Models in the Cloud: Exploring Next Generation Environmental Software Systems

There is growing interest in the application of the latest trends in computing and data science methods to improve environmental science. However we found the penetration of best practice from computing domains such as software engineering and cloud computing into supporting every day environmental science to be poor. We take from this work a real need to re-evaluate the complexity of software tools and bring these to the right level of abstraction for environmental scientists to be able to leverage the latest developments in computing. In the Models in the Cloud project, we look at the role of model driven engineering, software frameworks and cloud computing in achieving this abstraction. As a case study we deployed a complex weather model to the cloud and developed a collaborative notebook interface for orchestrating the deployment and analysis of results. We navigate relatively poor support for complex high performance computing in the cloud to develop abstractions from complexity in cloud deployment and model configuration. We found great potential in cloud computing to transform science by enabling models to leverage elastic, flexible computing infrastructure and support new ways to deliver collaborative and open science

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

Abstract. This paper presents the application of a modular approach for real-time streamflow forecasting that uses different system-theoretic rainfall-runoff models according to the situation characterising the forecast instant. For each forecast instant, a specific model is applied, parameterised on the basis of the data of the similar hydrological and meteorological conditions observed in the past. In particular, the hydro-meteorological conditions are here classified with a clustering technique based on Self-Organising Maps (SOM) and, in correspondence of each specific case, different feed-forward artificial neural networks issue the streamflow forecasts one to six hours ahead, for a mid-sized case study watershed. The SOM method allows a consistent identification of the different parts of the hydrograph, representing current and near-future hydrological conditions, on the basis of the most relevant information available in the forecast instant, that is, the last values of streamflow and areal-averaged rainfall. The results show that an adequate distinction of the hydro-meteorological conditions characterising the basin, hence including additional knowledge on the forthcoming dominant hydrological processes, may considerably improve the rainfall-runoff modelling performance

Implication of two new paradigms for futures studies

The paper considers the emergence of two recent perspectives in futures work. One is evolutionary futures studies. The other is critical futures studies. After describing aspects of each, the paper considers them as alternative rival paradigms in relation to criteria that include: the role of the human being as a subject, the role of interpretation and differences in methodological premises. It concludes that both have contributed to the development of futures methods but that a number of theoretical and methodological problems still remain unsolved

Selection-Based Learning: The Coevolution Of Internal And External Selection In High-Velocity Environments

To understand the effects of selection on firm-level learning, this study synthesizes two contrasting views of evolution. Internal selection theorists view managers in multiproduct firms as the primary agents of evolutionary change because they decide whether individual products and technologies are retained or eliminated. In contrast, external selection theorists contend that the environment drives evolution because it determines whether entire firms live or die. Though these theories differ, they describe tightly interwoven processes. In assessing the coevolution of internal and external selection among personal computer manufacturers across a 20-year period, we found that (1) firms learned cumulatively and adaptively from internal and partial external selection, the latter occurring when the environment killed part but not all of a firm; (2) internal and partial external selection coevolved, as each affected the other's future rate and the odds of firm failure; (3) partial external selection had a greater effect on future outcomes than internal selection; and (4) the lessons gleaned from prior selection were reflected in a firm's ability to develop new products, making that an important mediator between past and future selection events.Managemen

Good policies for bad governments: behavioral political economy

Politicians and policymakers are prone to the same biases as private citizens. Even if politicians are rational, little suggests that they have altruistic interests. Such concerns lead us to be wary of proposals that rely on benign governments to implement interventionist policies that "protect us from ourselves." The authors recommend paternalism that recognizes both the promise and threat of activist government. They support interventions that channel behavior without taking away consumers' ability to choose for themselves. Such "benign paternalism" can lead to very dramatic behavioral changes. But benign paternalism does not give government true authority to control our lives and does not give private agents an incentive to reject such authority through black markets and other corrosive violations of the rule of law. The authors discuss five examples of policy interventions that will generate significant welfare gains without reducing consumer liberties. They believe that all policy proposals should be viewed with healthy skepticism. No doctor would prescribe a drug that only worked in theory. Likewise, economic policies should be tested with small-scale field experiments before they are adopted.Macroeconomics ; Economics ; Economic policy

MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH

Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to put their relationship in words his answer will probably reveal a fuzzy nature like: "IF the inflation rate in the EURO-Zone is low and the GDP growth rate is larger than in the US THEN the EURO will rise against the USD". æLowÆ and ælargerÆ are fuzzy terms which give a gradual linguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy Fuzzy Rule base from examples we introduce Genetic Algorithms and Artificial Neural Networks as learning operators. These examples can either be empirical data or originate from an economic simulation model. The software GENEFER (GEnetic NEural Fuzzy ExplorER) has been developed for designing such a Fuzzy Rule Base. The design process is modular and comprises Input Identification, Fuzzification, Rule-Base Generating and Rule-Base Tuning. The two latter steps make use of genetic and neural learning algorithms for optimizing the Fuzzy Rule-Base.

Scenario Models of the World Economy

The purpose of this paper is threefold: First it discusses the case for building an IO-based model of the world economy, second, it discusses the type and the necessary characteristics of such a model and finally it highlights a road map for its construction. In the process it argues for the following set of propositions: a) the necessity of reviving the notion of world models b) the need to take on simultaneously the social-economic and environmental challenges and their interdependence c) the suitability of the IO framework as the basis of the next generation of world models d) the importance of bringing theory to bear on the development of the model and on policy analysis e) the importance of community for the development, support and diffusion of the model and its uses f) asserting the importance of the role of IIOA in building that community g) a focus on interdisciplinarity rather than on the export of economic logic alone to the social and environmental dimensions h) the importance of a scenario-based approach and i) the need to develop and integrate the financial side along with the real side of the economy.Forecast, World Economy;Input-Output Models