29,783 research outputs found
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Biology of Applied Digital Ecosystems
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the biological processes that contribute to
these properties have not been made explicit in Digital Ecosystems research.
Here, we discuss how biological properties contribute to the self-organising
features of biological ecosystems, including population dynamics, evolution, a
complex dynamic environment, and spatial distributions for generating local
interactions. The potential for exploiting these properties in artificial
systems is then considered. We suggest that several key features of biological
ecosystems have not been fully explored in existing digital ecosystems, and
discuss how mimicking these features may assist in developing robust, scalable
self-organising architectures. An example architecture, the Digital Ecosystem,
is considered in detail. The Digital Ecosystem is then measured experimentally
through simulations, with measures originating from theoretical ecology, to
confirm its likeness to a biological ecosystem. Including the responsiveness to
requests for applications from the user base, as a measure of the 'ecological
succession' (development).Comment: 9 pages, 4 figure, conferenc
Model Exploration Using OpenMOLE - a workflow engine for large scale distributed design of experiments and parameter tuning
OpenMOLE is a scientific workflow engine with a strong emphasis on workload
distribution. Workflows are designed using a high level Domain Specific
Language (DSL) built on top of Scala. It exposes natural parallelism constructs
to easily delegate the workload resulting from a workflow to a wide range of
distributed computing environments. In this work, we briefly expose the strong
assets of OpenMOLE and demonstrate its efficiency at exploring the parameter
set of an agent simulation model. We perform a multi-objective optimisation on
this model using computationally expensive Genetic Algorithms (GA). OpenMOLE
hides the complexity of designing such an experiment thanks to its DSL, and
transparently distributes the optimisation process. The example shows how an
initialisation of the GA with a population of 200,000 individuals can be
evaluated in one hour on the European Grid Infrastructure.Comment: IEEE High Performance Computing and Simulation conference 2015, Jun
2015, Amsterdam, Netherland
Beyond Biomass: Valuing Genetic Diversity in Natural Resource Management
Strategies for increasing production of goods from working and natural systems have raised concerns that the diversity of species on which these services depend may be eroding. This loss of natural capital threatens to homogenize global food supplies and compromise the stability of human welfare. We assess the trade off between artificial augmentation of biomass and degradation of biodiversity underlying a populations' ability to adapt to shocks. Our application involves the augmentation of wild stocks of salmon. Practices in this system have generated warnings that genetic erosion may lead to a loss of the “portfolio effect” and the value of this loss is not accounted for in decision making. We construct an integrated bioeconomic model of salmon biomass and genetic diversity. Our results show how practices that homogenize natural systems can still generate positive returns. However, the substitution of more physical capital and labor for natural capital must be maintained for gains to persist, weakens the capacity for adaptation should this investment cease, and can cause substantial loss of population wildness. We apply an emerging optimization method—approximate dynamic programming—to solve the model without simplifying restrictions imposed previously
Evaluation of agricultural ecosystem services in fallowing land based on farmers' participation and model simulation
Fallowing with green fertilizer can benefit agricultural ecosystem services (AES). Farmers in Taiwan do not implement fallow practices and plant green fertilizer because the current subsidy level (46,000 NTperha)istoolowtomanagefallowingThispaperdefinestheobjectiveofgovernmentagriculturepolicyorthefarmer’sobjectiveasmaximizationoffarmproductivityapproximatedtothevalueofsocialwelfareandAESFarmswhichdonotfollowproperfallowingpracticesoftenhavepoorlymaintainedfallowlandorleftfarmlandabandonedThisresultsinnegativeenvironmentalconsequencessuchascutworminfestationsinabandonedlandwhichinturncanaffectcropsinadjacentfarmlandsTheobjectivesofthisstudyaretwofoldFirstitdeterminestheproperfallowingsubsidybasedontheconceptofpaymentforecosystemservicestoenticemorefarmerstoparticipateinfallowingSeconditsimulatesthebenefitofplantinggreenmanureinfallowlandtothesupplyofAESbasedontherateoffarmerswhoarewillingtoparticipateinfallowlandpracticesandessentialparametersthatcanaffectsoilfertilitychangeTheapproachinvolvesaseriesofinterviewsandadevelopedempiricalmodelThevalueofAESwhentherateoffarmerparticipationis100 ) over the value at the current participation rate of 14%. This study further concludes that the appropriate fallowing subsidy has a large positive impact on AES and social welfare (e.g., benefit from food and biofuel supplies) and is seen as a basis of ecological governance for sustainable agro-ecosystems
Spatial optimization for land use allocation: accounting for sustainability concerns
Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined
An Agent-Based Model of Multifunctional Agricultural Landscape Using Genetic Algorithms
Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent.Multifunctional agriculture, Agent based modeling, Genetic Algorithm, Environmental Economics and Policy, Land Economics/Use,
Towards a Holistic CAD Platform for Nanotechnologies
Silicon-based CMOS technologies are predicted to reach their ultimate limits
by the middle of the next decade. Research on nanotechnologies is actively
conducted, in a world-wide effort to develop new technologies able to maintain
the Moore's law. They promise revolutionizing the computing systems by
integrating tremendous numbers of devices at low cost. These trends will have a
profound impact on the architectures of computing systems and will require a
new paradigm of CAD. The paper presents a work in progress on this direction.
It is aimed at fitting requirements and constraints of nanotechnologies, in an
effort to achieve efficient use of the huge computing power promised by them.
To achieve this goal we are developing CAD tools able to exploit efficiently
these huge computing capabilities promised by nanotechnologies in the domain of
simulation of complex systems composed by huge numbers of relatively simple
elements.Comment: Submitted on behalf of TIMA Editions
(http://irevues.inist.fr/tima-editions
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