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After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

This chapter provides a presentation about Chapter 1 of The Balance of the National Economy, 1923–24, edited by Pavel Illich Popov. The Balance was issued in June 1926 by the Central Statistical Administration (CSU or TsSU) of the USSR, which Popov had headed from July 1918 to January 1926. In the first part of our chapter, we show how Popov’s work on the balance of the national economy was rooted in the specific scientific and political culture of zemstvo statisticians inherited from the Tsar. Statistical inquiry was considered an objective scientific process based on international standards. Furthermore, like zemstvo statisticians, CSU statisticians developed great autonomous political power. The balance of the national economy was built according to these principles, which met harsh criticism from revolutionaries and Bolsheviks. In the second part, we analyze the contents of Popov’s Chapter 1, especially the theoretical foundations of the balance and its connection with Soviet planning. In the third part, we discuss the balance’s significance in the years 1926–1929, years which ended the NEP and launched the first Five-Year Plan, so as to understand how CSU’s balance didn’t become a standard Soviet statistical instrument and was discarded as a “bourgeois” device.

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.

This chapter presents several approaches for identifying and dating the speculative bubble on real estate market. Using the real estate price index (IPAI), statistical and structural approaches were combined in order to detect the existence of a bubble on the Moroccan real estate market. The results obtained affirm that the Moroccan real estate market experienced a speculative bubble during the period 2006–2008 explained mainly by the boom of credit during the same period. The use of the Markov switching model affirmed that the speculative bubble on Morocco is cyclic and consequently corroborates the critic formulated by Evans (1991) concerning the traditional approaches for the detection of financial bubbles. Thus, the analysis of the series of the bubble, extracted using the Kalman filter, affirms the existence of two regimes, namely an explosive regime and a normal regime. The first regime describes the periods of explosion of the bubble and lasts for about 9 quarters, while the second, lasting for 14 quarters, describes the periods of return to the average cycle.

The contrast of multilevel and comparative research may seem counterintuitive at first. After all, one might argue that comparative research on organizations by necessity spans several levels of analysis (Rokkan, 1966). Yet, multilevel and comparative research on organizations present rather distinct traditions in organization studies, each with its own epistemological assumptions and associated methods. Accordingly, an approach that aims to incorporate both multilevel and comparative ideas needs to start with taking inventory of these prior literatures to situate itself. In the following, we thus turn to the literatures on multilevel and comparative research as different traditions with surprisingly little overlap.

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

Jeliazkov and Poirier (2008) analyze the daily incidence of violence during the Second Intifada in a statistical way using an analytical Bayesian implementation of a second-order discrete Markov process. We tackle the same data and modeling problem from our perspective as cognitive scientists. First, we propose a psychological model of violence, based on a latent psychological construct we call “build up” that controls the retaliatory and repetitive violent behavior by both sides in the conflict. Build up is based on a social memory of recent violence and generates the probability and intensity of current violence. Our psychological model is implemented as a generative probabilistic graphical model, which allows for fully Bayesian inference using computational methods. We show that our model is both descriptively adequate, based on posterior predictive checks, and has good predictive performance. We then present a series of results that show how inferences based on the model can provide insight into the nature of the conflict. These inferences consider the base rates of violence in different periods of the Second Intifada, the nature of the social memory for recent violence, and the way repetitive versus retaliatory violent behavior affects each side in the conflict. Finally, we discuss possible extensions of our model and draw conclusions about the potential theoretical and methodological advantages of treating societal conflict as a cognitive modeling problem.

Policy-capturing is an experimental technique potentially capable of providing powerful insights into the cognitive bases of work-related decision processes by revealing actors’ “implicit” models of the problem at hand, thereby opening up the “black box” of managerial and organizational cognition. This chapter considers the strengths and weaknesses of policy-capturing vis-à-vis alternative approaches that seek to capture, in varying ways, the inner workings of people’s minds as they make decisions. It then outlines the critical issues that need to be addressed when designing policy-capturing studies and offers practical advice to would-be users concerning some of the common pitfalls of the technique and ways of avoiding them.