Exploring the roles of complex networks in linguistic categorization

This article adopts the category game model, which simulates the origins and evolution of linguistic categories in a group of artificial agents, to evaluate the effect of social structure on linguistic categorization. Based on the simulation results in a number of typical networks, we examine the isolating and collective effects of some structural features, including average degree, shortcuts, and level of centrality, on the categorization process. This study extends the previous simulations mainly on lexical evolution, and illustrates a general framework to systematically explore the effect of social structure on language evolution. © 2011 Massachusetts Institute of Technology.published_or_final_versio

A cross-model study on the effect of power-laws on language evolution

Based on three evolutionary computational models that respectively simulate lexical, categorical and syntactic evolutions, we explore the effect of power-law distributed social popularity on language origin and change. Simulation results reveal a critical scaling degree (λ ≈ 1.0) in power-law distributions that helps accelerate the diffusion of linguistic conventions and preserve high linguistic understandability in population. Other scaling degrees (λ = 0.0 or λ > 1.0), however, tend to delay such diffusion process and affect linguistic understandability. Apart from the conventionalization nature of language communications in these models, increase in population size could also contribute to select the critical scaling degree, since this scaling degree can accommodate the influence of population size on linguistic understandability and many power-laws in real-world systems have their scaling degrees around this critical value.published_or_final_versio

The Social Network Dynamics Of Category Formation

Category systems are remarkably consistent across societies. Stable partitions for concepts relating to flora, geometry, emotion, color, and kinship have been repeatedly discovered across diverse cultures. Canonical theories in cognitive science argue that this form of convergence across independent populations, referred to as ‘cross-cultural convergence’, is evidence of innate human categories that exist independently of social interaction. However, a number of studies have shown that even individuals from the same population can vary substantially in how they categorize novel and ambiguous phenomena. Contrary to findings on cross-cultural convergence, this individual variation in categorization processes suggests that independent populations should evolve highly divergent category systems (as is often predicted by theories of social constructivism). These puzzling findings raise new questions about the origins of cross-cultural convergence. In this dissertation, I develop a new mathematical approach to cultural processes of category formation, which shows that whether or not independent populations create similar category systems is a function of population size. Specifically, my model shows that small populations frequently diverge in their category systems, whereas in large populations, a subset of categories consistently reach critical mass and spread, leading to convergent cultural trajectories. I test and confirm this prediction in a large-scale online social network experiment where I study how small and large social networks construct original category systems for a continuum of novel and ambiguous stimuli. I conclude by discussing the implications of these results for networked crowdsourcing, which harnesses coordination in communication networks to enhance content management and generation across a wide range of domains, including content moderation over social media and scientific classification in citizen science

Estudi de la coautoria de publicacions científiques entre UPC i institucions de Xina

S'analitza la coautoria de la UPC amb autors vinculats a institucions de Xina, per totes les areas temàtiques i sense considerar límits cronològics o documentals.Postprint (author’s final draft

Tacit knowledge for business intelligence framework using cognitive-based approach

Tacit knowledge becoming a key issue in business intelligence approach to knowledge systems. Capturing the tacit knowledge is not a straightforward task, since it consists of unstructured data and related to a variety of information that is not always accessible through traditional means. This work presents a systematic approach for capturing tacit knowledge to be used in a business intelligence framework. The approach is based on the theory of systematic functional linguistics, developed into interview protocols to be asked to tacit knowledge owners. The data transformed into cognitive maps to supply the data warehouse. The framework was tested on 23 librarians from several university libraries in West Java and Yogyakarta, Indonesia. The algorithm starts with a content targeted interview to identify the list of problems faced by librarians. The problems were then converted into a questionnaire to identify qualities of the problems such as frequency, urgency, severity, and importance. From the questionnaire results, the best tacit knowledge performers were identified. They are respondents who can solve the problems, while the majority of the respondents are unable to solve them. The best performers were then subjected to grammar targeted interview to collect the solutions they made to the problems. The transcription of the interview results is then converted into cognitive maps that visualize the solutions. These cognitive maps are then stored in a data warehouse and ready to collect anytime for analytics purposes. The framework is validated through Power BI and reviewed by seven experts. Its applicability to other domains is justified as long as the domain, e.g., manufacturing, have experienced problems related to technical, managerial, and empirical problems faced by employees at work. This research contributes to the methods of capturing tacit knowledge using a cognitive-based approach, which important to ensure the continuity of business in various domains