This paper proposes a learning-efficiency explanation of modular structure in language. An optimal grammar arises as the solution to the problem of learning a language from a minimal number of observations of instances of the use of the language. Agents face symmetry constraints that limit their ability to make a priori distinctions among symbols used in the language and among objects (interpreted as facts, events, speaker’s intentions) that are to be represented by messages in the language. It (...) is shown that if it is commonly known that the object space is modular and messages are strings, then modularity of the language is sufficient and (essentially) necessary for learning efficiency. Journal of Economic Literature Classification Number: C72. (shrink)

The age of global risks and changes that have come into play where stable development used to be a norm and the era of postmodernism, as a possibility of the multiplicity of meanings and solutions, determine the vectors of human development in the 21st century. Human society is undergoing changes, digital technologies are increasingly penetrating various domains of life, and it has become clear, they are here to stay because they are already changing life itself. The postmodern generation, consumed by (...) "virtual reality", sees the world differently. They need knowledge different from what previous generations used where knowledge itself requires new methods of education. With all positive and negative implications, the e-learning format has established itself as a viable education option in times of the world pandemic. The way this learning format transforms the higher education system is irrevocable. Thus, an increasing number of universities are integrating online and personalized components into their courses. However, the role of the teacher remains invariably important. Although methods and format of their work may adjust and alter, it is the teacher who passes on human experience, imbues knowledge with emotional coloring and encourages reflection and reasoning. Therefore, the e-learning system rests on two factors, namely, the presence on the Internet and the development of student-teacher relations. All available research and resources are currently aimed at improving technical capabilities, whereas the "student-learner" system needs to be reconsidered. It will not be destroyed in the new conditions, rather transformed into new forms. (shrink)

One of the few certainties we have about our collective future is that it will require a massive amount of learning, by just about everybody, everywhere. The time for generating as many creative and collaborative knowledge builders has come. Therefore, improving the efficiency of learning could very well become a key leverage point for successfully meeting the challenges of the twenty-first century. This paper explores the possibilities of using mutual learning as a systemic means to improve (...) learning efficiencies. This is measured through three different metrics: (1) the time required to learn, (2) the quantity of learning that is retained over time, and (3) the leveraging of the cost of scholarships through the use of a complementary currency designed to track and encourage mutual learning. In all three metrics, mutual learning is shown as an important approach to increase the effectiveness of learning and, at the very least, can be an adjunct to the conventional educational methods. Mutual learning could apply not only to learning among peers, but also to social, intergenerational, or intercultural mutual learning. (shrink)

The study aimed to identify E- Learning strategies and their relation to the efficiency of research performance in foreign and Palestinian universities (University of Ottawa, Munster, Suez Canal, Al-Azhar, Islamic, Al-Aqsa). The analytical descriptive approach was used for this purpose, and relying on the questionnaire as a main tool for data collection. The study society is from the senior management, where the number of senior management in the universities in question is 206. The random stratified sample was selected (...) and (SPSS) was used for statistical data analysis. The study found a significant relationship between E- Learning strategies and the efficiency of research performance in universities. It also reached the participation of senior management in the research that develops the university performance in the Palestinian universities. The senior management indicated that they do not care to follow the policies of implementing the development of scientific research. While the senior management in foreign universities indicated that they are interested in following up the policies of implementing the development of scientific research. The study also showed that senior management in Palestinian universities does not care about providing the appropriate budget for E- Learning. The study recommended that the senior management of Palestinian universities should provide an E- Learning budget and encourage employees to continue using E- Learning strategies. The administration in the Palestinian universities should adopt and support outstanding research, and the need to encourage interest in the implementation of policies for the development of scientific research. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP (...) experiments with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

