A Workflow for Software Development within Computational Epidemiology

A critical investigation into computational models developed for studying the spread of communicable disease is presented. The case in point is a spatially explicit micro-meso-macro model for the entire Swedish population built on registry data, thus far used for smallpox and for influenza-like illnesses. The lessons learned from a software development project of more than 100 person months are collected into a check list. The list is intended for use by computational epidemiologists and policy makers, and the workflow incorporating these two roles is described in detail.NOTICE: This is the author’s version of a work that was accepted for publication in Journal of Computationa Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Computational Science, VOL 2, ISSUE 3, 6 June 2011 DOI 10.1016/j.jocs.2011.05.004.</p

Cultural transmission and optimization dynamics

We study the one-dimensional version of Axelrod's model of cultural transmission from the point of view of optimization dynamics. We show the existence of a Lyapunov potential for the dynamics. The global minimum of the potential, or optimum state, is the monocultural uniform state, which is reached for an initial diversity of the population below a critical value. Above this value, the dynamics settles in a multicultural or polarized state. These multicultural attractors are not local minima of the potential, so that any small perturbation initiates the search for the optimum state. Cultural drift is modelled by such perturbations acting at a finite rate. If the noise rate is small, the system reaches the optimum monocultural state. However, if the noise rate is above a critical value, that depends on the system size, noise sustains a polarized dynamical state.Comment: 11 pages, 10 figures include

Combining Computational Fluid Dynamics and Agent-Based Modeling: A New Approach to Evacuation Planning

We introduce a novel hybrid of two fields—Computational Fluid Dynamics (CFD) and Agent-Based Modeling (ABM)—as a powerful new technique for urban evacuation planning. CFD is a predominant technique for modeling airborne transport of contaminants, while ABM is a powerful approach for modeling social dynamics in populations of adaptive individuals. The hybrid CFD-ABM method is capable of simulating how large, spatially-distributed populations might respond to a physically realistic contaminant plume. We demonstrate the overall feasibility of CFD-ABM evacuation design, using the case of a hypothetical aerosol release in Los Angeles to explore potential effectiveness of various policy regimes. We conclude by arguing that this new approach can be powerfully applied to arbitrary population centers, offering an unprecedented preparedness and catastrophic event response tool

Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Unemployment expectations in an agent-based model with education

Why are unemployment expectations of the “man in the street” markedly different from professional forecasts? We present an agent-based model to explain this deep disconnection using boundedly rational agents with different levels of education. A good fit of empirical data is obtained under the assumptions that there is staggered update of information, agents update episodically their estimate and there is a fraction of households who always and stubbornly forecast that the unemployment is going to raise. The model also sheds light on the role of education and suggests that more educated agents update their information more often and less obstinately fixate on the worst possible forecast

Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley

A s the only social science that has access to data of sufficient duration to reveal long-term changes in patterned human behavior, archaeology traditionally has been concerned with describing and explaining how societies adapt and evolve in response to changing conditions. A major impediment to rigorous investigation in archaeology-the inability to conduct reproducible experiments-is one shared with certain other sciences, such as astronomy, geophysics, and paleontology. Computational modeling is providing a way around these difficulties. k Within anthropology and archaeology there has been a rapidly growing interest in so-called agent-based computational model

Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley

A s the only social science that has access to data of sufficient duration to reveal long-term changes in patterned human behavior, archaeology traditionally has been concerned with describing and explaining how societies adapt and evolve in response to changing conditions. A major impediment to rigorous investigation in archaeology-the inability to conduct reproducible experiments-is one shared with certain other sciences, such as astronomy, geophysics, and paleontology. Computational modeling is providing a way around these difficulties. k Within anthropology and archaeology there has been a rapidly growing interest in so-called agent-based computational model