Fast Convention Formation in Dynamic Networks Using Topological Knowledge
Authors: Mohammad Hasan, Anita Raja, Ana Bazzan
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive simulation results indicate that our proposed mechanism is both effective (able to converge into a large majority convention state with more than 90% agents sharing a high-quality lexicon) and efficient (faster) as compared to state-of-the-art approaches for social conventions in large convention spaces. |
| Researcher Affiliation | Academia | Mohammad Rashedul Hasan University of Nebraska-Lincoln Lincoln, NE 6588, USA hasan@unl.edu; Anita Raja The Cooper Union New York, NY 10003, USA araja@cooper.edu; Ana Bazzan Instituto de Informatica, UFRGS C.P. 15064 91501-970, P. Alegre, RS, Brazil bazzan@inf.ufrgs.br |
| Pseudocode | Yes | Algorithm 1: Topological Factor Computation; Algorithm 2: Topologically Aware Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about the release of open-source code for the described methodology, nor does it provide links to a code repository. |
| Open Datasets | No | The paper describes generating synthetic network topologies (Ring, Small-World, Random, Scale-Free) and initializing agent lexicons with randomized mappings. It does not use or provide access information for any publicly available or open datasets. |
| Dataset Splits | No | The paper describes its simulation setup, including network types and parameters, but it does not specify explicit training, validation, or test dataset splits. The experiments are conducted as simulations on generated networks. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used to run the simulations. |
| Software Dependencies | No | The paper does not mention any specific software components with version numbers (e.g., programming languages, libraries, or frameworks) used for the implementation or simulation. |
| Experiment Setup | Yes | We conduct experiments on four topologies: Ring, SW, RN and SF. (...) The average node degree in these networks are set to 20 (...). Initially the internal lexicon of every agent is set with 10 fixed concepts and a randomized mapping of one or more words (from a set of 10 words) for each concept. (...) the three parameters of equation 2 (CE, S & TF) are equally weighted (i.e., a = b = c = 1). The spreading and updating probabilities are set to 0.01. Only 10% of the agents are randomly selected to take part in network reorganization using the Fermi function in which the value of β is set to 1.0. Table 1 provides the setting of the threshold levels of the parameters for the TA mechanism. α is set to be greater than or equal to 0.95 and λ is equal to 1.0. (...) µ is set to a large number 1000. (...) 50 influencer agents are randomly deployed in the network (...). Each simulation consists of 100,000 timesteps... |