Search results for: decision making loop

Authors: Dinamarie Fonzone

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Employer-sponsored health insurance (ESI) strategic decision-making processes rely on financial analysis to guide leadership in choosing plans that will produce optimal organizational spending outcomes. These financial decision-making methods have not abated ESI costs. Previously unrecognized external social determinants, the impact on ESI plan spending, and other organizational strategies are emerging and are important considerations for organizational decision-makers and change management practitioners. The purpose of thisstudy is to examine the relationship between the social determinants of health (SDoH), employer-sponsored health insurance (ESI) plans, andthe unintended consequence of health inequity. A quantitative research design using selectemployee records from an existing employer human capital management database will be analyzed. Statistical regressionmethods will be used to study the relationships between certainSDoH (employee income, neighborhood geographic living area, and health care access) and health plan utilization, cost, and chronic disease prevalence. The discussion will include an application of the social gradient of health theory to the study findings, organizational transformation through changes in ESI decision-making mental models, and the connection of ESI health inequity to organizational development and changediversity, equity, and inclusion strategies.

Keywords: employer-sponsored health insurance, social determinants of health, health inequity, mental models, organizational development, organizational change, social gradient of health theory

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Authors: Sonia Anand Knowlton

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There are some cases where we as a society feel deeply uncomfortable with the use of Artificial Intelligence (AI) tools in the judicial decision-making process, and justifiably so. A perfect example is COMPAS, an algorithmic model that predicts recidivism rates of offenders to assist in the determination of their bail conditions. COMPAS turned out to be extremely racist: it massively overpredicted recidivism rates of Black offenders and underpredicted recidivism rates of white offenders. At the same time, there are certain uses of AI in the judicial decision-making process that many would feel more comfortable with and even support. Take, for example, a “super-breathalyzer,” an (albeit imaginary) tool that uses AI to deliver highly detailed information about the subject of the breathalyzer test to the legal decision-makers analyzing their drunk-driving case. This article evaluates the point at which a judge’s use of AI tools begins to undermine the public’s trust in the administration of justice. It argues that the answer to this question depends on whether the AI tool is in a role in which it must perform a moral evaluation of a human being.

Keywords: artificial intelligence, judicial reasoning, morality, technology, algorithm

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Authors: Reza Karami, Ali Akbar Ebrahimi

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This paper discusses concurrent engineering of robot manipulators, based on the Holistic Concurrent Design (HCD) methodology and by using a hardware-in-the-loop simulation platform. The methodology allows for considering numerous design variables with different natures concurrently. It redefines the ultimate goal of design based on the notion of satisfaction, resulting in the simplification of the multi-objective constrained optimization process. It also formalizes the effect of designer’s subjective attitude in the process. To enhance modeling efficiency for both computation and accuracy, a hardware-in-the-loop simulation platform is used, which involves physical joint modules and the control unit in addition to the software modules. This platform is implemented in the HCD design architecture to reliably evaluate the design attributes and performance super criterion during the design process. The resulting overall architecture is applied to redesigning kinematic, dynamic and control parameters of an industrial robot manipulator.

Keywords: concurrent engineering, hardware-in-the-loop simulation, robot manipulator, multidisciplinary systems, mechatronics

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Authors: Abdelhalim Kessal

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This paper treats a design of combined control of a single phase power factor correction (PFC). The strategy of the proposed control is based on two parts, the first, for the outer loop (DC output regulated voltage), and the second govern the input current of the converter in order to achieve a sinusoidal form in phase with the grid voltage. Two kinds of regulators are used, Fuzzy controller for the outer loop and predictive controller for the inner loop. The controllers are verified and discussed through simulation under MATLAB/Simulink platform. Also an experimental confirmation is applied. Results present a high dynamic performance under various parameters changes.

