Cognition & Foundations  (CF) Session 1

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Time and Date: 14:15 - 15:45 on 19th Sep 2016

Room: G - Blauwe kamer

Chair: Taha Yasseri

288 Automatic identification of relevant concepts in scientific publications [abstract]
Abstract: In recent years, the increasing availability of publication records has attracted the attention of the scientific community. In particular, many efforts have been devoted to the study of the organization and evolution of science by exploiting the textual information in the articles like words extracted from the title and abstract. However, just few of them analyzed the main part of an article, i.e., its body. The access to the whole text, instead, allows to pinpoint related papers according to their content, analyzing the network of similarity between them. Scientific concepts are extracted from the body of the articles available in the ScienceWISE platform, but the paper similarity network possesses a considerably high link density (36 %) which spoils any attempt of associating communities of papers to topics. This happens because not all the concepts inside an article are truly informative and, even worse, they may not be useful to discriminate articles with different contents. The presence of such ``generic'' concepts with a loose meaning implies that a considerable amount of connections is made up by spurious similarities. To eliminate generic concepts, we introduce a method to evaluate the concepts' relevance according to an information-theoretic approach. The significance of a concept $c$ is defined in terms of the distance between its maximum entropy, $S_{max}$, and the actual one, $S_c$, calculated using its frequency of occurrence inside papers $tf_c$. Generic concepts are automatically identified as the ones with an entropy close to their maximum and disregarded, while only ``meaningful'' concepts are retained when constructing the paper similarity network. Consequently, the number of links decreases, as well as the amount of noise in the strength of articles' similarities. Hence, the resulting network displays a more well defined community structure, where each community contains articles related to a specific topic.
Andrea Martini, Alessio Cardillo and Paolo De Los Rios
30 Japanese wood ants might use different inbound strategies depending on outbound visual experiences. [abstract]
Abstract: Ants use visual cues for their navigation. There are many studies reporting that ants cannot use map-like systems. Rather, they appear to adopt taxon-systems in which one-to-one correspondences between two different landmarks are realized. To this end, ants can use different paths on their inbound from paths on their outbound. Actually, they appear to head for or ignore visual cues depending on whether they consume food or not. In ant navigation systems therefore, arriving at one location after passing a certain visual cue can be strengthened via several training, resulting in associating each location with a certain visual landmark. After several foraging trips, ants might head for learned visual cues in order to reach goal locations. In this learning mechanism, there is no room for considering other landmarks when foragers head for certain locations. However, several studies reported that ants can exhibit latent learning. This phenomenon can be related to the problem whether or not foragers on inbound trips can use visual cues acquired on their outbound in order to return to their nest. Originally, several trips enhance the relationship between nest location and visual cues. However, in this case, latent learned cues on their outbound can be directly applied to inbound navigation systems. Our aim of this study is checking whether ants can establish effective foraging strategies by associating disconnected information with each other. In this paper, by exposing Japanese wood ants to right-angle-shaped maze or linear-shaped maze on their outbound, we observed trajectories of foragers on their initial inbound trips. On inbound trips, mazes were removed. Thus, foragers could move freely on the test arena. We found that foragers were able to follow their outbound paths when they were restricted to right-angle-shaped maze on their outbound compared with linear-shaped maze on their outbound.
Tomoko Sakiyama and Yukio-Pegio Gunji
37 Network-Oriented Modelling: a Temporal-Causal Network Modelling Approach to Complex Dynamical Systems [abstract]
