During the last decade, scholars have identified a number of factors that pose significant challenges to effective business ethics education. This article offers a "coping-modeling, problem-solving" (CMPS) approach (Cunningham, 2006) as one option for addressing these concerns. A rationale supporting the use of the CMPS framework for courses on ethical decisionmaking in business is provided, following which the implementation processes for this program are described. Evaluative data collected from N = 101 undergraduate business students enrolled in a third year (...) required course on ethical decision-making in business indicated that the CMPS model is a promising alternative for both overcoming teaching challenges and for facilitating skill acquisition in the areas of ethical recognition, judgment, and action. Limitations and directions for future research are discussed. (shrink)

During the last decade, scholars have identified a number of factors that pose significant challenges to effective business ethics education. This article offers a “coping-modeling, problem-solving” approach as one option for addressing these concerns. A rationale supporting the use of the CMPS framework for courses on ethical decision-making in business is provided, following which the implementation processes for this program are described. Evaluative data collected from N = 101 undergraduate business students enrolled in a third year required course on (...) ethical decision-making in business indicated that the CMPS model is a promising alternative for both overcoming teaching challenges and for facilitating skill acquisition in the areas of ethical recognition, judgment, and action. Limitations and directions for future research are discussed. (shrink)

Millions of Ugandan children have become orphaned over the last two decades, the primary cause being the increasing HIV/AIDS epidemic. This phenomenon has prompted the government to institute numerous legal reforms. These internal reforms, implemented in a legal environment based on English common law and increasingly, international standards, greatly influence the legal inheritance rights of Ugandan orphans and their chances for prosperity. In many regions, however, the traditional local mores trump both national and global standards, meaning that while Ugandan parents (...) may own appreciable property upon death, their children rarely receive it, but rather fall victim to “property grabbing” or mismanagement by relatives. The key impediments to solving this problem and to Uganda’s adoption of a more egalitarian system of inheritance include the ineffectiveness of local councils, the inadequate enforcement of the Children Stature, and the heavily centralized and cumbersome structure of the national government. Therefore, significant reforms are needed, including the pronouncement of a national policy regarding orphans, the reduction of national reliance on NGOs, and the restoration of a national legislative effort to codify orphans’ property rights. (shrink)

In adversarial problem solving (APS), one must anticipate, understand and counteract the actions of an opponent. Military strategy, business, and game playing all require an agent to construct a model of an opponent that includes the opponent's model of the agent. The cognitive mechanisms required for such modeling include deduction, analogy, inductive generalization, and the formation and evaluation of explanatory hypotheses. Explanatory coherence theory captures part of what is involved in APS, particularly in cases involving deception.

As a NP-hard problem that needs to be solved in real time, the dynamic task allocation problem of unmanned aerial vehicle swarm has gradually become a difficulty and hotspot in the current planning field. Aiming at the problems of poor real-time performance and low quality of the solution in the dynamic task allocation of heterogeneous UAV swarm in uncertain environment, this paper establishes a dynamic task allocation model that can meet the actual needs and uses the binary wolf (...) pack algorithm to solve it, so as to propose a dynamic task allocation method of heterogeneous UAV swarm in uncertain environment. In this method, a dynamic mechanism of attacking while searching and priority attacking of important targets is designed. A dynamic task allocation model of multitarget, multitask, heterogeneous multiaircraft platform and multiconstraint is established based on the target cost-effectiveness ratio and task execution time window. In addition, one-dimensional 0–1 coding method is adopted to encode the task allocation scheme. Furthermore, the wolf pack algorithm is introduced in brief. This paper focuses on the BWPA with the good computational robustness and strong global search ability to solve the dynamic allocation model. According to the simulation results, the designed task allocation method not only has good adaptability to the change of target and UAV number, as well as good stability and scalability, but also can effectively solve the dynamic task allocation problem of heterogeneous UAV swarm in unknown environment. Therefore, the established model and solution method can provide a useful reference for task allocation and other related problems. (shrink)

