ABSTRACT. The view that all natural languages are equally complex systems has been more
strongly challenged only recently; see the articles in Linguistic Typology 5:2/3 (2001) and
(Miestamo et al. 2008), for instance. Language complexity is usually identified with grammar
complexity, based on the grammatical representations of the languages under consideration.
In the absence of complete detailed grammars of entire languages, we must restrict our
studies to local complexity (Miestamo 2008), that is, to the complexity of specific separate
aspects of grammar across languages. Thus, we can talk about morphological complexity,
complexity of simple sentences, complexity of negation, etc. The issue of how to compare the
complexity of considerably different grammars immediately arises. This can be resolved by
considering relative grammar complexity which takes into account the linguistic information
(syntactic and semantics functions) that the grammar is supposed to provide (Vulanović 2003,
2007). Closely connected with relative grammar complexity is relative grammar efficiency
(their measures are defined as reciprocals of one another). The measure of relative grammar
efficiency is given by a mathematical formula, some components of which may require a
complicated and involved combinatorial procedure. This is why simpler modifications have
been considered in several papers following (Vulanović 2007). The original formula and the
simplifications will be surveyed in the first part of the talk. One particular simplification will

be discussed in the second part. This approach to grammar efficiency is based on the degree
of violation of the linguistic principle ‘one-meaning–one- form’ (Anttila 1972). Two main
components can be considered in any linguistic system. On the one hand, there are
grammatical forms and on the other, the linguistic information (meaning) that the forms
convey. The simplest and most efficient grammars are those in which there is a one-to- one
correspondence between the forms and the required linguistic information. The grammar is
less efficient if the departure from the one-meaning–one- form principle is greater. In the
abstract mathematical description, this means that a measure should be found that indicates
how far a relation between two sets is from a bijection between them. One possible measure
of this kind will be presented and illustrated by several linguistic examples.

ABSTRACT. Complex systems display at all scales skewed distributions, spirals and sigmoid curves which comply closely with lognormal or power law distributions. Moreover, oscillations and chaotic behavior are not random either but share the same characteristics independent of scale. Common patterns imply a universal law. Laws in turn are found by recognizing a phenomenon that is common and mathematizing an example of it. So, what is common to all systems that evolve to complexity? It is evolution itself, that is, change. In terms of physics a change takes place when a quantum moves from surroundings to the system, or vice versa. For example, the quantum of light carries energy and time, and hence evolution steps forward in terms of energy and time toward balance. The general equation of evolution can be formulated by inspecting an energy level diagram of a system. The system´s state can be given in terms of probability as pioneered by Boltzmann and Gibbs, however, including quanta of light from surroundings. This leads to statistical mechanics of open quantized systems. Subsequently logarithm of the state equation gives entropy whose rate of change is the evolutionary equation known as the 2nd law of thermodynamics. When the equation is analyzed, it is found to results in the ubiquitous patterns. Perhaps more intriguing, the evolutionary equation yields non-determinate history.

ABSTRACT. Experts remain divided about the nature of the sociopolitical system of ancient Teotihuacan, which was one of the earliest and largest urban civilizations of the Americas. Excavations hoping to find compelling evidence of powerful rulers, such as a royal tomb, keep coming away empty-handed. But the alternative possibility of a corporate or collective government still remains poorly understood. There is a lack of evidence of a powerful bureaucracy, but a decentralized network limited to the level of neighborhood representatives seems susceptible to problems of collective action. Previously we used a computational model to show that in principle this latter worry is unfounded, as long as we assume that the network’s topology could be transformed via community rituals and was not strongly subdivided (Froese, Gershenson, and Manzanilla 2014). Here we extended this model to investigate whether increased social hierarchy could mitigate the negative effects of strong divisions. We found a special synergy between hierarchy and community ritual in that only their combination improved the extent of cooperation, which is consistent with portrayals of the elite as religious specialists serving the public good.

