Investigating Responsible Nudge Design for Informed Decision-Making Enabling Transparent and Reflective Decision-Making

The coronavirus disease 2019 (COVID-19) pandemic has increased public awareness of the influence of epidemiological and economic decision models on public policy decisions. Alongside this is an increased scrutiny on the development, analysis, reporting and utilisation of decision models for public policy making. Therefore, it is important that model developers can clearly explain and justify to all stakeholders what is included and excluded from a model developed to support decision-making, to both improve transparency and trust in decision-making. Our aim is to provide tools for improving communication between modellers and decision-makers, leading to improved transparency in decision-making. To do so, we extend the recently described directed acyclic graphs with omitted objects displayed (DAGWOOD) approach from Haber et al. (Ann Epidemiol 68:64-71, 2022) to decision analytic models, giving the decision analytic models with omitted objects displayed (DAMWOOD) approach. DAMWOOD is a framework for the identification of objects omitted from a decision model, as well as for consideration of the effects of omissions on model outcomes. Objects omitted from a decision model are classed as either an exclusion (known and unknown confounders), misdirection (alternative model pathways) or structure (e.g. model type, methods for estimating relationships between objects). DAMWOOD requires model developers to use explicit statements and provide illustration of included and omitted objects, supporting communication with model users and stakeholders, allowing them to provide input and feedback to modellers about which objects to include or omit in a model. In developing DAMWOOD, we considered two challenges we encountered in modelling for pandemic policy response. First, the scope of the decision problem is not always made sufficiently explicit by decision-makers, requiring modellers to intuit which policy options should be considered, and/or which outcomes should be considered in their evaluation. Second, there is rarely sufficient transparency to ensure stakeholders can see what is included in models and why. This limits stakeholders' ability to advocate to decision-makers for the prioritisation of specific outcomes and challenge the model results. To illustrate the application of DAMWOOD, we apply it to a previously published COVID-19 vaccine allocation optimisation model. The DAMWOOD diagrams illustrate the ways in which it is possible to improve the communication of model assumptions. The diagrams make explicit which outcomes are omitted and provide information on the expected impact of the omissions on model results. We discuss the usefulness of DAMWOOD for framing the decision problem, communicating the model structure and results and engaging with those making and affected by the decisions the model is developed to inform.

The article explores the key role of political elites in shaping civil society in Ukraine. It analyzes the impact of political elites on the development of democratic institutions, their ability to engage in institutional interaction with civil organizations, and their participation in ensuring democratic transformations. It is established that political elites act as key agents determining the direction of political transformations. However, their influence is often accompanied by challenges such as corporate interests, corrupt practices, and limited transparency in decision-making processes. The stages of the development of political elites since Ukraine’s independence are examined. In the early stages, they consisted of former post-Soviet cadres, which slowed the democratization process. After the Orange Revolution, and especially following the 2014 Revolution of Dignity, the political elite underwent a renewal, contributing to greater transparency and openness in decision-making. The study reveals that the influence of oligarchic structures remains a significant factor that complicates the implementation of reforms. The role of public consultations and open meetings with the public in the democratization of political processes is analyzed. It is found that these mechanisms are important for engaging citizens in key political decision-making, but their effectiveness is often limited by the internal pressure of political elites’ interests. The research concludes that sustainable democratic development requires further improvement of mechanisms for citizen participation in the political process, increased transparency and accountability of the government, and enhanced public oversight of political elites. It is suggested that digital platforms be more actively only expand opportunities for citizen participation in political life but also provide timely feedback between the authorities and civil society. It is identified that such platforms could serve as effective tools for improving transparency in decisionmaking processes, promoting the openness of political elites, and strengthening public control over their activities. It is argued that achieving sustainable democratic development necessitates balancing the interests of political elites and civil society by creating inclusive mechanisms for discussion and decision-making. The article concludes that future research should focus on examining specific mechanisms of interaction between political elites and civil society and analyzing their effectiveness during political crises.

Transparency in decision-making, involving the establishment of explicit goals and objectives combined with open, facilitative procedures, has recently been advocated as an important principle for effective environmental impact assessment (EIA). This paper examines recent changes to EIA practice in Western Australia that emphasise clear procedures for decision-making. Current practices focus on objectives established for relevant environmental factors identified during the screening and scoping stages of EIA. These objectives are then used as decision criteria for project decision-making following public review. An example from a recent assessment is provided. Some strengths and weaknesses of this approach to transparent EIA decision-making are also examined.