Abstract: This contribution presents a Network-Oriented Modelling approach based on temporal-causal networks. The temporal-causal modelling approach incorporates a dynamic perspective on causal relations. Basic elements are networks of nodes and connections with for each connection a connection weight for the strength of the impact of the connection, for each node a speed factor for the timing of the effect of the impact, and for each node the type of combination function used to aggregate multiple impacts on this node. The approach covers specific types of neural networks, but it is more generic; it also covers, for example, probabilistic and possibilistic approaches in which product or max and min-based functions are used. The temporal-causal network modelling format used enables to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, of internal simulation and mirroring of mental processes of others, and of social interactions. Also adaptive networks are covered in which connection weights of the network change over time, which, for example, can be used to model Hebbian learning in adaptive neuro-cognitive models or evolving social interactions. By choosing suitable combination functions every process that can be modelled as a smooth state-determined system by first-order differential equations, also can be modelled by the presented temporal-causal network modelling approach. At the European Conference on AI ECAI’16, a tutorial is organised about the temporal-causal network modelling approach [1]. Moreover, [2] is a journal paper about the approach, and a book [3] on the approach will be published by Springer in the series Understanding Complex Systems. [1] [2] Treur, J., Dynamic Modeling Based on a Temporal-Causal Network Modeling Approach. Biologically Inspired Cognitive Architectures, 16, 131-168 (2016) [3] Treur, J., Network-Oriented Modelling: Addressing Complexity of Cognitive, Affective and Social Interactions. Series on Understanding Complex Systems, Springer Publishers, 2016, to appear.
Jan Treur
235 Exploring Power Law in School Dropout Rates for the State of Pennsylvania in the United States [abstract]
Abstract: Research on the origins of power law and observation and validation of power law distribution in empirical data is active in recent years. This paper is an interdisciplinary research, focusing on exploring the power law distribution in school dropout data for the State of Pennsylvania in the United States. By using the fitting method with goodness-of-fit test based on the Kolmogorov- Smirnov statistics and least-squares fitting, the data is tested in two types of power law distributions—the survival (rank) distribution (“Zipf distribution”) and the complementary cumulative probability distribution (CCDF). In both distributions, only the middle range of the data shows power law and the upper quantile of the distribution bends down from the power law fit. It has two implications: first, technical-wise, it reflects issues of empirical data to fit in the power law since empirical data is affected by the availability of datasets and in social systems, data is bounded by other societal factors. Second, social science wise, it indicates that the dropout rates obey a skewed distribution, which means that the average value of dropout rates loses its meaning. This paper argues that policy makers and researchers should instead focus on the extreme values of dropout rates to better understand the high dropout rates phenomenon in certain districts and areas.
Xiaoyi Yuan
486 Making Complexity Accessible Using 3D Printing [abstract]
Abstract: Complex systems science studies systems made of many components, where key information about system properties and structures are often conveyed through complex two-dimensional visualization. Such 2D visualization can be very difficult to understand, or even inaccessible at all, for learners whose sensory and/or cognitive modes are not compatible with it, including blind and visually impaired learners and learners who are more successful in understanding abstract materials through physical interaction with concrete, tangible objects. The importance, and difficulty, of making complex visualization more accessible to broader audience has been noted in STEM education literature, but not much development has been made to address this problem yet. Here we explore possibilities of making complexity more accessible to broader participants through three-dimensional manipulatable representations (3D “physicalization”) using 3D printing technologies. We assume that, given the optimal design, materials and 3D printing processes, 3D physicalization will substantially improve the learning of complex systems concepts for a variety of learners, compared to using 2D visualization only. We have conducted iterative designs of the following two physicalizations so far: (1) complex network diagrams, and (2) trajectories of chaotic systems. Several iterations of design and testing have revealed non-trivial design challenges. For example, conventional network embedding algorithms (e.g., spring embedding) are not suitable for physicalization as they tend to embed important nodes in inaccessible areas, which we have resolved by developing heuristic layout algorithms that place all nodes on a hollow (semi-)sphere. Another example of the challenges is how to make crowded parts haptically discernible (e.g., trajectories of strange attractors). This illustrates the importance of striking a right balance between scientific accuracy and pedagogical clarity when we physicalize complex systems. Future work includes further design optimization, experimental evaluation of educational effects of 3D physicalization on learners with diverse backgrounds/abilities, and applications to actual complex systems problem solving.
Hiroki Sayama and Prahalad Rao

