Reducing complexity in an agent based reaction model-Benefits and limitations of simplifications in relation to run time and system level output.

Agent based modelling is a methodology for simulating a variety of systems across a broad spectrum of fields. However, due to the complexity of the systems it is often impossible or impractical to model them at a one to one scale. In this paper we use a simple reaction rate model implemented using the FLAME framework to test the impact of common methods for reducing model complexity such as reducing scale, increasing iteration duration and reducing message overheads. We demonstrate that such approaches can have significant impact on simulation runtime albeit with increasing risk of aberrant system behaviour and errors, as the complexity of the model is reduced

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

EMU and fiscal discipline: the end of the depreciation threat

Are the characteristics of the exchange rate regime relevant for the degree of fiscal discipline? What are the conclusions for fiscal behavior in Europe after the transition to EMU? These are the central questions that are analyzed in this paper from a theoretical point of view. After a general discussion of these issues, the optimization process of fiscal agents is analyzed in the context of a model based on the monetary approach to the exchange rate. The model conclusion is that monetary union leads to more fiscal discipline for high debt countries that used to have a benign neglect stance on the exchange rate. Contrasting to that, low debt countries that used to pay much attention to the exchange rate in the past will behave less disciplined in the future. --EMU,Exchange Rate Regime,Fiscal Discipline,Globalization,Public Debt

Data and Design: Advancing Theory for Complex Adaptive Systems

Complex adaptive systems exhibit certain types of behaviour that are difficult to predict or understand using reductionist approaches, such as linearization or assuming conditions of optimality. This research focuses on the complex adaptive systems associated with public health. These are noted for being driven by many latent forces, shaped centrally by human behaviour. Dynamic simulation techniques, including agent-based models (ABMs) and system dynamics (SD) models, have been used to study the behaviour of complex adaptive systems, including in public health. While much has been learned, such work is still hampered by important limitations. Models of complex systems themselves can be quite complex, increasing the difficulty in explaining unexpected model behaviour, whether that behaviour comes from model code errors or is due to new learning. Model complexity also leads to model designs that are hard to adapt to growing knowledge about the subject area, further reducing model-generated insights. In the current literature of dynamic simulations of human public health behaviour, few focus on capturing explicit psychological theories of human behaviour. Given that human behaviour, especially health and risk behaviour, is so central to understanding of processes in public health, this work explores several methods to improve the utility and flexibility of dynamic models in public health. This work is undertaken in three projects. The first uses a machine learning algorithm, the particle filter, to augment a simple ABM in the presence of continuous disease prevalence data from the modelled system. It is shown that, while using the particle filter improves the accuracy of the ABM, when compared with previous work using SD with a particle filter, the ABM has some limitations, which are discussed. The second presents a model design pattern that focuses on scalability and modularity to improve the development time, testability, and flexibility of a dynamic simulation for tobacco smoking. This method also supports a general pattern of constructing hybrid models --- those that contain elements of multiple methods, such as agent-based or system dynamics. This method is demonstrated with a stylized example of tobacco smoking in a human population. The final line of work implements this modular design pattern, with differing mechanisms of addiction dynamics, within a rich behavioural model of tobacco purchasing and consumption. It integrates the results from a discrete choice experiment, which is a widely used economic method for study human preferences. It compares and contrasts four independent addiction modules under different population assumptions. A number of important insights are discussed: no single module was universally more accurate across all human subpopulations, demonstrating the benefit of exploring a diversity of approaches; increasing the number of parameters does not necessarily improve a module's predictions, since the overall least accurate module had the second highest number of parameters; and slight changes in module structure can lead to drastic improvements, implying the need to be able to iteratively learn from model behaviour

