Understanding The Influence Of Participants\u27 Preferences On The Affiliation Network Of Churches Using Agent-based Modeling

As the affiliation network of churches may potentially benefit public health, the impact of participants’ preferences on the affiliation network bears further study. This paper attempts to use agent-based modeling techniques associated with geographic information to study the affiliation network between churches and participants. Using churches in ZIP Code 30318 in Atlanta in Georgia, this study specifies the preferences of participants as personal radii and choice patterns. Results suggest (1) personal radii of participants are positively related to the size of affiliation network and the centralities of churches; (2) the change of choice pattern of participants can lead to a sharp reduction in size of the affiliation network of churches; (3) The centralities of churches among the affiliation network are corresponding to population density of census tracts. Findings can be used to understand the formulation of affiliation network of churches and their relationship with participants’ preferences

A scalable methodology for modeling cities as systems of systems

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 144-146).As cities evolve in size and complexity, their component systems become more interconnected. Comprehensive modeling and simulation is needed to capture interactions and correctly assess the impact of changes. This thesis presents a methodology for modeling cities from a systems of systems perspective. The framework supplies general modeling guidelines and key steps. Also addressed are the importance of stakeholder interactions, creating the model structure, using smart city sensor data, and applying the methodology to larger, traditional cities. As an initial step, four city modeling including CityNet, CityOne, Sim City 4, and SoSAT software programs were evaluated from both a user and mathematical perspective. From the assessments, a list was developed of features critical to successful city modeling software including visualization, a streamlined user interface, accurate mathematics, the ability to specify systems and attributes, and the ability to model interconnections between systems. SoSAT was selected as the modeling tool for the case study, which involved modeling the Army's Base Camp Integration Laboratory. A model of the camp's baseline configuration was built and the camp was simulated for 30 days with results recorded at one hour intervals. 100 trials were run with averaged results presented by time intervals and for the total simulation time. Results were presented at all levels of structural aggregation. Two sensitivity analyses were conducted to analyze the impact of maintenance personnel and the frequency of potable water deliveries. Adding or subtracting a maintenance person impacted the availability of the generator systems that were being serviced, in turn impacting the performance of the micro grid. Extending the time between deliveries by 24 and 48 hours revealed two systems experienced resource depletions. Lastly, two technology insertions cases were conducted to assess the impact of adding a laundry water reuse system (LWRS) and a solar powered hot water heater (SHWH). The LWRS provided 70% of the laundry system's water needs, significantly reducing dependency upon deliveries. The SHWH was expected to decrease electricity consumption and increase fuel consumption. However, the reduction in energy demand meant fewer generators were needed to power the micro grid and both electricity and fuel consumption decreased.by Amanda M. Wachtel.S.M

The knowledge management arena: agent-based modelling of the pig sector

Abstract belonging to PhD thesis: The knowledge management arena: agent-based modelling of the pig sector Sjoukje A. Osinga Wageningen University, Information technology group To be defended on 22nd of April, 2015 Promotor: Em. Prof. ir AJM (Adrie) Beulens, Information technology group Co-promotor: Dr ir GJ (Gert Jan) Hofstede, Information technology group Complex adaptive systems are characterized by multiple levels of behaviour: the behaviour of individual components and the behaviour of the entire system. In this thesis we study this relationship by means of agent-based models. By modelling individuals (agents) and their behaviour only, and simulating this behaviour over time, we generate emerging patterns: we did not explicitly put them in. We try to understand these patterns by reasoning back to individual level (multi-level analysis). Our application domain is knowledge management in the pig sector. Through a series of cases, we study the relationship between farmers' decision outcomes and their implications for the sector (bottom-up), and, vice versa, the relationship between sector-wide interventions and their effect on farmers' decision outcomes (top-down). Farmers make decisions based on knowledge, which diffuses through the population. We develop our agent-based models and the representation of knowledge throughout the thesis. Our final model is applicable to not only the pig sector, but to any sector with autonomous suppliers who need to make decisions based on criteria to be matched. A secondary aim of this thesis is methodological: to convey the merits of applying agent-based modelling to this type of multi-level research problem. Our cases concern each farmer's decision of which quality market to supply his pigs to (agent level). As outcome, we observe the spectrum of emerging quality market shares (sector level). Knowledge is assumed to be a prerequisite for market entry, and defined as everything a farmer needs to know to match the entrance criteria set by a market segment, as perceived by that farmer. Knowledge management refers to both the individual farmer's activities to coordinate a market's criteria with his own options, and the activities at sector level to influence all farmers' decision behaviour. One case addresses reproducing a well-known sector-level phenomenon (the pork cycle) by modelling individuals only. Other cases study the effect on emerging market shares of experimenting with agent-level properties: the amount of available knowledge and the conditions under which knowledge can be exchanged, and knowledge quality. The last case investigates the effect of experimenting with sector-level properties on individual farmer behaviour: two different policy interventions, and variations in demand. We apply multi-level analysis to seek explanations for emergent patterns in terms of individual farmer behaviour. Expert validation is used to evaluate the plausibility of model outcomes and explanations with respect to the real world. Results show that (1) the presence of sufficient knowledge in the system is more important than the network structure between knowledge exchanging agents for emerging quality market shares; (2) efficient knowledge management increases quality, but there is a limit to that efficiency; and (3) imposing policies on a sector the hard way is not necessarily more effective than making gradual changes, while the latter is more friendly for the individuals. Multi-level analysis proves to give added value to the results: in two cases, an unexpected pattern in model outcomes occurred, for which multi-level analysis could provide an explanation in model terms. Judged by the experts, the explanation for one of the patterns was deemed plausible in reality. In conclusion we can say that both varying individual properties and varying system-level properties result in responsive behaviour that can be explained in model terms, and that is to some extent plausible in reality. Knowledge representation power appears to differ per model. Dependent on the aim of the model, representation power can be kept deliberately modest (as in the pork cycle model), or can be rich (as in the final model, that allows representing different types of knowledge). We believe that the representation power of agent-based models make them sufficiently suitable to represent a real-world case, as long as the model has a well-defined purpose. We recommend agent-based modelling as a method, with multi-level analysis providing added value. We believe that extending this line of research is promising for any discipline where complex adaptive systems are object of study, of which knowledge management is an example.</p

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF