SlideShare a Scribd company logo
1 of 1
Glossary of Technical Terms
Volatility: The willingness of individuals in a society to learn a
language.
Prestige: The value of a language to a society as a whole.
Project Description
• Determine a model that stabilizes the coexistence of languages with
meaningful applications
• Understand the effect of language social standing and resistance to
assimilation on the current integration of language communities within a
bilingual model of language competition
• Understand the effect of temporal and spatial parameters on the social
standing and resistance to assimilation of a language community within
a bilingual model of language competition
Scientific Challenges
• Current literature reflects the Abrams-Strogatz model and its variants,
where one language always dominates
Potential Applications
• A method for prolonging the existence of a minority language
• A method for effectively modelling oscillating temporal and spatial
parameters within a given model
Modeling Language Competition: Coexistence of Languages
Team Members: Sana Shahid ● Rema Hamdan ● Catherine Gallardo ● Kevin Lendo
Original Numerical dynamics show language death
Methodology
1. Begin with the Bilinguals Minett-Wang model, as an extension of the
Abrams-Strogatz Model:
𝑝𝑖, 𝐴 → 𝐴𝐵 = 1 − 𝑠 𝜎𝑖, 𝐵
𝑎
, 𝑝𝑖, 𝐵 → 𝐴𝐵 = 𝑠 𝜎𝑖, 𝐴
𝑎
𝑝𝑖, 𝐴𝐵 → 𝐴 = 1 − 𝑠 1 − 𝜎𝑖, 𝐴
𝑎
, 𝑝𝑖, 𝐴𝐵 → 𝐵 = 𝑠 1 − 𝜎𝑖, 𝐵
𝑎
2. Alter prestige to be a function of space and time, with neutral volatility:
𝑠 𝑥, 𝑦, 𝑡 = 𝛽 sin 𝑐𝑥 sin 𝑑𝑦 + 𝑒−𝑡
sin(𝑡100𝑒 𝑡
) , 𝑎 = 1
3. Collect data
4. Alter volatility to be a function of space and time, with socially
equivalent languages:
𝑎 𝑥, 𝑦, 𝑡 = 𝛽 sin 𝑐𝑥 sin 𝑑𝑦 + 𝑒−𝑡sin(𝑡100𝑒 𝑡) , s =
1
2
5. Collect data
6. Alter prestige to be a function of space ad volatility to be a function of
time:
𝑠 𝑥, 𝑦 = 𝛽 sin 𝑐𝑥 sin 𝑑𝑦 +
1
2
, 𝑎(𝑡) = 𝑒−𝑡sin(𝑡100𝑒 𝑡)
7. Collect data
Results
1. Start with The original model. Set volatility to 1 and prestige to ½.
2. The analysis of this model shows two stable fixed points indicating
language death, as well as an unstable fixed point.
1. The numeric results agree with the analytic findings, showing language
death.
1. Next experiment by setting values of prestige and volatility as functions
(5)
1. Change value of prestige to obtain seemingly stable
References
1. Castello, X. Equiluz, V.M Loureiro-Porto, l. San Miguel, The
Fate of Bilingualism in a Model of Language Competition
(2007)
2. Castello, X. Vasquiz, Agent Based Models of Language
Competitons: Macroscopic Descritpions and Order_Disoder
Transitions (2010)
3. PPLANE (http://math.rice.edu/~dfield/) is developed by
John C. Polking, Department of Mathematics, Rice
University.
Acknowledgments
This project was mentored by Colin Clark, whose help is
acknowledged with great appreciation.
Support from a University of Arizona TRIF (Technology
Research Initiative Fund) grant to J. Lega is also gratefully
acknowledged.
Dynamics of the Oregonator model [2], plotted with the software
PPLANE [3].
(1)
(2)
(3)
(4)
(5)
Second trial of Dynamics
β = 0.5, c = 0.5, d = 0.5
Third trial of Dynamics
β = 0.5, c = 0.1, d = 0.1
Initialization
1. To Consider physical dynamics, a two space (x,y) torus grid was used,
as the modeled society.
2. Additionally, time models exponential growth with units in terms of the
square of the elapsed time in years.
3. To main initial societies were used in order to eliminate the possibility
of initial location as a condition on the asymptotic behavior.

