An artificial economy based on reinforcement learning and agent based modeling

In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.

Incorporation of a physically based melt pond scheme into the sea ice component of a climate model

The extent and thickness of the Arctic sea ice cover has decreased dramatically in the past few decades with minima in sea ice extent in September 2005 and 2007. These minima have not been predicted in the IPCC AR4 report, suggesting that the sea ice component of climate models should more realistically represent the processes controlling the sea ice mass balance. One of the processes poorly represented in sea ice models is the formation and evolution of melt ponds. Melt ponds accumulate on the surface of sea ice from snow and sea ice melt and their presence reduces the albedo of the ice cover, leading to further melt. Toward the end of the melt season, melt ponds cover up to 50% of the sea ice surface. We have developed a melt pond evolution theory. Here, we have incorporated this melt pond theory into the Los Alamos CICE sea ice model, which has required us to include the refreezing of melt ponds. We present results showing that the presence, or otherwise, of a representation of melt ponds has a significant effect on the predicted sea ice thickness and extent. We also present a sensitivity study to uncertainty in the sea ice permeability, number of thickness categories in the model representation, meltwater redistribution scheme, and pond albedo. We conclude with a recommendation that our melt pond scheme is included in sea ice models, and the number of thickness categories should be increased and concentrated at lower thicknesses.

Multi-agent based modeling and simulation of complex system in hospital

This study puts forward a method to model and simulate the complex system of hospital on the basis of multi-agent technology. The formation of the agents of hospitals with intelligent and coordinative characteristics was designed, the message object was defined, and the model operating mechanism of autonomous activities and coordination mechanism was also designed. In addition, the Ontology library and Norm library etc. were introduced using semiotic method and theory, to enlarge the method of system modelling. Swarm was used to develop the multi-agent based simulation system, which is favorable for making guidelines for hospital's improving it's organization and management, optimizing the working procedure, improving the quality of medical care as well as reducing medical charge costs.

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

Probabilistic physically-based cloud screening of satellite infra-red imagery for operational sea surface temperature retrieval

We propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar-orbiting platforms. In comparison with the established cloud-screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false-alarm rate by one quarter. The rate of occurrence of cloud-screening-related errors of >1 K in area-averaged SSTs is reduced by 83%. Copyright © 2005 Royal Meteorological Society.

Quantitative single-molecule localization microscopy combined with rule-based modeling reveals ligand-induced TNF-R1 reorganization toward higher-order oligomers

We report on the assembly of tumor necrosis factor receptor 1 (TNF-R1) prior to ligand activation and its ligand-induced reorganization at the cell membrane. We apply single-molecule localization microscopy to obtain quantitative information on receptor cluster sizes and copy numbers. Our data suggest a dimeric pre-assembly of TNF-R1, as well as receptor reorganization toward higher oligomeric states with stable populations comprising three to six TNF-R1. Our experimental results directly serve as input parameters for computational modeling of the ligand-receptor interaction. Simulations corroborate the experimental finding of higher-order oligomeric states. This work is a first demonstration how quantitative, super-resolution and advanced microscopy can be used for systems biology approaches at the single-molecule and single-cell level.

A physical based modeling approach for the dynamic behavior of ultrafine grained structures

This paper discusses some experimental results on the influence of grain refinement on the final mechanical properties of IF and microalloyed steels designed for auto-body components. It shows also some modeling approaches to understanding the dynamic behavior of fine-rained materials. The Zerilli–Armstrong (Z–A) and Khan–Huang–Liang (KHL) models for studied steels were implemented into FEM code in order to simulate the dynamic compression tests with different strain rates.

Enhancing procedural efficiency in multiple-industry ratio-based modeling of corporate collapse

This paper utilizes a methodological approach called Multi-Level Modeling (MLM) that addresses two major shortcomings in the two step analytic process that is traditionally adopted in the pertinent literature for modeling corporate collapse; thereby, enhancing procedural efficiency. The robustness of MLM vis-à-vis the traditional two-step procedure is ascertained using a data sample of Australian
publicly listed companies, equally split between collapsed and non collapsed, during the period 1989 to 2006. The results indicate that not only does MLM improve procedural efficiency, it does so while
enhancing the robustness of signaling corporate collapse; in particular, MLM signals collapse with an overall 6.6% increase in accuracy.

A physically based phenomenological model for deformation twinning in magnesium alloy

A partial differential equation is developed that captures the evolution of key characteristics of tensile twinning in magnesium base alloys. The objective is to provide a framework for ascertaining the effects of hardening – due to grain refinement, precipitation and dislocation substructure – on twin volume fraction, thickness and length. The model is developed with the help of observations made on alloy AZ31. It is shown that it is necessary to consider the nucleation of twins at locations where neighbouring twins impinge on the grain boundary. The model provides a reasonable approximation for the role of grain size on twinning. It predicts a period of low apparent work hardening following yielding and shows that this should be more extensive for finer grain sizes, in agreement with experiment. Finally, some predictions are made on the effect of changing the resistance to twinning.

