We believe that cities are important for humans as essential forms of social organisation in contemporary human life. Currently, the integrity of cities as enduring systems faces many challenges — ‘exogenous’ factors such as unsustainable consumption of energy and other resources and ‘endogenous’ factors such as ‘liveability’ and the ‘human scale’ of cities. Therefore we must work to ensure their future, hence the emerging importance of the concept of resilience. But how do we ensure the future of cities? Current slow, de-centralised and business-as-usual urban development is problematic. Instead, a planned approach to urban development is necessary, but how do we plan for cities to be resilient? Planning must inevitably rest on an understanding of how a city functions, and this leads us to thinking of developing mental or computational models of cities. In this paper we explore a number of mental models of cities, which could form the basis for directed urban planning. We identify three types of urban models, urban-state models, urban-learning models, and urban-systems models. Furthermore, we argue that all the current urban models are piecemeal and/or impractical and either do not adequately consider the complexity of the city or are not suitable for the interface with governance, We suggest that the best way forward is to embed multiple urban models within an adaptive governance framework, thereby providing a way for urban decision makers and planning organisations to better handle the complexity of their cities. To enable this, further work is required to identify suitable urban systems archetypes.
As writers including Heinz Pagels to Lee Smolin have noted, a new scientific paradigm is emerging to take the place the linear model of Descartes and Newton. This paper explores Complexity Theory studies the patterns that emerge as phenomena evolve in the world suggested by that new paradigm. The co-authors refer to the new paradigm as “processual”, because it depicts a world composed fundamentally of processes that flow through each other to create systemic causality, rather than the Newtonian image of a clock-like world of cause-and-effect. The paper relates how the co-authors used Complexity Theory to understand this emergent worldview as they wrote The Axial Ages of World History. In doing so, they discovered a way of understanding world history as extremely “thick” and multi-dimensional, less like a machine than an ecosystem. Complexity Theory, they conclude, stands as a gateway to such an understanding of disciplines from psychology to organizational development.
A review of the Lifecycle Assessment literature concerning biofuels found no conclusive answers on the important and policy-relevant questions of whether biofuels can help reduce emissions of greenhouse gases, and whether they are an efficient source of energy. This inconclusiveness is attributed to the problematic specification of these papers, which cannot give actionable and policy-relevant answers. The main problems in the specification of the papers are: the reliance on aggregate-based modeling rather than investigating the impact of specific policies, the absence of integration within a dynamic economic model that includes the price effects, the focus on emissions quantities rather than the environmental impact of the emissions, and missing. This paper draws on insights from economics and philosophy of science to explain the underlying reasons why LCA studies fail to reach conclusive answers.
Mental models can affect people’s actions and have the capacity to affect how people achieve organizational outcomes. An ethnographic complexity-based inquiry into the mental models of staff and management about work practices was undertaken within a not-for-profit organization. Interviews were conducted to uncover the mental models held by management and staff about actual work practices and ideal work practices. A comparison of the individual mental models revealed that individuals in the organization were in a state of chaotic edge thinking, where everything is perceived as a threat, procedures are formed to control, and people are reacting radically. This was a result of the miscommunication between members of the organization and an environment characterized by a negative phrase space. It is suggested that the identification of individual mental models about work practices is beneficial for knowing how a person’s actions are influenced, and in this case, why work practices failed.
Citizen trust and distrust perceptions have become an increasingly controversial problem in recent election turmoil regarding changes in governance. Key to this trust and distrust problem is that the physical perception process in the human brain is still not well understood. The ongoing trust and distrust debate in organizational literature was researched seeking a resolution. The framework of this debate argues whether trust and distrust are separate dimensions or merely opposite ends of a single continuum. Because the human perception neural binding process is so little understood, the debate has remained in the argument stage of how trust and distrust should be defined. This led the research exploration to examine the Artificial Intelligence (AI) community’s development of computers that mimic cognitive functions of humans. AI includes a multi-sensory data gathering and binding architecture that mimics the human neural multi-sensory data gathering and binding neural signals for people to perceive a conscious awareness of the world around them. This sensory fusion architecture was used in the exploration research to create a map to match the human neural multi-sensory binding phases. The AI computer developers used fMRI research to test the credibility of their system with human participants. They identified that trust and distrust each activate separate correlative sections of the brain. This paper proceeds to examines how the perceptions of trust and distrust are used by people to develop their organization of self-governance of their social behavior, as individuals, as social groups, and as citizens’ especially the self-governance of their political governments. However, when both trust and distrust perceptions are at extreme force, they can become fused into one. The results come often at the expense of most of the people involved, as described in the Polybius’ Cycle Governments.
