Predictive and stochastic rule-based models to evaluate the effect of panic in human populations under stressful situations
Abstract
Specific Objectives 1. To formalize the dynamic creation and removal of compartments In doing so, our framework will be able to dynamically create and remove compartments according to the local density of agents. Importantly, these compartments may represente either physical or logical groups such as towns or cities, and social clubs or sects, respectively. 2. To formalize the dynamic creation and removal of rules and rates, and links between compartments In doing so, our framework will be able to dynamically create and remove rules and rates during the dynamic of the system to model the effect of novel cultural imprinting or external influences such as media and/or social networks, among other information flows. 3. To formalize the usage of local synchronization mechanisms among compartments In doing so, our framework will be able to establish local synchronization events between compartments to deal with information flows among multiple compartments without the supervision of a centralized clock, therefore diminishing the transfer error. 4. To formalize the definition of geometrically explicit phase spaces In doing so, our framework will be able to automatically model complex environments where the geometry of the interaction among agents is relevant to the dynamic of the system such as hierarchical organizations and/or environments. 5. To formalize the implementation of historical track record for agents In doing so, our framework will be able to reconstruct the history of life of every agent on the model, providing an important tool to follow the individual experiences of agents during the dynamics. 6. To parameterize k In doing so, we will adjust the observables from our simulations to real data coming from historical records. Importantly, we expect that k will depend on both the social and cultural context and, therefore, it should be adjusted to different stressful scenarios and according to different populations.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 23, 2016
- Source ID
- FA95501610111
Entities
People
- Tomas Perez-Acle
Organizations
- Air Force Office of Scientific Research
- United States Air Force