A growing demand for transparency and accountability in local decision-making has grown substantially in recent years due to an increasing awareness of finite natural resources. Sustainable development captures the interrelatedness of the economy, the environment and social expectations. In addition, transparency in the decision-making process provides the public a method of accountability for public funds. The Capital Structures Model developed in this paper addresses the demand for accountability by focusing on local development projects classified by type of capital (built, human, social and natural) in order to provide information to the average constituent regarding specific county and community development projects. Local assets are initially categorized according to the type of capital to which they most contribute, and then indicators are employed to measure the contribution of a project with respect to increasing the capacity of each type of capital. These indicators are then cross-referenced with local policy to demonstrate how that policy is implemented in local development projects, and which policies contribute to which type of capital. It is the intention of this model to create an informative tool for planners and constituencies that monitors how development projects contribute to communities in terms of each of the four types of capital.

The scholarship recipient selection process often faces challenges such as subjectivity, time constraints, and a lack of transparency in decision-making. To address these issues, this study developed a web-based decision support system that implements the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method. This system is designed to assist scholarship administrators in determining recipients objectively and efficiently by considering several criteria, namely GPA, parental income, number of dependents, and non-academic achievements. The research was conducted using a quantitative approach that included the stages of problem identification, data collection, criterion weight determination, data normalization, calculation of Yi preference values, and alternative ranking. The results of the system implementation indicate that the MOORA method can provide accurate rankings based on the combination of criterion weights and the value of each alternative. This system not only accelerates the selection process but also enhances transparency and accountability in decision-making. In conclusion, the application of the MOORA method in the scholarship decision support system contributes significantly to a fair, structured, and accountable selection process.

The content of the article explores transparency in decision-making as an important factor in strengthening the relationship of public administration - citizens, stressing the need for a trial open and accessible decision-making for all actors involved. In this context, the aim of the research is demonstrate the importance of the transparency in decision-making and strengthening public administration dialogue with citizens. Theoretical and methodological support of the article is based on the studies edited by the Institute of Public Administration of the USM, the various issues elucidated by IDIS "Viitorul" and public policy issues analyzed by Expert-Grup. The methods applied have revealed numerous gaps in low participation of citizens to the decision-making process. Identification of solutions will represent the value of research and practical utility - transparency in the decision-making process is the decisionmaking pillar, and to be strengthened we need the openness of public administration, access to information and participatory will.

This study explores the development of an Open Artificial Intelligence (AI) decision-making solution, integrating blockchain technology with artificial intelligence to streamline organizational decision-making processes. Blockchain’s characteristics of transparency, incorruptibility, and decentralized validation are leveraged to build a platform that ensures secure and transparent decision-making. The platform’s architecture integrates a user-friendly frontend with a robust backend, enabling users to create accounts, manage tasks, participate in voting, and make collaborative decisions. The backend processes, including user authentication, error handling, and secure data management, ensure privacy and integrity throughout the decision lifecycle. The implementation details include organization management, task assignments, voting mechanisms, and profile management features, each facilitated through a user-friendly frontend interface. Workflow diagrams and a case study at DADWORD IT demonstrate the platform’s efficiency in handling complex decision-making processes while maintaining user engagement and data security. In conclusion, the developed platform demonstrates the ability of AI and blockchain technologies to improve collaborative decision-making, offering a secure and scalable solution for organizational management. The system can be adapted to various industries where transparency, accuracy, and efficient decision-making are crucial. Future work may explore further AI integration to refine decision support and predictive functionalities.

