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Questionnaire surveys made at currency markets around the world reveal that currency trade to a large extent not only is determined by an economy's performance or expected performance. Indeed, a fraction is guided by technical trading, which means that past exchange rates are assumed to provide information about future exchange rate movements. The purpose of this paper is to ask how a successful monetary policy should be designed when technical trading in the form of trend following is used in currency trading.

The paper embeds an optimal policy rule into Galí and Monacelli's dynamic stochastic general equilibrium (DSGE) model for a small open economy, which is augmented with trend following in currency trading, to examine the prerequisites for a successful monetary policy. Specifically, the conditions for a determinate rational expectations equilibrium (REE) that also is stable under least squares learning are in focus. The paper also computes impulse‐response functions for key variables to study how the economy returns to steady state after being hit by a shock.

The paper finds that a determinate REE that also is stable under least squares learning often is the outcome when there is a limited amount of trend following in currency trading, but that a more flexible inflation rate targeting in monetary policy sometimes cause an indeterminate REE in the economy. Thus, strict, or almost strict, inflation rate targeting in monetary policy is recommended also when there is technical trading in currency trading and not only when all currency trading is guided by fundamental analysis (in the form of rational expectations). This result is a new result in the literature.

There are already models in the literature on monetary policy design that incorporate technical trading in currency trading into an otherwise standard DSGE model. There is also a huge amount of DSGE models in the literature in which monetary policy is optimal. However, the model in this paper is the first model, to the best of the author's knowledge, where technical trading in currency trading and optimal monetary policy are combined in the same DSGE model.

This study aims to examine the relationship between undergraduates' course experience and their deep learning approach and to identify areas of improvement to facilitate students' deep learning in the private higher education context.

Data were collected from 844 Malaysian undergraduate students who studied in six private higher education institutions (HEIs) in Penang and Selangor. This study used partial least squares structural equation modelling (PLS-SEM) for data analysis.

The findings revealed that good teaching and appropriate assessment have no significant relationship with deep learning. Generic skills, clear goals and standards, appropriate workload and emphasis on independence are positively related to deep learning. Generic skills and emphasis on independence are two domains that deserve attention to enhance deep learning among undergraduates.

Lecturers need to focus on to the cultivation of generic skills to facilitate students' deep learning. Student autonomy and student-centred teaching approaches should be empowered and prioritised in teaching and learning.

The current study has its originality in providing empirical findings to inform the significant relationship between dimensions of course experience and deep learning in Malaysian private HEIs. Besides, it also identifies the areas of improvement concerning teaching and learning at the private HEIs using importance-performance matrix analysis (IPMA) in a non-Western context.

The evaluation of the conductivity profile of layered metallic structures is performed via the inversion of the impedance of a circular air cored probe coil of rectangular cross section. The inversion approach is based on the implementation of generalised radial basis function neural networks. The choice of the size of the network and the evaluation of its weights are handled by the orthogonal least squares learning algorithm. The merits of the proposed method are illustrated in the light of two examples concerning non‐destructive testing applications.

This paper seeks to explain how inefficient learning rules may lead to a perception of economic and ecological realities that may be systematically distorted in the long run.

The paper evaluates long‐term growth in standard growth‐pollution models. Expectations about future levels of pollution are formed under adaptive learning.

Socio‐economic players (private agents, governments, non‐profit organizations and/or groups of states) may fail in understanding, with full accuracy, long‐term environmental conditions. The perception about environment threats acquires a cyclical nature, even when ecological problems evolve steadily.

Relevant policy implications emerge if the agent is unable to compute the true levels of environmental pollution that will persist in the steady state. Authorities of several kinds are likely to underestimate or overestimate ecological problems.

The learning approach to the perception of the environment can be applied to other economic, social and biological issues, besides material growth. For instance, it can contribute to explain some cases of over‐exploitation of resources: even in the presence of a social planner capable of avoiding typical “tragedy of the commons” situations, this entity may fail in perceiving the reality and, thus, in applying the policies that prevent the exhaustion of resources.

The paper contributes to the literature on growth and environmental issues, but takes a step forward: it approaches not only the observed relation between economy and ecology, but also the impact over the observed relation of a systematically incorrect interpretation of such a connection.

The purpose of this paper is to determine the measurement constructs of learning within construction projects' milieu. The literature indicated some mechanisms of learning in projects under four aspects, namely knowledge sharing, knowledge creation, team action to learn, and learning support. The empirical study attempts to verify whether intra-project learning can be measured through these aspects.

