There have long been calls from industry for guidance in implementing strategies for sustainable development. The Circular Economy represents the most recent attempt to conceptualize the integration of economic activity and environmental wellbeing in a sustainable way. This set of ideas has been adopted by China as the basis of their economic development (included in both the 11th and the 12th ‘Five Year Plan’), escalating the concept in minds of western policymakers and NGOs. This paper traces the conceptualisations and origins of the Circular Economy, tracing its meanings, and exploring its antecedents in economics and ecology, and discusses how the Circular Economy has been operationalized in business and policy. The paper finds that while the Circular Economy places emphasis on the redesign of processes and cycling of materials, which may contribute to more sustainable business models, it also encapsulates tensions and limitations. These include an absence of the social dimension inherent in sustainable development that limits its ethical dimensions, and some unintended consequences. This leads us to propose a revised definition of the Circular Economy as “an economic model wherein planning, resourcing, procurement, production and reprocessing are designed and managed, as both process and output, to maximize ecosystem functioning and human well-being”.

/pdf/the-circular-economy-an-interdisciplinary-exploration-of-the-d4tbyk480i.pdf

The Circular Economy: An Interdisciplinary Exploration of the Concept and Application in a Global Context

(1963). Cybernetics, or Control and Communication in the Animal and the Machine. Technometrics: Vol. 5, No. 1, pp. 128-130.

Cybernetics, or Control and Communication in the Animal and the Machine

/pdf/millennials-rising-the-next-great-generation-3011lw78bf.pdf

Millennials Rising: The Next Great Generation

The Quark and the Jaguar

Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.

https://dl.acm.org/doi/pdf/10.1145/2792984

Many-Objective Evolutionary Algorithms: A Survey

Following current modeling paradigms, most processes are captured in the form of modeling a desired intent, often using success probabilities. In addition, only special roles that entities are intended to play are modeled. For effect-based modeling, the unintended but nonetheless resulting effects are as important as the intended effects, and they are therefore modeled. Also, un inspected actions based on alternative roles are important. Using agents to not only represent influencers and targets but also the processes, it becomes possible to capture all effects and move from ?what I intended to accomplish? to ?what I really accomplished,? including side and secondary effects. The agent architecture and a prototype for this effect-based model are presented in this paper.

/pdf/using-agent-technology-to-move-from-intention-based-to-1krg3og94n.pdf

Using agent technology to move from intention-based to effect-based models

https://digitalcommons.odu.edu/cgi/viewcontent.cgi?article=1025&context=emse_fac_pubs

Determining Stakeholder Influence Using Input-output Modeling☆

The operational and managerial independence, geographic distribution, emergent behaviour, and evolutionary development that characterise a system of systems (SoS) also ensure that it is impossible to truly optimise it. However, using the concept of satisficing, we can declare that a 'good enough' solution is in fact, sufficient. Why are we all right with this potentially unsettling notion in the context of a system of systems? In part, due to the principle of finite causality introduced in this paper, stating no system outcome can have infinitely bad (or good) implications; thus, the outcome of any action or series of actions is finite in nature. This realisation further bolsters the acceptability of an inherently sub-optimal SoS. This paper explores the notions of why optimisation of a SoS is both: 1) unattainable based on its inherent characteristics and associated systems principles; and 2) unnecessary in practice.

Why optimisation of a system of systems is both unattainable and unnecessary

The previous chapters in this section have addressed: (1) the who question through a discussion of problem stakeholders, their analysis, and management; and (2) the what question by decomposing the mess and constituent problems into relevant elements such as outputs and outcomes. In this chapter, we will address the why question through an analysis of motivation, and how each problem has a unique model of motivation and feedback between and among the stakeholders and the problem. The sections that follow will focus on the underlying fact or cause that provides logical sense for achieving goals and objectives as part of solving messes and their constituent problems. It will provide a short description of 20 theories of motivation that have informed the body of knowledge on the subject of motivation. Developing an understanding for the motives underlying the behaviors associated with why is the central tenet of each of these theories. The chapter will conclude by providing a theory or framework, for linking existing theories of motivation within a cybernetic model. The cybernetic model is provided as an aid in understanding the relationship between individual problems and the associated stakeholders, and the unique two-way relationship that contains both motivation and an associated feedback response.

The When of Systemic Thinking

Systems are increasing in complexity as the world becomes more globally connected. The pervasiveness of information, education, and connectivity has forced individuals to devise new ways to think about problems. Most complex problems no longer reside neatly within the boundaries of a single discipline. As a result, addressing these problems requires problem solvers to broaden their perspectives and to think across disciplines. This chapter explores the concept of complementarity as a motivation for seeking transdisciplinary solutions to problems. Through the lens of complementarity, the case will be made for the utility of adopting a transdisciplinary perspective to modern, complex problems unable to be addressed using a single discipline. It then provides three unique perspectives (engineering, global health, and education) through which to view the analysis of a singular problem, that of emerging health issues such as the current Ebola virus disease crisis in the world. Next, these perspectives will be examined using cognitive mapping, forming an approach to adopt a transdisciplinary perspective, which yields insights not possible by each of the disparate disciplines alone. This approach is then demonstrated on a sample problem. Finally, conclusions and recommendations for future research are identified.

Patrick T. Hester

Papers

Using agent technology to move from intention-based to effect-based models

Determining Stakeholder Influence Using Input-output Modeling☆

Why optimisation of a system of systems is both unattainable and unnecessary

The When of Systemic Thinking

On Complementarity and the Need for a Transdisciplinary Approach in Addressing Emerging Global Health Issues