In China, cultivation of the craftsmanship spirit is strongly advocated, but little attention is devoted to whether and how “working for the family” promotes heritage of this spirit. A configuration model of family motivation and organizational learning is proposed and expounded. Fuzzy set qualitative comparative analysis was used to further explore the conditional configuration. The results show that the fitting family motivation to organizational learning is important for promoting heritage of the craftsmanship spirit. There are two paths that (...) promote efficient heritage of this spirit: the institution-led path and the motivation-driven interpretation-led path. For the institution-led path, when apprentices have strong institution learning ability, the anterior-cause condition involves low family motivation, but this factor plays a weak role in promoting efficient heritage of the craftsmanship spirit. In a configuration consisting of intuition, interpretation, integration and institution, family motivation becomes irrelevant, which explains the phenomenon that organizational learning behavior reinforcement leads to a lack of family motivation. For the motivation-driven interpretation-led path, even without regular and institutionalized learning behaviors in the organization, the core conditions of high family motivation and high explanation learning, together with low intuition learning and low integration learning, will promote efficient heritage of the craftsmanship spirit. (shrink)

The importance of the topic of the article lies in the fact that in the era of postmodernism, informatization of the elementary school allows you to improve the quality of the educational process, through the use of information and communication technologies (ICT) to simplify management of the educational process, to organize the exchange of teaching experience, to expand didactic capabilities of the lesson. The purpose of the article is the need to study and justify the importance of using new ICT (...) in primary school lessons, to expand the experience of e-learning in primary school. The article expands the concept of information and communication technologies and their benefits in primary school; given the rationale for conducting a lesson using ICT; the experience of e-learning in the primary school of the postmodern era is characterized. Thus, one of the priorities of the development of education in postmodern society is the professional application in all areas of information technology and e-learning, which can increase the interest of present students and improve the ability to adapt to current electronic information society. The formation of knowledge and skills in the field of computer literacy of teachers and students will be an indicator of compliance of its level with current requirements. (shrink)

This paper studies efficient learning with respect to mind changes. Our starting point is the idea that a learner that is efficient with respect to mind changes minimizes mind changes not only globally in the entire learning problem, but also locally in subproblems after receiving some evidence. Formalizing this idea leads to the notion of uniform mind change optimality. We characterize the structure of language classes that can be identified with at most α mind changes by some learner (...) (not necessarily effective): A language class L is identifiable with α mind changes iff the accumulation order of L is at most α. Accumulation order is a classic concept from point-set topology. To aid the construction of learning algorithms, we show that the characteristic property of uniformly mind change optimal learners is that they output conjectures (languages) with maximal accumulation order. We illustrate the theory by describing mind change optimal learners for various problems such as identifying linear subspaces and one-variable patterns. (shrink)

In the educational hypermedia domain, adaptive systems try to adapt educational materials according to the required properties of a user. The adaptability of these systems becomes more effective once the system has the knowledge about how a student can learn better. Studies suggest that, for effective personalization, one of the important features is to know precisely the learning style of a student. However, learning styles are dynamic and may vary domain-wise. To address such aspects of learning styles, (...) we have proposed a computationally efficient solution that considers the dynamic and nondeterministic nature of learning styles, effect of the subject domain, and nonstationary aspect during the learning process. The proposed model is novel, robust, and flexible to optimize students’ domain-wise learning style preferences for better content adaptation. We have developed a web-based experimental prototype for assessment and validation. The proposed model is compared with the existing available learning style-based model, and the experimental results show that personalization based on incorporating discipline-wise learning style variations becomes more effective. (shrink)

The last two decades have produced unprecedented successes in the fields of artificial intelligence and machine learning (ML), due almost entirely to advances in deep neural networks (DNNs). Deep hierarchical memory networks are not a novel concept in cognitive science and can be traced back more than a half century to Simon's early work on discrimination nets for simulating human expertise. The major difference between DNNs and the deep memory nets meant for explaining human cognition is that the latter (...) are symbolic networks meant to model the dynamics of human memory and learning. Cognition-inspired symbolic deep networks (SDNs) address several known issues with DNNs, including (1) learning efficiency, where a much larger number of training examples are required for DNNs than would be expected for a human; (2) catastrophic interference, where what is learned by a DNN gets unlearned when a new problem is presented; and (3) explainability, where there is no way to explain what is learned by a DNN. This paper explores whether SDNs can achieve similar classification accuracy performance to DNNs across several popular ML datasets and discusses the strengths and weaknesses of each approach. Simulations reveal that (1) SDNs provide similar accuracy to DNNs in most cases, (2) SDNs are far more efficient than DNNs, (3) SDNs are as robust as DNNs to irrelevant/noisy attributes in the data, and (4) SDNs are far more robust to catastrophic interference than DNNs. We conclude that SDNs offer a promising path toward human-level accuracy and efficiency in category learning. More generally, ML frameworks could stand to benefit from cognitively inspired approaches, borrowing more features and functionality from models meant to simulate and explain human learning. (shrink)

