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robin gras

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About robin gras

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  • Favorite Area of Science
    artificial life
  1. They have the capability to perceive potential mates at distance and to choose to move in their direction.
  2. We designed a complex ecosystem simulation platform that we use to investigate many difficult theoretical ecology problems. In our recent PLOS ONE publication, we show that the notion of species is universal and automatically emerged from systems as long as they are complex enough to allow natural selection to occur. The species we observed emerging in our artificial world have the same properties as the ones observed in nature. We show that natural selection alone, with no need for spatial separation or sexual selection mechanisms, is enough for speciation events to occur. This is the first time that such result has been obtain without any bias from an external expert designed natural selection mechanism. This has been possible to achieve because our system is complex enough, with hundred of thousand of "intelligent" agents with unique evolving behavioral models, for natural selection to emerge from the interaction of the individuals with their environment. More information about our project is available here.
  3. Hello Dekan, For sure stoke and share exists and are not just "figments of human imagination". Moreover, information on the state of the world and the worldwide economy is well broadcast and directly influence the people behaviors. That means that stoke market do not vary completely randomly and abruptly making them potentially predictable even if it is difficult to do. Robin
  4. Hello Sensei, We are not specialist in finance. We do not know what the best way to use such kind of predictions is. We only propose a method and demonstrate what its possibilities are. However, the computational time of the method is very small (few seconds to few minutes). It means that even if the prediction of the method leads to some behaviors in the market that change completely the properties of the time series, new predictions taking into account the new state of the market could be done in real time giving some feedbacks about the effects of the reactions and giving the possibility to adjust the reactions and the behaviors of the financial institutions. Robin
  5. Thank you very much! Yes, we thought about that but we have not tried yet. It is in our to do list ;-) Effectively, we expect that it will also works pretty well.
  6. Time series forecasting is of fundamental importance for a variety of domains including the prediction of earthquakes, financial market prediction, and the prediction of epileptic seizures. We present an original approach that brings a novel perspective to the field of long-term time series forecasting. Nonlinear properties of a time series are evaluated and used for long-term predictions. We used financial time series, medical time series and climate time series to evaluate our method. The results we obtained show that the long-term prediction of complex nonlinear time series is no longer unrealistic. The new method has the ability to accurately predict the long-term evolutionary trend of stock market time series and successfully predict financial crises several weeks in advance, and it attained an accuracy level with 100% sensitivity and specificity for the prediction of epileptic seizures up to 17 minutes in advance based on data from 21 epileptic patients. Our new method also predicted the trend of increasing global temperature in the last 30 years with a high level of accuracy. Thus, our method for making long-term time series predictions is vastly superior to existing methods. We therefore believe that our proposed method has the potential to be applied to many other domains to generate accurate and useful long-term predictions. More information here.
  7. We study the evolutionary process and the emergence of species in a simulated ecosystem. We have conceived EcoSim, an individual-based evolving predator-prey ecosystem simulation. The agents evaluate their environment (e.g., distance to predator/prey, distance to potential breeding partner, distance to food, energy level), its internal state (e.g., fear, hunger, curiosity) and chooses among several possible actions such as evasion, eating or breeding. The behavioral model of each individual is unique and is the outcome of the evolution process. This is the only simulation modeling the fact that individual behaviors affect evolution and speciation. We were able to predict species extinction using information about spatial distribution of the individuals, to correlated genetic diversity with the fitness of a species, to show that the overall behaviour of our simulation is deterministic chaotic with multifractal properties and to predict speciation events using some features describing the species properties. This approach will be also used to study the species abundance distribution, patterns and rates of speciation, the evolution of sexual and asexual populations, the interaction and diffusion of an invasive species or a disease in an existing ecosystem, etc. We propose a weekly presentation of a run of our simulation that will last for tenth of thousand of time steps at this site: https://sites.google.com/site/ecosimgroup/research/ecosystem-simulation/long-run. Every week we will add a page presenting a detailed summary of what happen in the system during the last 500 or 1000 time steps. We will show many graphs, videos and figures presenting the evolution of the system, including the number of individuals, the number of species, the amount of food available, the tree of life of the world, the distribution of age of the individuals, the evolved behavioral maps of some individuals, the variability of the gene alleles in the populations… This will allow following the ongoing evolutionary process and its effects on the whole world for thousand of generations.
  8. We have created a generic platform to simulate complex ecosystems with intelligent predator and prey agents interacting and evolving in a large and dynamic environment. We have chosen to implement an individual-based model, built upon the predator–prey paradigm. The novelty of our model stems from the fact that each agent behavior is modeled by a fuzzy cognitive map (FCM, a computational tool, similar to a neural network, based on a graph that represents interaction between concepts, such as emotions and desires, perception or action), and evolves during the simulation. The FCM of each agent is unique, and is the outcome of the evolution process going on throughout the simulation. The notion of species is also implemented, in a way that species emerge from the evolution of agents. To our knowledge, our system has been the first one allowing modeling the links between speciation and individual behavior. More information about the project and some videos of the running simulation are available at: http://sites.google.com/site/ecosimgroup/research/ecosystem-simulation
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