Since first described by Markides and Coreil in 1986, multiple authors have attempted to unravel the curious finding that Hispanic Americans appear to, in spite of seemingly disadvantageous health factors, to have better outcomes than expected. While there have been some dissenting studies, the preponderance of evidence seems to support the finding, although the exact mechanism remains elusive. A computational analysis of the 2011-2016 data on the counties of Arizona and New Mexico contained in the Robert Wood Johnson Foundation’s County Health Rankings and Roadmaps confirmed that this Hispanic Paradox does indeed exist. However additional factors, such as the distribution and concentration of the Hispanic population, appear to be necessary in order for it to manifest. It is maximized once a critical level of population percentage and lack of acculturation are met. These levels are achieved in some counties in Arizona, but are absent in New Mexico, despite an average county Hispanic population percentage only 60% that seen in New Mexico. In this regard, the Hispanic Paradox appears to follow the same dynamics as that seen in the Roseto Effect described in the 1960’s and may be related to the differing effect of curanderismo in these areas.
Teams are framed as individuals embedded in hierarchical and knowledge networks, who interact among each other with the aim of accomplishing a common task. Social interactions are the means through which team members exert their mutual social influence, change opinions, and converge to a common understanding. In this paper, we investigate how the density and connectivity of the team knowledge network and the team organizational structure relate to team performance. The latter is measured in terms of level of agreement among the team members (consensus outcome). We first develop a theoretical model grounded on social influence theory and then a computational model based on the Ising approach. Successively, we carry out a broad simulation analysis in environments characterized by different levels of uncertainty. Results show that high-density values of the team knowledge network are beneficial in the majority of cases, but may become detrimental, when the uncertainty of the environment is low, the team knowledge network exhibits a random connectivity, and the team organizational structure is characterized by high centralization of the authority and a strong leadership behavior. We also find that scale-free connectivity of the team knowledge network hinders the achievement of consensus, compared to the random connectivity case. Based on the simulation results, we finally identify the best organizational structure that should be adopted to improve the consensus outcome.
Governments of the Republic of Korea have an exemplary record in providing leadership through published visions and strategies. This review is inspired by seven such endeavors conducted since 1999. All of these efforts use the future to motivate changes in the present. However, over time there has been a gradual shift in the content of the visions, revealing an unresolved tension between predictive targets based on benchmarking that is effective for emulation and a recognition that on the frontier of socio-economic change there are no knowable targets. As a result the capacity of strategic foresight processes to bring complexity and emergence to bear on the identification of opportunities in the present becomes more important. The perspective presented in this review can be summarized in three hypotheses: one is that South Korea is now at the frontier of existing socio-economic models and therefore catch-up and benchmarking are no longer sufficient foundations for South Korea’s vision, although still highly important for incremental improvement and competitive positioning on already existing markets/organizational aspects of society; two is that recent developments in strategic foresight theory and practice are enhancing the capacity to focus on complex, emergent systems that are rich with unforeseeable novelty and, critically, make a practical connection to the processes of sense making and making sense that are the basis for action; and three that South Korea is in a good position to invest in developing its strategic foresight capabilities on the basis of the outstanding track record of previous efforts to use the future for the benefit of all South Koreans.
The art of communication and the science of complexity are intriguing areas of thought and practice that can be examined through storytelling. In an increasingly complex world with many voices, a deeper understanding of complexity communication provides opportunities for researchers and practitioners. This paper discussion centers on Complexity Communication, and the Complex Responsive Processes within the storytelling environment. Complexity theory explores how independent agents interact with each other. Complexity is different from chaos and emerges through a process of human interaction, which is best seen through the art of storytelling. This article explores storytelling and the social construction and discursive elements within the process of human interaction. This discussion advances the call from Hoffman (2008) to embrace the ideas within complexity communication.
