The dominant model of professional policing in the United States is the so-called “standard model,” which coalesced during the 1960s and 1970s. Weisburd and Eck have identified key characteristics of the standard model of policing as a focus on random patrol, rapid response to 911 calls, and reactive criminal investigations. Calling it the standard model is a deliberate choice, for it denotes that police operations and services have, in large part, become standardized. The standard model assumes that a reactive template of policing can be applied without regard to the particular circumstances of any given community.
The standard model of policing is effectively a design for a production-oriented organization. This design is not a surprise, given that it evolved between 1930–1970; a time when American industry was crucial to winning the Second World War and positioning America as not only a global hegemon, but also the embodiment of capitalism during the Cold War. The standard model is industrial in its outlook that directly produces reports, investigations, arrests, and prosecutions with the underlying assumption that if enough of those goods are produced, a profit of public safety will be realized. Kelling observes that the innovators of the Reform Era were following in the footsteps of the principles of Frederick Taylor’s scientific management, a system for increasing productivity through essentially adopting assembly line techniques. Accordingly, police procedures were standardized, broken down into constituent steps, and police officers trained to perform their specific task efficiently while managers coordinated between different “production units” within the departments.
This reductive, production-oriented worldview falls squarely within a linear understanding of problems. Linear problems can be thought of as having three main qualities. First, they react in fixed proportions to various inputs. Secondly, they are constructed out of independent variables that only affect a limited set of known, dependent variables. Thirdly, a linear problem can be understood through reductionist thinking, or breaking it down into small individual parts and tallying the interactions of those atomized parts. Linear problems can be thought of as a strict math equation; the sum of the variables completely describes the problem.
However, since the 1970s, an ongoing revolution in communications technology has occurred, and people are more widely linked and interconnected than ever before in history. These dense webs of connections allow for new, nonlinear types of behaviors. Complexity theory is the study of nonlinear dynamic systems, which are formed when large numbers of elements, such as people, are able to interact with each other. The most prominent feature of complex problems is that they do not react proportionately or predictably. Exact predictions about the behavior of complex systems cannot be made because so many circumstances and interactions are occurring that obtaining sufficient knowledge about them to make accurate predictions is impossible. Variables in complex problems continuously link to each other in ways that can overlap and are not fixed. They can “link and let go and link again” and therefore cannot be neatly classified into categories of independent and dependent. Finally, complex problems do not yield to reductionist thinking because they feature emergent properties, which transcend individual parts of that whole and are not simply recreating the properties of the constituent parts at scale. As linear problems were earlier likened to strict mathematical sums, complex problems are noted for being more than the sum of their parts.
Many of those who investigate complexity across a variety of disciplines are discovering that “at the heart of many—if not most—problems of organized complexity are network problems.” The physicist Albert-Laszlo Barabasi goes a step further in establishing the link between complexity and networks: “One thing is increasingly clear: no theory of the cell, of social media or of the Internet can ignore the profound network effects that their interconnectedness cause. Therefore, if we are ever to have a theory of complexity, it will sit on the shoulders of network theory.”
Some scholars believe that, due to the Information Revolution, networks are becoming the driving force in society. Just as the Industrial Revolution led to industrial society, Manuel Castells believes that the IT revolution is leading to an “informational society.” The informational society is “oriented towards … the accumulation of knowledge and towards higher levels of complexity in information processing.” Castells acknowledges that people connecting with each other and forming a network is not a new phenomenon, but argues that prior to the IT Revolution, networks were limited in ability and were outperformed by the forces of hierarchy and centralization in human societies. Since the IT revolution, the capabilities of this new technology have allowed network dynamics to explode and overtake the actions of each individual agent within a network.
For police agencies practicing the standard model of policing, crime is thought of in linear terms, either the actions of scattered individuals or relatively formal hierarchical organizations. However, the technological advancements profoundly altering the nature of civil society are having the same effects on crime and criminals. This thesis applies a five-element framework to a street robbery pattern in Washington, DC and discovers that these robberies can be modeled as a complex adaptive system (CAS). In this CAS, individual criminals act as decentralized agents, with the cumulative effects of their individual actions self-organizing to form an interrelated system that adapts to changes in the environment around it, to include law enforcement responses to criminal activity. There are also indications that network effects are present, although current police doctrine limits the amount of pertinent information collected by police departments.