As one of the important 21st-century skills, collaborative problem solving (CPS) has caught much attention in the assessment area. Two initiative approaches have been created: the human-to-human and human-to-agent modes. Between the two modes, the human-to-human interaction is much closer to the real-world situation and its process stream data can reveal more detailed information about the cognitive processes. In order to measure CPS ability effectively by this mode, how to extract indicators from the data and model it is crucial, (...) while the existing methods all have some disadvantages. In the present study, we proposed a new paradigm for extracting indicators and modeling the dyad data in the human-to-human mode. Specifically, both individual and group indicators were extracted from the data stream, which served as evidence for demonstrating CPS skills. In addition, a within-item multidimensional Rasch model was used to fit the dyad data. To test the validity of the paradigm, we developed five tasks following the asymmetric mechanism and integrated them into an online testing platform. Four hundred thirty-four students from China participated in the assessment and the online platform recorded their crucial actions with time stamps. The new paradigm was used to deal with the generated process stream data. Results showed that the model fitted well. The item parameter estimates and fitting indexes were acceptable, and students were well differentiated. In general, the new paradigm of extracting indicators and modeling the dyad data is feasible for improving the human-to-human assessment of CPS. Finally, the limitations of the current study and further research directions are discussed. (shrink)

The undoubted truth is that there exist for the planetary motions true and constant configurations from which no impossibilities or contradictions follow; they are not the same as the configurations asserted by Ptolemy; and Ptolemy neither grasped them nor did his understanding get to imagine what they truly are.

Multiple theories of problem-solving hypothesize that there are distinct qualitative phases exhibited during effective problem-solving. However, limited research has attempted to identify when transitions between phases occur. We integrate theory on collaborative problem-solving with dynamical systems theory suggesting that when a system is undergoing a phase transition it should exhibit a peak in entropy and that entropy levels should also relate to team performance. Communications from 40 teams that collaborated on a complex problem were coded for (...) occurrence of problem-solving processes. We applied a sliding window entropy technique to each team's communications and specified criteria for identifying data points that qualify as peaks and determining which peaks were robust. We used multilevel modeling, and provide a qualitative example, to evaluate whether phases exhibit distinct distributions of communication processes. We also tested whether there was a relationship between entropy values at transition points and CPS performance. We found that a proportion of entropy peaks was robust and that the relative occurrence of communication codes varied significantly across phases. Peaks in entropy thus corresponded to qualitative shifts in teams’ CPS communications, providing empirical evidence that teams exhibit phase transitions during CPS. Also, lower average levels of entropy at the phase transition points predicted better CPS performance. We specify future directions to improve understanding of phase transitions during CPS, and collaborative cognition, more broadly. (shrink)

Open peer commentary on the article “Examining the Role of Re-Presentation in Mathematical Problem Solving: An Application of Ernst von Glasersfeld’s Conceptual Analysis” by Victor V. Cifarelli & Volkan Sevim. Upshot: Problem solving is an enormous field of study, where so-called “problems” can end up having very little in common. One of the least studied categories of problems is open-ended mathematical modeling research. Cifarelli and Sevim’s framework - although not developed for this purpose - may be a useful (...) lens for studying the development of mathematical modelers and researchers in applied mathematics. (shrink)

In the context of office work, learning to handle an Enterprise Resource Planning system is important as implementation costs for such systems and associated expectations are high. However, these expectations are often not met because the users are not trained adequately. Electronic Performance Support Systems are designed to support employees’ ERP-related problem-solving and informal learning. EPSS are supposed to enhance employees’ performance and informal workplace learning through task-specific and granular help in task performance and problem-solving. However, there is (...) little empirical research on EPSS. Two survey studies addressed this research gap. In the first study, 301 people working in Human Resource -related positions and functions evaluated the learning potential of EPSS as well as potential advantages and obstacles concerning the implementation and use of EPSS. Though other measures are currently assessed as more important for learning, HR employees expect a strong increase in the significance of EPSS for employee learning. In the second study, 652 users of ERP software completed a questionnaire on characteristics of their daily work tasks, team characteristics, individual dispositions, their coping with ERP-related problems, and characteristics of EPSS. Findings indicate that the most frequently available and used approach when dealing with an ERP-related problem is consulting colleagues. Three EPSS types can be distinguished by their increasing integration into the user interface and their context-sensitivity. While external and extrinsic EPSS are available to many users, intrinsic EPSS are less common but are used intensively if available. EPSS availability is identified to be a strong positive predictor of frequency of EPSS use, while agreeableness as well as the task complexity and information-processing requirements show small negative effects. Moreover, more intensive ERP users use EPSS more frequently. In general, ERP users value, features such as context-sensitivity, an integration of the EPSS into the ERP system’s user interface, the option to save one’s own notes, and information displayed in an extra window. It is expected that EPSS will play an important role in workplace learning in the future, along with other measures. (shrink)