Keywords: boost converter, harmonic distortion, Fuzzy, predictive, unity power factor

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Authors: Christoph Ostermair

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We deal with the effect of correlation between prospects on human decision making under uncertainty as proposed by the comparatively new and promising model of “salience theory of choice under risk”. In this regard, we show that the theory entails the prediction that the inconsistency of choices, known as the Allais paradox, should not be an issue in the context of “real-life decision making”, which typically corresponds to situations of subjective uncertainty. The Allais paradox, probably the best-known anomaly regarding expected utility theory, would then essentially have no practical relevance. If, however, empiricism contradicts this prediction, salience theory might suffer a serious setback. Explanations of the model for variable human choice behavior are mostly the result of a particular mechanism that does not come to play under perfect correlation. Hence, if it turns out that correlation between prospects – as typically found in real-world applications – does not influence human decision making in the expected way, this might to a large extent cost the theory its explanatory power. The empirical literature regarding the Allais paradox under subjective uncertainty is so far rather moderate. Beyond that, the results are hard to maintain as an argument, as the presentation formats commonly employed, supposably have generated so-called event-splitting effects, thereby distorting subjects’ choice behavior. In our own incentivized experimental study, we control for such effects by means of two different choice settings. We find significant event-splitting effects in both settings, thereby supporting the suspicion that the so far existing empirical results related to Allais paradoxes under subjective uncertainty may not be able to answer the question at hand. Nevertheless, we find that the basic tendency behind the Allais paradox, which is a particular switch of the preference relation due to a modified common consequence, shared by two prospects, is still existent both under an event-splitting and a coalesced presentation format. Yet, the modal choice pattern is in line with the prediction of salience theory. As a consequence, the effect of correlation, as proposed by the model, might - if anything - only weaken the systematic choice pattern behind the Allais paradox.

Keywords: Allais paradox, common consequence effect, models of decision making under risk and uncertainty, salience theory

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Authors: Sylvie Michel, Emmanuelle Gagnou, Joanne Hamet

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Researchers in the fields of AI and Robotics frequently encounter ethical dilemmas throughout their research endeavors. Various ethical challenges have been pinpointed in the existing literature, including biases and discriminatory outcomes, diffusion of responsibility, and a deficit in transparency within AI operations. This research aims to pinpoint these ethical quandaries faced by researchers and shed light on the mechanisms behind ethical decision-making in the research process. By synthesizing insights from existing literature and acknowledging prevalent shortcomings, such as overlooking the heterogeneous nature of decision-making, non-accumulative results, and a lack of consensus on numerous factors due to limited empirical research, the objective is to conceptualize and validate a model. This model will incorporate influences from individual perspectives and situational contexts, considering potential moderating factors in the ethical decision-making process. Qualitative analyses were conducted based on direct observation of an AI/Robotics research team focusing on collaborative robotics for several months. Subsequently, semi-structured interviews with 16 team members were conducted. The entire process took place during the first semester of 2023. Observations were analyzed using an analysis grid, and the interviews underwent thematic analysis using Nvivo software. An initial finding involves identifying the ethical challenges that AI/robotics researchers confront, underlining a disparity between practical applications and theoretical considerations regarding ethical dilemmas in the realm of AI. Notably, researchers in AI prioritize the publication and recognition of their work, sparking the genesis of these ethical inquiries. Furthermore, this article illustrated that researchers tend to embrace a consequentialist ethical framework concerning safety (for humans engaging with robots/AI), worker autonomy in relation to robots, and the societal implications of labor (can robots displace jobs?). A second significant contribution entails proposing a model for ethical decision-making within the AI/Robotics research sphere. The model proposed adopts a process-oriented approach, delineating various research stages (topic proposal, hypothesis formulation, experimentation, conclusion, and valorization). Across these stages and the ethical queries, they entail, a comprehensive four-point comprehension of ethical decision-making is presented: recognition of the moral quandary; moral judgment, signifying the decision-maker's aptitude to discern the morally righteous course of action; moral intention, reflecting the ability to prioritize moral values above others; and moral behavior, denoting the application of moral intention to the situation. Variables such as political inclinations ((anti)-capitalism, environmentalism, veganism) seem to wield significant influence. Moreover, age emerges as a noteworthy moderating factor. AI and robotics researchers are continually confronted with ethical dilemmas during their research endeavors, necessitating thoughtful decision-making. The contribution involves introducing a contextually tailored model, derived from meticulous observations and insightful interviews, enabling the identification of factors that shape ethical decision-making at different stages of the research process.