More Related Content

Similar to MATH 485_Poster_Team V

A Neural Probabilistic Language Model.pptx
A Neural Probabilistic Language Model.pptxA Neural Probabilistic Language Model.pptx
A Neural Probabilistic Language Model.pptx
Rama Irsheidat
 
CUHK intern PPT. Machine Translation Evaluation: Methods and Tools
CUHK intern PPT. Machine Translation Evaluation: Methods and Tools CUHK intern PPT. Machine Translation Evaluation: Methods and Tools
CUHK intern PPT. Machine Translation Evaluation: Methods and Tools
Lifeng (Aaron) Han
 
Grammatical encoding. PURMOHAMMAD
Grammatical encoding. PURMOHAMMADGrammatical encoding. PURMOHAMMAD
Grammatical encoding. PURMOHAMMAD
Mehdi Purmohammad
 
Vectorized Intent of Multilingual Large Language Models.pptx
Vectorized Intent of Multilingual Large Language Models.pptxVectorized Intent of Multilingual Large Language Models.pptx
Vectorized Intent of Multilingual Large Language Models.pptx
SachinAngre3
 

Similar to MATH 485_Poster_Team V (20)

LEARNING CROSS-LINGUAL WORD EMBEDDINGS WITH UNIVERSAL CONCEPTS
LEARNING CROSS-LINGUAL WORD EMBEDDINGS WITH UNIVERSAL CONCEPTSLEARNING CROSS-LINGUAL WORD EMBEDDINGS WITH UNIVERSAL CONCEPTS
LEARNING CROSS-LINGUAL WORD EMBEDDINGS WITH UNIVERSAL CONCEPTS
 
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...
 
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...
The Ins and Outs of Preposition Semantics:
 Challenges in Comprehensive Corpu...
 
Visual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on LanguageVisual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on Language
 
Plug play language_models
Plug play language_modelsPlug play language_models
Plug play language_models
 
ECAL11 Paris - August 2011
ECAL11 Paris - August 2011ECAL11 Paris - August 2011
ECAL11 Paris - August 2011
 
Distilling Linguistic Context for Language Model Compression
Distilling Linguistic Context for Language Model CompressionDistilling Linguistic Context for Language Model Compression
Distilling Linguistic Context for Language Model Compression
 
Distilling Linguistic Context for Language Model Compression
Distilling Linguistic Context for Language Model CompressionDistilling Linguistic Context for Language Model Compression
Distilling Linguistic Context for Language Model Compression
 
Esa act
Esa actEsa act
Esa act
 
Dodson_Honors_Thesis_2006
Dodson_Honors_Thesis_2006Dodson_Honors_Thesis_2006
Dodson_Honors_Thesis_2006
 
A Neural Probabilistic Language Model.pptx
A Neural Probabilistic Language Model.pptxA Neural Probabilistic Language Model.pptx
A Neural Probabilistic Language Model.pptx
 
Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...
Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...
Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...
 
CUHK intern PPT. Machine Translation Evaluation: Methods and Tools
CUHK intern PPT. Machine Translation Evaluation: Methods and Tools CUHK intern PPT. Machine Translation Evaluation: Methods and Tools
CUHK intern PPT. Machine Translation Evaluation: Methods and Tools
 
Modern Programming Languages classification Poster
Modern Programming Languages classification PosterModern Programming Languages classification Poster
Modern Programming Languages classification Poster
 
DH_syllabus_typology
DH_syllabus_typologyDH_syllabus_typology
DH_syllabus_typology
 
Embedding for fun fumarola Meetup Milano DLI luglio
Embedding for fun fumarola Meetup Milano DLI luglioEmbedding for fun fumarola Meetup Milano DLI luglio
Embedding for fun fumarola Meetup Milano DLI luglio
 
Grammatical encoding. PURMOHAMMAD
Grammatical encoding. PURMOHAMMADGrammatical encoding. PURMOHAMMAD
Grammatical encoding. PURMOHAMMAD
 
Vectorized Intent of Multilingual Large Language Models.pptx
Vectorized Intent of Multilingual Large Language Models.pptxVectorized Intent of Multilingual Large Language Models.pptx
Vectorized Intent of Multilingual Large Language Models.pptx
 
Master defence 2020 - Anastasiia Khaburska - Statistical and Neural Language ...
Master defence 2020 - Anastasiia Khaburska - Statistical and Neural Language ...Master defence 2020 - Anastasiia Khaburska - Statistical and Neural Language ...
Master defence 2020 - Anastasiia Khaburska - Statistical and Neural Language ...
 
