Data Visualization and Sonification for Financial Agent-Based Models

The corporate bond market is one of the areas that has witnessed profound changes since the last financial crisis, prompting regulators (and industry participants) to question its resilience under stress. We are building agent-based models to better understand bond market dynamics using simulations. Simulations offer an intriguing method of capturing the second-order feedback loops that can affect prices under conditions of stress. However, understanding all the data and emergent behaviors from these complex systems remains a difficult challenge. In this paper, we begin investigating visualization and sonification techniques that might help us meet this challenge at both agent (micro) and system-wide (macro) levels, with the goal of assembling an effective mixture of visual elements. Sonification offers a novel way to enrich our visualizations with sound, setting markets to music. An experiment assessing the impact of mutual fund market share on bond market stability provides an interesting context with meaningful outcomes

Using agent-based modelling algorithms to analyze the impacts of toxic contaminations on Lake Ontario ecosystem

Recent advances in computer technology have brought a revolution in ecological modelling. Ecoinformatics and computational ecology make use of various programs, including agent-based modeling algorithms, to study ecological systems. In this study, an in-silico analysis was performed using an agent based modelling software, to analyze the impacts of a potential toxin on Lake Ontario ecosystem. For easier duplication of the real world into the virtual system, the ecosystem was divided into 6 compartments. These compartments include phytoplankton, zooplankton, macroinvertebrates, forage fish, piscivores, and sea lamprey. The test model was performed under five different concentrations of toxin. Each test was repeated 15 times to reduce demographic stochasticity. The results suggest that toxic contaminations, such as mercury, could potentially lead to population reduction in forage fish, piscivores and sea lamprey compartments.Les progrès récents reliés à la technologie informatique ont amené une révolution dans la modélisation écologique. L’éco-informatique et l’écologie computationnelle utilisent plusieurs programmes, y compris des algorithmes basés sur les systèmes multiagents pour étudier les systèmes écologiques. Dans cette étude, une analyse insilico a été accomplie en utilisant les systèmes multiagents pour analyser les impacts d’une toxine potentielle dans l’écosystème du Lac Ontario. Afin de mieux améliorer la représentation du monde réel dans le système virtuel, l’écosystème du Lac d’Ontario a été divisé en six compartiments. Ces compartiments comprennent le phytoplancton, le zooplancton, les macroinvertébrés, les poissons fourragers, les piscivores et la lamproie marine. Ce modèle a été examiné sous cinq concentrations des toxines différentes. Chaque examen a été répété 15 fois pour réduire la stochasticité démographique. Les résultats suggèrent que des contaminations toxiques, comme la contamination par le mercure, pourraient potentiellement arriver à une réduction de la population des poissons fourragers, des piscivores et des compartiments de la lamproie marine

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

With the current surge of simulation studies in archaeology there is a growing concern for the lack of engagement and feedback between modellers and domain specialists. To facilitate this dialogue I present a compact guide to the simulation modelling process applied to a common research topic and the focus of this special issue of Human Biology—human dispersals. The process of developing a simulation is divided into nine steps grouped in three phases. The conceptual phase consists of identifying research questions (step 1) and finding the most suitable method (step 2), designing the general framework and the resolution of the simulation (step 3) and then by filling in that framework with the modelled entities and the rules of interactions (step 4). This is followed by the technical phase of coding and testing (step 5), parameterising the simulation (step 6) and running it (step 7). In the final phase the results of the simulation are analysed and re-contextualised (step 8) and the findings of the model are disseminated in publications and code repositories (step 9). Each step will be defined and characterised and then illustrated with examples of published human dispersals simulation studies. While not aiming to be a comprehensive textbookstyle guide to simulation, this overview of the process of modelling human dispersals should arm any non-modeller with enough understanding to evaluate the quality, strengths and weaknesses of any particular archaeological simulation and provide a starting point for further exploration of this common scientific tool