Modeling Spatial Organization with Swarm Intelligence Processes

International audienceUrban Dynamics modeling needs to implement spatial organization emergence in order to describe the development of services evolution and their usage within spatial centers. In this paper, we propose an extension of the nest building algorithm with multi-center, multi-criteria and adaptive processes. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. Typically, this model is suitable to analyse and simulate urban dynamics like the evolution of cultural equipment in urban area

The Scientific Problems with Using Non-Human Animals to Predict Human Response to Drugs and Disease

Every year, and in countries around the world, significant time and resources are devoted to the noble cause of developing drugs to treat and cure human disease. With rare exception, drug interventions cannot reach commercialization without safety and efficacy having first been demonstrated in animal models. The intention of regulations, which require the use of animal models in such contexts, is to ensure that only safe and effective drugs end up being used by patients. Similarly, it is standard practice for researchers to employ animal models in their attempts to understand the way diseases present and progress in humans. Unfortunately, there exist serious theoretical and empirical concerns regarding the standard practice of using non-human animals to model human response to perturbations, such as drugs and disease. These concerns are important because conducting disease research and drug development in a manner that is not supported by science will have suboptimal implications for the humans who rely on that research, which encompass the entire population. Based on complexity science, modern evolutionary biology, and empirical evidence, we demonstrate that animal models have failed as predictors of human response. That is, animal models do not and cannot have acceptably high predictive value for human response to drugs and disease. By this we mean that animal modeling, as a methodology, is for all practical purposes not predictive of human response to drugs and disease; and hence it should be abandoned in favor of human-based research and testing, such as personalized medicine, a new field that takes into account the unique genetic make-up of each individual patient

Understanding transfer from a dynamic system approach:Two studies of children using problem-solving tasks

Transfer is not static but a dynamic process of learning. In this article, the concept of transfer and the implications of its study are reconsidered from the theoretical basis of the complex dynamic system approach. We describe “transfer” as an emergent process that implies not a copy of knowledge applied to a new situation, but a new configuration of knowledge to solve new situations. Therefore, we discussed the concept of transfer based on the following dynamic principles: soft-assembly, multi causality, variability, self-organization, and iteration. To reconsider the concept of transfer, we provide empirical evidence, illustrating these principles by discussing two studies of transfer carried out with preschoolers. The participants were 34 children of 4 years old (M = 4,6), and 8 children of 4 to 6 years old (M = 5,2). Using repeated measure designs (3 weeks and 6 months, respectively), participants worked on sets of problem-solving situations in the domain of physics (i.e. Archimedes’ principle and Air pressure). By using time-series graphs we identified the relevant elements of the tasks used by the children during the problem-solving process to analyze how this process changes over time. Results show transfer as a self-organized and context-related process in which the information is not static but in constant transformation

Complejidad y la organización

Se vive en un mundo en el que la complejidad aumenta; y las políticas organizacionales dependen de comprender esa complejidad, la cual existe interna y externamente. Un mundo así lleva a que la ciencia de la organización debe acoger nuevos escenarios, con el fin acotar el caos y fuerzas imprevistas que podrían acabar todo. La gestión del conocimiento es esencial, hoy día, como proceso que determina cuál es el capital intelectual que posee una organización y, sobre todo, cómo aplicarse en situaciones complejas. La gestión tradicional indica que el mundo es objetivo, que las interacciones son lineales, que solo hay dos valores de verdad y que la predicción y el control proporcionan una perspectiva sobre el "caos" y los múltiples cambios del entorno; perspectiva que mejora con los aportes de las ciencias de la complejidad. Las teorías sobre la complejidad desafían muchos de los supuestos de la gestión tradicional, pues indican que las acciones humanas están sujetas a comportamientos emergentes, lo cual induce a analizar las organizaciones con nuevos enfoques

Agent-Based Models and Simulations in Economics and Social Sciences

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility.Agent-Based Models and Simulations ; Epistemology ; Economics ; Social Sciences ; Conceptual Exploration ; Model World ; Credible World ; Experiment ; Denotational Hierarchy