Can the mass utilization of biofuels help reduce greenhouse gas emissions and dependence on fossil fuels? Four particular types of biofuels have been the subject of extensive study in this regard: corn ethanol, sugar cane ethanol, cellulosic ethanol, and biodiesel. This paper overviews the evidence from the literature on Lifecycle Assessment studies pertaining to these four fuels and finds that no definitive answer to these questions exists in the literature. The example of biofuels co-products is used to illustrate the reason why no easy answer can be arrived at from these studies. The deeper sources of variation within the literature are identified as well as the requirements that would be needed to formulate an LCA which can provide conclusive and actionable answers.
Routines and organizational capabilities are central constructs in explanations of firms’ heterogeneity and sustained competitive advantage in industries and markets. Building on the foundational contribution of Nelson and Winter, the routines and knowledge capabilities of firms have been a major focus of theorizing in many areas within innovation and management research. Despite this, numerous issues still remain unresolved, particularly regarding the specific mechanisms by which capabilities emerge and develop over time. In this paper, we contend that the evolutionary approach to the firm pioneered by Nelson and Winter can resolve existing contentious issues about the topic by adopting a complexity perspective. In particular, we argue that a view of the firm as a complex adaptive system enhances evolutionary explanations of the origin of organizational capabilities, and how these emerge and change via processes of organizational learning. In addition, we show that embedding this view within a general evolutionary framework emphasizes the role of knowledge capabilities as sources of developmental variety in the evolution of firms, and allows accounting for their creation via self-organizing processes as a fundamental dynamic force in economic evolution.
This paper examines the dynamics in organizational innovation processes, and in particular, the role blockages. The case covers the process of designing a joint-stock enterprise that is partly owned by the employees and partly by the federation of municipalities, and is to deliver primary health care services to a set of municipalities. After a promising start, the process is now stuck before it has reached the implementation phase. The purpose of the paper is to examine the dynamics in the organizational innovation process, and in particular, the role of blockages and failures. By highlighting the value of complexity theoretical thinking, this paper seeks to contribute to our understanding of the nature of organizational innovation in the public sector and the analytical power of complexity. The data consists of interviews with the key actors in the process and is analysed by applying theory driven content analysis. Preliminary results suggest that the organizational innovation process is characterized by an active use of relational potential and a sequence of unexpected events resulting in emergent patterns. The space of possibilities not only frames the system but also enables co-evolutionary dynamics to emerge. Contrary to the fitness (or performance) landscape models, where the (organizational) structure is seen as an important determinant of the innovation potential, it does not seem to play a central role in this particular case. Results suggest that the innovation itself emerges in the complex responsive processes of relating between key actors, long before the end result of the process is realized. A structural failure might turn into a relational success.
Firm beginning matters because in the early days different configurations are tried to cope with challenges and opportunities of their environment. It is in this early days that a new organization emerge -it is forged- because of the continuous trial and error exercises. In this first stage, drastic changes might happen that determine and configure their internal organization, culture and values; that is possible because of the firm´s size, lack of path dependence and an incipient culture. If a fit between the new firm and the market´s requirements is found; then a second stage may be present were optimization as form of economizing become predominant, this efficiency comes with a cost: The firm´s righty. This paper is about the importance of the firm´s begging and how to some extent, its origin determines its future.
We argue the case that human social systems and social organizations in particular are concrete, non-metaphorical, cognitive agents operating in their own self-constructed environments. Our point of departure is Luhmann’s theory of social systems as self-organizing systems of communications. Integrating the Luhmannian theory with the enactive theory of cognition and Simondon’s theory of individuation, results in a novel view of social systems as complex, individuating sequences of communicative interactions that together constitute distributed yet distinct cognitive agencies. The relations of such agencies with their respective environments (involving other agencies of the same construction) is further clarified by discussing both the Hayek-Hebb and the perturbation-compensation perspectives on systems adaptiveness as each reveals different and complementary facets of the operation of social systems as loci of cognitive activity. The major theoretical points of the argument are followed and demonstrated by an analysis of NASA’s communications showing how a social organization undergoes a process of individuation from which it emerges as an autonomous cognitive agent with a distinct and adaptive identity. With this example we hope to invite a debate on how the presented approach could inform a transdisciplinary method of cognitive modeling applied to human social systems.
