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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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