ABSTRACT. Francesca Lipari (Lumsa University) francy.lipari@gmail.com Brais Alvarez-Pereira (European University Institute) brais.alvarez@eui.eu Alireza Goudarzi (Laboratory for Neural Computation and Adaptation) alireza.goudarzi@gmail.com Leonhard Horstmeyer (Max Planck Institute for Mathematics in the Sciences) horstmey@mis.mpg.de 

Keywords: Networks, consensus formation, non-Bayesian learning, social learning 

Understanding the mechanism of social learning is important in an uncertain and complex world in which, as Hayek said, knowledge of the circumstances is never presented in concentrated or integrated form, but rather as “dispersed bits of incomplete and frequently contradictory knowledge which all the separate individual posses”. Agents can rely on two mechanism of information aggregation, a local mechanism and global one. The first comes directly from individual interaction with their peers and neighbors on an ongoing basis. The second is displayed by policy intervention. Sometimes, these two mechanisms are in disagreement, some other times they reinforce each other on a particular issue, ultimately their interaction is important to understand the dynamics of agents’ opinion about social issues (i.e. vaccines, gender norms, political participation etc). In this work our aim is twofold. We study, first, the learning mechanism that produces the emergence of new opinion and, in it, what is the role of the two forms of information aggregation in shaping the mechanism of opinion and consensus formation; second, we study how the new opinion is endorse. We model a collection of interacting agents endowed with an initial multi-attribute identity and initial opinions over those attributes. The dispositions evolve over time in a social learning process, through exposure to local and external information. The model allows us to study how fast consensus is reached in this learning environment as a function of the strength of the external information and the divergence between agents’ individual disposition and the established norms.

ABSTRACT. The Science of Complex Systems is an emergent discipline rather successful in the last years. However, further progress in the physics is hampered by the lack of deep knowledge about how multi-level complex systems organize and operate. Preliminary results show that interactions at different levels behave in a significantly different way than in an isolated level. For example, such dependencies may induce cascading failures and sudden collapses of the entire system. This makes the science of complex networks particularly suitable for the exploration of the many challenges that we face today, including critical infrastructures and communication systems, as well as techno-social and socioeconomic networks. We have been working in the development of a mathematical, computational and algorithmic framework for the study of the physics of multiscale complex systems. The chosen framework consists in a set of layers in such a way that every single layer has exactly the same set of nodes, but they can have different patterns of connectivity. A clear example can be that of social networks, where a layer can be a set of Twitter users having its respective set of following and followed users and another layer can be the same set of Facebook users. A user can have different sets of neighbours in each network because one can think on more familiar or more professional links. But the set of users is exactly the same. In this case, one can imagine that information can flow in any of the two layers and shifting from one to the other when one of the users decide to do it. In this talk we emphasize on the dynamic properties of multiplex networks by looking at different phenomenologies and different types of interaction within and between layers.

ABSTRACT. Electrical power systems are one of the most important infrastructures that support our society. However, their vulnerabilities have raised great concern recently due to several large-scale blackouts around the world. In this talk, we will present several results concerning the robustness of power systems against cascading failures initiated by a random attack. This will be done under a simple yet useful model based on global and equal redistribution of load upon failures. We provide a comprehensive understanding of system robustness under this model by (i) deriving an expression for the final system size as a function of the size of initial attacks; (ii) deriving the critical attack size after which system breaks down completely; (iii) showing that complete system breakdown takes place through a first-order (i.e., discontinuous) transition in terms of the attack size; and (iv) establishing the optimal load-capacity distribution that maximizes robustness. In particular, we show that robustness is maximized when the difference between the capacity and initial load is the same for all lines; i.e., when all lines have the same redundant space regardless of their initial load. This is in contrast with the intuitive and commonly used setting where capacity of a line is a fixed factor of its initial load. We will also consider the case where an adversary can launch a targeted attack, and present several results on the hardness of attacking optimally. Time permitting, we will present several other application areas for the same model with pointers for future work.

ABSTRACT. In order to reduce the global energy consumption, the energy efficiency must be improved. Rotating electrical generators generate almost all electrical energy in the world. About 50% of this energy is used to supply electrical motors with average efficiency of 88%. Therefore, efficiency improvements to both electrical generators and motors can have a very large impact on energy consumption. The energy wasted in electric motors produces near 0.5 billion tons of CO2 emission per year. In the short to medium term, development of new materials, such as rare earth based (NdFeB), improving construction techniques and introducing new structures for motors can lead to more efficient electrical machines. The rare earth (NdFeB) based permanent magnets provide also opportunities to build more compact electrical machines. Nowadays, most manufacturers of electrical machines try to produce high efficiency, super premium efficiency and ultra-premium efficiency classes of motors. So, the challenge is to design an electric motor enabling to handle peak power, to stand thermal stress, and insulation aging, to be compact, low-cost and efficient.

