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Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

The consideration of alternative sources of material for construction is imperative to reduce the environmental impacts as two-fifths of the carbon footprint of materials is attributed to the construction industry. One alternative material with improved biodegradable attributes which can contribute to carbon offset is bamboo. The commercialisation of bamboo in modern infrastructures has significant potential to address few of the Sustainable Development Goals (SDGs) itemised by the United Nations, namely SDG 9 about industry, innovation and infrastructure. Other SDGs covering sustainable cities and communities, responsible consumption and production and climate action are also indirectly addressed when utilising sustainable construction materials. Being a natural material however, the full commercialisation of materials such as bamboo is constrained by a lack of durability. Besides fracture mechanisms arising from load-induced cracks and thermal modification, the durability of bamboo material is greatly impaired by biotic and abiotic factors, which equally affect its natural rate of degradation, hence fracture behaviour. In first instance, this chapter outlines the various factors leading to the durability limitations in bamboo material due to load-induced cracks and natural degradation based on recent findings in this field from the author's own work and from past literature. Secondly, part of this chapter is devoted to a new approach of processing the surge of information about the varied aspects of bamboo durability by considering the powerful technique of artificial intelligence (AI), specifically the artificial neural network (ANN) for prediction modelling. Further use of AI-enabled technologies could have an impactful outcome on the life cycle assessment of bamboo-based structures to address the growing challenges outlined by the United Nations.

This chapter contrasts two “careers in dope” (Waldorf, 1973), one a Hispanic crack dealer and the other a White trafficker of powder cocaine. The first dealer worked openly on the street, in the urban style; the latter dealt indoors, exclusively through networks of kin and friends, the only way to sell drugs in the suburbs. This chapter seeks to establish “suburban” drug sales as a particular modality, with dynamics specific to its context.

Two in-depth case cases are examined. They are drawn from a larger set of oral interviews that explore the life histories of drug dealers, with an emphasis on how they sold marijuana and cocaine, and how and why they quit selling.

First, the suburban style of drug sales has much to do with the mitigated risks White people face as dealers. Second, suburban dealing illuminates the limits of conventional economic theory to explain drug dealing universally.

Because suburban drug deals happen among friends and kin relations they are never anonymous. Making sense of economic transactions among intimates raises a number issues fundamental to economic anthropology: the ambivalence of gifts in socialeconomic relationships, and more generally the integration of economic phenomena in social dynamics.

The field of strategic management has advanced substantially in both theory and empirical research over the last 25 years. However, there are “cracks” beginning to occur in the methodology “dam.” To grow as a discipline, strategic management research must meet and deal effectively with methodological challenges in several areas. We address these challenges in each of the following areas: research questions, data collection, construct measurement, analysis of endogenous relationships, and applications. We present a concise view of the future suggesting ways in which these challenges can be overcome and explain the benefits to the field.

In this chapter, I examine how racial disparities in punishment for nonviolent drug crimes align with significant differences in how the black and white drug problems are constructed in media, law enforcement, and academia.

By examining differences in how the black and white drug problems have been constructed over the past 70 years for the opioids (heroin, prescription painkillers), cocaine (both powder and crack), and marijuana, I illustrate how these distinct representations of the black and white drug problems accompany more punitive policies in response to black drug epidemics even as white drug epidemics are typically met with tolerance or indifference.

Historically, powerful interest groups like media and law enforcement have benefitted from circulating myths and exaggerations about the illegal drug problem that encourage punitive drug policies. By contrast, at least some academics have benefitted from taking the opposite tack and debunking many of these myths. Unfortunately, academics have been less willing to challenge myths about the black drug problem than the white drug problem. Indeed, some academics actually reinforce many of the myths about the black drug problem promoted by media and law enforcement.

This chapter builds upon a substantial academic literature that challenges myths about illegal drug use by whites. However, it goes beyond this literature to consider the paucity of similar academic research exposing media and law enforcement myths about the black drug problem.

This chapter explores the work of Stephen Bunker through a review of the role of women in Amazonian extractive economies and how shifting ideas of development affected them in the Western Amazon. While the initial development programs for Extractive Reserves focused on green marketing, consumer coops and value added through processing carried out in an urban factory in the village of Xapuri, as structural adjustment programs gained importance, the development emphasis shifted to decentralized processing and piecemeal contracts on the individual seringal (rubber tapping estate) or in mini factories in forests. While this was an appealing approach given the kinds of development concerns at the time; non-timber forest products, income generation for women in the forest itself, and neoliberal ideologies of economic and labor decentralization, it failed to appreciate the demands and the opportunity costs on women's time in rural areas and underestimated the importance of formal employment in urban areas. The logics on which the shift was justified, enhanced production, efficiency and lower costs, did not materialize.

Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.