Ill-defined Problems Research Articles

An extended Pythagorean fuzzy complex proportional assessment approach with new entropy and score function: Application in pharmacological therapy selection for type 2 diabetes

In the context of medical decision making, the Type 2 Diabetes (T2D) pharmacological therapy selection problem involves several medications that can be stipulated to manage the blood glucose level of patients. The extensive range of hyperglycemia lowering agents with varying outcomes and several side effects makes the decision quite complicated and uncertain. Pythagorean Fuzzy Sets (PFSs) are proven as one of the valuable tools to handle ambiguous and ill-defined problems. This paper introduces an innovative Complex Proportional Assessment (COPRAS) to solve the T2D medication selection problem under the Pythagorean fuzzy environment. In this methodology, a new formula based on entropy measure and score function is introduced to evaluate the unknown criteria weights. To doing so, the new entropy measure and score function are developed under the PFSs context. Further, an illustrative case study of the T2D pharmacological therapy selection problem is taken to express the feasibility and usefulness of the proposed method in real-world decision-making problems. Finally, the obtained result is compared with some existing methods, which confirms the strength and stability of the proposed COPRAS method.

Ill-Defined Problem Solving Does Not Benefit From Daytime Napping.

The main goal of the present study was to explore the role of sleep in the process of ill-defined problem solving. The results of previous studies indicate that various cognitive processes are largely dependent on the quality and quantity of sleep. However, while sleep-related memory consolidation seems to be well-grounded, with regard to the impact of sleep on problem solving, existing research yields mixed and rather inconclusive results. Moreover, this effect has been mainly tested using simple and well-defined, common laboratory problems, such as the remote associate test (RAT), crossword and anagram puzzles, numeric and logic problems, etc. What is lacking is research on the effect of sleep on solving more complex and more real-life oriented ill-defined problems. In the present study, we hypothesized that sleep can improve performance in solving this kind of problems. The study involved 40 participants, randomly assigned to two experimental conditions: sleep group and waking group. The experimental protocol comprised three stages: problem presentation, retention interval, and testing stage. The problem was presented to the participants in the form of an interactive computer game concerning a complex, elaborate crime story. During the retention interval, the participants—depending on the condition—took a nap or stayed awake; sleeping participants underwent polysomnography recording, while waking participants performed activities not related to the experimental problem. In the testing stage, participants tried to solve the presented problem. The solutions generated were assessed both for quality (reasonableness, consistency, and story recall) and creativity (fluency, flexibility, originality, and elaboration). Contrary to expectations, we found no effect of sleep on ill-defined problem solving. Neither quality nor creativity of the solutions generated by the participants was higher in the nap group than in the waking group. There were also no performance improvements with regard to any sleep stage or incidence of dreams. Our study adds to a growing body of evidence that sleep probably might provide an incubation gap, but not a facilitating environment serving the purpose of problem solving, at least with regard to ill-defined problems.

Students’ Consciousness of their Problem Solving Approaches as a Key to Creativity in Design

Architectural engineering students are constantly dealing with ill-defined and tangled design problems. Many scholars accentuated the importance of creative thinking in tackling such wicked and complex problems. Accordingly, getting engaged in an ill-defined problem solving process requires specific personality traits that are often critical to creativity and innovation in design. In that sense, architectural engineering curricula need to provide various strategies through which such individual skills can be nurtured and developed. The objective of this study is to empirically identify the different patterns of students’ approaches in solving problems and the role of group discussions in such a process. The study adopted a qualitative approach, in a live class setup, through a series of workshops to allow for in-depth exploration of the students’ problem solving skills and abilities. The intention is to help students in discovering and in being aware of their own way of solving problems and identifying its strengths and weaknesses. This is considered a core and significant step towards the improvement and development of their design thinking skills. The findings of the study have emphasized the positive impact of the cyclical behavior in the creative problem solving process and highlighted the different key issues and lessons emerging from students’ consciousness of the mental processes that occurred during this iterative process. Such awareness and consciousness of those emergent issues is expected to encourage conscious design, increase tolerance for ambiguity and improve self-confidence which are believed to dramatically help students in creatively solving ill-defined architectural design problems.