This research presents a computer model called EUREKA that begins with novice‐like strategies and knowledge organizations for solving physics word problems and acquires features of knowledge organizations and basic approaches that characterize experts in this domain. EUREKA learns a highly interrelated network of problem‐type schemas with associated solution methodologies. Initially, superficial features of the problem statement form the basis for both the problem‐type schemas and the discriminating features that organize them in the P‐MOP (Problem Memory Organization (...) Packet) network. As EUREKA solves more problems, the content of the schemas and the discriminating features change to reflect more fundamental physics principles. This changing network allows EUREKA to shift from a novicelike means‐ends strategy to a more expertlike “knowledge development” strategy in which the presence of abstract concepts are triggered by problem features. In this model, the strategy shift emerges as a natural consequence of the evolving expertlike organization of problem‐type schemos. EUREKA captures many of the descriptive models of novice expert differences, and also suggests a number of empirically testable assumptions regarding problem‐solving strategies and the representation of problem‐solving knowledge. (shrink)

Instructions in Wason’s Selection Task underdetermine empirical subjects’ representation of the underlying problem, and its admissible solutions. We model the Selection Task as an interrogative learning problem, and reasoning to solutions as: selection of a representation of the problem; and: strategic planning from that representation. We argue that recovering Wason’s ‘normative’ selection is possible only if both stages are constrained further than they are by Wason’s formulation. We conclude comparing our model with other explanatory models, w.r.t. to (...) empirical adequacy, and modeling of bounded rationality. (shrink)

We investigate flexibility and problem solving in skilled action. We conducted a field study of mountain bike riding that required a learner rider to cope with major changes in technique and equipment. Our results indicate that relatively inexperienced individuals can be capable of fairly complex 'on-the-fly' problem solving which allows them to cope with new conditions. This problem solving is hard to explain for classical theories of skill because the adjustments are too large to be achieved by (...) automatic mechanisms and too complex and rapid to be achieved by cognitive processes as they are usually understood. A recent theory, Mesh, can explain these results because it posits that skill-specific cognitive abilities develop during skill learning and that control typically involves an interplay between cognitive and automatic mechanisms. Here we develop Mesh further, providing a detailed explanation for these problem solving abilities. We argue that causal representation, metacognitive awareness and other forms of performance awareness combine in the formulation and control of action strategies. We also argue that the structure of control present in this case is inconsistent with Bratman's model of intentions, and that, in the face of high uncertainty and risk, intentions can be much more labile than Bratman recognises. In addition, we found limitations and flaws in problem solving which illuminate the representations involved. Finally, we highlight the crucial role of social and cultural learning in the development of complex skills. (shrink)

Most psychological theories of problem solving have focused on modeling explicit processes that gradually bring the solver closer to the solution in a mostly explicit and deliberative way. This approach to problem solving is typically inefficient when the problem is too complex, ill-understood, or ambiguous. In such a case, a ‘creative’ approach to problem solving might be more appropriate. In the present paper, we propose a computational psychological model implementing the Explicit-Implicit Interaction theory of creative (...) class='Hi'>problem solving that involves integrating the results of implicit and explicit processing. In this paper, the new model is used to simulate insight in creative problem solving and the overshadowing effect. (shrink)

Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in (...) cognitive processes that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (shrink)