ABSTRACT. To study processes of self-organization we performed an agent-based simulation of non-interacting particles within a closed system. Without any interaction field, the system thus defined can be thermodynamically described as a canonical ensemble (constant energy flux and fixed number of agents) with all the agents dispersed equally throughout the system. The statistics of the system, in such a state, when an interacting field is absent, shows a symmetrical distribution of the average path lengths. Upon introduction of an internal interaction field, the system experiences emergence of order from a state of disorder as the agents form a path along the gradient of the interaction field. A steady state occurs when a stable path has been formed. Steady-state statistics of the simulations reveal the appearance of an exponential decaying tail for the average path-lengths, breaking the initial symmetry to a non-Gaussian distribution towards a more organized state. This result can be used to understand the evolution of self-organization in complex systems, which start from initially random and uncorrelated motions, to an ordered, strongly correlated ones. The interplay between a completely random state and an ordered state is used as a metric to quantify the magnitude of complexity of the system. Entropy and action efficiency of the organized state serve as numerical measure for the degree of organization. Those results should be applicable across wide range of non-equilibrium self-organizing systems from different disciplines.

ABSTRACT. What is evolution and why does it exist in the geophysical, biological, social and technological realms – in short, everywhere? Why is there a time direction – a time arrow – in the changes we know are happening every moment and everywhere? These are questions of physics, about everything. The physics answer is that nothing lives, flows, moves and morphs unless it is driven by power and has freedom to change. The power is destroyed by the flows, and the flow architectures evolve into configurations that provide progressively greater access for movement. The universal natural tendency to ‘evolve’ was placed in physics by the constructal law (1996). In this lecture I show why this law is useful to us. We are the evolving “human & machine species.” Evolution can be put to use in our lifetime in technology, transportation, urban design, spreading and collecting, miniaturization, communications, science, government and the unstoppable march to freedom, access, wealth and knowledge. The lecture is based on the book: THE PHYSICS OF LIFE: The Evolution of Everything (St. Martin’s Press, New York, 2016).

ABSTRACT. Jorge I. Campos (4E CognitionGroup, Instituto de Investigación en Matemáticas Aplicadas y Sistemas, UNAM) jorgeivan.campos.bravo@gmail.com 
Tom Froese (4E CognitionGroup, Instituto de Investigación en Matemáticas Aplicadas y Sistemas, UNAM) t.froese@gmail.com 

Keywords: referential communication, evolution of language, iterated learning model, collective intentionality 

Tomasello (2014) proposes a thought experiment inspired by Lord of the Flies: what would happen if a group of human infants grew up together in isolation? Tomasello argues that the infants could develop forms of joint intentionality during their lifetime (including communicating via pointing), but that forms of collective intentionality involving normative practices (including communicating via language) would only start to emerge over multiple generations as ontogeny becomes shaped by an increasingly complex sociocultural context. In contrast, groups of infant chimpanzees would not be able to develop either of these forms of sociality. This argument is based on two assumptions: (1) specifically human biology is necessary but not sufficient for the emergence of normative sociocultural practices, because (2) it is also necessary for humans to establish the required conventions over multiple generations. We probe both of these assumptions on the basis of a variation of the iterated learning model (ILM) of language evolution (Kirby and Hurford 2002). The results of our model allow us to suggest that (1) conventions resembling properties of language can emerge during repeated social interactions during a single generation’s lifetime, and (2) that this process does not depend on specificities of the learning agent’s cognitive structures. Nevertheless, a problem of initial conditions remains, in particular as sufficiently long and arbitrarily varied utterances are required for the ILM to work properly, and this is where biological and cultural intergenerational transmission may indeed play an essential role (Merker and Okanoya 2007).

ABSTRACT. Carlos Gracia-Lázaro (Instituto de Biocomputación y Física de Sistemas Complejos, Universidad de Zaragoza) carlos.gracia.lazaro@gmail.com 
Alexis R Hernández (Instituto de Física, Universidade Federal do Rio de Janeiro) elchechi@gmail.com
Edgardo Brigatti (Instituto de Física, Universidade Federal do Rio de Janeiro) edgardobrg@gmail.com
Yamir Moreno (Instituto de Biocomputación y Física de Sistemas Complejos) yamir.moreno@gmail.com 

Keywords: Social Dynamics, Axelrod's model, Homophily, Aversion 

In the Axelrod’s model of dissemination of culture, we consider the incorporation of new topics into the social debate as an agreement is reached in other topics, modeling this situation through the initialization of the topics in which a consensus has been reached. When the fraction of agents sharing the most abundant trait of a topic reaches a threshold, consensus on the topic is assumed and it is replaced by a new emerging topic through the initialization o traits in that topic. As an additional modification, we consider two mechanisms: homophily and aversion. If the cultural overlap between two agents is higher than a given threshold, the culture of one of those agents is allowed to change by imitation of an uncommon topic’s trait, with a probability proportional the overlap. Otherwise, individuals will be able to discern about common trait: the smaller the overlap, the greater the rate of unimitation. Our results show that once cultural groups are consolidated, the emergence of new themes in the social debate does not have a significant effect on them, but it does on cultural overlaps. We find conditions for stationary partial multiculturality, as well as a transition that exhibits the characteristic bimodal character of a discontinuous transition. In addition, we show that aversion minimizes the cultural overlap.

