Historical observations indicate a linear relationship between the increase of global
mean surface temperature from the pre-industrial level (the 1850 – 1900 average) and
the cumulative net CO2 emissions (accounted from 1876 onwards). Thus future cumulative CO2 emissions – a.k.a. carbon budget – are considered a good predictor for future warming.
The transition towards a low-carbon society will require a fundamental reorganization of
society’s metabolism, i.e. the material and energy flows associated with socioeconomic
activities. The 2°C and even more so the 1.5°C goal, agreed as fundamental climate
protection target in 2015, require a massive reduction of global greenhouse gas emissions
until 2050 and more or less complete decarbonization until 2100. Against this background, the European Union committed itself to reduce GHG emission by 80% compared to a 1990 baseline
Reducing global meat consumption can significantly help to alleviate agricultural
land-use change and greenhouse gas
emissions. Most modelling studies rely on an average value of meat consumption per capita, or on stylized diet types. They do not consider behavioral dynamics behind diet change. Recent studies show the importance of linking human behavior feedback to climate models. Therefore, exploring the implications of diet change requires considering the feedback
loops between dietary actions and environmental impacts.
Inequality is widely considered to hinder economic growth and can potentially trigger social unrest. Historical records show that inequality varies largely over time and
across countries, and ongoing trends are a source of growing concern. In this research, we aim to enhance the understanding of the driving forces of inequality, and propose market policies able to reduce international
inequality. We do this by developing a network model that consolidates the intertwined effects of migration and foreign direct investment on the consumption distribution.
The problem of constructing optimal closed-loop control strategies under uncertainty is one of the key problems of the mathematical control theory. Its solution would give a new impetus to the theory’s development and create the foundation for its new applications.
Models have traditionally been used to find the best-estimate futures, therefore the validation (evaluation, assessment) approaches focused on building a “robust” model that narrows all the complexity and uncertainty down to a single estimate. Models are increasingly used to explore a variety of scenarios, instead of generating a best-estimate future. Therefore, the validation approaches should be aligned with this changing model purpose. Before investigating potential validation techniques for uncertainty-oriented models, this study reviews the existing validation approaches.
The method of program packages is a tool for solution of the guaranteed positional control problems with incomplete information
on the initial state of the system. In this work the application of the method to a linear dynamical system with a linear observed system is considered. An example, for which the corresponding problem turns out to be ill-posed, is presented.
While the versatility of dynamic vegetation models (DVMs) continuously increases, their accuracy suffers from accumulating uncertainty as new processes and parameters are added. We propose that the key to solving this problem lies in a ‘missing law’ –adaptation and optimization principles rooted in natural selection.
In a supply chain, production disruptions may cascade from one firm to another. Because of this ripple effect, accidents or natural disasters
provoke economic losses in distant locations, exemplified by the 2011 flooding in Thailand.
Businesses, insurers, and policy-makers report a rising concern for such systemic risk. Through outsourcing and vertical specialization, supply chains are increasingly fragmented: the multiple production and delivery stages are run by many independent firms. How does this fragmentation affect the management of systemic risk? The way one firm mitigates risk impacts the profit of its supply chain partners, which may in turn change their mitigating strategy. We study these interactions using evolutionary game theory.
We analyze how seafood trade flows are redistributed under a range of shock scenarios and assess the food-security implications by comparing changes in regional fish supplies to indices of each region’s nutritional fish dependency. We introduce a model of shock propagation and distribution among regions on a network of historical bilateral seafood trade data from UN Comtrade using 205 reporting territories grouped into 18 regions. Shocks originate from decreased exports from one region, reducing the flows to importing regions. Regions with reduced imports either reduce their own exports, thus passing on a fraction of the shock, or reduce their domestic fish supply, thus absorbing the shock locally. For increased realism, we account for a larger willingness to pay when supplies drop in regions with higher GDP per capita.
Recessions are economic downturns that can be recognized from macro-indicators such as the Dow Jones Industrial Average (DJIA) and
the Federal Reserve Interest Rate (FRIR). To provide early-warning signals of recessions and similar systemic transitions, here we propose
a new approach based on pattern recognition, called inclination analysis. For this purpose, we develop a stochastic model based on time-series analysis to assess the probability of a recession to occur at a given moment in the past, present, or future. Calibrating our model
to data proceeds in three steps, involving the coarse-graining of the available input time series, the identification of short series motifs that foreshadow recessions, and the optimization of key model parameters according to the model’s desired forecasting horizon.
ClimTrans2050 makes use of IIASA's Emissions-Temperature-Uncertainty (ETU) framework to provide emission target
paths for Austria that are compatible with global warming targets for 2050, e.g., 2 °C. The ETU framework allows reconciling short-term GHG emission commitments with long-term efforts to meet global temperature targets in 2050 and beyond; and understanding uncertainty across temporal scales. In a nutshell, the ETU framework can be used to monitor a country's performance - past as well as projecte achievements - in complying with a future warming target in a quantified uncertainty-risk context.
