Fractal Pattern?

Talking about addictions with Reba we thought that one could think of an addiction as an attractor, a gravity well. One common pattern with addictions is that people often need to bottom out before they can get over the addiction. Is that similar to a small mass body speeding up around a larger mass and then if the configuration / context is right, it can reach escape velocity from the attractor / gravity well or achieve a more “stable” orbit around the attractor.



Grouping model use case

alpha grouping model

a simulation where a distribution of agents is created with some distribution of alpha levels for each agent. The levels can range from -10 to plus 10. Each agent has an initial specified direction for movement. [At each step, an agent looks for the highest alpha level in it neighborhood. If it finds an alpha level greater than itself, it changes its direction to the direction of that alpha. (Does it respond to other agents with positive or negative alpha as well?) ]{ alternate approach: At each step an agent is influenced by other agents within its sphere of interaction, which can be chosen to range from a few cells to the most distant cells.  The attraction or repulsion due to the presence of other agents will, by the laws of mechanics, change the location and velocity of the specified agent.  In turn the specified agent just affected will affect other agents, so we have what is called an N-body problem (for programming methods, unless u have it handled, I would suggest looking at folks who have done this since it is computationally intensive and is not a simple problem. There are “easy” ways to do this problem and hard ways. It is beyond my expertise. )  The base environment is a two or three dimensional grid space.

the purpose of the simulation is to begin to explore the dynamics of group formation, group stability, and the relationships between groups in conflict with or in agreement with each other. The initial simulations are one dimensional in terms of agent attributes which means the results may be dominated by high attribute agents (leaders).  Also we can vary initial conditions to determine their effect on group evolution.  We plan to add more complexity as our methods are validated by the initial results. By varying parameters we hope to be able to determine the parameters that dominate the evolution of group behavior and determine if a equilibrium system of groups is possible. Continue reading “Grouping model use case”

Rule set continued


1. For agents need to specify attributes in all relevant dimensions, initial conditions, locations, movements.  The attributes can be seen as forming a vector  for each agent.

2. Need to define meaning of all dimensions, properties, initial conditions

3. Need to specify nature of interaction (eg forces) between agents and between agents and other entities, eg groups, memories, in same or other dimension and response to these forces.

4. Need to define conditions that indicate the presence of and characterize a collective entity or emergent property, eg so many agents per space.  Define different class groups, eg x agents in standard radius 1, volume of group or net force, center of gravity of group, strength of emergent property, range, eg 10% of population in 1% of space (dimensionless measures are good).

5. Need to define some “global” properties of agents, eg length of vector representing agent (this would require defining a metric for defining an effective length of the vector), some global interactions for agents, eg the scalar product of two agent vectors, which would be a representation of their similarity, the vector product of two agent vectors, which might represent the force of their differences.

6. Need to define some global properties of a group or other entity, eg the sum of a bunch of vectors perhaps indicating the net force of a group.  Quality of a group? Etc.