The lostness measure, an implicit and unobtrusive measure originally designed for assessing the usability of hypertext systems, could be useful in Virtual Reality (VR) games where players need to find information to complete a task. VR locomotion systems with node-based movement mimic actions for exploration and browsing found in hypertext systems. For that reason, hypertext usability measures, such as “lostness” can be used to identify how disoriented a player is when completing tasks in an educational game by examining steps made (...) by the player. An evaluation of two different lostness measures, global and local lostness, based on two different types of tasks, is described in a VR educational game using 13 college students between 14 and 18 years old in a first study and extended using 12 extra participants in a second study. Multiple Linear Regression analyses showed, in both studies, that local lostness, and not global lostness, had a significant effect on a post-game knowledge test. Therefore, we argued that local lostness was able to predict how well-participants would perform on a post-game knowledge test indicating how well they learned from the game. In-game experience aspects (engagement, cognitive interest, and presence) were also evaluated and, interestingly, it was also found that participants learned less when they felt more present in the game. We believe these two measures relate to cognitive overload, which is known to have an adverse effect on learning. Further research should investigate the lostness measure for use in an online adaptive game system and design the game system in such a way that the risk of cognitive overload is minimized when learning, resulting in higher retention of information. (shrink)

This study aims to examine the main determinants of efficiency of both conventional and Islamic Saudi banks and then choose the best fit model among machine learning prediction models, Chi-squared automatic interaction detector, linear regression, and neural network ). The data were collected from the annual financial reports of Saudi banks from 2014 to 2018. The Saudi banking sector consists of 11 banks, 4 of which are Islamic. In this study, the major financial ratios are subgrouped into the (...) profitability ratios, managerial practices, asset and loans, capital adequacy ratios, and liquidity. First, regression analysis is implemented with efficiency ratio as a dependent variable and the proxies of banks’ profitability, liquidity, asset quality, management ratios, and capital adequacy ratios as independent variables. Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models were developed to choose the best fit. The performance metrics have also been evaluated. Regression results exhibit that the efficiency of both conventional and Islamic banks is highly affected by profitability, liquidity, and managerial practices. Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. The best predictors of cost efficiency for Saudi banks are the capital ratios, namely, CAR total and CAR tier 1. Findings are theoretically and practically important to academics, investors, and policymakers. Policymakers can benefit from the novelty of this study in building an early warning system using the CHAID model to predict different financial distress scenarios. (shrink)

The study aims to identify the efficiency of the university education performance from the perspective of postgraduate and undergraduate students in international and Palestinian universities. The analytical descriptive approach was used for this purpose and the questionnaire was used as a main tool for data collection. The study community consists of: post graduate students, (23850) graduate students and (146355) undergraduate students. The sample of the study was 378 graduate students and 383 undergraduate students. The random stratified sample was used. (...) The Statistical Package of Social Sciences (SPSS) was also used for data analysis. The study reached a number of results, the most important of which are: The level of efficiency of educational performance in Palestinian and international universities from the point of view of postgraduate and undergraduate students was high. And that there are significant differences between the average views of the sample of the study on the efficiency of educational performance in Palestinian and international universities attributed to the University and to the benefit of international universities. The study concluded many recommendations, the most important of which is the necessity of continuing to develop e-learning strategies that affect the efficiency of educational performance and research commensurate with the university's position in the local and international community, which puts it on the best classification between local and international universities through e-learning. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

This paper proposes a nonlinear autoregressive neural network method for the investment performance evaluation of state-owned enterprises. It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises. Secondly, a set of investment (...) class='Hi'>efficiency evaluation index system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage. (shrink)