Augmented Reality in Paramedic Training: a Formative Study

Emergency management of critically ill patients and a high expectation of mastery of technical medical skills are an essential aspect in a paramedics’ scope of practice. An entry level paramedic with limited exposure to high-risk critical patients is expected to perform interventions and utilize critical thinking skills at a mastery level when entering the workforce as reported by Zautchke et al. (The Journal of Emergency Medicine, 505–512, 1987). Paramedic programs in the USA continue to deliver the bulk of the course curriculum in a traditional classroom lecture format using 2-dimensional methods such as PowerPoint and lecture. Traditional, lecture-driven teaching methods rarely provide opportunities for paramedic students to directly witness the impact of their decisions and develop automaticity of their skills to reduce reaction times and prevent cognitive overload. Automacity is defined as making more automatic decisions and practices in the face of random incidences that occur in authentic conditions and is the result of repeated learning and practice. This definition is a classic example of a problematic circumstance which can utilize design thinking to resolve it. If the definition of design thinking is to use methods and processes for investigating ill-defined problems and finding solutions, then it is important for instructional designers to be innovative to assist them in recognizing these contexts, being creative in the generating appropriate solutions, and rationalizing/analyzing potential “best fit’ solutions. For years, EMT’s and paramedics have utilized mannequins (often called “Resuscitation Annies”) to teach recovery skills. Over the years, these mannequins have been augmented with technology to assist with skill development and to make them more interactive. Yet, as effective as they have been for the state of the art they represented, they have not been multifaceted enough for trainers to create an appropriate level of randomness that represent the ever complex situations paramedics and EMTs are becoming involved with. On top of that, the granularity and authenticity of the visual aspects caused these mannequins to suffer from a lack of a third dimensional realism, especially as it related, for example, to bodily fluids breakage and other sources of complications, for example.

How Well Do Teachers Predict Students’ Actions in Solving an Ill-Defined Problem in STEM Education: A Solution Using Sequential Pattern Mining

Predicting students' line of actions helps educators give adequate guidance to students, but this remains a challenge in science, technology, engineering, and mathematics (STEM) education. Given this, there is a scarcity of related research that will help improve teachers' prediction capabilities on students' line of actions when tackling ill-defined problems (IDPs), as well as how emerging data mining techniques could contribute to such prediction. The present study aims to fill the gap by measuring the quality of teachers' predictions (labeled expert prediction), where 43 elementary teachers predict students' step-by-step actions when solving an IDP through the light path task (LPT), and then comparing its quality with that of machine prediction, executed via sequential pattern mining techniques. Data on students' lines of action were collected from 501 5th- and 6th-grade students, aged 11-12. The results showed the significantly lower accuracy of expert prediction compared to machine prediction, which highlights the advantages of using data mining in predicting students' actions and shows its possible application as a recommendation system to provide adaptive guidance in future STEM education.

The Role of Design in Policy Making

The design discipline is of increasing appeal to a public sector confronted with ill-defined problems consistent with the socially-embedded. This paper explores the role of design in policy making projects, by means of two case empirical case studies. We establish and apply a wicked problems perspective to analyse data from; (1) MindLab and (2) Helsinki Design Lab. Findings reveal that design is specifically useful in the mitigation of wicked policy problems when harnessed by a strategically composed multidisciplinary team including designers. The characteristics of design that are identified as essential are an interactive approach to problems, a holistic perspective, and a user-centred way of working. This paper contributes empirical evidence toward the role of design in policy making, drawing the two domains together via wicked problems theory.

The Predictive Creative Mind: A First Look at Spontaneous Predictions and Evaluations During Idea Generation.

Idea generation, the process of creating and developing candidate solutions that when implemented can solve ill-defined and complex problems, plays a pivotal role in creativity and innovation. The algorithms that underlie classical evolutionary, cognitive, and process models of idea generation, however, appear too inefficient to effectively help solve the ill-defined and complex problems for which one would engage in idea generation. To address this, these classical models have recently been redesigned as forward models, drawing heavily on the “predictive mind” literature. These pose that more efficiency can be achieved by making predictions based on heuristics, previous experiences, and domain knowledge about what material to use to generate ideas with, and evaluate these subsequently generated ideas based on whether they indeed match the initial prediction. When a discrepancy occurs between prediction and evaluation, new predictions are made, and thus shaping what actions, and how these actions, are undertaken. Although promising, forward models of idea generation remain theoretical and thus no empirical evidence exists about whether such predictions and evaluations indeed form part of the idea generation process. To take a first empirical look at this, a mixed-methods study was conducted by analyzing people’s self-reports for the reasons of the actions that they take during an idea generation task. The results showed that predictions and evaluations are pervasive in the idea generation process. Specifically, switching between concept selection and conceptual combination and idea generation, as well as repeating idea generation based on earlier selected conceptual combination, and possibly (but to a lesser extent) concept selection and the repetition thereof, are likely to be driven by predictions and evaluations. Moreover, the frequencies of the predictions and evaluations that drive these actions influenced the amount of ideas generated, amount of concepts used, and within-concept fluency (the ratio of the amount of ideas generated per concept used). Therefore, the contribution of this paper is the first empirical evidence that indicates that the idea generation process is driven by both predictions and evaluations.

Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits

We investigate the seemingly ill-defined problem of extracting a ground-state mass from a lattice simulation where the extent of the lattice is not long enough to project out the ground-state properly. We regulate the problem using a Bayesian method. We show that controlling meta-parameters (overconfidence) can allow the data to overcome the input priors (bias). We can write the method as a black-box technique which allows extraction of a ground-state mass, even on a relatively short lattice.

The W-model: a pre-college design pedagogy for solving wicked problems

Many design frameworks introduced to novices are not compatible with the behaviours and habits of mind of expert designers. This creates a barrier to effective practice, especially when novice designers tackle ill-defined, wicked problems. The W-model is a pedagogical framework that provides a prescriptive design model for novices, enabling them to effectively engage with ill-defined problems. Co-evolution of the problem and solution are mandated through rapid iterations of five design phases: define, ideate, synthesize, assess and reflect. The W-model was tested on pre-college novice designers who used this framework to solve a wicked problem. Results demonstrate that students using the W-model engaged in behaviours associated with informed designers, and were able to effectively and confidently tackle a real-world problem.

‘I'm lost’ – a qualitative analysis of student teams' strategies during their first experience in problem-based learning

ABSTRACTFuture global challenges that engineering graduates face have placed demands on engineering education and how graduates develop competency in collaborative problem-solving. Such demand has seen an increase in the use of pedagogies like problem-based learning (PBL) that provide opportunities for developing collaborative problem-solving skills. PBL has been well studied however there is still much to understand about ‘how’ students solve problems collaboratively in PBL settings. This work investigates the processes taking place when students set out to solve problems in a group. Naturalistic data from video recordings of participants in chemical process design PBL sessions is used. Participants conversations were transcribed and their language analysed using qualitative content analysis to provide a description of ‘what’ strategies students use. The findings indicate that students tend to adhere to relatively rigid structures and minimise effort when tackling unfamiliar ill-defined problems. Additionally, students appear to struggle making connections between knowledge domains.

Potentials for the Integration of Design Thinking along Automotive Systems Engineering Focusing Security and Safety

AbstractThe increasingly intelligent, highly complex, technical systems of tomorrow - for instance autonomous vehicles - result in the necessity for a systematic security- and safety-oriented development process that starts in the early phases of system design. Automotive Systems Engineering (ASE) as one approach is increasingly gaining ground in the automotive industry. However, this approach is still in a prototype stage. The consideration of security and safety within the early stages of systems design leads to so- called ill-defined problems. Such are not covered by ASE, but can be addressed by means of Design Thinking. Therefore we introduce an approach to combine both approaches. Based on this combination, we derive potentials in the context of the consideration of security and safety. Essential advantages are the possibility to think ahead of threat scenarios at an early stage in system design. Due to an incomplete database, this is not supported or only partially supported by conventional approaches. The resulting potentials are derived based upon a practical example.

Non-backtracking cycles: length spectrum theory and graph mining applications

Graph distance and graph embedding are two fundamental tasks in graph mining. For graph distance, determining the structural dissimilarity between networks is an ill-defined problem, as there is no canonical way to compare two networks. Indeed, many of the existing approaches for network comparison differ in their heuristics, efficiency, interpretability, and theoretical soundness. Thus, having a notion of distance that is built on theoretically robust first principles and that is interpretable with respect to features ubiquitous in complex networks would allow for a meaningful comparison between different networks. For graph embedding, many of the popular methods are stochastic and depend on black-box models such as deep networks. Regardless of their high performance, this makes their results difficult to analyze which hinders their usefulness in the development of a coherent theory of complex networks. Here we rely on the theory of the length spectrum function from algebraic topology, and its relationship to the non-backtracking cycles of a graph, in order to introduce two new techniques: Non-Backtracking Spectral Distance (NBD) for measuring the distance between undirected, unweighted graphs, and Non-Backtracking Embedding Dimensions (NBED) for finding a graph embedding in low-dimensional space. Both techniques are interpretable in terms of features of complex networks such as presence of hubs, triangles, and communities. We showcase the ability of NBD to discriminate between networks in both real and synthetic data sets, as well as the potential of NBED to perform anomaly detection. By taking a topological interpretation of non-backtracking cycles, this work presents a novel application of topological data analysis to the study of complex networks.

