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I am interested in a set X of entities and refer to this as a multiple-entity system unless the set contains just a single entity. Each entity can be characterised either by a single attribute or by many attributes. In general, then we have a system of n entities with m attributes, giving nm attributes in all. A model of a system usually focuses on the variables associated with the attributes. So a model for a unitary entity with nm attributes, a model for a system of nm entities each with just one attribute and a model for a system of n entities with m attributes may be all formally identical with one another.

Following from the cutting-edge work of Stephen Wolfram in A New Kind of Science (2002), in this chapter we propose “a new kind of OB” (organizational behavior) based on the varient approach to theory building and testing. In particular, we offer four simple, yet comprehensive theories to account for individual behavior, interpersonal relationships, group dynamics, and collectivized processes in organizations. In each case, two constructs, their association, and the levels of analysis of their operation are proposed. While the four theories proposed here are simple notions, they can explain a variety of complex phenomena and behavior in organizations.

Herbert A. Simon and Alan Newell won the Turing Award jointly in Computer Science for foundational work on Artificial Intelligence. Simon also won the Nobel Prize in Economics for the concept of “bounded rationality.” In both cases, the same heuristic was deemed fundamental: “Search till a satisfactory solution is found.” We argue that behavioral strategy can learn a great deal from the Theory of Computational Complexity and Artificial Intelligence. These fields can provide a sounder theoretical grounding for bounded rationality and for the necessity and usefulness of heuristics. Finally, a concept of “organizational intractability” based roughly on the metaphor provided by the Theory of Computational Complexity may be useful in determining what analytical decision technologies are actually intractable in real organizations with constraints on time and managerial attention.

Managing complex construction projects is a challenging task because it involves multiple factors and decision-making processes. A systematic evaluation of these complex factors is imperative for achieving project success. As most of these factors are qualitative or intangible in nature, decision makers often rely on subjective judgements when comparing and evaluating them. The hybrid techniques that integrate fuzzy set theory and the analytic hierarchy process (AHP) are able to deal with such problems. This chapter discusses various hybrid techniques of the fuzzy AHP and presents an application of these techniques to the evaluation of transportation project complexity, which is essential for prioritising resource allocation and assessing project performance. Project complexity can be quantified and visualised effectively with the application of the fuzzy AHP. This chapter enhances the understanding of construction project complexity and fuzzy hybrid computing in construction engineering and management. Future research should address the calibration of fuzzy membership functions in pairwise comparisons for each individual decision maker and develop computational tools for solving optimisation problems in the constrained fuzzy AHP. In the area of construction project complexity, future research should investigate how scarce resources are allocated to better manage complex projects and how appropriate resource allocation improves their performance.

Roger Koppl (2009, p. 1) argues that “Austrian economics is a school of thought within the broader complexity movement in economics.” Is he correct? Although there are many who have argued for some overlapping between the two, I shall argue that this is probably an overly strong statement. The main reason is that there are substantial elements and strands within Austrian economics that do not fit in with any of the multiple varieties of complexity theory, even though there are some that clearly do.

Economic models based on simple rules can result in complex and unpredictable deterministic dynamics with emergent features similar to those of actual economies. I present several such models ranging from cellular automaton and register machines to quantum computation. The additional benefit of such models is displayed by extending them to model political entanglement to determine the impact of allowing majority redistributive voting. In general, the insights obtained from simulating the computations of simple rules can serve as an additional way to study economics, complementing equilibrium, literary, experimental, and empirical approaches. I culminate by presenting a minimal model of economic complexity that generates complex economic growth and diminishing poverty without any parameter fitting, and which, when modified to incorporate political entanglement, generates volatile stagnation and greater poverty.

As global economic systems become increasingly more complex and dynamic and the universal language of historical accounting is being profoundly altered, the theory and tools we use in neo-classical economics, traditional finance, and valuation are beginning to prove inadequate to the tasks being required of them. Hence, there is a need to consider new avenues of thought and new tools. In this conceptual chapter, I explore the use of real options “in” engineering systems design as a means to achieve more rigorous and insightful results in the design and valuation of economic systems, particularly that of the firm. In the process, I gain further insight into the causes and cures for systemic disturbances generated by the presence and selection of real options in economic systems.

This chapter discusses the empirical application of a class of strategic network formation models, using the approach to identification introduced by de Paula, Richards-Shubik, and Tamer (2018). The author emphasizes the interplay between model specification and computational complexity, and suggests tactics to make empirically realistic models become tractable. Two detailed examples, on friendship networks and coauthorship networks, are used to illustrate these issues and to demonstrate the performance of the approach with both simulation and empirical evidence. Also, the author presents extensions to the estimation method, which expand the potential range of applications, and which provide statistical inference with minimal computational burden.

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.