Simplified physically based model for estimating effective floc density

This paper applies dimensional analysis to propose an alternative model for estimating the effective density of flocs (Δρf). The model takes into account the effective density of the primary particles, in addition to the sizes of the floc and primary particles, and does not consider the concept of self-similarity. The model contains three dimensionless products and two empirical parameters (αf and βf), which were calibrated by using data available in the literature. Values of αf=0.7 and βf=0.8 were obtained. The average value of the primary particle size (Dp) for the data used in the analysis, inferred from the new model, was found to vary from 0.05 μm to 100 μm with a mean value of 2.5 μm. Good comparisons were obtained in comparing the estimated floc-settling velocity on the basis of the proposed model for effective floc density with the measured value.

Business competition dynamics: agent-based modeling simulations of firms in search of economic performance

A intenção deste trabalho é explorar dinâmicas de competição por meio de “simulação baseada em agentes”. Apoiando-se em um crescente número de estudos no campo da estratégia e teoria das organizações que utilizam métodos de simulação, desenvolveu-se um modelo computacional para simular situações de competição entre empresas e observar a eficiência relativa dos métodos de busca de melhoria de desempenho teorizados. O estudo também explora possíveis explicações para a persistência de desempenho superior ou inferior das empresas, associados às condições de vantagem ou desvantagem competitiva

iSPD: An iconic-based modeling simulator for distributed grids

Simulation of large and complex systems, such as computing grids, is a difficult task. Current simulators, despite providing accurate results, are significantly hard to use. They usually demand a strong knowledge of programming, what is not a standard pattern in today's users of grids and high performance computing. The need for computer expertise prevents these users from simulating how the environment will respond to their applications, what may imply in large loss of efficiency, wasting precious computational resources. In this paper we introduce iSPD, iconic Simulator of Parallel and Distributed Systems, which is a simulator where grid models are produced through an iconic interface. We describe the simulator and its intermediate model languages. Results presented here provide an insight in its easy-of-use and accuracy.

Risk-based Modeling to Develop Zoning Criteria for Land-use Near Canadian Airports

Land development in the vicinity of airports often leads to land-use that can attract birds that are hazardous to aviation operations. For this reason, certain forms of land-use have traditionally been discouraged within prescribed distances of Canadian airports. However, this often leads to an unrealistic prohibition of land-use in the vicinity of airports located in urban settings. Furthermore, it is often unclear that the desired safety goals have been achieved. This paper describes a model that was created to assist in the development of zoning regulations for a future airport site in Canada. The framework links land-use to bird-related safety-risks and aircraft operations by categorizing the predictable relationships between: (i) different land uses found in urbanized and urbanizing settings near airports; (ii) bird species; and (iii) the different safety-risks to aircraft during various phases of flight. The latter is assessed relative to the runway approach and departure paths. Bird species are ranked to reflect the potential severity of an impact with an aircraft (using bird weight, flocking characteristics, and flight behaviours). These criteria are then employed to chart bird-related safety-risks relative to runway reference points. Each form of land-use is categorized to reflect the degree to which it attracts hazardous bird species. From this information, hazard and risk matrices have been developed and applied to the future airport setting, thereby providing risk-based guidance on appropriate land-uses that range from prohibited to acceptable. The framework has subsequently been applied to an existing Canadian airport, and is currently being adapted for national application. The framework provides a risk-based and science-based approach that offers municipalities and property owner’s flexibility in managing the risks to aviation related to their land use.

Packing a trunk : a physically based approach with full motional freedom

Geometric packing problems may be formulated mathematically as constrained optimization problems. But finding a good solution is a challenging task. The more complicated the geometry of the container or the objects to be packed, the more complex the non-penetration constraints become. In this work we propose the use of a physics engine that simulates a system of colliding rigid bodies. It is a tool to resolve interpenetration conflicts and to optimize configurations locally. We develop an efficient and easy-to-implement physics engine that is specialized for collision detection and contact handling. In succession of the development of this engine a number of novel algorithms for distance calculation and intersection volume were designed and imple- mented, which are presented in this work. They are highly specialized to pro- vide fast responses for cuboids and triangles as input geometry whereas the concepts they are based on can easily be extended to other convex shapes. Especially noteworthy in this context is our ε-distance algorithm - a novel application that is not only very robust and fast but also compact in its im- plementation. Several state-of-the-art third party implementations are being presented and we show that our implementations beat them in runtime and robustness. The packing algorithm that lies on top of the physics engine is a Monte Carlo based approach implemented for packing cuboids into a container described by a triangle soup. We give an implementation for the SAE J1100 variant of the trunk packing problem. We compare this implementation to several established approaches and we show that it gives better results in faster time than these existing implementations.