Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude

Compositional Ecological Modelling via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

Centre for Intelligent Systems and their ApplicationsCompositional modelling is one of the most important knowledge-based approaches to automating domain model construction. However, its use has been limited to physical systems due to the specific presumptions made by existing techniques. Based on a critical survey of existing compositional modellers, the strengths and limitations of compositional modelling for its application in the ecological domain are identified and addressed. The thesis presents an approach for effectively building and (re-)using repositories of models of ecological systems, although the underlying methods are domainindependent. It works by translating the compositional modelling problem into a dynamic constraint satisfaction problem (DCSP). This enables the user of the compositional modeller to specify requirements to the model selection process and to find an appropriate model by the use of efficient DCSP solution techniques. In addition to hard dynamic constraints over the modelling choices, the ecologist/ user of the automated modeller may also have a set of preferences over these options. Because ecological models are typically gross abstractions of very complex and yet only partially understood systems, information on which modelling approach is better is limited, and opinions differ between ecologists. As existing preference calculi are not designed for reasoning with such information, a calculus of partially ordered preferences, rooted in order-of-magnitude reasoning, is also devised within this dissertation. The combination of the dynamic constraint satisfaction problem derived from compositional modelling with the preferences provided by the user, forms a novel type of constraint satisfaction problem: a dynamic preference constraint satisfaction problem (DPCSP). In this thesis, four algorithms to solve such DPCSPs are presented and experimental results on their performance discussed. The resulting algorithms to translate a compositional modelling problem into a DCSP, the order-of-magnitude preference calculus and one of the DPCSP solution algorithms constitute an automated compositional modeller. Its suitability for ecological model construction is demonstrated by applications to two sample domains: a set of small population dynamics models and a large model on Mediterranean vegetation growth. The corresponding knowledge bases and how they are used as part of compositional ecological modelling are explained in detail

Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude

Approximate model composition for explanation generation

This thesis presents a framework for the formulation of knowledge models to sup¬ port the generation of explanations for engineering systems that are represented by the resulting models. Such models are automatically assembled from instantiated generic component descriptions, known as modelfragments. The model fragments are of suffi¬ cient detail that generally satisfies the requirements of information content as identified by the user asking for explanations. Through a combination of fuzzy logic based evidence preparation, which exploits the history of prior user preferences, and an approximate reasoning inference engine, with a Bayesian evidence propagation mechanism, different uncertainty sources can be han¬ dled. Model fragments, each representing structural or behavioural aspects of a com¬ ponent of the domain system of interest, are organised in a library. Those fragments that represent the same domain system component, albeit with different representation detail, form parts of the same assumption class in the library. Selected fragments are assembled to form an overall system model, prior to extraction of any textual infor¬ mation upon which to base the explanations. The thesis proposes and examines the techniques that support the fragment selection mechanism and the assembly of these fragments into models. In particular, a Bayesian network-based model fragment selection mechanism is de¬ scribed that forms the core of the work. The network structure is manually determined prior to any inference, based on schematic information regarding the connectivity of the components present in the domain system under consideration. The elicitation of network probabilities, on the other hand is completely automated using probability elicitation heuristics. These heuristics aim to provide the information required to select fragments which are maximally compatible with the given evidence of the fragments preferred by the user. Given such initial evidence, an existing evidence propagation algorithm is employed. The preparation of the evidence for the selection of certain fragments, based on user preference, is performed by a fuzzy reasoning evidence fab¬ rication engine. This engine uses a set of fuzzy rules and standard fuzzy reasoning mechanisms, attempting to guess the information needs of the user and suggesting the selection of fragments of sufficient detail to satisfy such needs. Once the evidence is propagated, a single fragment is selected for each of the domain system compo¬ nents and hence, the final model of the entire system is constructed. Finally, a highly configurable XML-based mechanism is employed to extract explanation content from the newly formulated model and to structure the explanatory sentences for the final explanation that will be communicated to the user. The framework is illustratively applied to a number of domain systems and is compared qualitatively to existing compositional modelling methodologies. A further empirical assessment of the performance of the evidence propagation algorithm is carried out to determine its performance limits. Performance is measured against the number of frag¬ ments that represent each of the components of a large domain system, and the amount of connectivity permitted in the Bayesian network between the nodes that stand for the selection or rejection of these fragments. Based on this assessment recommenda¬ tions are made as to how the framework may be optimised to cope with real world applications

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Looking Back, Looking Forward: Progress and Prospect for Spatial Demography

In 2011 a specialist meeting on the “Future Directions in Spatial Demography” was held in Santa Barbara, California (Matthews, Goodchild, & Janelle, 2012).1 This specialist meeting was the capstone to a multi-year National Institutes of Health training grant that had supported workshops in advanced spatial analysis methods increasing used by population scientists.2 Early-career scholars who had participated in the training workshops and senior demographers and geographers drawn from across the United States participated in the specialist meeting.3 The application process to attend the 2011 meeting, required that each of the forty-one attendees submit a statement that reviewed challenges and identifed new directions for spatial demography, including gaps in current knowledge regarding innovations in geospatial data, spatial statistical methods, and the integration of data and models to enhance the science of spatial demography in population and health research. Reading again some of the ruminations of these scholars is an interesting exercise in its own right. The level of optimism back in 2011 was high, and especially regarding anticipated changes in computational capacity, leveraging big data (including volunteered geographic information), developments in data systems (including new data high resolution data products and online resources such as multi-scale map interfaces and dashboards), and in methods such as time–space models, agent-based models, microsimulation, and small-area estimation. There were also several challenges identifed including, but not limited to, study designs, data integration, data validation, confdentiality, non-representative data, historic data, defnitions of place, residential selection and mobility as well as two overarching challenges related to the role and contribution of spatial demographers in interdisciplinary population and health research, and many, many comments on training issues. Substantively the attendees research focused on all forms of interaction between people and place (and the reciprocal relations between the people in social, built, and physical environment contexts) covering the gamut of demographic processes from reproductive health to mortality, though with perhaps an overrepresentation of researchers in areas related to population and environment research, racial and residential segregation, and migration.The R25 Training Grant was funded through the Eunice Kennedy Shriver National Institutes of Child Health and Human Development (NICHD 5R-25 HD057002; Principal Investigator: Stephen A. Matthews).