Simulación de Catástrofes

OpenStreetMap es un mapa libremente editable por todo el mundo.

Gracias a él podremos geolocalizar todas las carreteras y ríos, y podremos extraer toda esa información para poder integrarla con nuestra aplicación a fin de hacer la simulación mucho mas real y exacta.

Difficulty of writing scenarios causes a serious problem.

In order to remove this problem and to help scenario writers with multi- agent simulations, we set up the following three research objectives.

• Develop scenario description languages:
  We need a general purpose scenario description language to describe social interaction between humans and agents that employs the vocabulary used in the application domain. We also need to provide a domain dependent language to capture the patterns of interaction observed in the application domain.
• Establish a scenario description process:
  Unlike computer programming, no specification is given in advance for a scenario description. We need to establish a process for scenario development that determines vocabulary, describes scenarios, extracts interaction patterns, and integrates real world observations with virtual world simulations.
• Validate technologies using real-world problems:
  We need to validate the scenario description languages and scenario description process by examining how well the scenario- based simulation accurately reproduces real life situations.

Extracted from Scenario Description for Multi-Agent Simulation by Yohei Murakami, Toru Ishida, Tomoyuki Kawasoe and Reiko Hishiyama

On when to use Agent Based Modelling (ABM):

• The behavior of individuals cannot be clearly defined through aggregate transition rates.
• Individual behavior is complex. Everything can be done with equations, in principle, but the complexity of differential equations increases exponentially as the complexity of behavior increases. Describing complex individual behavior with equations becomes intractable.
• Activities are a more natural way of describing the system than processes.
• Validation and calibration of the model through expert judgment is crucial. ABM is often the most appropriate way of describing what is actually happening in the real world, and the experts can easily ‘‘connect’’ to the model and have a feeling of ‘‘ownership.’’
• Stochasticity applies to the agents’ behavior. With ABM, sources of randomness are applied to the right places as opposed to a noise term added more or less arbitrarily to an aggregate equation.

Extracted from Agent-based modeling: Methods and techniques for simulating human systems by Eric Bonabeau.