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The DisruptSC model assesses the indirect economic impacts of disasters by explicitly quantifying the disturbances on supply chains. It simulates, in space and time, how transport infrastructure disruptions perturb the flows of goods in supply chains and how these perturbations affect households, firms, and trade.

Pathfinder is designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: (1) the capacity to be calibrated using Bayesian inference, (2) the capacity to be coupled with integrated assessment models (IAMs), and (3) the capacity to explore a very large number of climate scenarios to narrow down those compatible with limiting climate impacts.

The Integrated Catastrophe Analysis and Management Modeling (ISCRiMM) approach addresses systemic risks by analyzing socio-economic and structural vulnerabilities, employing statistical and machine learning methods, and enabling stakeholders to update vulnerability indices for effective risk management.

Welcome to the world of PlantFATE, a novel eco-evolutionary vegetation model designed to unravel the intricate dynamics of forests and their response to environmental change. PlantFATE stands as a powerful tool to guide conservation efforts, inform policy decisions, and foster a deeper appreciation for the wonders of nature. Embark on a journey through the intricate tapestry of forest resilience with PlantFATE as your guide.

This is a GIS-based model which explicitly accounts for the interplay between national and local ex-ante measures, e.g., investment in prevention/mitigation measures (on the part of the public authorities, the citizens and the insurance industry) and ex-post policies for sharing the financial costs after the disaster.

A set of global zoning layers are provided that identify broad homologues of foodscape classes. These are comparable in a minimum set of biophysical and management characteristics and can help to plan for possible interventions and leverage points for more sustainable agriculture.

The CAMALIOT mobile app for crowdsourcing is used to collect data transmitted by the Global Satellite Navigation System (GNSS) for scientific applications such as weather forecasting and improving positional accuracy

A model to reproduce historical wildfire events and to project future burned areas, as well as to assess climate change impacts and adaptation options.

Stochastic Quasi-Gradient (SQG) methods have been developed for solving general optimization problems without exact calculation of objective function and constraints (let alone of their derivatives). SQG methods enable a sequential revision of approximate solutions towards the optimal using newly acquired information on the system, obtained via either direct on-line observations or(and) simulations. 

The framework for eco-genetic modeling offers flexible tools for exploring the course and rates of multi-trait life-history evolution in natural populations.

The Working Group on Fisheries-Induced Evolution (WGEVO) of the International Council for the Exploration of the Sea (ICES) has developed a general framework for investigating eco-evolutionary changes in fish stocks and their utilities in terms of ecosystem services and for assessing the management implications of fisheries-induced evolution through Evolutionary Impact Assessments (EvoIAs).

The complexity and dynamical nature of community interactions make modelling a useful tool for understanding how biodiversity patterns in communities develop over time and how they respond to external perturbations.

Ensuring that common goods and open-access resources—everything from clean air and the global climate to the internet and civil security—are equitably and fairly available to everyone requires incentive mechanisms.

Despite modern medical interventions, infectious diseases continue to generate huge socio-economic losses.

The Working Group on Fisheries-Induced Evolution (WGEVO) of the International Council for the Exploration of the Sea (ICES) has developed a general framework for investigating eco-evolutionary changes in fish stocks and their utilities in terms of ecosystem services and for assessing the management implications of fisheries-induced evolution through Evolutionary Impact Assessments (EvoIAs).

The linkage algorithms solve the problem of linking models, e.g. sectorial and/or regional, into an inter-sectorial inter-regional integrated model. Linkage enables to avoid “hard linking” of models in a single code, which saves the programming time and enables parallel distributed computations of individual models instead of a large scale integrated model. Models linkage preserves the structure of the original models taking into account critically important details, which are usually missing in aggregate models.

Scenathons: Interactive Scenario and Development Pathway Planning

Where are the best places for a romantic date? Where can you skate? Where is it cool on a hot summer's day? Find the perfect places to spend the day and share them with the City Oases App!

The current state of the world affairs calls for a revival of systems thinking to improve decision-making. Recognizing that the tightening of socio-economic links heightens the need for holistic responses, that disciplinary and sectorial solutions are of limited effectiveness and efficiency, and that big data is not generating integrative perspectives by itself, highlights the need for policymakers to become thoroughly familiar with the promises and pitfalls of systems analysis. Challenges are systemic, dynamic, and interconnected, and systems analysis, coupled with an improved anticipation, provides a coherent methodology and necessary tools to develop new approaches so urgently required for more coherent and effective policy planning.

We are developing and applying a range of methods for integrated multi-attribute evaluation under risk, subject to incomplete or imperfect information, and evaluations of decision situations using imprecise utilities, probabilities, and weights, as well as qualitative estimates between these components derived from sets of weight, utility and probability measures. To avoid some mathematical aggregation problems when handling set membership functions and similar, we use higher-order distributions for better discrimination between the possible outcomes.