Integrating complex business networks in Tourism is a wicked problem. Many different business owners have various goals and management approaches. A tourist network is often managed through coordination and partnerships because the sheer complexity of trying to be competitive makes little sense when so many businesses have a common goal. In this paper we explore how thinking in network terms in tourist business networks actually sheds light on how to manage wicked problems in general. In particular, we focus on how the network approach to managing complex networks in business may produce leverage points for synthesising managerial tension points between partners and thereby facilitate innovation systems. We argue that the network approach may shed light on how to build platforms for gaining traction and synthesis in wicked problems. We conclude with suggestions for future research.
We live in a world where war rages between nations, where revolution erupts within nations, where global terrorism is the norm, where new forms of conflict are emerging on the internet, and where class struggle is exacerbated by rising levels of income inequality. The very existence of these ongoing problems suggests that we do not have the highly effective theories needed to deal with them. In seeking to improve our theories, previous scholars have claimed that theories with a higher level of structure would be more effective. However, they did not provide a useful measure of that structure. In the present paper, Propositional Analysis (PA) is presented as an emerging methodology for determining the structure of theories with some level of objectivity. Using PA, this article investigates the change in structure of theories of conflict over a century-long span of time. The outcomes of these analyses suggest the need for new standards for creating theory, integrating theories, and choosing theory for research and/or practice. This study shows that our theories are not evolving toward a higher level of structure. Instead, the level is nearly stable. These results suggest a new understanding as to why the field of conflict theory has not increased in relevance and usefulness. And, as a result, suggests new directions for accelerating the improvement of theories of conflict. While this is a small study, it is expected that these results and insights may be generalized to the broader field of sociology.
The analysis of force-fields for managing social change developed by Kurt Lewin, Eric Trist, Fred Emery, and other pioneers in Action Research is used as a guide to explore the role of energy’s force-fields in bring about emergent change regarding people, social groups, and ecology. Action Research uses force-fields as dynamic placeholders to follow the forces influencing people’s interactions develop into emerging organization complexities of social change. The paper charts a course of exploration that follows energy’s force-fields. The exploratory view is through the lens of energy and proceeds along three interlinked paths: 1) Energy: force-fields interacting for change, 2) Complexity: cooperative self-reorganization for change, and 3) Process: Energy’s Work Domains for enacting change. By focusing strictly on energy’s force-fields in action we can see better how change emerges from the processes of energy’s force-fields’ interactions. We can see anew our options for managing social change and develop better ways for us to enact them.
Originating from a concern on the linkage between health policies and immigration policies within healthcare organizations, our goal is to understand how and why healthcare organizations adapt their services to the needs and characteristics of migrant populations. In doing so, we used three angles of analysis: (1) interactions between the stakeholders within an organization viewed as a Complex Adaptive System (CAS), especially between an organization’s various levels of governance; (2) the levers of action implemented by the multiple stakeholders; and (3) the factors that influence the stakeholders. We propose a conceptual model of multilevel adaptive governance able to reconcile two paradoxical adaptation mechanisms: (1) multiple autonomous stakeholders are able to self-organize while acting in a heterogeneous manner; (2) governance allows these heterogeneous actions, through levers of action, to converge toward a more homogeneous collective process.
The clustering phenomenon for inanimate and living systems and the herding effect for living systems are analyzed under the light of physical sciences. The suggested approach derives from the classical mechanics, the variational principle of least action and fluid dynamics theory is used, qualitatively, to enlighten some irrational behavior of financial systems.
It is almost 30 years ago since Richard Dawkins was given credit for introducing the meme as a concept for studying cultural evolution. Despite the growing interest for both evolution and complexity in the social sciences, the highly controversial field of Memetics has been accused for being a fad and a pseudoscience, never able to establish itself as a recognized research program. Some 10 years ago it was even sentenced to death by some academics in the field. This article examines the status of Memetics, and suggests ways to make it grow further as a scientific discipline.