To achieve their goals, organizations for individuals with intellectual disability have to stimulate high-quality relationships between professionals and family members. Therefore, achieving professionals’ trust in family members has become a challenge. One relevant factor in explaining professional’s trust in families is the degree to which family members use the “problem-solving” conflict management strategy in their disputes–disagreements with professionals. It is reasonable to argue that when family members use problem-solving conflict management, professionals’ trust increases. Professionals’ trust, in turn, stimulates (...) the use of problem-solving strategies by family members. However, it is also plausible that professionals are the initiators of this positive spiral. To examine this relationship between problem solving and trust over time, we conducted a longitudinal survey study in which 329 professionals reported on these two constructs three times. Using structural equation modeling, we compared four nested models: stability, causality, reversed causation, and reciprocal. The results of the χ2 difference tests, regarding the comparison of the models, showed that the reciprocal model was significantly superior to the alternative proposals. Our findings supported a complex view of the relationships between problem-solving conflict management and trust, based on dynamic reciprocal relationships over time. (shrink)

This paper argues that historical research is an important tool for modeling problem-solving in scientific invention and discovery. Two important cases in the history of modern physics—the invention of the transistor by John Bardeen and Walter Brattain and the development of the theory of superconductivity by Bardeen, Leon Cooper, and J. Robert Schrieffer—reveal factors essential to include in such a model. The focus is on problem-solving practices: problem decomposition, analogy, bridging principles, team-work, empirical tinkering, and library research. (...) A complete framework must encompass the full range of factors, including contingent individual traits and environmental circumstances. (shrink)

Multiple goals balancing is an important but not yet fully validated dimension of complex problem solving (CPS). The present study used process data to explore how solvers clarify goals, set priorities, and balance conflicting goals. We extracted behavioral indicators of goal pursuit from the log data of 3,201 students on the third subtask of the “Ticket” task in the PISA 2012 CPS test. Cluster analysis was used to identify 10 groups that varied in goal pursuit behavior. Logistics and least-squares (...) regression analysis were used to explore how goal pursuit affected task scores and CPS proficiency. The results showed that competent solvers clarified goals and weighed priorities more effectively. They also made trade-offs between conflicting goals. The importance of theoretically-driven log data analysis and coping strategies in the face of multiple goals conflict scenarios was discussed. (shrink)

Peer interaction has been found to be conducive to learning in many settings. Knowledge co-construction has been proposed as one explanatory mechanism. However, KCC is a theoretical construct that is too abstract to guide the development of instructional software that can support peer interaction. In this study, we present an extensive analysis of a corpus of peer dialogs that we collected in the domain of introductory Computer Science. We show that the notion of task initiative shifts correlates with both KCC (...) and learning. Speakers take task initiative when they contribute new content that advances problem solving and that is not invited by their partner; if initiative shifts between the partners, it indicates they both contribute to problem solving. We found that task initiative shifts occur more frequently within KCC episodes than outside. In addition, task initiative shifts within KCC episodes correlate with learning for low pre-testers, and total task initiative shifts correlate with learning for high pre-testers. As recognizing task initiative shifts does not require as much deep knowledge as recognizing KCC, task initiative shifts as an indicator of productive collaboration are potentially easier to model in instructional software that simulates a peer. (shrink)

The meaning of spontaneous skin conductance activity, and its relevance to appraisal theory, are examined. Spontaneous skin conductance activity is hypothesised to reflect task engagement, and thus to be correlated with appraisals of problem-focused coping potential. In a within-subjects design, subjects solved anagrams in which task difficulty was manipulated by varying both the difficulty of the anagrams and the amount of time available to solve them. In the most difficult condition, appraisals of coping potential were expected, and observed, to (...) be especially low, and these appraisals were predicted to produce selective disengagement from the task, yielding reduced skin conductance activity. Within trials, skin conductance activity increased at the start of the trials in all conditions, but decreased by the end of the trials in the most difficult condition only. At the end of the trials, both spontaneous response rate and maximum response amplitude were positively correlated, within-subjects, with appraisals of coping potential, and, even after controlling for task difficulty, with solving the anagram. These findings support the hypothesised relation between appraised coping potential and spontaneous skin conductance activity, and the interpretation of this activity as reflecting task engagement. (shrink)

