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The purpose of this chapter is to investigate the adoption and use of building information modelling (BIM) for residential real estate development in Nigeria (using Lagos as a case study), with a view to providing information towards improving BIM uptake, which could enhance sustainable housing delivery in the country. A quantitative research methodology was adopted involving the use of questionnaire survey to collect primary data. The data were obtained from private real estate developers in Lagos State. The self-administered questionnaire was distributed to all the 72 active real estate developers in the study area, and the response rate was 62.5%. The collected data were analysed using statistical tools such as frequency and percentages, mean rating and chi-square. The results revealed a low level of awareness and usage of the transformative and contemporary BIM technology (6D BIM version) by real estate developers. It was established that the 2D and 3D BIM traditional versions were the most utilised across the phases of real estate development process. It was also found that the level of BIM utilisation has a significant relationship with the age and asset base of the real estate developers. The chapter concludes by advocating increase in the asset base and organisational profile of real estate developers to enhance BIM adoption, especially, the 6D BIM, which could facilitate sustainable real estate development.

Many instructors have implemented decision-based learning (DBL) into their courses. This chapter is a careful qualitative analysis of the narratives in this book done by the editors. The author found common themes among all the narratives. The first theme was that many instructors discovered that they were missing conditional knowledge in their instruction. Second, the author found common issues around the complexity of designing an expert decision model (EDM). Included in this theme are stories about selecting problems and organizing the EDM, building the EDM around specific course learning outcomes, providing just-enough, just-in-time instruction, and introducing the decision model and software to students. Instructors also discovered that assessing the learning of students needed to go beyond traditional goals and began to include new goals related to conditional knowledge. Finally, the author describes the comments made by both faculty and students about the experience of using DBL. Several authors described the value of using DBL in the process of taking students from novice thinkers to expert thinkers. Many students expressed that they enjoyed the process that DBL presented to them and that they had a new level of confidence to be able to approach problems in the content area. Summaries and quotes from the chapters in this book are referenced by the authors’ names and the content areas they were teaching.

Transitioning engineering knowledge to practice is critical for the success of a working engineer. Decision-Based learning (DBL) provides a path for developing this capability during the educational experience rather than relying on post-educational experiences in the field. In this chapter, DBL is framed in the context of an engineering educational experience, with a discussion of how certain attributes of engineering and DBL can complement each other. An overall class structure conducive to implementing DBL instruction methods is presented. A possible process for defining an expert decision model (EDM) for engineering topics is explored. This type of decision model can help communicate and teach students the small decisions that culminate to an expert conclusion on a topic, giving the student a clear way to justify analysis and design decisions. An EDM for selecting of a static failure theory with an accompanying learning module and representative assignment problems that accompany the module are presented. The implementation of a DBL approach in an engineering classroom is discussed, and reflections are given about the experience for both the instructor and the students.

This paper surveys the treatment of expectations in estimated Dynamic Stochastic General Equilibrium (DSGE) macroeconomic models.

A recent notable development in the empirical macroeconomics literature has been the rapid growth of papers that build structural models, which include a number of frictions and shocks, and which are confronted with the data using sophisticated full-information econometric approaches, often using Bayesian methods.

A widespread assumption in these estimated models, as in most of the macroeconomic literature in general, is that economic agents' expectations are formed according to the Rational Expectations Hypothesis (REH). Various alternative ways to model the formation of expectations have, however, emerged: some are simple refinements that maintain the REH, but change the information structure along different dimensions, while others imply more significant departures from rational expectations.

I review here the modeling of the expectation formation process and discuss related econometric issues in current structural macroeconomic models. The discussion includes benchmark models assuming rational expectations, extensions based on allowing for sunspots, news, sticky information, as well as models that abandon the REH to use learning, heuristics, or subjective expectations.

We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend existing approaches by permitting for heterogenous lead–lag patterns of the various indicators along the business cycles. The framework is well suited for high-frequency monitoring of current economic conditions in real time – nowcasting – since inference can be conducted in the presence of mixed frequency data and irregular patterns of data availability. Our assessment of the underlying index of business cycle conditions is accurate and more timely than popular alternatives, including the Chicago Fed National Activity Index (CFNAI). A formal real-time forecasting evaluation shows that the framework produces well-calibrated probability nowcasts that resemble the consensus assessment of the Survey of Professional Forecasters.

Chapter 6 synthesizes the psychophysics of sensation into a plausible model for the design and configuration of the learning engagement dimension of a learning system. In sensation, the task is to collect and review stochastic information collected from an external stimulus. In learning systems design, the task is the opposite: to design learning objects and activities that communicate the intended learning to the learner effectively and efficiently. The sensation systems focus their attention on the structure of the stimulus. Likewise, a psychophysical learning system emphasizes the interconnections within categories of content to configure the learning experiences. The curriculum embeds this information into a learning plan.