scholarly journals A bird’s-eye view of naming game dynamics: From trait competition to Bayesian inference

2020 ◽  
Vol 30 (6) ◽  
pp. 063119
Author(s):  
Gionni Marchetti ◽  
Marco Patriarca ◽  
Els Heinsalu
Keyword(s):  
Bayesian Inference ◽  
Game Dynamics ◽  
Naming Game

scholarly journals IN-DEPTH ANALYSIS OF THE NAMING GAME DYNAMICS: THE HOMOGENEOUS MIXING CASE

2008 ◽  
Vol 19 (05) ◽  
pp. 785-812 ◽  
Author(s):  
ANDREA BARONCHELLI ◽  
VITTORIO LORETO ◽  
LUC STEELS
Keyword(s):  
Numerical Simulations ◽  
Cultural Learning ◽  
Game Dynamics ◽  
Naming Game ◽  
Depth Analysis ◽  
Multi Agent ◽  
Language Emergence

Language emergence and evolution have recently gained growing attention through multi-agent models and mathematical frameworks to study their behavior. Here we investigate further the Naming Game, a model able to account for the emergence of a shared vocabulary of form-meaning associations through social/cultural learning. Due to the simplicity of both the structure of the agents and their interaction rules, the dynamics of this model can be analyzed in great detail using numerical simulations and analytical arguments. This paper first reviews some existing results and then presents a new overall understanding.

scholarly journals Naming Game Dynamics on Pairs of Connected Networks with Competing Opinions

2015 ◽  
pp. 368-379
Author(s):  
Albert Trias Mansilla ◽  
Mingming Chen ◽  
Boleslaw K. Szymanski ◽  
Josep Lluís de la Rosa Esteva
Keyword(s):  
Game Dynamics ◽  
Naming Game

Unifying Bayesian Inference and Vector Space Models for Improved Decipherment

2015 ◽  
Author(s):  
Qing Dou ◽  
Ashish Vaswani ◽  
Kevin Knight ◽  
Chris Dyer
Keyword(s):  
Bayesian Inference ◽  
Vector Space ◽  
Vector Space Models

How do academics assess the results of multiple experiments?

2018 ◽  
Author(s):  
Olmo Van den Akker ◽  
Linda Dominguez Alvarez ◽  
Marjan Bakker ◽  
Jelte M. Wicherts ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Bayesian Inference ◽  
Secondary Analysis ◽  
Individual Participant Data ◽  
Additional Experiment ◽  
Underlying Theory ◽  
Individual Participant ◽  
Vote Counting

We studied how academics assess the results of a set of four experiments that all test a given theory. We found that participants’ belief in the theory increases with the number of significant results, and that direct replications were considered to be more important than conceptual replications. We found no difference between authors and reviewers in their propensity to submit or recommend to publish sets of results, but we did find that authors are generally more likely to desire an additional experiment. In a preregistered secondary analysis of individual participant data, we examined the heuristics academics use to assess the results of four experiments. Only 6 out of 312 (1.9%) participants we analyzed used the normative method of Bayesian inference, whereas the majority of participants used vote counting approaches that tend to undervalue the evidence for the underlying theory if two or more results are statistically significant.

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