The outbreak of the COVID-19 epidemic has promoted the popularity of online learning, but has also exposed some problems, such as a lack of interaction, resulting in loneliness. Against this background, students' attitudes toward peer interaction may have become even more important. In order to explore the impact of attitude toward peer interaction on students' mindset including online learning motivation and critical thinking practice that could affect their problem-solving self-efficacy during the COVID-19 pandemic, we developed and administered a questionnaire, (...) receiving 1,596 valid responses. The reliability and validity of the questionnaire were re-tested, and structural equation modeling was applied. It was found that attitude toward peer interaction could positively predict middle school students' online learning motivation and critical thinking. Learning motivation and critical thinking also positively supported problem-solving self-efficacy. It is expected that the results of this study can be a reference for teachers to adopt student-centered online learning in problem solving courses. (shrink)

How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (...) (SIM), an agent‐based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that “exploration” may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self‐obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone is sufficient. (shrink)

It is frequently assumed that dreaming can be likened to such waking cognitive activities as imagination, analogical reasoning, and creativity, and that these models can then be used to explain instances of problem solving during dreams. This paper emphasizes instead the lack of reflexivity and intentionality within dreams, which undermines their characterization as analogs of the waking world, and opposes claims that dreams can complement and aid waking world problem solving. The importance of reflexivity in imagination, in analogical (...) reasoning and in creativity means that dreaming, being usually single-minded, cannot be subsumed into these categories. Freud’s hypothesis that dreams result from the translation of latent thoughts into manifest content is taken to support this idea of cognitive deficiency during dreaming. Dream content, however, can still represent and reflect the dreamer’s waking concerns. (shrink)

Evans’ 1968 ANALOGY system was the first computer model of analogy. This paper demonstrates that the structure mapping model of analogy, when combined with high‐level visual processing and qualitative representations, can solve the same kinds of geometric analogy problems as were solved by ANALOGY. Importantly, the bulk of the computations are not particular to the model of this task but are general purpose: We use our existing sketch understanding system, CogSketch, to compute visual structure that is used by our existing (...) analogical matcher, Structure Mapping Engine (SME). We show how SME can be used to facilitate high‐level visual matching, proposing a role for structural alignment in mental rotation. We show how second‐order analogies over differences computed via analogies between pictures provide a more elegant model of the geometric analogy task. We compare our model against human data on a set of problems, showing that the model aligns well with both the answers chosen by people and the reaction times required to choose the answers. (shrink)

BackgroundMental health problems have emerged as a significant health complication in United States colleges during COVID-19, and as a result, they have been extensively investigated in the United States and internationally. In contrast, research on coping among the college population during the pandemic is scant. Hence, this study investigated coping strategies proposed by undergraduate students attending a Midwestern university.ObjectivesThe purpose of this preliminary study was to obtain college students’ feedback/opinions about coping strategies for mental health problems, suicide ideation, and self-harm (...) during COVID-19.MethodsIn December 2021, one-hundred and four undergraduate students completed an online survey on coping strategies using Qualtrics. Major topics included: Types of coping strategies/styles expressed by students for general mental health problems, Types of coping strategies for suicide ideation and self-harm behaviors, Preferred platforms for receiving coping therapy, and Reasons for accepting or refusing parent involvement with mental health problems.ResultsThe most beneficial coping strategies for mental health were ranked by college students as follows: a skills training development program, meditation, and mindfulness exercises, and physical education. The respondents’ best coping strategies for preventing self-harm and suicide ideation/behaviors during COVID-19 were ranked as: improving support from friends, building self-esteem, and addressing anger, depression, stress, and loneliness. Finally, a total of 50% of participants felt that parents should be involved in college student interventions. Students stated that the most important type of support that they received from their parents were: emotional support, direction and/or assistance with solutions, and problem-solving.ConclusionThis study identified potential avenues which could be implemented into action during future outbreaks. Specifically, employing interventions that: train undergraduate students to employ more effective skills training coping strategies or practicing mindfulness or meditation; integrate mental health, suicide, and self-harm prevention into the curriculum; offer more in-person campus services targeted toward the psychological and emotional effects of a pandemic, and involve support persons in students’ lives to enhance their well-being during and after COVID-19. (shrink)

