| Multi-Scenario World II. Description |
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This model is the EMIL-S replication of the Multi-Scenario World model. In this model, the agents repeatedly traverse through four different stations of a virtual airport. At each station the agents have to choose between two possible behaviours. Two types of agents are defined: social conformers and norm recognizers.
1. Purpose Simplifying the highly complex process of behaviour by different actors in the social context, this scenario is a simulation model of the interaction of agents of two different kinds. On the one hand, there are social conformers who strictly react on their environment and copy the commonly observed action of others without recognizing the norm from which the decision is arising. On the other hand, norm recognizers have knowledge about the existing norm in social matters, and their actions are based on this knowledge. Joining the different agents in a simulated environment reveals the consequences of acting based on different principles. 2. State variables and scales In this model the social behaviour of two types of agents, the “norm recognizers” and the “social conformers”, is analysed and compared. The agents are interacting within a shared environment, representing several stations of an artificial “airport”, and for each of the stations two context-depended actions are defined:
3. Process overview and scheduling The agents repeatedly pass through the three stations in a fixed order. Each cycle is equal with one single visit of the airport.The norm recognizer agents make the decisions for actions to perform on base of their experiences (induced by norm invocations) from former visits. The social conformer agents draw on the current behaviour of the majority of the agent population. 4. Design concepts
4.1 Emergence Emergence of norms in the artificial society of norm recognizer regarding the behaviour within each of the three stations. 4.2 Adaptation Agents adapt their behaviour by changing their action preferences, based on received feedback from the environment or received messages from other agents (punishment). 4.3 Fitness The fitness of an agent relates to it’s norm compliant behaviour. 4.4 Prediction No prediction in this model. 4.5 Sensing Agents are able to perceive their environment consisting of topographical elements and other agents. 4.6 Interaction Individuals interact “physically” within the topographical environment and by sending and receiving norm invocation messages. 4.7 Stochasticity The action selection involves a stochastic process. Furthermore, the agent mobility is influenced by random variables. Group of norm recognizers, group of social conformers. 4.9 Observation Results are shown as animation sequences (during the simulation run using TRASS) and as output graphs (using the experimentation environment MEME). 5. Initialization All agents are initialized with uniform distributed selection probabilities. The input parameters of a simulation run are: 7. Submodels None.
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