Keywords: ethical decision making, artificial intelligence, robotics, research

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Authors: Katarina Rogulj, Ivana Racetin, Jelena Kilic

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In this study, the fuzzy logic approach (FLA) was developed for construction project management (CPM) under uncertainty and duality. The focus was on decision making in selecting the type of the glass facade for a residential-commercial building in the main design. The adoption of fuzzy sets was capable of reflecting construction managers’ reliability level over subjective judgments, and thus the robustness of the system can be achieved. An α-cuts method was utilized for discretizing the fuzzy sets in FLA. This method can communicate all uncertain information in the optimization process, taking into account the values of this information. Furthermore, FLA provides in-depth analyses of diverse policy scenarios that are related to various levels of economic aspects when it comes to the construction projects' valid decision making. The developed approach is applied to CPM to demonstrate its applicability. Analyzing the materials of glass facades, variants were defined. The development of the FLA for the CPM included relevant construction projec'ts stakeholders that were involved in the criteria definition to evaluate each variant. Using fuzzy Decision-Making Trial and Evaluation Laboratory Method (DEMATEL) comparison of the glass facade was conducted. This way, a rank, according to the priorities for inclusion into the main design, of variants is obtained. The concept was tested on a residential-commercial building in the city of Rijeka, Croatia. The newly developed methodology was then compared with the existing one. The aim of the research was to define an approach that will improve current judgments and decisions when it comes to the material selection of buildings facade as one of the most important architectural and engineering tasks in the main design. The advantage of the new methodology compared to the old one is that it includes the subjective side of the managers’ decisions, as an inevitable factor in each decision making. The proposed approach can help construction projects managers to identify the desired type of glass facade according to their preference and practical conditions, as well as facilitate in-depth analyses of tradeoffs between economic efficiency and architectural design.

Keywords: construction projects management, DEMATEL, fuzzy logic approach, glass façade selection

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Authors: Zahra Hamedani

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Many wonderful technological developments in recent years has opened up the possibility of using intelligent buildings for a number of important applications, ranging from minimizing resource usage as well as increasing building efficiency to maximizing comfort, adaption to inhabitants and responsiveness to environmental changes. The concept of an intelligent building refers to the highly embedded, interactive environment within which by exploiting the use of artificial intelligence provides the ability to know its configuration, anticipate the optimum dynamic response to prevailing environmental stimuli, and actuate the appropriate physical reaction to provide comfort and efficiency. This paper contains a general identification of the intelligence paradigm and its impacts on the architecture arena, that with examining the performance of artificial intelligence, a mechanism to analyze and finally for decision-making to control the environment will be described. This mechanism would be a hierarchy of the rational agents which includes decision-making, information, communication and physical layers. This multi-agent system relies upon machine learning techniques for automated discovery, prediction and decision-making. Then, the application of this mechanism regarding adaptation and responsiveness of intelligent building will be provided in two scales of environmental and user. Finally, we review the identifications of sustainability and evaluate the potentials of intelligent building systems in the creation of sustainable architecture and environment.

Keywords: artificial intelligence, intelligent building, responsiveness, adaption, sustainability

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Authors: M. Mohan, J. P. Roise, G. P. Catts

