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Multi-Scenario World Description |
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| Model name: | Multi-Scenario World | Model title:
| Norm Recognizers acting in a Multi Scenario World | Replicated model?
| Yes | | Keywords: | Agent Architecture, Normative Agent, Norm Recognition Module, Simulation | Model authors:
| Marco Campennì, Giulia Andrighetto, Federico Cecconi, Rosaria Conte
| | Programming language? | Matlab |
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 World: four different scenarios defined by 9 types of actions (2 scenario-specific + 1 common action); Agents: norm recognition module implemented by a three layers architecture + a normative board.
3. Process overview and scheduling
Agents are randomly paired at each time tick and they exchange messages; they can process received information to generate new normative beliefs (that will affect their future actions, i.e. the subject of future messages)
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
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Multi-Scenario World Results |
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Figures File
| Title | Description
| Figure 1
| Social input | | Figure 2
| The norm recognition module (in action) | On the right side of the figure, from the bottom the Input and the two layers of the module (layer 0 and layer 1) plus the normative belief (generated or recognized); on the left side, the normative board. Vertical arrows in the block on the right side indicate the process regulating the generation of a new normative belief. The input action ( alpha) can match with a norm present in the normative board (see the arrows path on the left side of the figure); or a new normative belief can be formed if the agent receives an input action ( alpha) (at least one time as deontic or normative valuation) for a given number of times (as fixed by the normative threshold; see the arrows path on the right side of the figure). If the agent receives no other occurence of the same input action ( alpha), after a fixed time t action exits from the higher level and the process is finalized (see Exit). | | Figure 3/a | Number of agents | Number of agents in each context runtime - with external barrier. | Figure 3/b
| Number of agents | Number of agents in each context runtime - without external barrier. | | Figure 4/a | Overall number of new normative beliefs | Overall number of new normative beliefs generated for each type of possible action - with external barrier. | | Figure 4/b | Overall number of new normative beliefs | Overall number of new normative beliefs generated for each type of possible action - without external barrier. | Figure 5/a
| New normative beliefs generated runtime | New normative beliefs generated runtime - with external constraint. | Figure 5/b
| New normative beliefs generated runtime | New normative beliefs generated runtime - without external barrier. | Figure 6/a
| Number of performed actions | Number of performed actions - case with barriers. | Figure 6/b
| Number of performed actions | Number of performed actions - case without barriers. | Figure 7/a
| Convergence rate | Convergence rate - case with barriers.
| Figure 7/b
| Convergence rate | Convergence rate - case without barriers. | Figure 8/a
| Number of performed actions | Number of performed actions - case with barriers.
| | Figure 8/b | Number of performed actions | Number of performed actions - case without barriers. | Figure 9/a
| Convergence rate | Convergence rate - case with barriers.
| Figure 9/b
| Convergence rate | Convergence rate - case without barriers. | Videos Textual Results File
| Description
| results1 (.mat MATLAB 5.0 file)
| Results using parameter array: 100, 100, 4, 1, 3, 10, NR, ND, EB
| | results2 (.mat MATLAB 5.0 file) | Results using parameter array: 100, 100, 4, 1, 3, 10, NR, YD, EB | | results3 (.mat MATLAB 5.0 file) | Results using parameter array: 100, 200, 4, 1, 3, 10, ND, EB | | results4 (.mat MATLAB 5.0 file) | Results using parameter array: 100, 200, 4, 1, 3, 10, YD, EB | Publications - Campenni, M., Andrighetto, G., Cecconi, F., Conte, R. (2008) Normal = Normative? The Role of Intelligent Agents in Norm Innovation. ESSA 2008.
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