This exposition considers perspectives underpinning contemporary leadership studies given we are located in what Hawking describes as the ‘century of complexity’, also understood as a Knowledge Era. Social complexity as context allows consideration of the turbulence our times without looking for guaranteed, certain, or ‘right’ answers and allows us to work with these conditions, rather than succumb to threat rigidity, pretend they do not exist, or think they are someone else’s problem. To make sense of these conditions requires ontological and cognitive shifts of mindset that more closely match the ‘requisite variety’ of the complexities of our times. The paper draws upon a PhD interpretive inquiry which identified cogent leadership literacies for the 21st century and explored them within Australian university settings. Various cognitive frames feature in this paper and serve to illuminate possibilities for scholars and practitioners seeking fresh approaches for leadership studies for a Knowledge Era. Whilst there are many contemporary scholars already doing so it is also clear that the ontological shifts are not easy and that archaic mindsets are difficult to dislodge even in light of wicked problems like the Global Financial Crisis of 2008 or environmental disasters.
Big History might be considered the study of an evolving, large, complex adaptive system with three very different phases progressing geometrically from the early universe to the present day. A geometrical progression rate would suggest transitions to life evolution beginning at about 5 billion years ago; to brain evolution around 5 million years ago; and further transition to technological civilization development about 5,000 years ago. Characteristic properties of complex adaptive systems include: (1) a resource which drives the level of complexity, such as energy flow; (2) new options at critical nonlinear decision points along development paths tied to levels of energy flow; and (3) continuous logistic learning as the options are explored; (4) scaling of other dimensions besides energy, such as length and time scales of important processes. This paper presents indications that these processes are occurring through historical trends in energy, environment, economics, and organization. The understanding of these phenomena could contribute to our ability to develop and anticipate potential future scenarios with more integrated, systemic, and effective approaches and expectations.
This paper applies systems thinking and emergence theory to present an understanding of sustainability in terms of the human actions and attitudes required for sustainability to emerge. Sustainability is viewed as an emergent quality that occurs when the interactions within the system, and between the system and its environment are nourishing. We suggest this conception is useful because it indicates the kinds of relationships individuals and groups need to engage in as actors; the responsibilities and importance of observers in recognising emergent patterns; and the significance of the relationship between the actor and observer scales. We aim to identify strategies in these three areas that can best facilitate the emergence of sustainability. Emergence theory is found to be a fruitful framework for generating solutions and stimulating new thinking about defining, monitoring, or acting for sustainability.
Complexity scholars have identified two distinct catalysts of emergence: (1) Far-from-equilibrium dynamics that trigger order creation, and (2) adaptive tension (McKelvey, 2004) which can push a system toward instability, leading to the emergence of new order. Each of these provides a necessary but incomplete explanation of the catalyst for emergent order. In particular, the far-from-equilibrium framework, when taken to its logical ends, would conclude that most dynamic and fluid organizations are the ones farthest-from-thermodynamic equilibrium—like Exxon or GM, for example. Adaptive tension on the other hand identifies an exogenous force of market change, but doesn’t explain how emergence is actually triggered. As a solution I propose “Opportunity Tension,” which integrates the endogenous intention of an entrepreneur to create a new venture to the exogenous changes that open up an entrepreneurial opportunity—a market that will exchange money for the value being created. Opportunity tension occurs in “pulses,” each cycle leading to a new dynamic state of the system. This model, which is consonant with the notion of “dynamic creation” (Chiles et al., 2010), contributes to a complexity science that is moves us beyond a far-from-equilibrium framework.
As more scholars join the conversation around complexity theory (CT), it seems a useful time to ask ourselves if we are talking about the “same thing?” This concern is highlighted by the present survey, which finds more conflict than agreement between definitions. In contrast to the conflict, a path toward common ground may be found by applying the idea of a “robust” theory. A robust theory is expected to be more effective in application and more reasonably falsifiable. In this paper, Reflexive Dimensional Analysis (RDA) is used to analyze existing definitions of CT. These definitions are deconstructed, redefined as scalar dimensions, combined, and investigated to identify co-causal relationships. The robustness of CT is identified as 0.56 on a scale of zero to one. Paths for advancing the theory are suggested, with important implications for complexity science.