Cognition & Foundations  (CF) Session 2

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Time and Date: 10:45 - 12:45 on 22nd Sep 2016

Room: H - Ontvangkamer

Chair: Massimo Stella

561 Similarity of Symbol Frequency Distributions with Heavy Tails [abstract]
Abstract: How similar are two examples of music or text from different authors or disciplines? How fast is the vocabulary of a language changing over time? How can one distinguish between coding and noncoding regions in DNA? Quantifying the similarity between symbolic sequences is a traditional problem in information theory which requires comparing the frequencies of symbols in different sequences. We will address this problem for the important case in which the frequencies of symbols show heavy-tailed distributions (e.g. the famous Zipf's law for word-frequencies), which hinder an accurate finite-size estimation of entropies, and for a family of similarity measures based on the generalized entropy of order α. We will show analytically how the systematic (bias) and statistical (fluctuations) errors in these estimations depend on the sample size N, on the exponent γ of the heavy-tailed distribution and the order alpha of the similarity measure. Our primary finding is that, for heavy-tailed distributions, (i) the error decay is often much slower than 1/N, illustrating the difficulty in obtaining accurate estimates even for large sample sizes, and (ii) there is a critical value of the order of the entropy α∗=1+1/γ≤2 for which the normal 1/N dependence is recovered. We emphasize the importance of these findings in the example of quantifying how fast the English vocabulary has changed within the last 200 years, showing that these finite-size effects have to be taken into account even for very large databases (N≳1,000,000,000 words). Associated reference: DOI:
Martin Gerlach, Francesc Font-Clos and Eduardo Altmann
145 Universal properties of culture: evidence for the ``multiple self'' in preference formation [abstract]
Abstract: Understanding the formation of subjective human traits, such as preference and opinions, is an important, but poorly explored problem. It is essential that traits collectively evolve under the repeated action of social influence, which is the focus of many studies of cultural dynamics. In this paradigm, other mechanisms potentially relevant for trait formation are reduced to specifying the initial cultural state for the social influence models, state which usually is generated in a uniformly random way. However, recent work has shown that the outcome of social influence dynamics strongly depends on the nature of the initial state: a higher level of cultural diversity is found after long-term dynamics, for the same level of propensity towards collective behaviour in the short term, if the initial cultural state is sampled from empirical data instead of being generated in a uniformly random way. First, this study shows that this effect is remarkably robust across data sets. In a certain sense, the analysis suggests that systems from which such data is extracted function close to criticality. Second, this study presents a stochastic model for generating cultural states that retain the universal properties. One ingredient of the model, already used in previous work, assumes that every individual's set of traits is partly dictated by one of several ``cultural prototypes'', which are abstract entities informally postulated by several social science theories. A second, new ingredient, taken from the same theories, assumes that apart from a dominant prototype, each individual also has a certain exposure to the other prototypes. The fact that this combination of ingredients is compatible with regularities of empirical data suggests that cultural traits in the real world form under the combined influence of several cultural prototypes, thus providing indirect evidence for the class of social science theories compatible with this description.
Alexandru-Ionut Babeanu, Leandros Talman and Diego Garlaschelli
380 Temporal Network Analysis of Small Group Discourse [abstract]
Abstract: The analysis of school-age children engaged in engineering projects has proceeded by examining the conversations that take place among those children. The analysis of classroom discourse often considers a conversational turn to be the unit of analysis. In this study, small-group conversations among students engaged in a robotics project are analyzed by forming a dynamic network with the students as nodes and the utterances of each turn as edges. The data collected for this project contained more than 1000 turns for each group, with each group consisting of 4 students (and the occasional inclusion of a teacher or other interloper). The conversational turns were coded according to their content to form edges that vary qualitatively, with the content codes taken from prior literature on small group discourse during engineering design projects, resulting in approximately 10 possible codes for each edge. Analyzed as a time sequence of networks, clusters across turns were created that allow for a larger unit of analysis than is usually used. These larger units of analysis are more fruitfully connected to the stages of engineering design. Furthermore, the patterns uncovered allow for hypotheses to be made about the dynamics of transition between these stages, and also allow for these hypotheses to be compared to expert consideration of the group’s stage at various times. Although limited by noise and inter-group variation, the larger units allowed for greater insight into group processes during the engineering design cycle.
Bernard Ricca and Michelle Jordan
403 Ranked communities and the detection of dominance and influence hierarchies [abstract]