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Despite living in an era where conservation zones are de-facto the central element in any sustainable wildlife management strategy, we still find ourselves grappling with several pareto-optimal situations regarding resource allocation and area distribution for the same. In this paper, a multi-objective decision making (MODM) model is presented to answer the question of whether or not we can establish mutual relationships between these contradicting objectives. For our study, we considered a Red-cockaded woodpecker (Picoides borealis) habitat conservation scenario in the coastal plain of North Carolina, USA. Red-cockaded woodpecker (RCW) is a non-migratory territorial bird that excavates cavities in living pine trees for roosting and nesting. The RCW groups nest in an aggregation of cavity trees called ‘cluster’ and for our model we use the number of clusters to be established as a measure of evaluating the size of conservation zone required. The case study is formulated as a linear programming problem and the objective function optimises the Red-cockaded woodpecker clusters, carbon retention rate, biofuel, public safety and Net Present Value (NPV) of the forest. We studied the variation of individual objectives with respect to the amount of area available and plotted a two dimensional dynamic graph after establishing interrelations between the objectives. We further explore the concept of interdependency by integrating the MODM model with GIS, and derive a raster file representing carbon distribution from the existing forest dataset. Model results demonstrate the applicability of interdependency from both linear and spatial perspectives, and suggest that this approach holds immense potential for enhancing environmental investment decision making in future.

Keywords: conservation, interdependency, multi-objective decision making, red-cockaded woodpecker

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Authors: Jitender Sodhi, Shweta Talati, A. K. Gupta, Pankaj Arora

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Background:Equipment donation to hospitals in resource-limited settings can significantly benefit services in these settings albeit requires important ethical, practical and financial issues to be considered before accepting donations. Objective: To understand the decision making process leading to acceptance/ rejection/ deferment of equipment donation from the perspective of a public sector teaching tertiary care hospital. Design: Retrospective, record based study. Setting: 2000-bedded public sector teaching tertiary care hospital in North India. Methods: A total of 30 cases of equipment donation from March 2010-October 2013, were analysed for their decision process leading to acceptance/rejection/deferment.Each case was studied retrospectively and data pertaining to the agenda and decision taken was collected. Results: A total of 30 cases of equipment donation received from March 2010- October 2013 were screened, out of which 17 (56.6%) were for diagnostic purpose and 13 (43.3%) for therapeutic purpose. Out of 30 cases, 16 (53.3%) were accepted and 8 (26.6%) were rejected. The remaining 6 cases included 3 (10%) which required further clarification and other 3 (10%) which were out of the domain of committee. Conclusion: This study highlights the importance of equipment donation in resource limited settings and considerations involved while making decisions for acceptance/rejections/defermentof such donations.

Keywords: equipment donation, teaching hospital, decision-making, North India

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Authors: Ritsuko Kawasaki, Takeshi Hiromatsu

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Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Therefore, this paper provides a model which supports the selection of measures by applying multi-objective analysis to find an optimal solution. Additionally, a list of measures is also provided to make the selection easier and more effective without any leakage of measures.

Keywords: information security risk treatment, selection of risk measures, risk acceptance, multi-objective optimization

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Authors: Serhat Tüzün, Tufan Demirel

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Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: Boukrida Rafiq

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Democracy is based on the notion that all citizens have the right to participate in the managing of political affairs and that every citizens input is of equal importance. This basic assumption clearly places emphasis on public participation in maintaining a stable democracy. The level of public participation, however is highly contested with many theorists arguing that too much public participation would overwhelm and ultimately cripple democratic systems. On the other hand, others who favor high levels of participation argue that more citizen involvement leads to greater representation. Regardless of these disagreements over the utopian level of participation, there is widespread agreement amongst scholars that, at the very least, some participation is necessary to maintain democratic systems. The ways in which citizens participate vary greatly and depending on the method used, influence political decision making at varying levels. The method of political participation is a key in controlling public influence over political affairs and therefore is also an integral part of maintaining democracy, whether it be "thin" (low levels of participation) or "Robust" (high levels of participation). High levels of participation or "robust" democracy are argued by some theorists to enhance democracy through providing the opportunity for more issues to be represented during decision making. The notion of widespread participation was first advanced by classical theorists.