ABSTRACT. When looking at the history of technology, we can see that all inventions are not of equal importance. Only a few technologies have the potential to start a new branching series (specifically, by increasing diversity), have a lasting impact in human life, and ultimately become turning points. Technological transitions correspond to times and places in the past when a large number of novel artifact forms or behaviors appeared together or in rapid succession. Why does that happen? Is technological change continuous and gradual, or does it occur in sudden leaps and bounds? The evolution of information technology (IT) allows for a quantitative and theoretical approach to study technological transitions. The coexistence of episodes of gradual improvements and discontinuous technological change is a consequence of the asymmetric relationship between complexity and hardware and software. Using a cultural evolution approach, we suggest that sudden changes in the organization of information technology systems depend on the high costs of maintaining and transmitting reliable information.

ABSTRACT. Keywords: cultural computation, evolutionary growth, knowledge propagation, digital network analysis 

This paper presents the concept of knowledgebits, which extends on mainstream working models about evolutionary knowledge growth (e.g., see Hayek, 1945; Popper, 1963; Boulding, 1977; Arthur, 2009), as well as more recent advances at the intersection of culture and computation (e.g., see Hidalgo, 2014; Hartley & Potts, 2014; Bentley & O’Brien 2017). The concept of knowledgebits contributes towards a better understanding of the dynamics of sociocultural complexity by providing a framework that assesses the combinability (combination + compatibility) of the world’s knowledge in our computationally assisted, complex cultural networks. It starts from the assumption that every individual disposes of an ever-changing set of entities of knowledge as each person internalises and externalises by using human and artificial assistance. The concept of knowledgebits is grounded in empirical evidence derived from both largescale digital network analysis as well as individual case studies research about how various bits of knowledge from a diverse range of languages may combine (Petzold, 2017). It allows to tackle questions such as what bits of knowledge are more likely to survive, or how fast knowledgebits may spread. Overall, the paper contributes to the satellite’s anticipated efforts by providing a conceptual harbinger that helps to explain the global propagation and loss of cultural complexity in our computationally assisted, complex cultural world

ABSTRACT. The rising adoption of plug-in electric vehicles (PEVs) leads to the alignment of their electricity and their mobility demands. Therefore, transportation and power infrastructures are becoming increasingly interdependent. In this work, we uncover patterns of PEV mobility by integrating for the first time two unique data sets: (i) mobile phone activity of 1.39 million Bay Area residents and (ii) charging activity of PEVs in 580,000 sessions obtained in the same region. We present a method to estimate individual mobility of PEV drivers at fine temporal and spatial resolution integrating survey data with mobile phone data and income information obtained from census. Thereupon, we recommend changes in PEVs charging times of commuters at their work stations that take into account individual travel needs and shave the pronounced peak in power demand. Informed by the tariff of electricity, we calculate the monetary gains to incentivize the adoption of the recommendations. These results open avenues for planning for the future of coupled transportation and electricity needs using personalized data.

ABSTRACT. A conventional view of biological evolution typically assumes that the fitness is an attribute of individual organisms or genes. However, this simplistic view is known to be invalid in realistic scenarios where organisms' fitnesses depend on their surrounding environments. Here we present a theoretical framework that mathematically formulates the evolutionary dynamics of such organism-environment couplings using graph product multilayer networks. Specifically, one factor network represents different options of environments and their mutual physical accessibility, and another factor network represents possible types of organisms and their mutual evolutionary accessibility. The organism-environment coupling space is given by a Cartesian product of these two networks, whose nodes represent specific organism-environment combinations. We studied a simple evolutionary model using a reaction-diffusion equation on this organism-environment coupling space. We numerically measured correlations between the inherent fitness of organisms and the actual average fitness obtained from the graph product-based evolutionary model, varying the spatial diffusion rate while keeping the type diffusion rate small. Results demonstrated that, when the spatial diffusion is sufficiently slow (i.e., when the system is effectively spatial), the correlation between inherent and actual fitnesses drops significantly, where it is no longer valid to assume that fitness can be attributed only to organisms.

