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This chapter uses the theory of complex systems as a conceptual lens through which to compare the work of Friedrich Hayek with that of Vincent and Elinor Ostrom. It is well known that, from the 1950s onwards, Hayek conceptualised the market as a complex adaptive system. It is argued in this chapter that, while the Ostroms began explicitly to describe polycentric systems as a class of complex adaptive system from the mid-to-late 1990s onwards, they had in fact developed an account of polycentricity as displaying most if not all of the hallmarks of organised complexity long before that time. The Ostromian and Hayekian approaches can thus be seen to share a good deal in common, with both portraying important aspects of society – the market economy in the case of Hayek, and public economies, legal and political systems, and environment resources in the case of the Ostroms – as complex rather than simple systems. Aside from helping to bring out this aspect of the Ostroms’ work, using the theory of complex systems as a framework for comparing the Hayekian and Ostromian approaches serves two other purposes. First, it can be used to show how one widely criticised aspect of Hayek’s theory of society as a complex system, namely his account of cultural evolution via group selection, can be strengthened by an appeal to the work of Elinor Ostrom. Second, it also helps to resolve a tension – ultimately acknowledged by the Ostroms themselves – between some of their explicit methodological pronouncements and the actual, substantive approach they adopted in their analysis of polycentric systems.

In this chapter, we raise awareness of the larger network in which multiteam systems (MTSs) are situated. We posit that in the complex operations conducted by military units, MTSs are not isolated entities, but rather exist in exponentially complex systems that include additional challenges for both research and practice.

An operational example involving an Army Brigade Combat Team Headquarters is presented to explain the details of the exponentially complex MTSs inherent in military operations, raise awareness about challenges that plague successful mission accomplishment, and discuss the way forward for research and practice.

The Army Brigade Combat Team Headquarters is characterized as a traditional MTS, embedded in a system of hierarchical MTSs, further embedded within a parallel structure of MTSs. Challenges inherent in these organizational structures provide direction for research and practice to address the exponentially complex meta-systems that are prevalent throughout the military.

While researchers have begun to address teams existing in larger networks, or MTSs (Mathieu, Marks, & Zaccaro, 2001), much of the existing research is based on small or isolated systems. As a result, our understanding of the meta-systems in which many of these MTSs exist is limited. This chapter provides concrete examples of an exponentially complex MTS within a military environment and highlights challenges to be addressed in both research and practice.

Organizations desire more accurate cost systems as competition increases, and consequently increase cost system complexity, as cost systems with greater complexity are potentially more accurate than simpler systems. However, even complex systems are prone to impactful inaccuracies, for example, due to design or calculation issues, that can adversely affect decision-making and firm performance. The authors investigate whether and the extent to which cost system complexity and competition decrease managers’ attribution of cost-system-driven adverse firm effects to the cost system. The authors find greater cost system complexity (by inspiring greater confidence in the cost system) and higher competition (by providing a plausible external cause) decrease managers’ attribution of cost-system-driven adverse firm effects to the cost system. With both greater cost system complexity and higher competition, managers observing signals of material cost inaccuracies are potentially the least likely to attribute cost-system-driven adverse firm effects to the cost system.

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.

Standard financial risk management practices proved unable to provide an adequate understanding and a timely warning of the financial crisis. In particular, the theoretical foundations of risk management and the statistical calibration of risk models are called into question. Policy makers and practitioners respond by looking for new analytical approaches and tools to identify and address new sources of financial risk. Financial markets satisfy reasonable criteria of being considered complex adaptive systems, characterized by complex financial instruments and complex interactions among market actors. Policy makers and practitioners need to take both a micro and macro view of financial risk, identify proper transparency requirements on complex instruments, develop dynamic models of information generation that best approximate observed financial outcomes, and identify and address the causes and consequences of systemic risk. Complexity analysis can make a useful contribution. However, the methodological suitability of complexity theory for financial systems and by extension for risk management is still debatable. Alternative models drawn from the natural sciences and evolutionary theory are proposed.