Keywords: assumption clearly places emphasis, ultimately cripple, influence political decision making at varying, classical theorists

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Authors: Faruk Aras, Melih Inal, Tansel Cinar

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The coordination of airport staff especially in the operations and maintenance departments is important for the airport operation. As a result, this coordination will increase the efficiency in all operation. Therefore, a Collaborative Decision-Making (CDM) system targets on improving the overall productivity of all operations by optimizing the use of resources and improving the predictability of actions. Enlarged productivity can be of major benefit for all airport operations. It also increases cost-efficiency. This study explains how predictive maintenance using IoT (Internet of Things), predictive operations and the statistical data such as Mean Time To Failure (MTTF) improves airport terminal operations and utilize airport terminal equipment in collaboration with collaborative decision making system/Airport Operation Control Center (AOCC). Data generated by the predictive maintenance methods is retrieved and analyzed by maintenance managers to predict when a problem is about to occur. With that information, maintenance can be scheduled when needed. As an example, AOCC operator would have chance to assign a new gate that towards to this gate all the equipment such as travellator, elevator, escalator etc. are operational if the maintenance team is in collaboration with AOCC since maintenance team is aware of the health of the equipment because of predictive maintenance methods. Applying predictive maintenance methods based on analyzing the health of airport terminal equipment dramatically reduces the risk of downtime by on time repairs. We can classify the categories as high priority calls for urgent repair action, as medium priority requires repair at the earliest opportunity, and low priority allows maintenance to be scheduled when convenient. In all cases, identifying potential problems early resulted in better allocation airport terminal resources by AOCC.

Keywords: airport, predictive maintenance, collaborative decision-making system, Airport Operation Control Center (AOCC)

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Authors: B. Güney, Ç. Teke

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In recent years, rapid and correct decision making is crucial for both people and enterprises. However, uncertainty makes decision-making difficult. Fuzzy logic is used for coping with this situation. Thus, fuzzy linear programming models are developed in order to handle uncertainty in objective function and the constraints. In this study, a problem of a factory in food industry is investigated, required data is obtained and the problem is figured out as a fuzzy linear programming model. The model is solved using Zimmerman approach which is one of the approaches for fuzzy linear programming. As a result, the solution gives the amount of production for each product type in order to gain maximum profit.

Keywords: food industry, fuzzy linear programming, fuzzy logic, linear programming

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Authors: Dhiraj Neupane

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Decentralized policy and practices have largely concentrated on the transformation of decision-making authorities from central to local institutions (or people) in the developing world. Such policy and practices always aimed for the equitable and efficient management of resources in the line of poverty reduction. The transformation of forest decision-making autonomy has also glorified as the best forest management alternatives to maximize the forest benefits and improve the livelihood of local people living nearby the forests. However, social heterogeneity and poor decision-making capacity of local institutions (or people) pose a nexus while managing the resources and sharing the forest benefits among the user households despite the policy objectives. The situation is severe in the lowland of Nepal, where forest resources have higher economic potential and user households have heterogeneous socio-economic conditions. The study discovered that utilizing the power of decision-making autonomy, user households were putting low values of timber considering the equitable access of timber to all user households as it is the most valuable product of community forest. Being the society is heterogeneous by socio-economic conditions, households of better economic conditions were always taking higher amount of forest benefits. The low valuation of timber has negative consequences on equitable benefit sharing and poor support to livelihood improvement of user households. Moreover, low valuation has possibility to increase the local demands of timber and increase the human pressure on forests.

Keywords: decentralized forest policy, Nepal, poverty, social heterogeneity, Terai

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Authors: Varsha Prasad, S. Sandya

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Cosmic Ray effects in microelectronics such as single event effect (SET) and total dose ionization (TID) have been of major concern in space electronics since 1970. Advanced CMOS technologies have demonstrated reduced sensitivity to TID effect. However, charge pump Phase Locked Loop is very much vulnerable to single event transient effect. This paper presents an SET analysis model, where the SET is modeled as a double exponential pulse. The time domain analysis reveals that the settling time of the voltage controlled oscillator (VCO) depends on the SET pulse strength, setting the time constant and the damping factor. The analysis of the proposed SET analysis model is confirmed by the simulation results.