ABSTRACT. Dynamic systems in our universe equalize natural or forced gradients through flows of matter/energy. For this reason such systems are also called ‘flow systems’. Examples are stars, rivers, plants, animals and cities. Such systems receive their current form as a consequence of free energy consumption. For a flow system to persist or grow it must develop its form in the direction of giving easier access to the currents that flow through them (the Constructal Law, Bejan). In relation to this, energy efficiency can be quantified by a measure called action efficiency that focuses on the number of events the system realizes over a certain length of time while degrading a given amount of energy. 
In the presentation I aim at demonstrating that the newly developed Operator Theory suggests that when focusing on action efficiency it can be useful to distinguish between two major groups of systems that possess different kinds of organization. The contrasting groups are either called ‘operators’ (the Lego bricks) on the one hand, or so called ‘interaction systems’ (the Lego cities, Lego houses, etc.) on the other hand. I will then focus on the links between flow-based thinking and efficiency while discussing the different kinds of organization of some example operators in the Operator Hierarchy, e.g. fundamental particles, atoms, cells, or organisms with brains. The analysis will demonstrate that flows developed from mere condensation in e.g. atoms and molecules, to water-mill like production systems in organisms.
 

ABSTRACT. The study of self-organization of complex systems has been a significant importance to the scientific community for a while now. Self-organization can be defined as a spontaneous formation of an ordered pattern from complete disorder due to local interactions and energy flows. These patterns are formed in various systems throughout nature. One of the more recognizable patterns are the formation of Rayleigh-Benard Convection cells. When a liquid is evenly heated from the bottom and evenly cooled from its surface the liquid tends to self-organize into successively more efficient patterns, such as rolls and hexagonal cells, with an upward flow of the hotter liquid from the bottom, and a downward flow of the cooler liquid from the top. In our experiments, we use a thermocouple that is attached to a copper plate and an infrared camera that is positioned directly above the plate to measure the temperatures of the hotter and colder fluid. A heater is attached to the bottom of the copper plate and voltages of 10V-80V is introduced to heat the working fluid. The fluid we use is Silicone oil with varying viscosities (5cst,10cst,150cst) and varying thicknesses (50mm-300mm). Using ImageJ software we analyze the thermal profile of each one of these experiments and calculate the entropy, dissipation of heat and the work done on the sample during convection which will give us the action efficiency of the energy transmission through each pattern. The principle of least action leads to self-organization for maximum action efficiency. The working hypothesis is that the energy rate density increases with increased action efficiency in the system, up to a limit. The nature of the dependency is the goal of this study. These results are compared to similar findings about this dependency in other physical systems in search for universality.

ABSTRACT. In this paper, we present a new way of analyzing the CPU data in order to study trends in technological evolution. We analyze the existing data using nonlinear regression methods. In the light of our analysis we observed that the data follows two different trends. Such an insight was absent from the empirical analysis in our previous papers, and also the existing literature.The empirical analysis was carried out by using log-transformed data points on a linear scale. Our aim is to find whether the Principle of Stationary Action apply to artificial systems despite human intervention. This was done by plotting different, direct and derived, parameters for a wide array of CPU architectures, and looking for trends in the graphs. For easy visualization of the graphs, the raw data points were plotted on a logarithmic scale. We found out that points on most of the graphs do not fit into a single fit-line. In fact, those points seemed to change slope around a certain region that corresponds to the introduction of the Pentium generation of the CPU models in the year, 1994. Thus, the nonlinear regression model fits the points with two different allometric fits having two different slopes. Moreover, we found that the most frequent action generated by all CPUs analysed in our study is the lowest in value which corresponds to the Principle of Least Action. In addition, we notice that entropy follows a decreasing trend with increasing efficiency, but an increasing trend with increasing power across the different generations of CPU studied. Keywords: Principle of Least Action, Nonlinear fitting, Allometric fits, Entropy, Statistical analysis.

