results show that the  relationship between corporate profitability, size, and environmental disclosure is mediated by pressure of stakeholders. Furthermore, the findings imply  that the relationship between size of firm and environmental disclosure is mediated by visibility by the media. However, the legal need does not operate  as a buffer between any firm characteristic and environmental disclosure, indicating that laws, regulations, and rules are not the only drivers of  environmental disclosure. For the first time, the study introduces, quantifies, and examines the business environment as the only justification for  environmental disclosure. The study combines legitimacy and stakeholder theories, treating businesses as entities with internal decision-making  processes that are also influenced by pressure from the outside world. The study also suggests that enhancing environmental disclosure and business  participation may not be possible with just rules or legislation. The study demonstrates that stakeholder theory works more effectively in situations when  powerful stakeholders put significant pressure on businesses to disclose environmental information. In terms of society, the study would promote  social involvement in ensuring that businesses disclose and protect the environment so that people can live in safety. 

Integrating the Decision-Making Process in the Work- Family Field: An Action-Based Approach

The aim of this article is to integrate the decision-making process in the work-family field using an action-based approach. Drawing from the theory of Pérez López, we develop a conceptual framework as a way of understanding the internal decision-making process among managers in the work-family field. We use the core concepts of his theory, motivational structure, and motivation learning, and link them with caregiving ambition and family-supportive supervisor behaviors to build our framework. This iterative model illustrates how motivational factors influence caregiving ambition and family-supportive supervisor behaviors from which motivation learning is derived, and future decisions are constantly shaped.

Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results.

Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems. Yet complexity is difficult in practice, particularly when strings are short. By numerically approximating algorithmic (Kolmogorov) complexity (K), we establish an objective tool to characterize behavioral complexity. Next, we approximate structural (Bennett's Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioral string. We apply our toolbox to three landmark studies of animal behavior of increasing sophistication and degree of environmental influence, including studies of foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition (e.g., predator-prey) strategies. We find that ants harness the environmental condition in their internal decision process, modulating their behavioral complexity accordingly. Our analysis of flight (fruit flies) invalidated the common hypothesis that animals navigating in an environment devoid of stimuli adopt a random strategy. Fruit flies exposed to a featureless environment deviated the most from Levy flight, suggesting an algorithmic bias in their attempt to devise a useful (navigation) strategy. Similarly, a logical depth analysis of rats revealed that the structural complexity of the rat always ends up matching the structural complexity of the competitor, with the rats' behavior simulating algorithmic randomness. Finally, we discuss how experiments on how humans perceive randomness suggest the existence of an algorithmic bias in our reasoning and decision processes, in line with our analysis of the animal experiments. This contrasts with the view of the mind as performing faulty computations when presented with randomized items. In summary, our formal toolbox objectively characterizes external constraints on putative models of the "internal" decision process in humans and animals.

The Globalization of Language: Exploring the Dominance and Internationalization of English

In the past 20 years, the significant trend of globalization has had a considerable impact on language. An illustration of this is the fact that English is recognized on a global scale as a dominant and mostly uncontested medium of communication. Despite this undeniable internationalization of English, the question of whether or not a translation from or into English still has a place in this fast-evolving world given the advancements in communication technology remains for the professional. This study has three components of exploration and illustration. First, despite fierce competition from other languages, it aims to establish benchmarks for the internationalization of English as a language. The following are examples of the variables that affect power in language and society: 1. Being granted access to resources, including, material, political, and fiscal. 2. Participation in the international decision-making process. 3. A willingness to introduce and deal with global technical developments, including the knowledge of superhighway and communication technology among others. The study further, establishes and does the advocacy with relevant ideas and justifications that a strong language like English can take the lead and maintain dominance in a global society due to its significant role in disseminating and facilitating information technology and the consequent tools, such as email service and the internet among others. The notion that every translation is a recreation originated with Derrda's followers, whose philosophy of deconstruction investigated every claim or fabrication through the process of tearing down the preexisting construction or framework. As a result, the term "translation" came to denote "recreation" from structuralism to deconstruction to post-structuralism. It sums up that regardless of the translator’s changeable characteristics, such as culture, language, or age, the production would always include all three aspects of reinterpretation, reappropriation, and recreation.