Keywords: charge pump, phase locked loop, SET, VCO

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Authors: M. Pushparani, A. Sagaya

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Embedded systems have emerged as important elements in various domains with extensive applications in automotive, commercial, consumer, healthcare and transportation markets, as there is emphasis on intelligent devices. On the other hand, Business Intelligence (BI) has also been extensively used in a range of applications, especially in the agriculture domain which is the area of this research. The aim of this research is to create a framework for Embedded Weight Comparison Algorithm with Business Intelligence (EWCA-BI). The weight comparison algorithm will be embedded within the plantation management system and the weighbridge system. This algorithm will be used to estimate the weight at the site and will be compared with the actual weight at the plantation. The algorithm will be used to build the necessary alerts when there is a discrepancy in the weight, thus enabling better decision making. In the current practice, data are collected from various locations in various forms. It is a challenge to consolidate data to obtain timely and accurate information for effective decision making. Adding to this, the unstable network connection leads to difficulty in getting timely accurate information. To overcome the challenges embedding is done on a portable device that will have the embedded weight comparison algorithm to also assist in data capture and synchronize data at various locations overcoming the network short comings at collection points. The EWCA-BI will provide real-time information at any given point of time, thus enabling non-latent BI reports that will provide crucial information to enable efficient operational decision making. This research has a high potential in bringing embedded system into the agriculture industry. EWCA-BI will provide BI reports with accurate information with uncompromised data using an embedded system and provide alerts, therefore, enabling effective operation management decision-making at the site.

Keywords: embedded business intelligence, weight comparison algorithm, oil palm plantation, embedded systems

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Authors: Jacquelyn Burkell, Jane Bailey

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Artificial intelligence (AI) solutions deployed within the justice system face the critical task of providing acceptable explanations for decisions or actions. These explanations must satisfy the joint criteria of public and professional accountability, taking into account the perspectives and requirements of multiple stakeholders, including judges, lawyers, parties, witnesses, and the general public. This research project analyzes and integrates two existing literature on explanations in order to propose guidelines for explainable AI in the justice system. Specifically, we review three bodies of literature: (i) explanations of the purpose and function of 'explainable AI'; (ii) the relevant case law, judicial commentary and legal literature focused on the form and function of reasons for judicial decisions; and (iii) the literature focused on the psychological and sociological functions of these reasons for judicial decisions from the perspective of the public. Our research suggests that while judicial ‘reasons’ (arguably accurate descriptions of the decision-making process and factors) do serve similar explanatory functions as those identified in the literature on 'explainable AI', they also serve an important ‘justification’ function (post hoc constructions that justify the decision that was reached). Further, members of the public are also looking for both justification and explanation in reasons for judicial decisions, and that the absence of either feature is likely to contribute to diminished public confidence in the legal system. Therefore, artificially automated judicial decision-making systems that simply attempt to document the process of decision-making are unlikely in many cases to be useful to and accepted within the justice system. Instead, these systems should focus on the post-hoc articulation of principles and precedents that support the decision or action, especially in cases where legal subjects’ fundamental rights and liberties are at stake.

Keywords: explainable AI, judicial reasons, public accountability, explanation, justification