ABSTRACT. Mario A. Zarco-López (IIMAS-UNAM) mario.60as@gmail.com 

Tom Froese (IIMAS-UNAM) t.froese@gmail.com 

Keywords: Computational model of cultural evolution, Agent-based model, Neural network based agents, Liane Gabora, Material engagement, Embodied model for cultural evolution 

Liane Gabora developed a computational model of cultural evolution named MAV (Meme And Variations) (Gabora 1995), which she later extended into a model called EVOC (EVOlution of Culture) (Gabora 2008b). The models simulate neural network based agents with an array-based body in a grid-cell world. Agents generate ideas for body configurations, or they copy them from their neighbors, depending on the relative fitness of the ideas. The simulations are motivated by her theory that internal models of the world are the units of culture that evolve (Gabora 1995). Ideas and artifacts are treated as merely the external reflection of the evolved state of an internal worldview, and they are expressed in the behavior of agents. We highlight a tension between treating behavior as an epiphenomenon and the growing recognition in cognitive archaeology of the constitutive role of material engagement (Malafouris 2013). As a consequence, we criticize the internalist view of mind adopted by Gabora. Moreover, we point out concerns with the fact that these models reduce the dynamics of cultural complexification to an evolutionary process based on fitness, even though Gabora argues that her culture evolution theory is non-Darwinian (Gabora 2008a). Also, we note technical problems related to the way her theory is implemented in the simulations. Finally, we conclude that her attempt of introducing an embodied model for cultural evolution is pointing in the right direction, but unfortunately falls short of its ambitious goal because the models become processes of learning and transmission of information using isolated neural networks.

ABSTRACT. Millennia, sociocultural complexity increased (and occasionally decreased) gradually over many human generations, as people inherited traditional knowledge within kin-based local communities. In these settings, where knowledge was shared within populations and across generations, socio-technological complexity tended to scale with population size. In the 21st century, however, knowledge is transmitted across populations and within generations — sociocultural complexity may not scale with population size in the way it had before. To span these different scales and modes of cultural evolution, different representations are useful, including fitness landscapes and a heuristic representing the transparency of payoffs in social learning. I will use these approaches to discuss how cultural evolution may have profoundly changed — as a process — from prehistoric societies to mass/social media and future artificial intelligence.

ABSTRACT. Keywords: Cave Art, Upper Palaeolithic Era, Symmetry operations 

Cave art of Upper Palaeolithic Era consists of paintings and engravings made on parietal and portable contexts which are considered to be among the first indicators of human artistic creativity and abstraction. Generally, these works fall into two categories: first category is the figurative art, where animals of various kind are depicted, and second is the non-figurative art, consisting of geometric and abstract marks. Although figurative art is thoroughly studied and attempted to explain, non-figurative art lacks these efforts and is yet to be examined. This study is an attempt to provide insights into non-figurative marks. Considered as purely geometric entities, these marks are observed to indicate inter-and intra-regularities. Exploring these patterns is a way of understanding the marks without necessarily trying to interpret them or assuming that they have representational purposes. In this study, 90 of these marks found in Upper Palaeolithic caves worldwide, but more concentrated on Northern Spain and Southern France, are analysed through their photographs. The marks are investigated according to presence or absence of five symmetry operations possible in plane, i.e. translation, horizontal and vertical reflection, glade reflection and rotation. Distribution of symmetry operations present within the marks, together with their distribution among different time periods will be the main outcome of this study. Moreover, these results will be used to further answer whether there exists correlation between temporal distribution of different symmetry operations and cultural complexity, and thus whether complexity of the marks can be considered as a sign of evolution in cultural complexity.

ABSTRACT. The human brain is a complex organ characterized by heterogeneous patterns of interconnections across disparate physical scales and phenomenological levels. New non-invasive imaging techniques now allow for these patterns to be carefully and comprehensively mapped in individual humans, paving the way for a better understanding of how wiring supports our thought processes. While a large body of work now focuses on descriptive statistics to characterize these wiring patterns at a single scale, a critical open question lies in how the organization of these networks across scales constrains the potential repertoire of brain dynamics. In this talk, I will describe an approach for understanding how perturbations to brain dynamics propagate through complex writing patterns, driving the brain into new states of activity. Drawing on a range of disciplinary tools – from graph theory to network control theory and optimization – I will identify control points in brain networks, characterize trajectories of brain activity states following perturbation to those points, and propose a mechanism for how network control evolves in our brains as we grow from children into adults. Finally, I will describe how these computational tools and approaches can be used to better understand how the brain controls its own dynamics (and we in turn control our own behavior), but also how we can inform stimulation devices to control abnormal brain dynamics, for example in patients with severe epilepsy.

