Abstract: Network models of language have provided a way of linking cognitive processes to the structure and connectivity of language. This has been of particular importance in language acquisition as a means to explore the relational role of language and its influence on cognitive processes. Steyvers and Tenenbaum proposed that language is learned by a process similar to preferential attachment, with highly connected nodes being learned earliest, accounting for high-level lexical network structure and also capturing the empirical age of acquisition reports. Hills and colleagues suggested, instead, that language learning is driven by contextual diversity, or the degree of unknown words in the adult language graph, accounting for and predicting normative acquisition trends. Here we extend and test these previous ideas on acquisition trajectories of individual children as opposed to normative acquisition. We further explore the types of relationships between words that are meaningful to toddlers when learning new words. We construct not only theoretical models of acquisition but models that are capable of predicting what words a specific child is likely to learn next. We find that the best fitting network model varies systematically across children and across the course of development. We also find that the choice of network representation influences our ability to model the acquisition trends of young children. This work suggests that the use of network models to understand language acquisition trends of toddlers may not only provide predictive models of what words a child is likely to learn next but may provide insight into the cognitive processes of acquisition itself.

Abstract: Interdisciplinary scientific teams are increasingly funded, but how is knowledge co-produced, in practice, across various disciplines? This paper presents results from a three-year study using a complex systems approach to track the emergence of interdisciplinary science in a heterogeneous researchers network – a “Coupled Human and Natural Systems” research team. I conceptualize the research team as a heterogeneous social network, and analyze the process of knowledge co-production as emergent from the properties and dynamics of the network. The case study is based on epistemological data collected weekly, over three years, from team members (anthropologists, geographers, ecologists, hydrologists, climate scientists and computer scientists) after joint meetings, combined with individual interviews and thematic analyses of research outputs (e.g. articles). I argue that a complex system framework is useful to supports the assessment of what fosters or blocks the emergence of joint knowledge for successful collaborative science.

Abstract: By using a huge Japanese blog data base with the author’s ID, we can observe not only the number of entries per day for any words, but also personal dynamics of blog entries. In this presentation, we report statistical properties and modeling for four major categories of words. The First is “ordinary words” which is used in our daily life, for example “soon”. The number of entries of “soon” has a steady fluctuation. The Second is “News words”, for example “Michael Jackson”. We can observe clear jump and power law decaying in the number of entries of “Michael Jackson” after the news of which Michel Jackson died. The Third is “Trending words”, for example “Twitter”. The number of entries of “Twitter” was increasing exponential from Oct. 2008 to Jun. The fourth is "event words" which has growth and relaxation characterized by a power function around the peak day such as national holidays. We reproduced these dynamics by an agent-based model based on the SIR model which is well known in mathematical epidemiology to clarify the origin of these dynamics from the view point of bloggers interactions. In order to reproduce not only an exponential but also a power law growth and relaxation behaviors observed in trending words, we developed the base model by adding some effects to our model, for example an external shock effect, a deadline effect and an amorphous effect. The amorphous effect, inspired by solid physics studies, gives bloggers individual characteristics, in other words individual duration of interest for the specific word. As a result of adding these essential effects, our model reasonably reproduces the dynamics observed from our data.

Abstract: This presentation argues that crises are a distinctive feature of complex social systems. A quest for connectivity of communication leads to increase systems’ own robustness by constantly producing further connections. When some of these connections have been successful in recent operations, the social system tends to reproduce the emergent pattern, thereby engaging in a non-reflexive, repetitive escalation of more of the same communication. This compulsive growth of systemic communication in crisis processes, or logic of excess, resembles the dynamic of self-organized criticality. Our theoretical model contend that crises in complex social systems are not a singular event, but result from a process that unfolds in three stages: incubation, in which the system incrementally develops a recursive dynamics of non-reflexive repetitions that weakens both its adaptive capabilities and connections; contagion, whereby the effects of that dynamics expands to different systems or clusters in the network; and restructuring, namely, a reorganization of both the system’s own conditions of functioning and its interrelationships with the environment. Next, we argue that percolation and sand pile models are suitable techniques for both modeling this process and analytically distinguishing between three phases of social crises. We illustrate our propositions with a view on the crisis of the educational system in Chile—a country in which over the last forty neoliberal reforms led to an incremental monetization of public education. Accordingly, we first construct the conceptual foundations of our approach. Second, we present three core assumptions related to the generative mechanism of social crises, their temporal transitions (incubation, contagion, restructuring), and the suitable modeling techniques to represent them. Third, we illustrate the conceptual approach with a percolation model of the crisis in Chilean education system.

Abstract: According to psycholinguistics, the human mind organises words in a mental lexicon (ML), i.e. a dictionary where words are stored and retrieved depending on their correlations. Until recently, network theory has been used for investigating one type of interactions at a time, without providing cross-correlational information. Our novel approach overcomes this limitation by modelling the mental lexicon of English speakers as a multiplex lexicon network (MLN), where nodes/words are connected according to: (i) word associations (“A” makes one think of “B”), (ii) feature norms (“A” shares features with “B”), (iii) co-occurrences (“A” and “B” are frequently adjacent), (iv) synonyms ("A" means also "B") and (v) phonological similarities (“A” differs from “B” by the addition, deletion or substitution of one phoneme). We build two MLNs: one for children up to 32 months (with 529 words) and one for adults (with almost 5000 words). Both the MLNs are irreducible, i.e. projecting all the edges onto one aggregate layer only would imply losing information about the word patterns in the system. In children, we show that the multiplex topology is more powerful in predicting the ordering with which words are acquired than individual layer statistics. Also, multiplexity allows for a quantification of the most important layers (semantic vs. phonological) that dynamically determine word acquisition. For adults, we propose a novel toy model of lexicon growth driven by the phonological level, in which real words are inserted along different orderings and they can be also rejected for memorization. Our model shows that when similar-sounding words are preferentially learned, the lexicon grows according to the multiplex structure, while when novel learned words sound different from the known ones, both semantic layers and frequency become predominant, instead. Our results indicate that the MLN topology is a meaningful proxy of the cognitive processes shaping the mental lexicon.