This package implements an agent scenario in which agents share information with each other in a networked setting. The package incorporates many adjustable parameters that are easily extensible. We give an overview of these characteristics in this document.
The code is available on:
Agents are connected to each other through a network that determines their connectivity. Each edge is currently of the same strength and/or bandwidth. The connectivity can be generated using different algorithms. Currently, we have two basic methods:
The GraphGen module implements this function:
Agents have characteristics that determine their behaviors. These characteristics are summarized below.
The Agent module sets these parameters.
Agents can base their behavior based on trust. In this case, they need to assess their trust for others based on their prior trust and evidence. An agent’s trust belief for another agent consist of two components:
Competence is the belief that the other agent has the ability to distinguish signal from noise. The competence belief is given with a prior value, i.e. initial belief, and evidence obtained within the simulation. The initial belief is given by a trust value and associated uncertainty. If uncertainty is low, new evidence will have little effect in the given belief. The evaluation of evidence is tied to the agent’s competence: a person who can tell signal from noise can also better judge other agents’ competence.
Willingness is the belief that the other agent will dedicate their energy to sending the agent information, instead of helping others or serving self-interest. Willingness models rational reciprocal information sharing behavior. It is seeded with a prior belief and associated uncertainty. If uncertainty is very low, simulation based evidence has no effect on willingness beliefs. Unlike competence, willingness beliefs are relative to others. When there is no information to share, then agents share nothing. This does not impact the beliefs about their willingness negatively.
Both beliefs are used to determine whether an agent can be trusted. Currently, the competence and willingness levels are used to determine a qualitative rating of trust, as given by the table below:
| Comp > 0.8 | T = 3 | T = 4 | T = 5 |
| Comp <= 0.8 | T = 3 | T = 4 | T = 4 |
| Comp <= 0.6 | T = 1 | T = 2 | T = 2 |
| 0 | Will <= 0.6 | Will <= 0.8 | Will > 0.8 |
Note that the thresholds can be changed in the Trust module:
https://github.com/rpitrust/agentsimulation/blob/master/Trust.py
In this case, trust values go from 5 (highest) to 1 (lowest). Trust value of 1 is considered not trust. We current do not model distrust in the simulation.
The trust module allows new evidence to be entered periodically and old evidence to be forgotten at a specified rate.
Currently, the simulation environment models a scenario in which agents send information to each other. Originally, the network is seeded with some initial set of information. Each agent has access to some facts by having it in its inbox. Some of the information is valuable and some of it is noise. The signal to noise ratio can be changed.
The agents in the model are limited. They can only do one action at a time (unless they explicitly have higher capacity). The main action that is implemented in the simulation is sharing of information in one’s inbox. At each point in time, if the agent is willing for that period, it can do one of two actions:
Send one piece of information that is in its outbox to a neighbor: from most trusted to the least trusted if trust is used, randomly otherwise.
Pop one piece of information that is in its inbox, determine if it is valuable, and then queue it to be sent to all neighbors by placing it in its outbox.
When determining whether information is valuable or not, the competence of the agent determines how frequently the agent will get it correctly.
Then, the agent will decide who to send it to based on spamminess and selfishness parameters.
The simulation starts by processing all information that is in agents’ inbox and placing them in their outbox based on their personalities. The agents are also seeded with initial trust if trust is used. The simulation then proceeds for a predetermined number of steps.
Periodically a set of performance statistics area collected from the networked. These statistics are summarized at the end of the simulation. Simulations can be repeated for a number of prespecified times and the average performance is reported at the end.
Performance statistics considered include:
The simulation is implemented in the simulation module: