David Huber

Semantic links in integrated modelling frameworks

It is commonly accepted that modelling frameworks offer a powerful tool for modellers, researchers and decision makers, since they allow the management, re-use and integration of mathematical models from various disciplines and at different spatial and temporal scales. However, the actual re-usability of models depends on a number of factors such as the accessibility of the source code, the compatibility of different binary platforms, and often it is left to the modellers own discipline and responsibility to structure a complex model in such a way that it is decomposed in smaller re-usable sub-components. What reusable and interchangeable means is also somewhat vague; although several approaches to build modelling frameworks have been developed, little attention has been dedicated to the intrinsic re-usability of components, in particular between different modelling frameworks. In this paper, we focus on how models can be linked together to build complex integrated models. We stress that even if a model component interface is clear and reusable from a software standpoint, this is not a sufficient condition for reusing a component across different integrated modelling frameworks. This reveals the need for adding rich semantics in model interfaces.

A Generic Farming System Simulator

The aim of this chapter is to present a bio-economic modelling framework established to provide insight into the complex nature of agricultural systems and to assess the impacts of agricultural and environmental policies and technological innovations. This framework consists of a Farm System Simulator (FSSIM) using mathematical programming that can be linked to a cropping system model to estimate at field level the engineering production and environmental functions. FSSIM includes a module for agricultural management (FSSIM-AM) and a mathematical programming model (FSSIM-MP). FSSIM-AM aims to define current and alternative activities and to quantify their input output coefficients (both yields and environmental effects) using a cropping system model, such as APES (Agricultural Production and Externalities Simulator) and other sources (expert knowledge, surveys, etc.). FSSIM-MP seeks to describe the behaviour of the farmer given a set of biophysical, socio-economic and policy constraints and to predict its reactions under new technologies, policy and market changes. The communication between these different tools and models is based on explicit definitions of spatial scales and software for model integration.

Approximating credal network inferences by linear programming

An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion. A complexity analysis for both the problem and the algorithm is reported together with numerical experiments, which confirm the good performance of the method. While the inner approximation produced by the algorithm gives rise to a classifier which might return a subset of the optimal class set, preliminary empirical results suggest that the accuracy of the optimal class set is seldom affected by the approximate probabilities.

Approximate credal network updating by linear programming with applications to decision making

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.