B. Initial Parameters

In order to change the initial configuration of the simulation, you go to Simulation -> Parameters, and adjust the parameters. Explanations for each parameter are given in this chapter, below the image:

• Model: This drop-down button gives you multiple options for basic model implementation for the simulation. Four models are available in this version. Identifiers of the models (internally, the name of the class), are the initial letters of the descriptions:

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

  • E2: Multilateral social influence without homophily - based on Experiment 2, Flache (2011): This implementation includes multilateral social influence, such that interactions can occur between multiple agents at the same time instead of in dyadic formation where only two agents can interact with each other at one time. This implementation does not consider homophily. Mutation and selection error are included as in E1.

  • E3: Multilateral social influence with homophily - based on Experiment 3, Flache(2011): This implementation is equivalent to E2, and includes homophily as presented in E1.

  • INST: Institutions with homophily based on Axelrod (1997) and Ulloa(2016): This implementation is based on E1, and thus includes homophily, mutation and selection error. It additionally introduces institutions. An institution can influence agents that belong to it by making them adopt or keep traits that are equivalent to the institution's traits. The table below (taken from the original publication) presents all rules inherent in this model.

• 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 has all agents belonging to one (the same) institution, and all cultural vectors contain exactly the same traits (i.e. agents all belong to the same culture). This provides an interesting baseline to compare effects of events.

• Iterations: Sets number of iterations after which the simulation stops. One iteration is defined as the time span after which all agents have had on average one opportunity of interaction. (Average, as the initiator agent is picked randomly, so not necessarily all agents will participate each turn. Opportunity, as the interaction might not actually occur, for example when the homophily rule prevents an interaction, or due to selection error.)

• Speed: Sets number of iterations that occur between checkpoints. Several important things happens during checkpoints: (1) Results are calculated from the current state of the simulation, (2) Current response variables are sent to the result output files, (3) Interface is updated with the current results, and (4) Simulation checks for current queued events and executes them, if any. (It is called speed because it affects how fast the simulation will run, as calculation and output of results is costly. Events are always implemented at checkpoints to make sure they are visualized properly on the interface.)

• Buffer Size: Controls the size of the file buffer sizes. A larger buffer size makes the simulation more efficient, but waiting times to check intermediate results in the files are slower. (Buffer size can be important when Batch Mode is executed.)

• World: Sets informational space (vector sizes) of the model. These traits cannot be modified after Intitialization (See Events.)

• Rows: Number of rows of the world grid.

• Cols: Number of columns of the world grid.

• Radius: The radius of the (Von Neumann neighborhood)[https://en.wikipedia.org/wiki/Von_Neumann_neighborhood], also known as the Manhattan distance. A Von Neumann neighborhood of radius 6 can be seen here:

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

• Traits: Number of possible values that a feature can adopt. Each trait represent a possible cultural item for the feature, for example if the feature is music, one possible trait can be rock music, another jazz.

• Noise: Sources of perturbation inside the simulation.

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

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

• Institutions: Sets the levels at which institutions can affect agents.

• Influence: Level of importance that is given to institutional influence (alpha value in the rule table above). Alpha is multiplied with the similarity with the institution, and a beta value (1 - alpha) to the homophily. The resulting probability determines whether the interaction (an agent accepting other agent's trait) will be successful.

• Loyalty: Likehood of an agent staying or changing their institution after a successful interaction between agents (alpha prime value in the rule table above). Alpha prime is multiplied by a value that depends on the similarity with the institution, and a beta (1 - alpha) to the similarity with the neighbor's institution. The resulting probability determines whether and agent changes its institution to instead adopt the institution of its neighbor.

• Democracy: Inverse frequency (called period) of a democratic process. A democratic process is a bottom-up process which consists on an institution changing its trait as a result of a referendum in which multiple agents vote to change a trait, increasing similarity with their institution. The most voted trait is changed in the institution.

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

• Load and save configurations: This section at the bottom of the dialog helps to load pre-set configurations, for example which are similar to experiments executed previously in literature, and others that the users can set up and save themselves.

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

• Save: The user can save their 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.