Prediction and control in human neuromusculoskeletal models

Computational neuromusculoskeletal modelling enables the generation and testing of hypotheses about human movement on a large scale, in silico. Humanoid models, which increasingly aim to replicate the full complexity of the human nervous and musculoskeletal systems, are built on extensive prior knowledge, extracted from anatomical imaging, kinematic and kinetic measurement, and codified as model description. Where inverse dynamic analysis is applied, its basis is in Newton's laws of motion, and in solving for muscular redundancy it is necessary to invoke knowledge of central nervous motor strategy. This epistemological approach contrasts strongly with the models of machine learning, which are generally over-parameterised and largely data-driven. Even as spectacular performance has been delivered by the application of these models in a number of discrete domains of artificial intelligence, work towards general human-level intelligence has faltered, leading many to wonder if the data-driven approach is fundamentally limited, and spurring efforts to combine machine learning with knowledge-based modelling. Through a series of five studies, this thesis explores the combination of neuromusculoskeletal modelling with machine learning in order to enhance the core tasks of prediction and control. Several principles for the development of clinically useful artificially intelligent systems emerge: stability, computational efficiency and incorporation of prior knowledge. The first study concerns the use of neural network function approximators for the prediction of internal forces during human movement, an important task with many clinical applications, but one for which the standard tools of modelling are slow and cumbersome. By training on a large dataset of motions and their corresponding forces, state of the art performance is demonstrated, with many-fold increases in inference speed enabling the deployment of trained models for use in a real time biofeedback system. Neural networks trained in this way, to imitate some optimal controller, encode a mapping from high-level movement descriptors to actuator commands, and may thus be deployed in simulation as \textit{policies} to control the actions of humanoid models. Unfortunately, the high complexity of realistic simulation makes stable control a challenging task, beyond the capabilities of such naively trained models. The objective of the second study was to improve performance and stability of policy-based controllers for humanoid models in simulation. A novel technique was developed, borrowing from established unsupervised adversarial methods in computer vision. This technique enabled significant gains in performance relative to a neural network baseline, without the need for additional access to the optimal controller. For the third study, increases in the capabilities of these policy-based controllers were sought. Reinforcement learning is widely considered the most powerful means of optimising such policies, but it is computationally inefficient, and this inefficiency limits its clinical utility. To mitigate this problem, a novel framework, making use of domain-specific knowledge present in motion data, and in an inverse model of the biomechanical system, was developed. Training on simple desktop hardware, this framework enabled rapid initialisation of humanoid models that were able to move naturally through a 3-dimensional simulated environment, with 900-fold improvements in sample efficiency relative to a related technique based on pure reinforcement learning. After training with subject-specific anatomical parameters, and motion data, learned policies represent personalised models of motor control that may be further interrogated to test hypotheses about movement. For the fourth study, subject-specific controllers were taken and used as the substrate for transfer learning, by removing kinematic constraints and optimising with respect to the magnitude of the medial knee joint reaction force, an important biomechanical variable in osteoarthritis of the knee. Models learned new kinematic strategies for the reduction of this biomarker, which were subsequently validated by their use, in the real world, to construct subject-specific routines for real time gait retraining. Six out of eight subjects were able to reduce medial knee joint loading by pursuing the personalised kinematic targets found in simulation. Personalisation of assistive devices, such as limb prostheses, is another area of growing interest, and one for which computational frameworks promise cost-effective solutions. Reinforcement learning provides powerful techniques for this task but the expansion of the scope of optimisation, to include previously static elements of a prosthesis, is problematic for its complexity and resulting sample inefficiency. The fifth and final study demonstrates a new algorithm that leverages the methods described in the previous studies, and additional techniques for variance control, to surmount this problem, improving sample efficiency and simultaneously, through the use of prior knowledge encoded in motion data, providing a rational means of determining optimality in the prosthesis. Trained models were able to jointly optimise motor control and prosthesis design to enable improved performance in a walking task, and optimised designs were robust to both random seed and reward specification. This algorithm could be used to speed the design and production of real personalised prostheses, representing a potent realisation of the potential benefits of combined reinforcement learning and realistic neuromusculoskeletal modelling.Open Acces

EMU and Fiscal Discipline: The End of the Depreciation Threat

Are the characteristics of the exchange rate regime relevant for the degree of fiscal discipline? What are the conclusions for fiscal behavior in Europe after the transition to EMU? These are the central questions that are analyzed in this paper from a theoretical point of view. After a general discussion of these issues, the optimization process of fiscal agents is analyzed in the context of a model based on the monetary approach to the exchange rate. The model conclusion is that monetary union leads to more fiscal discipline for high debt countries that used to have a benign neglect stance on the exchange rate. Contrasting to that, low debt countries that used to pay much attention to the exchange rate in the past will behave less disciplined in the future

Aeronautical life-cycle mission modelling framework for conceptual design

This thesis introduces a novel framework for life cycle mission modelling during conceptual aeronautical design. The framework supports object-oriented mission definition using Geographical Information System technology. Design concepts are defined generically, enabling simulation of most aeronautical vessels and many non-aeronautical vehicles. Moreover, the framework enables modelling of entire vessel fleets, business competitors and dynamic operational changes throughout a vessel life cycle. Vessels consist of components deteriorating over time. Vessels carry payload that operates within the vessel environment.An agent-based simulation model implements most framework features. It is the first use of an agent-based simulation utilising a Geographical Information System during conceptual aeronautical design. Two case studies for unmanned aircraft design apply the simulation. The first case study explores how the simulation supports conceptual design phase decisions. It simulates four different unmanned aircraft concepts in a search-and-rescue scenario including lifeboats. The goal is to learn which design best improves life cycle search performance. It is shown how operational and geographical impacts influence design decision making by generating novel performance information. The second case study studies the simulation optimisation capability: an existing aircraft design is modified manually based on simulation outputs. First, increasing the fuel tank capacity has a negative effect on life cycle performance due to mission constraints. Therefore, mission definition becomes an optimisation parameter. Changing mission flight speeds during specific segments leads to an overall improved design