Abstract: In directed networks, edges often represent a transfer of power, social influence, or confidence. In some cases, this is explicit, as in the case of directed acts of dominance inflicted by one animal upon another. In other cases, the relationship is more implicit: when one university hires another's graduate, the hiring department is expressing confidence in the quality of the graduate's department's training. Given such a network, it is possible to rank individual vertices using a variety of methods, such as minimum violations or PageRank. However, in many real-world systems, groups of individuals are ranked, and expressions of influence or dominance by individuals, captured by network edges, simply reflect group affiliations--a type of large-scale network structure that has not been previously described. In this work, we introduce a definition of ranked communities in directed networks and propose an algorithm to efficiently identify them. The mathematical framework of our method is placed in the context of the related problem of classic modularity maximization and we introduce an important distinction between strict (dominance) and inclusive (endorsement) hierarchies. We confirm our method's ability to extract planted ranked community structure in synthetic networks before applying it to learn about real-world networks. In particular, this method allows us to quantitatively study a question posed over half a century ago regarding the relationship between social network structure and the Indian caste system. We show that our recently collected data of a social support network in two South Indian villages is structured according to a mixture of ranked caste-based communities and community-transcending individual relationships. In this system, organization by ranked community is related to known social structure, but we also apply our method to learn about other social and ecological networks in which the organizing mechanisms have yet to be identified.
Daniel Larremore, Laurent Hebert-Dufresnse and Eleanor Power
81 The Assessment of Self-organized Criticality in Daily High School Attendance Rates [abstract]
Abstract: One important aspect of studying the behavior of dynamical systems is the analysis of processes of stability and change over time. This requires an estimation of auto-correlative and cyclical patterns in sets of frequently repeated measurements of the behavior of such systems. Traditional mean and (co)variance computations typically used for cross-sectional data are not adequate to characterize these distributions, and may actually be misleading (Beran, 1994). Daily school attendance is presented as a case in point. Since 2004 and to this day, the New York City Department of Education has published daily attendance rates on its website for all of its schools. These data exhibit the degree of resolution needed to detect underlying systems dynamics that are hidden in the conventionally reported weekly, monthly or yearly average rates. Daily attendance rates in six small high schools were analyzed over a ten-year period (2005 – 2014). The analysis proceeded as follows: 1. intervention models were fitted to handle the most extreme values in the series (usually low attendance) for each school, 2. Conventional time series analyses were used to estimate short-term dependencies and cyclical patterns, 3. The goodness of fit of those models was compared with that of models including long-range estimates (fractional differencing parameter, Hurst exponent). Preliminary analyses suggest significant long-range dependencies in two of these six schools, suggesting self-organized criticality (tension-release, unpredictable cycles), and strong weekly cycles in the four others. The presentation will illustrate how the initial appearance of the data in the various diagnostic plots suggests long-range dependencies, and the parameters of the best fitting models are interpreted. Implications of the findings for the field are discussed, and a note is included about the available software options for conducting these types of analyses. References: Beran, J. (1994). Statistics for long-memory processes. Boca Raton, FL: Chapman & Hall/CRC.
Matthijs Koopmans
59 Organisational decision making as network coupled oscillators: validation and case study [abstract]
Abstract: Organisational decision making, where many individuals interact to share information, formulate decisions and perform actions, is at heart a social system oriented towards outcomes - success in a military mission or business venture. In recent years I have proposed the Kuramoto model of synchronising oscillators to represent such a system that may be developed towards predictive modelling against specific scenarios. Essentially here, the oscillator limit cycle represents what is known in cognitive psychology as a Perception-Action cycle, or in military parlance an Observe-Orient-Decide-Action loop; the network of interactions may represent the range of formal, information and technology based exchanges of information; finally, a native frequency represents the individual speed of decision-making for an agent left to themselves. To such a system may be added stochastic influences, or “noise”, to represent the human properties of intuition, indecision or degrading of communication under stress or mood shifts. In this paper, I propose the model in application to a military organisation as may be located in a deployed headquarters. I use data based on a recent study of such an organisation where participants responded to surveys and interviews probing their pattern of interactions and level of cognition (in the sense of the Perception-Action cycle) for two scenarios: routine business and an emergency response. For noise I use a combination of stable Lévy noise both in spatial and temporal dimensions, where the underlying probability distributions for this exhibit power-law heavy tails. I summarise an initial validation of the model, invoking another approach in organisation theory known as Contingency Theory. The model thus integrates ideas from quantitative and qualitative complexity theory. To illustrate the utility of the model, I study a number of interventions in the model: local network modifications and/or training in order to tighten frequency distributions. I conclude with prospects for future work.
Alexander Kalloniatis