The article examines modern European strategies of intersectoral cooperation in public administration, with a focus on the project approach. It discusses how these strategies promote innovation, efficiency, and transparency in decision-making. By combining theoretical perspectives with practical examples, the research identifies key mechanisms that support collaboration among public, private, and civil sectors, contributing to improved governance. Special emphasis is placed on the project approach, which fosters innovation, enhances efficiency, and ensures transparency in decision-making. These principles are crucial for improving cooperation between sectors in line with societal and organizational goals. The research examines various forms of cross-sectoral collaboration, including public-private partnerships, public-public partnerships, and tripartite partnerships, which bring together collective efforts to address complex challenges. European strategies highlight the significance of transparency in decision-making by ensuring stakeholders have access to relevant information, monitoring outcomes closely, and upholding accountability. The integration of advanced technologies such as digitalization and AI further enhances these initiatives. These technologies promote efficient resource use and facilitate real-time data sharing, enhancing sector communication. The project-based approach is recognized as an essential mechanism for addressing complex governance challenges. This method promotes collaborative planning, where responsibilities and roles are well-defined, ensuring shared accountability for risks and benefits. It brings stakeholders together around shared goals, enhancing resource distribution and encouraging creative solutions to evolving challenges. This approach facilitates the efficient management of large-scale projects that require coordination across various sectors. European strategies for cross-sectoral cooperation emphasize the importance of collaboration among public, private, and civil sectors to tackle complex problems. By aligning efforts effectively, they ensure the long-term success and flexibility of initiatives, helping build more resilient public administration systems capable of addressing global challenges and adapting to changing circumstances.

The recognition of transparency as an essential element of good governance is very important for new democracies because it leads to greater public support for their governments' economic and political decisions. This has been clearly understood by the countries in the Western Balkans which following the fall of communism entered a path of rapid democratization struggling to be opened and transparent. This paper explores transparency and open government in Albania looking at how the Albanian legal administrative framework and practices guarantee the principle of transparency in decision-making and the role the EU in complying with this principle. It introduces the concept of 'transparency through integration' as a model which encapsulates the transparency developments in Albania and Western Balkans and looks at how transparency can be transformed from a principle of good governance to a legalistic instrument holding a place in the hierarchy of legal norms. This research focuses on how transparency becomes part of a policy paradigm which can transform a country's politics from secretive and authoritarian to transparent and democratic. In addition, it suggests that the EU has a role to play as a transformative power to induce positive reforms and improve transparency in the decision-making in Albania.

IntroductionEnsuring accessible and affordable healthcare continues to be a key priority in low- and middle-income countries, including Ethiopia. To enhance healthcare coverage and provide financial protection, especially for vulnerable populations such as women, community-based health insurance (CBHI) schemes have been introduced.Despite these efforts, dropout rates from the program remain a challenge, threatening its long-term sustainability. This study investigated determinants of CBHI dropout among women in Sidama Regional State, Ethiopia.MethodsA community-based cross-sectional study was conducted between December 15, 2023, and January 12, 2024, involving 835 women aged 18 years and above in the central zone of Sidama. Participants were selected via a multistage sampling technique. Data were collected through structured, pre-tested face-to-face interviews encompassing demographic, household, and scheme-related factors.Multilevel logistic regression analysis was performed using Stata version 17 to identify factors associated with CBHI dropout. Adjusted Odds Ratios (AOR) with 95% Confidence Intervals (CI) were reported, with statistical significance set at p < 0.05.FindingsAmong the 845 women sampled, 835 participated in the interviews with a response rate of 98.8%. Of these, 77 women (9%) dropped from the CBHI scheme (AOR = 9; 95% CI: 7.43% −11.38%; p < 0.001). Lower dropout likelihood was significantly associated with increased age (AOR = 0.93; 95% CI: 0.89–0.97; p < 0.001) and larger family size (AOR = 0.28; 95% CI: 0.17–0.50; p < 0.001).Conversely, reduced transparency in decision-making (AOR = 2.05; 95% CI: 1.39–3.00; p < 0.001) and insufficient advocacy (AOR = 3.01; 95% CI: 2.29–7.00; p = 0.011) were positively associated with dropout. At the community level, residing in low-poverty areas (AOR = 0.29; 95% CI: 0.09–0.98; p = 0.046) and high-autonomy communities (AOR = 0.14; 95% CI: 0.04–0.50; p = 0.002) were associated with reduced dropout rates.ConclusionThis study identified factors linked with women’s dropout from the CBHI program that threaten sustainability. Lower dropout rates were linked with older age and larger family size, while poor transparency in decision-making and weak advocacy were associated with increased dropout rates. Communities residing in low-poverty and high-autonomy areas were also associated with decreased dropouts.