This paper argues that humans possess unique cognitive abilities due to the presence of a functional system that exists in the human brain that is absent in the non-human brain. This system, the frontal feedback system, was born in the hominin brain when the great phylogenetic expansion of the prefrontal cortex relative to posterior sensory regions surpassed a critical threshold. Surpassing that threshold effectively reversed the preferred direction of information flow in the highest association regions of the neocortex, producing the (...) frontal feedback system. This reversal was from the caudo-rostral bias characteristic of non-human, or pre-human, brain dynamics to a rostro-caudal bias characteristic of modern human brain dynamics. The frontal feedback system works through frontal motor routines, or action schemes, manipulating the release and reconstruction of stored sensory memories in posterior sensory areas. As an obligatory feature of frontal feedback, a central character, or self, emerges within this cortical network that manifests itself as agent in these reconstructions as well as in the experience of sensory perceptions. Dynamical-systems modeling of cortical interactions is combined in the paper with recent neuroimaging studies of “resting-state” brain activity to bridge the gap between microscopic and macroscopic levels of cortical behavior. This synthesis is used to support the proposal of an information flow reversal occurring in the hominin brain and also to explain how such a reversal generates the wide variety of cognitive and experiential phenomena that many consider to be uniquely human. (shrink)

The present study examined a relational model of appraisal that specifies the situational and dispositional antecedents of appraised problem-focused coping potential, itself a hypothesised antecedent of the emotions of hope/challenge and resignation. The hypothesised relational antecedents of this appraisal were tested in a quasi-experiment in which individuals varying in self-perceived and objectively assessed math ability attempted to solve math problems on which difficulty was manipulated. Findings for the critical test problem largely conformed to predictions: Under difficult conditions, but (...) not easy ones, increasing math ability was generally associated with elevated appraisals of problem-focused coping potential, increased hope/challenge, reduced resignation, and increased likelihood of solving the problem. However, problem-focused coping potential, hope/challenge, and the likelihood of solving the problem were all lower, and resignation was higher, for the highest ability participants, than would be predicted from their ability levels. Comparable findings were not observed for appraisals of emotion-focused coping potential or its theoretically associated emotion of anxiety, providing evidence of discriminant validity for the examined model. The results of a mediational analysis supported the hypothesis that the effects of the quasi-experimental design on hope/challenge and resignation were mediated by their effects on appraised problem-focused coping potential, lending support to the proposition that appraisals play a causal role in emotion elicitation. (shrink)

Word problems are notoriously difficult to solve. We suggest that much of the difficulty children experience with word problems can be attributed to difficulty in comprehending abstract or ambiguous language. We tested this hypothesis by (1) requiring children to recall problems either before or after solving them, (2) requiring them to generate f'mal questions to incomplete word problems, and (3) modeling performance pattems using a computer simulation. Solution performance was found to be systematically related to recall and question generation performance. (...) Correct solutions were associated with accurate recall of the problem structure and with appropriate question generation. Solution "errors" were found to be correct solutions to miscomprehended problems. Word problems that contained abstract or ambiguous language tended to be miscomprehended more often than those using simpler language, and there was a great deal of systematicity in the way these problems were miscomprehended. Solution error pattems were successfully simulated by manipulating a computer model’s language comprehension strategies, as opposed to its knowledge of logical set relations. o was.. (shrink)

This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can be (...) defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools. (shrink)

We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem‐solving strategy, the relative difficulty of faults, and average fault‐finding time. More important, because the model learns (...) and transfers its learning across problems, it also accounts for the faster problem‐solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition‐based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. Overall, the quality of the correspondence appears to have arisen from procedural, declarative, and episodic learning all taking place within individual problem‐solving episodes. (shrink)

This paper has two objectives. The first is to formulate a critique of present-day cognitive linguistics concerning the inner workings of the cognitive system during language use, and the second is to put forward an alternative account that is inspired by the phenomenology of Merleau-Ponty. Due to its third-person methodology, CL views language use essentially as a problem-solving activity, as coping with two subproblems: the problem of minimum and maximum, which consists in selecting the appropriate expression out of (...) an unlimited multitude of possibilities, and the problem of the underdetermination of signification. This approach presupposes a notion of an isolated subject and a representationalist view of perception. We defend an alternative view of man's relation to the world in which intersubjectivity is constitutive of embodied subjectivity and which exchanges the representationalist view of perception for a direct nonrepresentationalism. We describe the ensuing view of linguistic action as intra- and interpersonal “all-at-onceness.” This approach dismisses the two subproblems CL implicitly identifies as constitutive of language use. The first is countered by rethinking what it means to be a situated speaking subject and results in the concept of “style.” The second is tackled by opposing the concept of “overdetermination” to CL's notion of underdetermination. (shrink)

Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual (A ∨ B) > C at a causal model M as a weighted (...) average of the probability of C in those submodels that truthmake A∨ B (Briggs 2012; Fine 2016, 2017). The weights of the submodels are given by the inverse distance to the original model M, based on a distance metric proposed by Eva, Stern, and Hartmann (2019). Apart from solving a major problem in the epistemology of counterfactuals, our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined. (shrink)

The bookModeling Reality covers a wide range of fascinating subjects, accessible to anyone who wants to learn about the use of computer modeling to solve a diverse range of problems, but who does not possess a specialized training in mathematics or computer science. The material presented is pitched at the level of high-school graduates, even though it covers some advanced topics (cellular automata, Shannon's measure of information, deterministic chaos, fractals, game theory, neural networks, genetic algorithms, and Turing machines). These advanced (...) topics are explained in terms of well known simple concepts: Cellular automata - Game of Life, Shannon's formula - Game of twenty questions, Game theory - Television quiz, etc. The book is unique in explaining in a straightforward, yet complete, fashion many important ideas, related to various models of reality and their applications. Twenty-five programs, written especially for this book, are provided on an accompanying CD. They greatly enhance its pedagogical value and make learning of even the more complex topics an enjoyable pleasure. (shrink)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools (...) to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and thus alternative with one (...) another, as sometimes claimed nowadays. The crucial problem is whether assuming this standpoint implies definitely renouncing to maintain a role for truth and the related concepts, particularly accuracy, in measurement. It is argued here that the well known objections against true values in measurement, which would lead to refuse the concept of accuracy as non-operational, or to maintain it as only qualitative, derive from a not clear distinction between three distinct processes: the metrological characterization of measuring systems, their calibration, and finally measurement. Under the hypotheses that (1) the concept of true value is related to the model of a measurement process, (2) the concept of uncertainty is related to the connection between such model and the world, and (3) accuracy is a property of measuring systems (and not of measurement results) and uncertainty is a property of measurement results (and not of measuring systems), not only the compatibility but actually the conjoint need of error-based and uncertainty-based modeling emerges. (shrink)

This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence (AI) has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can (...) be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools. (shrink)

Behavior oftentimes allows for many possible interpretations in terms of mental states, such as goals, beliefs, desires, and intentions. Reasoning about the relation between behavior and mental states is therefore considered to be an effortful process. We argue that people use simple strategies to deal with high cognitive demands of mental state inference. To test this hypothesis, we developed a computational cognitive model, which was able to simulate previous empirical findings: In two-player games, people apply simple strategies at first. They (...) only start revising their strategies when these do not pay off. The model could simulate these findings by recursively attributing its own problem solving skills to the other player, thus increasing the complexity of its own inferences. The model was validated by means of a comparison with findings from a developmental study in which the children demonstrated similar strategic developments. (shrink)

Behavior oftentimes allows for many possible interpretations in terms of mental states, such as goals, beliefs, desires, and intentions. Reasoning about the relation between behavior and mental states is therefore considered to be an effortful process. We argue that people use simple strategies to deal with high cognitive demands of mental state inference. To test this hypothesis, we developed a computational cognitive model, which was able to simulate previous empirical findings: In two-player games, people apply simple strategies at first. They (...) only start revising their strategies when these do not pay off. The model could simulate these findings by recursively attributing its own problem solving skills to the other player, thus increasing the complexity of its own inferences. The model was validated by means of a comparison with findings from a developmental study in which the children demonstrated similar strategic developments. (shrink)

Behavior oftentimes allows for many possible interpretations in terms of mental states, such as goals, beliefs, desires, and intentions. Reasoning about the relation between behavior and mental states is therefore considered to be an effortful process. We argue that people use simple strategies to deal with high cognitive demands of mental state inference. To test this hypothesis, we developed a computational cognitive model, which was able to simulate previous empirical findings: In two-player games, people apply simple strategies at first. They (...) only start revising their strategies when these do not pay off. The model could simulate these findings by recursively attributing its own problem solving skills to the other player, thus increasing the complexity of its own inferences. The model was validated by means of a comparison with findings from a developmental study in which the children demonstrated similar strategic developments. (shrink)

