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Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

Section 404 of the Sarbanes–Oxley Act (SOX) altered the relationship between auditors and their clients by requiring an external audit of companies’ internal controls. Regulatory guidance is interpreted and applied by external auditors to comply with SOX. The purpose of this paper is to apply service operations management theories and techniques to the internal control audit process to better understand the role regulatory guidance plays in audit services. We discuss service operations management theories that apply to the production of audit services and employ the operations management technique of simulation to examine the effects of a historical relationship between the client and the auditor, information sharing between the client and the auditor, and the auditor’s perceived risk of the client on the internal control audit process. The application of service operations management theories and the simulation results illustrate that risk and information sharing are key factors for the audit process. The results suggest the updated Public Company Accounting Oversight Board guidance from Auditing Standard 2 to Auditing Standard 5 appropriately increased audit effectiveness by encouraging risk-based judgments and information sharing. This paper merges accounting and service operations management research to examine the effects of regulatory guidance on the internal control audit process. The paper uses simulation to illustrate the importance of interpreting regulatory guidance and the specific effects of risk and information sharing on the internal control audit process.

This chapter conceptualizes computational methods across three related, yet distinct approaches: (1) Social Simulation, (2) Data Science, and (3) Big Data. Group communication research is then situated and reviewed along these three lines of research. Although some areas have considerable visibility (e.g., network analysis, text mining), some areas are less visible in group communication research (e.g., Social Simulation, Big Data designs). The chapter concludes with suggestions for issues regarding reliability, validity, and ethics.

Emerging thoughts and models in strategic management increasingly involve complex hypotheses at different levels of analysis and multiple sides of relationships. Such complexities often result in less than ideal empirical testing, with the ensuing implications being limited or sometimes even wrong. One such case is global relationship management (GRM). The effective implementation of GRM has been argued to be a principal source of a firm's value creation but the testing of GRM scenarios have been very limited. Using GRM as a case example, we introduce a new methodology to the strategic management literature that alleviates many of the limitations of existing techniques – static triangulation simulation (STS). A series of GRM hypotheses are briefly introduced and then tested via the STS technique. Starting values for the simulation, based on input from companies, are included from two sides of each GRM relationship (customer and supplier) and two levels (company and account) from each side. Such elaborate testing is typically not feasible via “normal” methodology – the STS technique, however, allows for a robust assessment of the different drivers that affect GRM outcomes.

Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

Within spatial econometrics a whole family of different spatial specifications has been developed, with associated estimators and tests. This lead to issues of model comparison and model choice, measuring the relative merits of alternative specifications and then using appropriate criteria to choose the “best” model or relative model probabilities. Bayesian theory provides a comprehensive and coherent framework for such model choice, including both nested and non-nested models within the choice set. The paper reviews the potential application of this Bayesian theory to spatial econometric models, examining the conditions and assumptions under which application is possible. Problems of prior distributions are outlined, and Bayes factors and marginal likelihoods are derived for a particular subset of spatial econometric specifications. These are then applied to two well-known spatial data-sets to illustrate the methods. Future possibilities, and comparisons with other approaches to both Bayesian and non-Bayesian model choice are discussed.