B. Initial Parameters

In order to change the initial configuration of the simulation, you can click in Simulation -> Parameters, and adjust the parameters that are explained below the image:

• Model: The selector allows you to select the basic model implementation that will be used in the simulation. There are 4 models available, the initial letters of the description is an identifier of the model (internally, the name of the class):

  • E1: Homophily (Axelrod, 1997) including mutation and selection error - Experiment 1, Flache(2011): This implementation is based on (Axelrod, 1997) where homophily, the principle that "like attracts like", is used to decide which agent can influence another. This model also includes two noise sources: mutation, where individual traits can change randomly (Klemm, 2003), and selection error, where individual can make a judgment error regarding the homophily of its neighbors (Flache, 2011). This model is equivalent to the model used in Experiment 1 of Flache (2011).

  • E2: Multilateral social influence without homophily - Experiment 2, Flache(2011): This implementation is based on the experiment 2 (Multilateral Social influence without homophily) of Flache (2011), in which multilateral social influence (i.e. the interactions between agents happen between many agents) instead of dyadic is used. This implementation don't consider homophily, however mutation and selection error are part of the implemenation

  • E3: Multilateral social influence with homophily - Experiment 3, Flache(2011): This implementation is based on the experiment 3 (Multilateral Social influence with homophily) of Flache (2011). It is equivalent to the previous except that this one includes homophily.

  • INST: Institutions including homophily Axelrod (1997) - Ulloa(2016): This implementation is based on the first; it cointains homophily, mutation and selection error, but also it introduces institutions. An institution can influence the agents that belong to it by making them keep the traits that are equivalent to the institution's traits. The table below (taken from the original publication) introduces all the rules that this model includes.

• Controls:

• Random initialization: When selected, the initial traits of the agents cultural vectors are initialized randomly with a uniform distribution. When not selected, the initial state of the simulation consiste of all agents belong to the same institution, and all cultural vectors containly exactly the same trait (i.e. belonging to the same culture). This provides an interesting baseline to compare the effects of events.

• Iterations: An iteration has passed when all agents have had on average on opportunity of interaction. First, it is on average, because the agent that initiates the possible interaction is picked randomly, so not necessarily all agents will be participating each turn. Second, it is just an opportunity because the interaction might not happen because of several reasons, e.g. the agents were not very similar so the homophily rule avoided the interaction, or simply there was a selection error.

• Speed: This is the number of iterations that occur between checkpoints; lower values causes the simulation to end later, but (and because) updates the simulation more often. Several important things happens during checkpoints: (1) results are calculated from the current state of the simulation, (2) the current response variables are sent to the results files, (3) the interface is updated with the current results, and (4) the simulation checks for current queued events and execute them, if any. It is called speed because it affects how fast the simulation will take since the calculation of the results is costly. The events are calculted always in a checkpoint because, in this way, we can make sure they are visualized properly in the interface.

• Buffer Size: It controls the size of the file buffer sizes. A larger buffer size makes the simulation more efficient, but the waiting times to check intermidiate results in files slower. Sometimes, it is an important parameter when it comes to the batch mode.

• World: this parameters affect the informational space (vectors size) of the model. This traits cannot be modified after the intitialization (See Configuring Events)

• Rows: Number of rows that the world grid contains

• Cols: Number of columns of the world grid contains

• Radius: The radius of the (Von Neumann neighborhood)[https://en.wikipedia.org/wiki/Von_Neumann_neighborhood] also known as the Manhattan distance. For example, the image below shows a Von Neumann neighborhood of radius 6.

• Features: The size of the cultural vector. Each feature represents a possible dimension of the culture, e.g. music.

• Traits: The number of possible values that can be store in a feature. Each trait represent a possible cultural item for the feature, e.g. rock

• Noise: Sources of perturbation inside the simulation

• Mutation: The probability of a random trait change in the agent's cultural vector after an interaction

• Selection Error: The probability of making a judgement mistake in the selection of the agent with which the interaction will happen.

• Institution: This parameters controls the ways the institution influence the agents

• Influence: It reffers to the importance that is given to the institutional influence. More precisely, it is the alpha showed in the above table for the institutional model. The alpha is multiplied by the similarity with the institution, and a beta (1 - alpha) to the homophily. The resulting probability determines whether the interaction (an agent accepting other agent's trait) is succesful.

• Loyalty: It reffers to the likehood of an agent staying or changing their institution after a succesful interaction between agents. More precisely, it is the alpha prime showed in the above table for the agent's loyalty. The alpha prime is multiplied by a value that depends on the similarity with the institution, an a beta (1 - alpha) to the similarity with the neighbor's institution. The resulting probability determines whether and agent changes its institution towards its interacting neighbor.

• Democracy: The inverse frequency (called period) of a democratic process. A democratic process is a bottom-up process which consists on an institution changing its trait according to a referendum in which agents support to change a trait that would help them to be more similar to their institution. The most voted trait is changed in the institution.

• Propaganda: The inverse frequency (called period) of a propagandistic process. A propagandistic process is a top-down process which consists on an institution propagating a trait among the agents that belong to it. Which trait (and corresponding feature) is decided based on the most conflicting trait, i.e. the one that produces more dissimilarity between the institution and its agents.

• Load and save configurations: This section at the bottom of the dialog is helpful to load interesting configurations, some that are similar to experiments executed previously in literature, and others that the user can save.

• Load Preset: These configurations resemble experiments that have been used in previous literature.

• Save: The user can save her own configurations. Saving configuration is important in order to run simulations in batch mode (see Batch Mode)

• Load: A user can load a previously saved configuration.