Turkish language modeling using BERT
Turkish language modeling using BERTTurkish language modeling using BERT
Turkish language modeling using BERT
 

MATH 485_Poster_Team V

  • 1. Glossary of Technical Terms Volatility: The willingness of individuals in a society to learn a language. Prestige: The value of a language to a society as a whole. Project Description • Determine a model that stabilizes the coexistence of languages with meaningful applications • Understand the effect of language social standing and resistance to assimilation on the current integration of language communities within a bilingual model of language competition • Understand the effect of temporal and spatial parameters on the social standing and resistance to assimilation of a language community within a bilingual model of language competition Scientific Challenges • Current literature reflects the Abrams-Strogatz model and its variants, where one language always dominates Potential Applications • A method for prolonging the existence of a minority language • A method for effectively modelling oscillating temporal and spatial parameters within a given model Modeling Language Competition: Coexistence of Languages Team Members: Sana Shahid ● Rema Hamdan ● Catherine Gallardo ● Kevin Lendo Original Numerical dynamics show language death Methodology 1. Begin with the Bilinguals Minett-Wang model, as an extension of the Abrams-Strogatz Model: 𝑝𝑖, 𝐴 → 𝐴𝐵 = 1 − 𝑠 𝜎𝑖, 𝐵 𝑎 , 𝑝𝑖, 𝐵 → 𝐴𝐵 = 𝑠 𝜎𝑖, 𝐴 𝑎 𝑝𝑖, 𝐴𝐵 → 𝐴 = 1 − 𝑠 1 − 𝜎𝑖, 𝐴 𝑎 , 𝑝𝑖, 𝐴𝐵 → 𝐵 = 𝑠 1 − 𝜎𝑖, 𝐵 𝑎 2. Alter prestige to be a function of space and time, with neutral volatility: 𝑠 𝑥, 𝑦, 𝑡 = 𝛽 sin 𝑐𝑥 sin 𝑑𝑦 + 𝑒−𝑡 sin(𝑡100𝑒 𝑡 ) , 𝑎 = 1 3. Collect data 4. Alter volatility to be a function of space and time, with socially equivalent languages: 𝑎 𝑥, 𝑦, 𝑡 = 𝛽 sin 𝑐𝑥 sin 𝑑𝑦 + 𝑒−𝑡sin(𝑡100𝑒 𝑡) , s = 1 2 5. Collect data 6. Alter prestige to be a function of space ad volatility to be a function of time: 𝑠 𝑥, 𝑦 = 𝛽 sin 𝑐𝑥 sin 𝑑𝑦 + 1 2 , 𝑎(𝑡) = 𝑒−𝑡sin(𝑡100𝑒 𝑡) 7. Collect data Results 1. Start with The original model. Set volatility to 1 and prestige to ½. 2. The analysis of this model shows two stable fixed points indicating language death, as well as an unstable fixed point. 1. The numeric results agree with the analytic findings, showing language death. 1. Next experiment by setting values of prestige and volatility as functions (5) 1. Change value of prestige to obtain seemingly stable References 1. Castello, X. Equiluz, V.M Loureiro-Porto, l. San Miguel, The Fate of Bilingualism in a Model of Language Competition (2007) 2. Castello, X. Vasquiz, Agent Based Models of Language Competitons: Macroscopic Descritpions and Order_Disoder Transitions (2010) 3. PPLANE (http://math.rice.edu/~dfield/) is developed by John C. Polking, Department of Mathematics, Rice University. Acknowledgments This project was mentored by Colin Clark, whose help is acknowledged with great appreciation. Support from a University of Arizona TRIF (Technology Research Initiative Fund) grant to J. Lega is also gratefully acknowledged. Dynamics of the Oregonator model [2], plotted with the software PPLANE [3]. (1) (2) (3) (4) (5) Second trial of Dynamics β = 0.5, c = 0.5, d = 0.5 Third trial of Dynamics β = 0.5, c = 0.1, d = 0.1 Initialization 1. To Consider physical dynamics, a two space (x,y) torus grid was used, as the modeled society. 2. Additionally, time models exponential growth with units in terms of the square of the elapsed time in years. 3. To main initial societies were used in order to eliminate the possibility of initial location as a condition on the asymptotic behavior.