This repository contains the updated scripts in Python 3.8 to simulate the Naming Game (NG) model and its many variants. During the last recent years while I was developing a new variant of NG based upon the Bayesian inference, I wrote many scripts for simulating the dynamics of NG and its variants, including their time-evolution in different types of complex networks such as random graphs, graphs with small-world properties and scale-free networks. However, the scripts were written in Python 2.7 as this version was required for running the scripts in parallel on computer clusters. However, Python 2.7 reached the end of its life on January 1st, 2020, however until one year I had to make scripts in Python 2.7. For this reason, I am rewriting the previous scripts in Python 3.8, adding also some changes/improvement when required. It is worth noting here that very good pseudo-random generators are crucially required for simulating the dynamics of such semiotic models. Luckily, Python provides very good algorithms for random number generators from its library numpy. Also, note that in some scripts of mine the agents are implemented as objects for in such a case, it is easier to simulate a real-life dynamics of consensus, either embeddeding the agents on nodes of a specific complex networks by means of the Python library NetworkX or making them Bayesian agents. For instance, in Figure below from my multi-agent simulations it is shown the average memory per agent in the system (top panel), and the number of different words (bottom panel), as functions of the rescaled time, for the Erdos-Renyi (ER) network, the Barabasi-Albert (BA) network (both networks have average degree equal to four), and the complete graph (or equivalently mean-field (MF) approximation). We refer the reader to G. Marchetti, at al., A bird's-eye view of naming game dynamics: From trait competition to Bayesian inference, Chaos: An Interdisciplinary Journal of Nonlinear Science, 30, 6, 063119, 2020, and references therein for details. Please, if you find any mistake contact me via email at gionnimarchetti@gmail.com or gionnimarchettiminstp@gmail.com. Thank you very much.
The script has been tested with Python 3.8.10. 3.8.16, 3.11.5
The following library is also required for running the scripts
The script ng2c.py simulate the semiotic dynamics of an ensemble of agents in number N restricted to two names A and B corresponding to
integers 0 and 1, respectively, starting from an initial configuration chosen by the user. Note that in such a model there will be always
consensus at some time (the convergence time), that is, convergence to a state where all the agents have one name, either A or B, in their inventories. For such a simple model, except for the convergence time, there are two only observables: the total number of names and the success rate of the agents' pairwise interaction. The simulation outputs, including the convergence time and the maximum number of names are
stored into file results.text.
Before to launch the script the user needs to input the following values in the input file input.text: number of agents N, initial number of agents with A, initial number of agents with B, number of steps for the simulation and the seed for the random number generator. The suitable choice of number of steps depends upon the system size, or equivalently the number N of agents, can be inferred by the corresponding scaling law in Gionni Marchetti et al., Chaos: An Interdisciplinary Journal of Nonlinear Science, 30, 6, 063119, 2020 and references therein. The seed is necessary for the reproducibility of the results.
G. Marchetti, et al., A bird's-eye view of naming game dynamics: From trait competition to Bayesian inference, Chaos: An Interdisciplinary Journal of Nonlinear Science, 30, 6, 063119, 2020