Press the Start button in the menu to start the simulation, following parameters can be set to produce different volunteering scenarios:
Scheme Type: Three different organizational schemes in real-world crowdsourced systems
Number of Agents: Number of Agents that allocated in the grid simulation
Observation Demand Rate: The fraction that a decision maker can observe (NA under the self-organized scheme)
Grid Value Change Frequency: The frequency of changing grid values every 100 time steps. Each grid has been initialized by a random value within 5, when
the grid value changes, the corresponding columns will increase by 5
Number of Centralized Steps: The number of time steps that the system executes the centralized under the hybrid scheme
Current Step: 0
About User Behavior Model
User behavior model from paper: https://www.nature.com/articles/s41599-022-01127-2.
This simulation implements a minimal volunteer crowdsource scenario that can exhibit self-organization effects in a controlled environment. The simulated environment is a 10-by-10 grid world, where each grid cell represents a task with a specific value. The task value measures a level of task importance and is set to change at frequency (a.k.a. Grid Value Change Frequency) to reflect the changing community demand. Each grid represents an organized task. For simplicity, we assumed that all tasks needed the same number of people to complete. To prevent unbalanced volunteer allocation, each grid cell (task) can be taken by at most 3 agents at a given time (The darker the grid has the more users joined into the task). At each time step of the simulation, agents representing individual volunteers can participate in a task by moving to the corresponding cell. The collective goal of the agents is to participate in tasks that maximize combined values.
We defined three task selection strategies based on different organizational schemes in real-world crowdsourced systems: a self-organized scheme, a centralized scheme and a hybrid scheme. As in the real-world case where local community demands are difficult to learn precisely, a decision maker can only observe a fraction of the task values (a.k.a. the observable demand rate) and assign agents optimally to the observable tasks. Under the hybrid scheme, the system executes the centralized strategy for several time steps (a.k.a. Number of centralized Steps) whenever the task value changes and then switches to the self-organized strategy.