Simplicity and complexity preferences in causal explanation: An opponent heuristic account

People often prefer simple to complex explanations because they generally have higher prior probability. However, simpler explanations are not always normatively superior because they often do not account for the data as well as complex explanations. How do people negotiate this trade-off between prior probability (favoring simple explanations) and goodness-of-fit (favoring complex explanations)? Here, we argue that people use opponent heuristics to simplify this problem—that people use simplicity as a cue to prior probability but complexity as a cue to goodness-of-fit. Study 1 finds direct evidence for this claim. In subsequent studies, we examine factors that lead one or the other heuristic to predominate in a given context. Studies 2 and 3 find that people have a stronger simplicity preference in deterministic rather than stochastic contexts, while Studies 4 and 5 find that people have a stronger simplicity preference for physical rather than social causal systems, suggesting that people use abstract expectations about causal texture to modulate their explanatory inferences. Together, we argue that these cues and contextual moderators act as powerful constraints that can help to specify the otherwise ill-defined problem of what distributions to use in Bayesian hypothesis comparison.

Machine auscultation: enabling machine diagnostics using convolutional neural networks and large-scale machine audio data

Acoustic signals play an essential role in machine state monitoring. Efficient processing of real-time machine acoustic signals improves production quality. However, generating semantically useful information from sound signals is an ill-defined problem that exhibits a highly non-linear relationship between sound and subjective perceptions. This paper outlines two neural network models to analyze and classify acoustic signals emanating from machines: (i) a backpropagation neural network (BP-NN); and (ii) a convolutional neural network (CNN). Microphones are used to collect acoustic data for training models from a computer numeric control (CNC) lathe. Numerical experiments demonstrate that CNN performs better than the BP-NN.

Parametric analysis of collapse load factor of planar frames

The plastic analysis and determining the collapse load factor (CLF) of frames have received much attention in all kinematical methods applied for designing these frames. CLF in frames depends on many effective parameters such as length of the bay, the height of story, loads, and plastic moments. As the number of these parameters increases, the analysis becomes more complex and time-consuming. In this regard, artificial neural networks (ANNs) have been found many applications in prediction and optimization of ill-defined problems. In the present paper, two planar frames were considered and analyzed as examples for modeling via ANN. In order to develop the ANNs, results of thirty samples were gathered from every two examples. Parameters including the length of the bay, the height of story, loads, and plastic moments were regarded as inputs and the CLF was considered as the output of the networks. After developing the networks, the model function was generated and the effects of the input parameters were investigated on the CLF of frames. The results showed tuning networks are finely and adjusting their parameters would lead to very good and acceptable results for the design of frames.

An Integral Approach to Sustainable Decision-Making within Maritime Spatial Planning—A DSC for the Planning of Anchorages on the Island of Šolta, Croatia

The planning of nautical tourism development and especially, the planning of its supporting infrastructure development, is important topic of the maritime spatial planning. The focus of research is the integration of multicriteria analysis and stakeholders within concept modeling that will provide support to the spatial planning specialists in the design of plans related to the development of anchorage capacities for small vessels. It examines economic, environmental, ecological, social, and civil engineering concerns related to the use of coastal water. It is a complex and ill-defined civil engineering problem because of multiple stakeholders with diverse interests, numerous conflicting goals and criteria, huge quantities of information and data, limited resources, etc. The research is concentrated on an integral approach to sustainable decision-making within maritime spatial planning by the modeling decision support concept to the processes of identification, validation, comparison, and the selection of locations for anchorage construction, based on multicriteria methods, goal analysis, and the logic of the decision support system. The concept is tested on the island of Šolta, Croatia, and has been proven as being an applicable, consistent, efficient, and effective methodology for the planning of the anchorage locations.

The Use of Patterns as an Urban Design Approach

Urban design is a complex problem-solving activity that commonly requires the aid of a variety of methods to support the process and enhance the quality of the outcomes. How to help designers with suitable methods to deal with ill-defined urban problems constitutes a major challenge in the urban design domain. In this regard, the use of urban design patterns is considered as a method that can contribute to urban design problem-solving. However, this tool was never investigated to understand its role in the task-related activities that take place during the design process by designers working in a team, and its influence on the creativity of the final design outcome as perceived by urban designers and students. Therefore, an empirical research based on a controlled experiment was carried out to explore the aid provided by design patterns during the conceptual stages of the process. Sixty-three master students working in teams of three were assigned a set of patterns to solve an urban design task. They were requested to use this material to produce as many creative conceptual solutions as possible. At the end of the sessions, they evaluated the creativity of their outcomes, and they completed a questionnaire about the aid afforded by the patterns. The solutions were also assessed by two independent urban designers. The study contributed to gain a better insight into the main design activities derived from the use of patterns as problem-solving tools and to unveil their contribution to urban design. The aid provided by the urban patterns was mainly concerned with task-related activities, and thereafter with team interactions. Overall, the use of patterns was seen to enhance the functionality of the design. Implications for design practice and design education are discussed.

Fuzzy inference system for modelling machining parameters in electrical discharge machining

The monitoring of the machining process has become a preliminary step in the mechanical manufacturing processes, especially for the electrical discharge machining (EDM). However, the monitoring of the electrical discharge machining process remains an ill-defined problem and is generally based on heuristics that are difficult to model. Therefore, artificial intelligence especially the fuzzy logic technique can be applied to processes such as electrical discharge machining in which the experiences and knowledge of the experts play an important role. This study presents a method for the identification of electric discharge machining parameters using the fuzzy inference system. The fuzzy inference system was used to determine the values of material removal rate; surface roughness and radial shrinkage based on process parameter values (open circuit voltage, discharge current, Pulse, duty factor and rinse pressure). The purpose of this is to highlight the development of mathematical models using artificial intelligence (fuzzy logic technique) to correlate relationships of various parameters of the process for determine the influence of those parameters on the quality of the machined parts. The fuzzy model developed for the determination of material removal rate, surface roughness and radial shrinkage formed (fuzzy rules) and tested using experimental data. The mean deviation of the test data did not exceed 3% for the three objectives used, which corresponds to an accuracy of 97%. The results of the tests have shown that the proposed fuzzy model can be used successfully for the selection of the parameters of the electric discharge machining process. The elaborate model offers a more precise and easy selection of EDM parameters.

Relationship between student designers’ reflective thinking and their design performance in bioengineering project: exploring reflection patterns between high and low performers

The purpose of this study is to investigate the relationship between students’ patterns of reflective thinking and their performance in solving design problems. Reflective thinking is a critical element in the process of solving ill-defined design problems. Many educators are dedicated to finding ways to promote students’ reflection. Yet few empirical studies attempt to explore the relationship between reflective thinking and design performance. 44 students enrolled in a Biomedical Microelectromechanical Systems and Medical Devices course participated in this study. Through a self-assessed questionnaire, students’ reflection patterns were collected in three areas: timing of reflection, objects of reflection, and levels of reflection. Also, students’ performance scores on their team project in biomedical device design were collected. The results revealed a general pattern of student designers’ reflection behaviors and a number of significant different reflection patterns between high-performing and low-performing students as they approached a design problem-solving task. Implications for supporting students’ reflective thinking and enhancing their problem-solving abilities in design tasks were discussed.

Inequality, Education, Workforce Preparedness, and Complex Problem Solving.

Economic inequality has been described as the defining challenge of our time, responsible for a host of potential negative societal and individual outcomes including reduced opportunity, decreased health and life expectancy, and the destabilization of democracy. Education has been proposed as the “great equalizer” that has and can continue to play a role in reducing inequality. One means by which education does so is through the development of complex problem solving skills in students, skills used to solve novel, ill-defined problems in complex, real-world settings. These are highly valued in the workforce and will likely continue to be so in the future workforce. Their importance is evident in results from employer surveys, as well as by their inclusion in large scale international and domestic comparative assessments. In this paper, I review various definitions of complex problem solving and approaches for measuring it, along with findings from PISA 2003, 2012, and 2015. I also discuss prospects for teaching, assessing, and reporting on it, and discuss the emerging importance of collaborative problem solving. Developing and monitoring complex problem solving skills, broadly defined, is a critical challenge in preparing students for the future workforce, and in overcoming the negative effects of inequality and the diminishment of individual opportunity.