ABSTRACT. In complex network theory a core consists of a group of central and densely connected nodes which often control the overall behavior of a network. It is recognised as one of the key meso-scale structures in complex networks, and it is typical of several real-world systems, such as the world trade web, many social and transportation networks, and the brain. In multiplex networks the relevance of nodes may be very heterogeneous across the different layers. For instance, a node belonging to the core at one layer might be extremely peripheral in the other ones. Conversely, it is possible that nodes that do not belong to any of the cores of the single layers, still emerge as the most important ones when the system is considered as a whole. Starting from the concept of rich core, we develop a method to detect uniquely multilayer core-periphery structure by accounting for interactions of different nature, test it against null-models and apply it to extract such meso-scale structure in multiplex brain networks. We consider two-layer multiplex networks constructed from structural and functional brain information on several healthy subjects, respectively obtained by Diffusion Tensor Imaging and resting-state functional MRI, and apply our algorithm to highlight novel key-areas of interest in the human brain neglected from previous analysis.

ABSTRACT. Mahmoud Shokrollahi-Far^1&2 and Peyman Passban^2&3

1.        Communication and Information Sciences Dept., Tilburg University, the Netherlands

2.        Computational Linguistics Dept., University College of Nabi-Akram, Iran

3.        Computing School, Dublin City University, Ireland

Formal Grammars, as finite sets of production rules to form structurally correct constituents of a language from its basic elements, originally emerged trying to employ a mathematical representation to model structures of natural languages. Now they are also employed to model mathematically any sequential system other than natural languages such as DNA sequences. However, the grammars employed have been effective in modelling just the coding sections of DNA sequences comprising only 2% to 5% of the molecule, leaving the rest 98% to 95% of the molecule ‘non-coding’. Moreover, these grammars are commonly complex in the sense that they impose costly computational overheads. Accordingly, more interesting would be less complex grammars which model the maximum number phenomena using simpler and less number of rules, hence more efficient. Our studies demonstrate that Quranic grammar developed on Quranic structural constituents, here called MOBIN Grammar, has the reliable grammatical properties for modeling DNA shuffling structures. In other words, this grammar that is at the least complex level in Chomsky Hierarchy of Formal Grammars provides a very efficient set of rules with the minimum length and the maximum coverage for modelling DNA shuffling structures very effectively.

ABSTRACT. none.

ABSTRACT. Iza Romanowska (Barcelona Supercomputing Center) i.romanowska@soton.ac.uk 
Seth Bullock (University of Bristol) seth.bullock@bristol.ac.uk 

Keywords: evolution, migration, behavioural plasticity, simulation, agent-based modelling archaeology

Human migration events have been a major focus of archaeological research in the last 50 years. However, despite extensive research, many of the key questions related to this topic remain unanswered. The study presented here looks at the relationships between climate change, migration and the evolution of behavioural plasticity using a formal computational framework. It is based on the Variability Selection Hypothesis proposed by Potts (1996) and formalised by Grove (2011), which concerns the evolution of versatility among human groups arising during periods of strong environmental fluctuations. The model was translated into a stochastic agent-based simulation to investigate the dynamics between individuals with different adaptations (including ‘versatilist’ individuals) within a heterogenous population. The results show that dispersal accelerates the evolution of versatilism in the population, therefore promoting a more flexible and robust range of adaptations. It also demonstrates the particularity of natural selection conditions at the front of a dispersal wave and at the core area from which individual migrate. Such conditions enable the evolution of adaptations beneficial in the long turn but not optimal at any one point in time thus highlighting the role of events which keep populations below their full capacity. Potts, R. 1996. Evolution and Climate Variability. Science 273: 922–23. Grove, M. 2011. Speciation, diversity, and Mode 1 technologies: The impact of variability selection. J Hum Evol 61: 306–19.

ABSTRACT. Keywords: social complexity, socio-political organisation, Sagalassos, complexity archaeology 

In archaeology, social complexity has long been considered within an evolutionary discourse of fixed stages of increasingly complex societies. In recent years, another approach has started to gain tract, studying complex societies starting from the more general phenomenon of complex adaptive systems (CAS). Too often however, complexity approaches in archaeology have been used merely on a metaphorical level, hardly taking into account why and how complexity in human societies arises. If archaeologists are ever to move beyond the level of metaphor, we need to uncover the underlying mechanisms of social complexity. CAS are open systems, requiring constant external energy input to maintain its internal structures, transforming energy into exergy, or useful energy. As a general property, self-organizing dissipative processes arise in complex systems whenever sufficient exergy is available to sustain them. Social systems are different from other complex systems through the factor of human agency. Still, given sufficient exergy input from their environment, increasingly complex configurations commonly develop. This process can be explained through the approach to complexity as a problem-solving tool. A social system develops solutions to internal and external disturbance events through development of its social, political, economic, and ideological structures. Complex social systems develop as independent components become increasingly interrelated within nested hierarchies of functional, informational, and decision-making roles. In this paper I will further elucidate this development through a case-study based on the development of socio-political organisation in Sagalassos and Düzen Tepe (SW Anatolia) during the late Achaemenid and Hellenistic periods (5th to 2nd centuries BCE).

