In our simulation model, the environment consists of four scenarios, in which the agents can produce three different kinds of actions. We define two context specific actions for every scenario, and one action common to all scenarios. Therefore, we have nine actions. Each of our agents is provided with a personal agenda (i.e. a sequence of contexts randomly chosen), an individual and constant time of permanence in each scenario (when the time of permanence is expired, the agent moves to the next context) and a window of observation (i.e. a capacity for observing and interacting with a fixed number of agents) of the actions produced by other agents. Agents are also provided with the three-layer architecture described above, necessary to analyze the received information, and a normative board in which the normative beliefs, once arisen, are stored. The agents can move across scenarios: once expired the time of permanence in one scenario, each agent moves to the subsequent scenario following her agenda.

DOCUMENTATION

1. Purpose

The Model aims to show the effects of the norm recognition module use in a multi scenario world.

2. State variables and scales

3. Process overview and scheduling

4. Design concepts

  4.1 Emergence

 The convergence to a single normative belief can emerge

 4.2 Adaptation

 4.3 Fitness

 4.4 Prediction

 4.5 Sensing

 Agents exchange messages defined by a modal, a subject, a sender and an addressee.

 4.6 Interaction

 Agents are randomly paired at each time tick

 4.7 Stochasticity

 A stochastic approach to initialize the modals sent at the beginning of the simulation (when agents have no normative beliefs in their minds) is used

 4.8 Collectives

 4.9 Observation

5. Initialization

Agents properties are randomly initialized (agenda, time of permanence)

6. Input

7. Submodels