A person's cognitive map, or knowledge of large‐scale space, is built up from observations gathered as he travels through the environment. It acts as a problem solver to find routes and relative positions, as well as describing the current location. The TOUR model captures the multiple representations that make up the cognitive map, the problem‐solving strategies it uses, and the mechanisms for assimilating new information. The representations have rich collections of states of partial knowledge, which support many of (...) the performance characteristics of common‐sense knowledge. (shrink)

Much of our thinking focuses on deciding what to do in situations where the space of possible options is too large to evaluate exhaustively. Previous work has found that people do this by learning the general value of different behaviors, and prioritizing thinking about high-value options in new situations. Is this good-action bias always the best strategy, or can thinking about low-value options sometimes become more beneficial? Can people adapt their thinking accordingly based on the situation? And how do we (...) know what to think about in novel events? Here, we developed a block-puzzle paradigm that enabled us to measure people's thinking plans and compare them to a computational model of rational thought. We used two distinct response methods to explore what people think about—a self-report method, in which we asked people explicitly to report what they thought about, and an implicit response time method, in which we used people's decision-making times to reveal what they thought about. Our results suggest that people can quickly estimate the apparent value of different options and use this to decide what to think about. Critically, we find that people can flexibly prioritize whether to think about high-value options (Experiments 1 and 2) or low-value options (Experiments 3, 4, and 5), depending on the problem. Through computational modeling, we show that these thinking strategies are broadly rational, enabling people to maximize the value of long-term decisions. Our results suggest that thinking plans are flexible: What we think about depends on the structure of the problems we are trying to solve. (shrink)

Possibility semantics offers an elegant framework for a semantic analysis of modal logic that does not recruit fully determinate entities such as possible worlds. The present papers considers the application of possibility semantics to the modeling of the indeterminacy of the future. Interesting theoretical problems arise in connection to the addition of object-language determinacy operator. We argue that adding a two-dimensional layer to possibility semantics can help solve these problems. The resulting system assigns to the two-dimensional determinacy operator a well-known (...) logic (coinciding with the logic of universal modalities under global consequence). The paper concludes with some preliminary inroads into the question of how to distinguish two-dimensional possibility semantics from the more established branching framework. (shrink)

Background Moral distress has negative effects on physical and mental health. However, there is little information about nurses’ coping strategies reducing moral distress. Aim The purpose of this study was to investigate the coping strategies of intensive care unit nurses reducing moral distress in Iran. Study design This is a qualitative study with a content analysis approach. Participants and research context The research sample consisted of nurses working in intensive care units of teaching hospitals affiliated to Tehran University of Medical (...) Sciences. Samples were selected among eligible nurses by purposive sampling. Data were obtained through 17 in-depth, individual, and semi-structured interviews with 17 nurses. Graneheim and Lundman’s (2004) 6-step content analysis method was used to analyze the data. Data management was also performed by MAXQDA software version 20. The COREQ checklist was used to report the study. Ethical considerations This project was approved by the Ethics Committee of Tehran University of Medical Sciences. All ethical guidelines in research were followed. Finding The data analysis resulted in the formation of two main categories of desirable coping strategies (with two subcategories of compensation and rejuvenation) and less desirable coping strategies (with three subclasses of indifference over time, escape and concealment). Discussion The intensive care unit nurses in dealing with ethical problems first try to solve the problem through discussion, but when they fail to resolve it peacefully, they resort to several coping strategies. Factors, such as increasing experience, lack of support from hospital managers and officials, poor communication between colleagues, the need to maintain hierarchy, fear of reprimand, and a sense of powerlessness, changed the nurses’ preferred strategies. It is important for managers to provide a blame/punishment-free atmosphere for expression of ethical experiences; a supportive atmosphere in which staff can engage in ethical discussions without fear of punishment. They should also provide opportunities for rest, rejuvenation, and adequate training for their employees. (shrink)