This chapter addresses two identified weaknesses in entrepreneurial ecosystem studies: there is a lack of focus on the relationships between the components of entrepreneurial ecosystems and little understanding of the underlying processes that determine how entrepreneurial ecosystems change over time. Both entrepreneurial ecosystems and solutions ecosystems from social entrepreneurship studies are place-based complex adaptive systems that are emergent in nature. While neither of these ecosystem types can be controlled, they can be influenced and guided to follow a direction by designing conditions for emergence and transitions. In this chapter, the proposition that an online tool, that is used to strengthen solution ecosystems and support their emergence and transition, could also be used to strengthen entrepreneurial ecosystems and guide their emergence and transition is examined. Two cases are used to investigate this proposition: a food security solution ecosystem case study that demonstrates how the online tool is used for solution ecosystems, and an impact economy entrepreneurial ecosystem case study that highlights how the online tool could be used for an entrepreneurial ecosystem. It is demonstrated in this chapter that the online tool can be used to address the current weaknesses of entrepreneurial ecosystem studies. In addition, it is suggested that by combining solution ecosystems with an impact economy entrepreneurial ecosystem, the online tool can be used to support the creation of conditions for social entrepreneurial places to emerge that are capable of addressing the most pressing problems that places face including the sustainable development goals.

This chapter discusses the emerging network science approach to the study of complex adaptive systems and applies tools derived from statistical physics to the analysis of tourism destinations. The authors provide a brief history of network science and the characteristics of a network as well as different models such as small world and scale free networks, and dynamic properties such as resilience and information diffusion. The Italian resort island of Elba is used as a case study allowing comparison of the communication network of tourist organizations and the virtual network formed by the websites of these organizations. The study compares the parameters of these networks to networks from the literature and to randomly created networks. The analyses include computer simulations to assess the dynamic properties of these networks. The results indicate that the Elba tourism network has a low degree of collaboration between members. These findings provide a quantitative measure of network performance. In general, the application of network science to the study of social systems offers opportunities for better management of tourism destinations and complex social systems.

This chapter provides an alternative conception of universities and the higher education systems in which they operate in an attempt to comprehend the ways in which such institutions and systems adapt and maintain themselves over time. Conceptually, it builds on complex systems theory, most notably critical insights from the study of complexity. We base our empirical analysis on developments across the European continent in the light of recent efforts to modernize university systems in the context of rising competition and pressures toward vertical and horizontal differentiation. We contrast two models of the university – strategic versus resilient – and critically reflect on the implications their differences have for the development of systems and universities and future research work in the area.

Although it is widely acknowledged that health care delivery systems are complex adaptive systems, there are gaps in understanding the application of systems engineering approaches to systems analysis and redesign in the health care domain. Commonly employed methods, such as statistical analysis of risk factors and outcomes, are simply not adequate to robustly characterize all system requirements and facilitate reliable design of complex care delivery systems. This is especially apparent in institutional-level systems, such as patient safety programs that must mitigate the risk of infections and other complications that can occur in virtually any setting providing direct and indirect patient care. The case example presented here illustrates the application of various system engineering methods to identify requirements and intervention candidates for a critical patient safety problem known as failure to rescue. Detailed descriptions of the analysis methods and their application are presented along with specific analysis artifacts related to the failure to rescue case study. Given the prevalence of complex systems in health care, this practical and effective approach provides an important example of how systems engineering methods can effectively address the shortcomings in current health care analysis and design, where complex systems are increasingly prevalent.

On many occasions, organizational science research has been referred to as fragmented and disjointed, resulting in a literature that is, in the opinion of many, difficult to navigate and comprehend. One potential explanation is that scholars have failed to comprehend that organizations are complex and intricate systems. In order to move us past this morass, we recommend that researchers extend beyond traditional rational, mechanistic, and variable-centered approaches to research and integrate a more advantageous pattern-oriented approach within their research program. Pattern-oriented methods approximate real-life phenomena by adopting a holistic, integrative approach to research wherein individual- and organizational-systems are viewed as non-decomposable organized wholes. We argue that the pattern-oriented approach has the potential to overcome a number of breakdowns faced by alternate approaches, while offering a novel and more representative lens from which to view organizational- and HRM-related issues. The proposed incorporation of the pattern-oriented approach is framed within a review and evaluation of current approaches to organizational research and is supplemented with a discussion of methodological and theoretical implications as well as potential applications of the pattern-oriented approach.