Complex adaptive system

About: Complex adaptive system is a research topic. Over the lifetime, 3190 publications have been published within this topic receiving 111947 citations. The topic is also known as: Complex adaptive system, CAS.

Defining new markets for intelligent agents

Abstract: Most agent applications are fairly straightforward: access a Web site, fetch material; in short, perform a simple fixed mission. Others do more personalized tasks such as filtering e-mail or updating legacy systems. From a programming viewpoint, agents are simply active objects that have been defined to simulate parts of a model. Agent based modeling and simulation then become a natural extension of the object oriented paradigm. Simulations of events that involve these kinds of agents (known as actors or demons) have assisted human decision making for decades in batch manufacturing, transportation, and logistics, for example. But work in complex adaptive systems (CAS) may be defining a new kind of agent: one that can actually evolve over time in response to its environment. The beginnings of these adaptive systems are already evident in more advanced agents, which can do simple negotiations on a user's behalf to secure goods and services in an auction, for example. The challenge now is to see how agents bargain and learn in a more complex environment. The Electric Power Research Institute (EPRI), for example, has funded research into agent based auctioning as a way to address the fierce competition for resources. As electric power marketers become available, wholesale electric customers are learning to shop around for the best suppliers. Like agents that represent individual human users, the agents bargaining on behalf of these suppliers and wholesalers decide things like how much to buy, which agent to buy from, how much to pay, and how to manage the exchange of power and money.

Towards complexity of agricultural sustainability assessment: Main issues and concerns

Abstract: The sustainability of agricultural systems is of paramount concern in order to ensure the survival and wellbeing of humans throughout the world. Sustainability is a complex issue involving multiple factors that fit broadly within economic, social and environmental areas. Given its complexity, this paper examines the question of how sustainability can be assessed in a way that gives a holistic picture of the separate and interrelated factors. The paper then presents a literature review, field experience and the use of complex adaptive systems to identify the issues and concerns that need to be addressed during agricultural sustainability assessment and categorizes them into in seven groups: integration of capitals; maintaining resilience, adaptation and transformation; ensuring system performance; involving stakeholders; mixing interdisciplinary views; integration of scales; and practicing good governance. Based on these issues and concerns, a set of indicators are suggested that will assist with holistic agricultural sustainability assessment in a given area.

Seeing the forest and the trees: A complex adaptive systems lens for mentoring

Abstract: We offer a complex adaptive systems (CAS) lens as a potentially fruitful cross-disciplinary perspective for mentoring. Understanding mentoring relationships as CAS will invigorate the field and assist it to be responsive and relevant given our increasingly complex and turbulent environment. We begin by exploring the key properties of CAS. We then review the current mentoring literature’s connection to the systems and complexity theories that underpin CAS, demonstrating CAS’s relevance. We further examine four specific benefits that such a lens could bring, including attention to process, the reconceptualization of context, the adoption of new methodologies, and the fostering of interdisciplinary conversation. Research questions the CAS lens might stimulate for mentoring are then considered and difficulties with the CAS approach explored. We conclude by suggesting how CAS-informed research might shape mentoring knowledge and practice.

Toward teaching agent-based simulation

Abstract: Agent-based simulation (ABS) is a relatively recent modeling technique that is being widely used to model complex adaptive systems by many disciplines. Few full length courses exist on agent-based modeling and a standard curriculum has not yet been established, but there is considerable demand to include ABS into simulation courses. Modelers often come to agent-based simulation by way of self-study or attendance at tutorials and short courses. Although there is substantial overlap, there are many aspects of ABS that differ from discrete-event simulation (DES) and System Dynamics (SD), including applicable problem domains, disciplines and backgrounds of students, and the underpinnings of its computational implementation. These factors make ABS difficult to include as an incremental add-on to existing simulation courses. This paper reports on some approaches to teaching the modeling of complex systems and agent-based simulation that the authors have used in a range of classes and workshops.