The Role of Strategic Management Accounting Tools in Developing Accounting Measurement and Disclosure for The Effects of Climate Changes

This research aims to study climate changes and know the international efforts made to reduce the negative effects of climate changes, and the impact of climate changes on management accounting and how to account measure and disclosure of information on greenhouse gas emissions, and to identify the role of strategic management accounting tools in developing the accounting measurement and disclosure of the effects of climate changes. The current study is the first, to the researcher’s knowledge, to specifically examine the role of strategic management accounting tools in developing accounting measurement and disclosure for the effects of climate changes. The results of the research showed that strategic management accounting, with its tools and methods, can help establishments identify and measure costs associated with climate changes, in addition to the possibility of allocating and distributing them to activities, products or the stages that cause them instead of merging them with the additional costs elements of the establishments, which methods and tools of strategic management accounting have contributed to their measurement and disclosure, and perhaps the most important of which is the sustainable balanced score card, which works to rationalize all internal and external decision-making processes related to the economic and environmental activity of these establishments.

Stable and actionable explanations of black-box models through factual and counterfactual rules

AbstractRecent years have witnessed the rise of accurate but obscure classification models that hide the logic of their internal decision processes. Explaining the decision taken by a black-box classifier on a specific input instance is therefore of striking interest. We propose a local rule-based model-agnostic explanation method providing stable and actionable explanations. An explanation consists of a factual logic rule, stating the reasons for the black-box decision, and a set of actionable counterfactual logic rules, proactively suggesting the changes in the instance that lead to a different outcome. Explanations are computed from a decision tree that mimics the behavior of the black-box locally to the instance to explain. The decision tree is obtained through a bagging-like approach that favors stability and fidelity: first, an ensemble of decision trees is learned from neighborhoods of the instance under investigation; then, the ensemble is merged into a single decision tree. Neighbor instances are synthetically generated through a genetic algorithm whose fitness function is driven by the black-box behavior. Experiments show that the proposed method advances the state-of-the-art towards a comprehensive approach that successfully covers stability and actionability of factual and counterfactual explanations.

Zwischen Universitätsreformen und katholischer Renaissance

Abstract With the first edition of the Philosophical Criticism, published in the 1870s and 1880s, Alois Riehl became the founder and most important representative of Realistic Criticism, and emerged as one of the leading figures in German-speaking philosophy at the turn of the century. In 1901, he applied for a chair at the Faculty of Philosophy at the University of Vienna. In the appointment procedure for the succession to Ernst Mach, he was chosen by the committee with the recommendation unico loco, and also confirmed by the faculty. Nevertheless, the Minister of Education, Wilhelm von Hartel, rejected the recommendation for political and ideological reasons. Using previously unpublished archival sources, the present study reconstructs Riehl’s academic career, the conditions of the appointment procedure, the internal decision-making processes, and the reasons that ultimately led to his rejection. The aim of the article is to develop a case study that, first, makes educational policy in Austria visible and, second, critically evaluates the term Austrian Philosophy that dates back to this time.

Sustainable transition and food democracy: The role of decision making process in Solidarity Purchasing Groups

This study investigates how the decision making process in Solidarity Purchasing Groups (SPGs), intended as hybrid organizations, supports the sustainability transition in food systems. The process of sustainability transition in food systems involves many kinds of tensions, especially in the process of pursuing a multiplicity of economic, social and environmental objectives. This study focuses on the SPGs in Italy and study how they organize their internal decision-making process and their search for the group objectives. This paper argues that the decentralization of the decision rights in SPGs sustains the integration of such different objectives and coordinates efficiently the multifaceted values of their members. The empirical analysis shows that the decision rights are decentralized and that the decentralized decision rights positions in solidarity purchasing groups are associated with the pursuing of different objectives. Our findings indicate that SPGs contributes to the transitions toward sustainability in food systems by using organizational democracy mechanisms to coordinate tensions among social, market and environmental values.