The Java applet below visualizes the dynamics of a Threshold Model of Collective Behavior. The interaction dynamics is based on an extension of the model introduced by Mark Granovetter [1] so that agents change their state between two options in both directions.

The system consists of N agents placed at the nodes of a two dimensional network with periodic boundary conditions and Moore's neighborhood (k = 8). Each agent i can choose between two possible states, denoted by S_i=+1 and S_i=-1. Each agent is assigned a threshold 0 < T < 8 which determines the number of neighbors required for agent i to change her state.
For T = 4 and S =0.00 this dynamics coincides with the Spin Flip Kinetic Ising model at zero temperature.


The system evolves by iterating the following time steps:


1) A agent i is randomly chosen in the lattice (called active agent).

2) The threshold value of the active node is assigned at random. This threshold follows a Gaussian distribution with "T" as mean value and standard deviation "S"

3) If the the number of neighbors of agent i in a state different that S_i is larger than the threshold "T", then the active agent changes its state (S_i) to (-S_i). Otherwise it keep the state.

    In the case that the fraction of neighbors in a different state is equal to the threshold "T", then with probability 0.5, the active agent changes its state.