The study used a survey method to collect the data from 36 mega-sized building projects in Malaysia. In total, 203 questionnaires were collected from professionals working in the sites of these projects. The data were analysed using principal component analysis (PCA) to determine the constructs of intra-project learning. Partial least squares-path modeling was used then to confirm the results of PCA and determine the contribution of each construct to intra-project learning.

The results affirmed two constructs of intra-project learning, named, social and technical and each consisted of four indicators of learning.

The paper emphasized the socio-technical perspective of learning and contributed to developing a hierarchical measurement model of learning in construction project. A project manager can propose new initiatives in response to the new perspective of learning for team building and continuous development. Lastly, the paper provides a comprehensive presentation of how to estimate the hierarchical measurement models of project learning as a latent variable.

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the aggregate macro behavior they co-create. The aim of this article is to provide a comprehensive literature survey on laboratory experiments on expectations in macroeconomics and finance. In particular, we discuss the extent to which expectations are rational or may be described by simple forecasting heuristics, at the individual as well as the aggregate level.

This chapter examines a class of interest rate rules that respond to public expectations and to lagged variables. Varying levels of commitment correspond to varying degrees of response to lagged output and targeting of the price level. If the response rises (unintentionally) above the optimal level, the outcome deteriorates severely. Hence, the optimal level of commitment is sensitive to the method of expectations formation and partial commitment is the robust, optimal policy. The policymaker should adjust the price level toward a target, but complete adjustment is neither necessary nor desirable.

The purpose of this paper is to propose an improved optimization methodology of information granulation‐based fuzzy radial basis function neural networks (IG‐FRBFNN). In the IG‐FRBFNN, the membership functions of the premise part of fuzzy rules are determined by means of fuzzy c‐means (FCM) clustering. Also, high‐order polynomial is considered as the consequent part of fuzzy rules which represent input‐output relation characteristic of sub‐space and weighted least squares learning is used to estimate the coefficients of polynomial. Since the performance of IG‐RBFNN is affected by some parameters such as a specific subset of input variables, the fuzzification coefficient of FCM, the number of rules and the order of polynomial of consequent part of fuzzy rules, we need the structural as well as parametric optimization of the network. The proposed model is demonstrated with the use of two kinds of examples such as nonlinear function approximation problem and Mackey‐Glass time‐series data.

The type of polynomial of each fuzzy rule is determined by selection algorithm by considering the local error as performance index. In addition, the combined local error is introduced as a performance index considered by two kinds of parameters such as the polynomial type of each rule and the number of polynomial coefficients of each rule. Besides this, other structural and parametric factors of the IG‐FRBFNN are optimized to minimize the global error of model by means of the hierarchical fair competition‐based parallel genetic algorithm.

The performance of the proposed model is illustrated with the aid of two examples. The proposed optimization method leads to an accurate and highly interpretable fuzzy model.

The proposed hybrid optimization methodology is interesting for designing an accurate and highly interpretable fuzzy model. Hybrid optimization algorithm comes in the form of the combination of the combined local error and the global error.

Online forums are increasingly deployed as important e‐learning tools for facilitating student learning in classrooms. However, building an online forum does not guarantee participation by students. The purpose of this paper is to advance our knowledge of facilitating student participation in this context by studying the role of communication environment.

The model was tested using data collected from a survey administered in a university in Hong Kong.

Results revealed that psychological safety communication climate influenced the intention of students to continue their participation both directly and indirectly through perceived responsiveness and self‐efficacy.

This study builds on social cognitive theory and extends the existing understanding of participation in e‐learning by highlighting the roles of psychological safety communication climate and perceived responsiveness, two communication environment factors critical to student learning but not yet addressed seriously in the e‐learning context.

Empirical work in macroeconomics almost universally relies on the hypothesis of rational expectations (RE).

This chapter departs from the literature by considering a variety of alternative expectations formation models. We study the econometric properties of a popular New Keynesian monetary DSGE model under different expectational assumptions: the benchmark case of RE, RE extended to allow for “news” about future shocks, near-RE and learning, and observed subjective expectations from surveys.

The results show that the econometric evaluation of the model is extremely sensitive to how expectations are modeled. The posterior distributions for the structural parameters significantly shift when the assumption of RE is modified. Estimates of the structural disturbances under different expectation processes are often dissimilar.

The modeling of expectations has important effects on the ability of the model to fit macroeconomic time series. The model achieves its worse fit under RE. The introduction of news improves fit. The best-fitting specifications, however, are those that assume learning. Expectations also have large effects on forecasting. Survey expectations, news, and learning all work to improve the model's one-step-ahead forecasting accuracy. RE, however, dominate over longer horizons, such as one-year ahead or beyond.