ABSTRACTThe paper examines Newport City Council’s Fairness Commission’s (NFC) understanding of fairness, alongside a survey of Newport citizens’ views on fairness. These views focus on two parameters of debate identified by the NFC – equality versus differential treatment, and the accountability and transparency of decision-making – reflecting competing interpretations of the political concept of fairness, and as explored by W.B. Gallie. Moreover, these contested interpretations also have a profound bearing on post-1945 debates about citizenship instigated by T.H. Marshall. While many contemporary policy-recommenders and politicians reject Marshallesque social rights to citizenship, dismissing these rights as encouraging so-called passive conceptions of citizenship emphasising unconditional individual entitlements to local services, the views of Newport citizens tend to broadly support these rights. These rights are distinct from ‘active’ conceptions, emphasising the values of interdependency and reciprocity, and citizens’ obligations to positively participate in community life which then underpin conditions for receiving entitlements.

This study explored academics’ and institutional leaders’ perception of transparency in decision-making in Ethiopian higher education institutions. Three universities were selected from the 31 public universities using a stratified sampling technique. The study employed a mixed-method exploratory research design. Descriptive statistics (mean and standard deviation) and inferential statistics (ANOVA and Tukey’s post-hoc test) were used to analyse the quantitative data while thematic analysis was used to identify emerging themes and meanings within qualitative data. The findings of this study showed that the Ethiopian public universities have hierarchal (top-down) governance lacking in transparency in decision-making. Academics and institutional leaders called for greater transparency in the flow of information and decision-making for the academic community to accomplish their academic core activities effectively. Transparency is a key element of the university governance, and we argue that a lack of transparency at the university upper levels of the university will discourage transparency at lower levels. We recommend shift to more shared governance or consultative governance for mutual accommodation if the Ethiopian universities are to become transparent and optimise their contribution to national development.

The increasing adoption of artificial intelligence(AI) in financial services has raised concerns about the transparency of product recommender systems. This study investigates how explanations in personalized product recommendations influence users’ perceived transparency and purchase decisions to help customers make informed decisions and improve product adoption. This research builds on concepts from Explainable Artificial Intelligence (XAI) and Dual-Process Theory. It draws from these theories to understand how users respond to financial product suggestions and how explanations support decision-making. The methodology adopted a comparative experimental design with a within-subject approach. Participants from different backgrounds interacted with the system versions. Structured questionnaires were administered to gather responses on perceived transparency and purchase decisions using a 5-point Likert scale. Statistical analysis included descriptive statistics and the Mann-Whitney U test. Results revealed that users perceived the Personalized Product Recommender (PPR) system with explanations as significantly more transparent (U1 = 2279.5, U2 = 2837, U3 = 3192, p &lt; 0.05) and were more likely to act on the recommendations (U = 2889, p &lt; 0.05). Explanations improved transparency and trust, supporting informed purchase decisions. This research provides insight for designing transparent, user-aligned financial recommender models, which implies improved customer experience strategies and AI decision support. This study presents a working explanation framework with real user evaluation, revealing its value for advancing transparency in financial services. Keywords: Personalized Product Recommender(PPR) System, Transparent explanations, Decision-making, Artificial Intelligence(AI) in Financial Services, Explainable Artificial Intelligence(XAI). Ene U.M., Bakpo F.S., Ebem D.U., Udanor C.N. &amp; Eneh A.H. (2025): Enhancing Perceived Transparency and Purchase Decisions through Explanations in Personalized Product Recommendations. Journal of Advances in Mathematical &amp; Computational Science. Vol. 13, No. 2. Pp 53-66. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V13N2P5

TIDI Addresses Uncertainty But Not Indeterminism

Catheter ablation has emerged as an effective treatment strategy for atrial fibrillation (AF) in recent years. During AF, complex fractionated atrial electrograms (CFAE) can be recorded and are known to be a potential target for ablation. Automatic algorithms have been developed to simplify CFAE detection, but they are often based on a single descriptor or a set of descriptors in combination with sharp decision classifiers. However, these methods do not reflect the progressive transition between CFAE classes. The aim of this study was to develop an automatic classification algorithm, which combines the information of a complete set of descriptors and allows for progressive and transparent decisions. We designed a method to automatically analyze CFAE based on a set of descriptors representing various aspects, such as shape, amplitude and temporal characteristics. A fuzzy decision tree (FDT) was trained and evaluated on 429 predefined electrograms. CFAE were classified into four subgroups with a correct rate of 81±3%. Electrograms with continuous activity were detected with a correct rate of 100%. In addition, a percentage of certainty is given for each electrogram to enable a comprehensive and transparent decision. The proposed FDT is able to classify CFAE with respect to their progressive transition and may allow objective and reproducible CFAE interpretation for clinical use.