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.
We can’t get here from there
Sustainability from complexity vs. conventional perspectives
Sustainability problems are today becoming more prevalent, more systemic and more serious than ever before. And they are expanding, from operational inconveniences that could largely be addressed through line-level fixes, to boardroom enigmas and political groundswells that defy traditional boundaries. This paper argues that these shifts in the nature of sustainability problems are highly significant for researchers as well. They indicate that the ontology of sustainability issues is also shifting: it is growing increasingly complex. We can no longer speak meaningfully about social, environmental and economic sustainability issues as isolated, independent incidents. With growing acceptance that “everything is connected to everything else”,1 we recognize that we must progress beyond sole use of conventional reductionist epistemologies. Growing complexity is not a descriptive term, but rather an ontological watershed between classical Newtonian assumptions of linearity, stability, and reductionistic inquiry on the one hand, and nonlinear, self-organizing, and emergent complexity theory on the other. While readers of this journal are likely to be well aware of these changes, there is value in a careful examination of this apparent shift toward complexity-based inquiry in sustainability research. Indeed, there are dangers in not doing so: not only is conventional research growing more limited for revealing the nonlinear nuances that increasingly make up sustainability problems, but further, it may obscure the actual dynamics and dynamic elements in play in a given situation.2 Hence, there is a need to both distinguish the two approaches from each other, and to highlight how each may be better suited to address particular problemscapes, or econo-social-environmental systems situated in space and time.3 This paper attempts to address the above situation in three ways. First, in a brief review of current literature, it finds several types of confusion in conventional research and research calls. Second, it offers a distinguishing framework that clearly differentiates complexity-based sustainability from conventional views, and shows how both are valuable but each is incommensurable. Third, it presents an original, longitudinal and quantitative case study of a sustainability initiative in a UK organization, using competing hypotheses from each perspective. Results are unexpected and anomalous from a conventional perspective, but these “negative” findings may be interpreted as consistent with a complexity perspective on the organization and initiative. In sum, the neoclassical, positivist, and reductionist model of sustainability is certainly not the only, and may not be the best way to study internal organizational shifts towards sustainability. From literature to theory, and theory to practice, it appears that complexity perspectives are fast becoming the “there” needed in sustainability inquiry in order to get to the “here” of today’s sustainability issues and problems.
Assessing the benefits of supply chain trust
NK simulation-based methodology and application*
Previous literature has emphasized that developing trust among supply chain (SC) firms is a critical element in achieving SC effectiveness. Since developing trust is an expensive task, however, making an informed decision whether to invest or not in trust requires careful assessment of trust benefits. Therefore, we advance a simulation-based methodology to quantify performance improvements associated with trust in SCs. We develop an NK simulation model of a generic SC that captures the SC dynamics under two alternative scenarios, characterized by the presence and absence of trust respectively. A procedure is then illustrated to quantify the benefits of trust in the SC. We also apply our proposed methodology to a real-world SC. Results show that, when trust is pervasive across the SC, performance increases at both the levels of the overall SC and its leading firm.