Biosemiosis is a process of choice-making between simultaneously alternative options. It is well-known that, when sufficiently young children encounter a new word, they tend to interpret it as pointing to a meaning that does not have a word yet in their lexicon rather than to a meaning that already has a word attached. In previous research, the strategy was shown to be optimal from an information theoretic standpoint. In that framework, interpretation is hypothesized to be driven by the minimization of (...) a cost function: the option of least communication cost is chosen. However, the information theoretic model employed in that research neither explains the weakening of that vocabulary learning bias in older children or polylinguals nor reproduces Zipf’s meaning-frequency law, namely the non-linear relationship between the number of meanings of a word and its frequency. Here we consider a generalization of the model that is channeled to reproduce that law. The analysis of the new model reveals regions of the phase space where the bias disappears consistently with the weakening or loss of the bias in older children or polylinguals. The model is abstract enough to support future research on other levels of life that are relevant to biosemiotics. In the deep learning era, the model is a transparent low-dimensional tool for future experimental research and illustrates the predictive power of a theoretical framework originally designed to shed light on the origins of Zipf’s rank-frequency law. (shrink)

With the development of the globalization of science and technology, innovation has become an important driving force for regional economic development. As a core element of regional innovation, financial R&D resources have also become a key element to enhance national innovation capabilities and national economic competitiveness. National and regional innovation capabilities have a direct impact. There are also many deep-seated problems behind the world-renowned achievements, such as irrational industrial structure, insufficient independent innovation capabilities, low resource utilization efficiency, and the (...) service quality and efficiency of financial institutions for the transformation of total factor productivity. These problems extremely restrict the efficiency upgrade and further development of our country’s total factor productivity. This study uses the DEA-Malmquist index model to measure the efficiency of fiscal R&D resource allocation in 28 provinces and regions in China in the past 10 years and uses Mapinfo12.0 software to analyze regional differences in the efficiency of fiscal R&D resource allocation in China from a spatial perspective. During the year, the overall R&D resource allocation efficiency of 28 provinces and autonomous regions in China has shown an upward trend. The efficiency of fiscal R&D resource allocation and the concentration of financial factors have had a positive impact on total factor productivity, transform and upgrade factors, increase total factor productivity, and provide empirical evidence for building a strong country. (shrink)

Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting (...) from adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions. (shrink)

Teaching the art of profession-related communication to students of non-linguistic study fields allows instructors to explain their students how to keep up the conversation using facts, data, concepts etc. specific to the area of their future profession. It activates the acquisition processes as well as increases students' motivation to study. The formation of oral monologue speaking skills in students of non-linguistic study fields is one of the tasks within the course of Foreign ( English) Language for Specific Purposes.This process is (...) associated with certain difficulties, which can inhibit learning and demotivated students to study. Among strategies and techniques aiding learning that can be used to improve performance and study outcomes during the course and can be implemented for the enhancement of speaking on occupation-related topics, the mind mapping technique is of specific relevance. It can serve as an interesting and useful tool serving to enhance quality of oral speaking skills in students majoring in Psychology as the representatives of non-linguistic study fields. The article analyses the carried out research concerning the facilitating impact of mind mapping on the oral speech performance of Psychology students. The study hypothesis implies that the use of the mind-mapping technique in the process of preparing for and presenting the talk in the study sessions enhances students' oral monologue speech performance. In order to check the hypothesis, we have conducted the experiment in Bohdan Khmelnytsky National University of Cherkasy at the Department of Psychology. The study involved 46 first-year students, including 34 female and 12 male participants. The respondents were randomly assigned to the experimental mind-mapping group of 23 students ( including 6 males) and the control group of 23 students ( including 6 males as well). The students in the experimental group were involved into the experiment using the mind-mapping technique during their study, and the students in the control group continued their study in the usual way. The hypothesis was confirmed. The mind map use stimulates activity of both brain hemispheres, thus making it easier for students to memorize thematic vocabulary, follow the logical sequence of the talk when speaking. The research findings have shown the effectiveness of implementing the mind-mapping technique into the university course teaching English for Specific Purposes to develop, advance and master students' professional communicative competence. Consistent use of mind maps will also promote the educational process efficiency and have positive effects on the general formation of competitive skilled specialists. (shrink)