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Authors: Olga Kokoulina

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Today, algorithms are assuming a leading role in various areas of decision-making. Prompted by a promise to provide increased economic efficiency and fuel solutions for pressing societal challenges, algorithmic decision-making is often celebrated as an impartial and constructive substitute for human adjudication. But in the face of this implied objectivity and efficiency, the application of algorithms is also marred with mounting concerns about embedded biases, discrimination, and exclusion. In Europe, vigorous debates on risks and adverse implications of algorithmic decision-making largely revolve around the potential of data protection laws to tackle some of the related issues. For example, one of the often-cited venues to mitigate the impact of potentially unfair decision-making practice is a so-called 'right to explanation'. In essence, the overall right is derived from the provisions of the General Data Protection Regulation (‘GDPR’) ensuring the right of data subjects to access and mandating the obligation of data controllers to provide the relevant information about the existence of automated decision-making and meaningful information about the logic involved. Taking corresponding rights and obligations in the context of the specific provision on automated decision-making in the GDPR, the debates mainly focus on efficacy and the exact scope of the 'right to explanation'. In essence, the underlying logic of the argued remedy lies in a transparency imperative. Allowing data subjects to acquire as much knowledge as possible about the decision-making process means empowering individuals to take control of their data and take action. In other words, forewarned is forearmed. The related discussions and debates are ongoing, comprehensive, and, often, heated. However, they are also frequently misguided and isolated: embracing the data protection law as ultimate and sole lenses are often not sufficient. Mandating the disclosure of technical specifications of employed algorithms in the name of transparency for and empowerment of data subjects potentially encroach on the interests and rights of IPR holders, i.e., business entities behind the algorithms. The study aims at pushing the boundaries of the transparency debate beyond the data protection regime. By systematically analysing legal requirements and current judicial practice, it assesses the limits of the transparency requirement and right to access posed by intellectual property law, namely by copyrights and trade secrets. It is asserted that trade secrets, in particular, present an often-insurmountable obstacle for realising the potential of the transparency requirement. In reaching that conclusion, the study explores the limits of protection afforded by the European Trade Secrets Directive and contrasts them with the scope of respective rights and obligations related to data access and portability enshrined in the GDPR. As shown, the far-reaching scope of the protection under trade secrecy is evidenced both through the assessment of its subject matter as well as through the exceptions from such protection. As a way forward, the study scrutinises several possible legislative solutions, such as flexible interpretation of the public interest exception in trade secrets as well as the introduction of the strict liability regime in case of non-transparent decision-making.

Keywords: algorithms, public interest, trade secrets, transparency

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Authors: Kingkan Pongsiri

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The study of marketing mix influencing on hotel booking decision making: Japanese senior traveler case aims to study the individual factors that are involved in the decision-making reservation for Japanese elderly travelers. Then, it aims to study other factors that influence the decision of tourists booking elderly Japanese people. This is a quantitative research methods, total of 420 completed questionnaires were collect via a Non-Probability sampling techniques. The study found that the majority of samples were female, 53.3 percent of 224 people aged between 66-70 years were 197, representing a 46.9 percent majority, the marital status of marriage is 212 per cent.50.5. Majority of samples have a bachelor degree of education with number of 326 persons (77.6 percentages) 50 percentages of samples (210 people) have monthly income in between 1,501-2,000 USD. The Samples mostly have a length of stay in a short period between 1-14 days counted as 299 people which representing 71.2 percentages of samples. The senior Japanese tourists apparently sensitive to the factors of products/services the most. Then they seem to be sensitive to the price, the marketing promotion and people, respectively. There are two factors identified as moderately influence to the Japanese senior tourists are places or distribution channels and physical evidences.

Keywords: Japanese senior traveler, marketing mix, senior tourist, hotel booking

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Authors: Krambia-Kapardis Maria, Neophytidou Christina

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Lobbying is an inherent aspect within the democratic regimes across the globe. Although it can provide decision-makers with valuable knowledge and grant access to stakeholders in the decision-making process, it can also lead to undue influence and unfair competition at the expense of the public interest if it not transparent. Given the multi-level governance structure of the EU, it is no surprise that the EU policy-making arena has become a place-to-be for lobbyists. However, in order to ensure that influence is legitimate and not biased of any business interests, lobbying must be effectively regulated. A comparison with the US and Canadian lobbying regulatory framework and utilising some good practices from EU countries it is apparent that lobbying is the Achilles’ heel to transparency in the EU. It is evident that EU institutions suffer from ineffective regulations and could in fact benefit from a more robust, mandatory and better implemented system of lobbying regulation.