This research aims at advanced learning from the past, namely, at treating uncertainty and its change seamlessly across time, from the past to the immediate future (near-term goal); and at providing a measure of reference for prognostic scenarios (long-term goal).
Crowdsourcing is a new approach for solving data processing problems for which conventional methods appear to be inaccurate, expensive, or time-consuming. Nowadays, the development of new crowdsourcing techniques is mostly motivated by so called Big Data problems, including problems of assessment and clustering for large datasets obtained in aerospace imaging, remote sensing, and even in social network analysis. By involving volunteers from all over the world, the Geo-Wiki project tackles problems of environmental monitoring with applications to flood resilience, biomass data analysis and classification of land cover. For example, the Cropland Capture Game, which is a gamified version of Geo-Wiki, was developed to aid in the mapping of cultivated land, and was used to gather 4.5 million image classifications from the Earth’s surface.
The model is able to carry out an integrated systems analysis of interdependent energy-food-water-environmental systems under security targets while accounting for the competition to those systems posed by restricted natural resources under inherent uncertainties and systemic risks. The case study focuses on developments of coal industry and agricultural production in Shanxi province of China.
Studies of complex systems are non-separable from the analysis of partial and imprecise information received from alternative sources. A system analysist deals with a set of ensemble outcomes which needs to be integrated into one estimate in order to install the ensemble into the modelling chain or provide support for the informed decision making.
Urban development has accelerated across the globe in recent decades. Much new urbanization has not been concentrated in cities, but has occurred as dispersed, low density development outside of major centers but within their area of economic influence. Province of Seville (Spain) has experienced notable urban expansion in recent years and is subject of the case study.
Diffusion processes are instrumental to describe the movement of a continuous quantity in a generic network of interacting agents. Diffusion processes are relevant
to describe the dynamical behavior of large-scale networks, for instance in the case of opinion dynamic and epidemic propagation, or in the context of trade and financial networks.
Our main contributions can be listed as follows:
i. We describe the network dynamics for asymmetric networks with stochastic updating.
ii. We introduce a classification of agent interactions according to two protocols where the total network quantity is conserved or variable.
iii. We capture network control by allowing external time-varying input functions or by considering network structure modifications. The proposed framework is relevant in the context of group coordination, herding behavior, distributed algorithms, and network control.
The poster is devoted to studies in the framework of the Economic Growth Project at IIASA. The research deals with the interdisciplinary approach for constructing optimal trajectories of growth based on the analysis of real time-series. The background of the study is the following:
• economic growth theory (Arrow, Solow, Shell);
• optimal control theory (Pontryagin’s maximum principle for problems on infinite horizon);
• econometric analysis of the model;
• numerical simulation and forecasting of future scenarios.
Prognostic systems analysis is widely applied to generate 'sharp' projections into the future. However, prognostic scenarios and 'sharp' futures are a physical impossibility!
At this stage the goal is to identify pre-cursors of the early 2000s recession (March - November 2001). We consider three-year-long financial time series for the DJIA (Dow Jones Industrial Average) and the FRIR (Federal Reserve Interest Rate), representing the monthly data preceding the recession. We identify some short patterns in the time series as “minus” signals (which primarily occur close to the time of the recession) and some short patterns occurring, primarily, in earlier periods as "plus" signals.
A dynamic optimization model of investment in improvement of the resource productivity index is analyzed for obtaining balanced economic growth trends including both the consumption index and natural resources use. The research is closely connected with the problem of shortages of natural resources stocks, the security of supply of energy and materials, and the environmental effectiveness of their consumption. The main idea of the model is to introduce an integrated environment for elaboration of a control policy for management of the investment process in development of basic production factors such as capital, energy and material consumption. An essential feature of the model is the possibility to invest in economy's dematerialization. Another important construction is connected with the price formation mechanism which presumes the rapid growth of prices on exhausting materials. The balance is formed in the consumption index which negatively depends on growing prices on materials. The optimal control problem for the investment process is posed and solved within the Pontryagin maximum principle. Specifically, the growth and decline trends of the Hamiltonian trajectories are examined for the optimal solution.
Species losses have always occurred as a natural phenomenon, but the pace at which species are going extinct has recently
accelerated dramatically as a result of human activities. The disappearance of a species can have far-reaching and often unexpected consequences for other species, since changes can propagate throughout ecosystems. Hence, the following questions arise:
• How does the collapse of one ecosystem compartment (species or functional groups) influence the remaining ecosystem compartments?
• How is an ecosystem’s structure related to its vulnerability to compartment collapses?
Last edited: 14 November 2019
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