Due to the increasing number of people traveling by air, the passenger flow at the airport is increasing, and the problem of passenger drop-off and pickup has a huge impact on urban traffic. The difficulty of taking a taxi at the airport is still a hot issue in the society. Aiming at the problem of optimizing the allocation of taxi resource, this paper is based on the cost-benefit analysis method to determine the factors that affect the taxi driver’s decision-making. The (...) mathematical methods such as function equation, BP neural network algorithm, and queuing theory were used to establish a complete decision-making model for taxi drivers and an optimization model of dispatching efficiency at the airport. A conclusion has been drawn that the allocation of airport taxi resource should be arranged closely related to drivers’ revenue and the layout of airport line. (shrink)

Deep Neural Networks (DNNs) are popular for classifying large noisy analogue data. However, DNNs suffer from several known issues, including explainability, efficiency, catastrophic interference, and a need for high‐end computational resources. Our simulations reveal that psychologically‐inspired symbolic deep networks (SDNs) achieve similar accuracy and robustness to noise as DNNs on common ML problem sets, while addressing these issues.

To explain social learning without invoking the cognitively complex concept of imitation, many learning mechanisms have been proposed. Borrowing an idea used routinely in cognitive psychology, we argue that most of these alternatives can be subsumed under a single process, priming, in which input increases the activation of stored internal representations. Imitation itself has generally been seen as a This has diverted much research towards the all-or-none question of whether an animal can imitate, with disappointingly inconclusive results. In (...) the great apes, however, voluntary, learned behaviour is organized hierarchically. This means that imitation can occur at various levels, of which we single out two clearly distinct ones: the a rather detailed and linear specification of sequential acts, and the a broader description of subroutine structure and the hierarchical layout of a behavioural Program level imitation is a high-level, constructive mechanism, adapted for the efficient learning of complex skills and thus not evident in the simple manipulations used to test for imitation in the laboratory. As examples, we describe the food-preparation techniques of wild mountain gorillas and the imitative behaviour of orangutans undergoing to the wild. Representing and manipulating relations between objects seems to be one basic building block in their hierarchical programs. There is evidence that great apes suffer from a stricter capacity limit than humans in the hierarchical depth of planning. We re-interpret some chimpanzee behaviour previously described as and suggest that all great apes may be able to imitate at the program level. Action level imitation is seldom observed in great ape skill learning, and may have a largely social role, even in humans. (shrink)

Due to the huge amount of data being generating from different sources, the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data optimization problems comes from many factors such as the high number of features, and the existing of lost data. The feature selection process becomes an important step in many data mining and machine learning algorithms to reduce the dimensionality of the optimization problems and increase the (...) performance of the classification or clustering algorithms. In this paper, a set of hybrid and efficient genetic algorithms are proposed to solve feature selection problem, when the handled data has a large feature size. The proposed algorithms use a new gene-weighted mechanism that can adaptively classify the features into strong relative features, weak or redundant features, and unstable features during the evolution of the algorithm. Based on this classification, the proposed algorithm gives the strong features high priority and the weak features less priority when generating new candidate solutions. In the same time, the proposed algorithm tries to more concentrate on unstable features that sometimes appear and sometimes disappear from the best solutions of the population. The performance of proposed algorithms is investigated by using different datasets and feature selection algorithms. The results show that our proposed algorithms can outperform the other feature selection algorithms and effectively enhance the classification performance over the tested datasets. (shrink)

Extended and distributed cognition theories argue that human cognitive systems sometimes include non-biological objects. On these views, the physical supervenience base of cognitive systems is thus not the biological brain or even the embodied organism, but an organism-plus-artifacts. In this paper, we provide a novel account of the implications of these views for learning, education, and assessment. We start by conceptualising how we learn to assemble extended cognitive systems by internalising cultural norms and practices. Having a better grip on (...) how extended cognitive systems are assembled, we focus on the question: If our cognition extends, how should we educate and assess such extended cognitive systems? We suggest various ways to minimize possible negative effects of extending one’s cognition and to efficiently find and organise (online) information by adopting a virtue epistemology approach. Educational and assessment implications are foregrounded, particularly in the case of Danish students’ use of the Internet during exams. (shrink)