Keywords: EU, lobbying regulation, transparency, democratic regimes

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Authors: Carolinda Douglass

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Institutional Effectiveness (IE) is the purposeful integration of functions that foster student success and support institutional performance. IE is growing rapidly within higher education as it is increasingly viewed by higher education administrators as a beneficial approach for promoting data-informed decision-making in campus-wide strategic planning and execution of strategic initiatives. Specific IE tools, including, but not limited to, project management; impactful collaboration and communication; commitment to continuous quality improvement; and accountability through rigorous evaluation; are gaining momentum under the auspices of IE. This research utilizes a case study approach to examine the use of these IE tools, highlight successes of this use, and identify areas for improvement in the implementation of IE tools within higher education. The research includes three case studies: (1) improving upon academic program review processes including the assessment of student learning outcomes as a core component of program quality; (2) revising an institutional vision, mission, and core values; and (3) successfully navigating an institution-wide re-accreditation process. Several methods of data collection are embedded within the case studies, including surveys, focus groups, interviews, and document analyses. Subjects of these methods include higher education administrators, faculty, and staff. Key findings from the research include areas of success and areas for improvement in the use of IE tools associated with specific case studies as well as aggregated results across case studies. For example, the use of case management proved useful in all of the case studies, while rigorous evaluation did not uniformly provide the value-added that was expected by higher education decision-makers. The use of multiple IE tools was shown to be consistently useful in decision-making when applied with appropriate awareness of and sensitivity to core institutional culture (for example, institutional mission, local environments and communities, disciplinary distinctions, and labor relations). As IE gains a stronger foothold in higher education, leaders in higher education can make judicious use of IE tools to promote better decision-making and secure improved outcomes of strategic planning and the execution of strategic initiatives.

Keywords: accreditation, data-informed decision-making, higher education management, institutional effectiveness tools, institutional mission, program review, strategic planning

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Authors: Ian McCulloh, Andrew Placona, Darren Stewart, Daniel Gause, Kevin Kiernan, Morgan Stuart, Christopher Zinner, Laura Cartwright

Abstract:

An estimated 22-25% of viable deceased donor kidneys are discarded every year in the US, while waitlisted candidates are dying every day. As many as 85% of transplanted organs are refused at least once for a patient that scored higher on the match list. There are hundreds of clinical variables involved in making a clinical transplant decision and there is rarely an ideal match. Decision makers exhibit an optimism bias where they may refuse an organ offer assuming a better match is imminent. We propose a semi-parametric Cox proportional hazard model, augmented by an accelerated failure time model based on patient specific suitable organ supply and demand to estimate a time-to-next-offer. Performance is assessed with Cox-Snell residuals and decision curve analysis, demonstrating improved decision support for up to a 5-year outlook. Providing clinical decision makers with quantitative evidence of likely patient outcomes (e.g., time to next offer and the mortality associated with waiting) may improve decisions and reduce optimism bias, thus reducing discarded organs and matching more patients on the waitlist.

Keywords: decision science, KDPI, optimism bias, organ transplant

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Authors: Lisandy Arinta Suryana, Santi Novani, Utomo Sarjono

Abstract:

Paradox of business strategic is associated with the contradictory practice. It becomes one of the critical way to survive and win in the dynamic competitive landscape – high level of uncertainty and rapid change in the business environment. Those characteristics are similar with the environment of hijab fashion business, especially in Indonesia. This paper aims to describe the success of paradoxical strategic based on historical data of hijab fashion business which have been validated by qualitative approach. This paper discusses two main aspects of paradoxical strategic such as paradox in human resource management, and logistic center management. Then, the detail effects from each practice are described in term of causal loop diagram. Moreover, the practice of paradoxical strategic depends on leadership that can make a brave and dynamic decision by capturing the main problems and opportunities in their business, and also build commitment to achieve a specific goal.

Keywords: paradox of business strategic, paradoxical strategic, causal loop diagram, sustainable business, hijab fashion business, business strategic

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Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye

Abstract:

In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.

Keywords: prediction, machine learning, artificial intelligence, digital agriculture

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Authors: Peng Liu, Chyng Wen Tee, Xiaofei Xu

Abstract:

This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike.

Keywords: multi-period portfolio optimization, look-ahead constrained optimization, machine learning, sequential decision making

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Authors: Zahid Ullah, Atlas Khan

Abstract:

The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

Abstract:

We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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