ABSTRACT. Keywords: archaeology, history, economy 

In this presentation I will explore long-term change in Roman amphorae distributions caused by recycling and consumer preference, contributing to a better understanding of long-term trends in a particular socio-cultural system: the Roman Empire. What kinds of empire-wide distribution patterns of amphorae would we expect to see if we assume amphorae might have been recycled as containers to transport different types of goods? How does an individual’s or a community’s preference for one type of product over another affect the distribution of both? Are these processes even archaeologically visible: under what conditions do they lead to undeniably different distribution patterns as compared to processes that exclude these factors? A set of increasingly elaborate computational models will be presented to explore these questions. Particular emphasis will be placed on understanding the effects of the Roman transport network and the demand of urban centres on these processes. This work is part of the larger research agenda of project MERCURY, a multidisciplinary project that will explore the most hotly debated questions about the Roman economy: was the Roman Imperial trade market equally integrated as nowadays? How important were social networks for structuring this flow of information? It will address two methodological issues currently preventing scholars from answering these questions: limited use of archaeological big data and the lack of quantitative comparisons of complex hypotheses. MERCURY will combine recent advances in computational network science and simulation methods with increasingly available archaeological big datasets.

ABSTRACT. Mean field approximations have allowed an important advancement in the understanding of spreading processes in networks, e.g. yielding first approximations to the epidemic thresholds for different models of networks. However, they are not appropriate for the analysis of a given network, only for families of them with well-defined statistical structural properties. The Microscopic Markov Chain Approach (MMCA) constitutes the main analytical alternative for the theoretical examination of discrete-time spreading processes in fixed (synthetic or real) networks. It has been applied to diverse dynamics such as SIS epidemic spreading, threshold-like models, the coupling of epidemic and information spreading in multiplex networks, metapopulation models, and routing dynamics. In this presentation, we will review MMCA in several single-layer and multiplex dynamics, and we will introduce a new paradigm in which the microscopic variables are defined at the level of links instead of nodes. We will show the derivation of Epidemic Link Equations for a SIS dynamics, which leads to important concepts such as the conductance of the links or the hostility felt by a node.

ABSTRACT. Over the last decades network science, and more recently the methods developed for the study of multilayer networks, have been successfully applied to study problems in diverse fields, from sociology to biology. In this talk, I will give an overview of applications of multilayer network methods for socio-economic development and humanitarian response. I will focus in particular on a study investigating the diffusion of microfinance within rural India villages accounting for the whole multilayer structure of the underlying social networks. We define a new measure of node centrality on multilayer networks, diffusion versatility, and show that this is a better predictor of microfinance participation rate than previously introduced measures defined on aggregated single-layer social networks. Moreover, we untangle the role played by each social dimension and find that the most prominent role is played by the nodes that are central on the layers related to trust (asking for help in a medical emergency, asking for money if in need, and asking for advice), shedding new light on the key triggers of the diffusion of microfinance.

ABSTRACT. Kubernetes is a fiction “edudrama” about the past, present and future of Cybernetics. The writer and producer of the film was a very close friend and disciple for more than 20 years of Stafford Beer, the creator of Management Cybernetics and author of many groundbreaking books, among them The Brain of the Firm and The Heart of Enterprise. Stafford Beer was also the chief scientist behind the creation of PROJECT CYBERSYN in the early 1970’s. The project died with the abrupt ending of Salvador Allende’s tenure as President of Chile in September 11th, 1973.

In this film, many of Stafford Beer’s ideas are discussed, including a look at the iconic “operations room”. The plot has ramifications to the 1970’s as a group of beautiful minds with very different cybernetic backgrounds are invited to a meeting to find a way to change the world by changing the way organizations of all types operate. Each one of the organizer’s guest brings a statement to the meeting. Once there, they ask questions about the issue of complexity, religion, government, Newtonian science, the purpose of human beings, criticize organizations and reflect on the possible existence of God. 

The screening will be followed by a Q&A session with the writer and producer of Kubernetes.

[Mexico, 2017, 95 min. In Spanish with English subtitles]