Learning to write in a foreign language is a complex cognitive process. The process-genre approach is a common instructional practice adopted by language teachers to develop learners’ writing abilities. However, the interacting elements of procedural knowledge, linguistic knowledge, and generic knowledge in this approach may exceed the capacity of an individual learner’s working memory, thus actually hindering the acquisition of writing skills. According to the collective working memory effect, it was hypothesized that teaching writing skills of English as a (...) foreign language by adopting a process-genre approach in collaborative conditions could lead to better writing performance, lower cognitive load, and higher instructional efficiency. The reported experiment compared learning writing skills of English as a foreign language in individual and collaborative instructional conditions from a cognitive load perspective, a rarely adopted approach in this field. The results indicated that the collaborative instructional condition was more effective and efficient than the individual instructional condition in improving the quality of written products as well as in optimizing the cognitive load experienced by the learners. Measures of cognitive load were used to support the cognitive load theory’s interpretation of the results, which is the unique contribution of this research study to the field. (shrink)

We propose an approach for texture segmentation based on weak supervised learning. The weak supervision implies that the user marks only a single small patch for each class in the input image. These patches are used for training. We employ the method of graph cuts for the segmentation task. Our work demonstrates that even under such weak training, texture segmentation can be achieved efficiently and with good accuracy via graph cuts. Moreover, our approach uses a simpler feature representation than (...) that in similar contemporary segmentation approaches. We also provide a brief discussion indicating the good performance of our approach. We validate our method on various standard texture mosaics and also on segmentation of natural images with large texture variations. (shrink)

Recent work on the evolution of social contracts and conventions has often used models of bargaining games, with reinforcement learning. A recent innovation is the requirement that every strategy must be invented either through through learning or reinforcement. However, agents frequently get stuck in highly-reinforced “traps” that prevent them from arriving at outcomes that are efficient or fair to the both players. Agents face a trade-off between exploration and exploitation, i.e. between continuing to invent new strategies and reinforcing (...) strategies that have already become highly reinforced by yielding high rewards. In this paper I systematically study the relationship between rates of invention and the efficiency and fairness of outcomes in two-player, repeated bargaining games. I use a basic reinforcement learning model with invention, and five variations of this model, designed introduce various forms of forgetting, to prioritize more recent reinforcement, or to maintain a higher rate of invention. I use computer simulations to investigate the outcomes of each model. Each models shows qualitative similarities in the relationship between the efficiency and fairness of outcomes, and the relative amount of exploration or exploitation that takes place. Surprisingly, there are often trade-offs between the efficiency and the fairness of the outcomes. (shrink)

While organizational learning literature has generated significant insight into the effective and efficient achievement of organizational goals as well as to the modus of learning, it is currently unable to describe moral learning processes in organizations consistently. Corporations need to learn morally if they want to deal effectively with stakeholders criticizing their conduct. Nongovernmental organizations do not ask corporations to be more effective or efficient in what they do, but to become more responsible or to learn morally. (...) Current research on the moral aspect of organizational learning has been primarily of a theoretical nature and is in need of empirical verification. Results of a longitudinal case study as Citigroup’s conflict with the Rainforest Action Network show that current organizational moral learning theories do not fit the moral learning path observed at Citigroup. More empirical research is needed to describe organizational moral learning. (shrink)

Online learning has received extensive attention in the field of education in the recent decade, especially after COVID-19 swept the globe in 2020. Online learning satisfaction has become the focal point of the research, since it is of vital significance to enhance online learning efficiency. This paper reviews the research on OLS from the dimensions of online learners, online instructors, online platforms and online instructional design to have a clear picture of factors affecting OLS. Based on (...) the review of previous studies, this mini review presents the prospect of future research on OLS and believes that breakthroughs on OLS research can be achieved by innovating research methods, expanding research subjects, and enriching research topics. OLS is a complicated dynamic system influenced by a diversity of factors, and it is worth more in-depth research by scholars and educators in future. (shrink)