There is a growing recognition that the standard model of policing may no longer be effective, but there is as yet little appreciation for why that is so. There is no shortage of alternative models of policing proposed to replace it, but none has been conceived with the changes to the underlying structure and fundamentals of the network society or the Information Age in mind. However, at least some of these models suggest practices that may serve as building blocks or stepping stones to the day-to-day operations of a complexity-based policing model. This thesis examines four of the major alternatives to the standard model, and finds that to the extent that they recommend practices that are well suited to a complex environment, it appears to be either accident or narrowly tailored responses to recognized shortcomings of the standard model, and not a coherent, theoretically informed understanding of the modern age. However, adopting complexity as the theoretical underpinning of a model of policing would provide an internally consistent means to integrate elements of these various models and address the shortcomings of the standard model of policing.
It is vital to recognize that society in the United States is transitioning into an informational, network-based society increasingly governed by nonlinear, dynamic processes. The dissatisfaction with the state of policing occurs since it is an institution that is no longer aligned with the dynamics that drive and organize society. Failure to recognize this misalignment makes attempting solutions ineffective or inefficient.
This thesis concludes with recommendations for implementing a complexity-based model of policing. Firstly, it recommends educating law enforcement personnel in complexity and network theories, and developing the mindset to work in complex environments. Secondly, it recommends incorporating data analysts into police departments as front-line, operational personnel working side-by-side with sworn officers. Finally, it offers a number of recommendations about operational changes. Departments should avoid structuring themselves around deployment of officers based on geography. Instead, the base unit of organization should be squads of officers with integrated analysts. The primary tasking of these squads should be investigation and intervention in complex networks and systems. Departments need to begin building and formally tracking networks of resources that they can bring to bear when they decide that interventions are necessary. All these recommendations are intended to orient police agencies towards an informational, rather than production-based view of the world.
 George L. Kelling and Mark H. Moore, The Evolving Strategy of Policing, Perspectives on Policing (Washington, DC: National Institute of Justice, U.S. Department of Justice, and the Program in Criminal Justice Policy and Management, John F. Kennedy School of Government, Harvard University, 1988), 2, https://www.ncjrs.gov/pdffiles1/nij/114213.pdf.
 David Weisburd and John Eck, “What Can Police Do to Reduce Crime, Disorder, and Fear?,” Annals of the American Academy of Political and Social Science 593, no. 1 (2004): 49.
 Weisburd and Eck, 44.
 Kelling and Moore, The Evolving Strategy of Policing, 6; Britannica s.v. “Taylorism,” November 15, 2018.
 Kelling and Moore, 6.
 Ben Ramalingam, Aid on the Edge of Chaos (Oxford, United Kingdom: Oxford University Press, 2013), 224–25.
 Michael Agar, “Complexity Theory: An Exploration and Overview Based on John Holland’s Work,” Field Methods 11, no. 2 (1999): 100; Glenn Walters, Modelling the Criminal Lifestyle: Theorizing at the Edge of Chaos (Switzerland: Palgrave Macmillan, 2017), vi.
 David L. Levy, “Applications and Limitations of Complexity Theory in Organization Theory and Strategy,” in Handbook of Strategic Management, ed. Jack Rabin, Gerald J. Miller, and W. Bartley Hildreth, 2nd ed. (New York: Marcel Dekker, 2000), 79, http://www.faculty.umb.edu/david_levy/complex00.pdf.
 Friedrich Hayek, “The Theory of Complex Phenomena,” Emergence: Complexity and Organization 9, no. 1–2 (2007): 157–59.
 Agar, “Complexity Theory,” 100.
 Hayek, “The Theory of Complex Phenomena,” 149.
 Ramalingam, Aid on the Edge of Chaos, 192–93.
 Albert-Laszlo Barabasi, “The Network Takeover,” Nature Physics 8, no. 1 (January 2012): 15.
 Manuel Castells, The Rise of the Network Society, 2nd ed., vol. 1, The Information Age: Economy, Society, and Culture (Malden, MA: Blackwell Publishing, 2000), 17.
 Manuel Castells, “Materials for an Exploratory Theory of the Network Society,” The British Journal of Sociology 51, no. 1 (January 1, 2000): 15.
 Castells, The Rise of the Network Society, 500.