The central theme of this book is the extent to which the structure of the free dynamical boundaries of a system controls the evolution of the system as a whole. Applying three orthogonal types of thinking - mathematical, constructivist and morphological, it illustrates these concepts using applications to selected problems from the social and life sciences, as well as economics.
In a broader context, it introduces and reviews some modern mathematical approaches to the science of complex systems. Standard modeling approaches (based on non-linear differential equations, dynamic systems, graph theory, cellular automata, stochastic processes, or information theory) are suitable for studying local problems. However they cannot simultaneously take into account all the different facets and phenomena of a complex system, and new approaches are required to solve the challenging problem of correlations between phenomena at different levels and hierarchies, their self-organization and memory-evolutive aspects, the growth of additional structures and are ultimately required to explain why and how such complex systems can display both robustness and flexibility.
Infomercialist and pop psychologist Barbara De Angelis puts it this way: “Love is a force more formidable than any other.” Whether you agree with her or not, De Angelis is doing something we do all the time—she is using the language of physics to describe social phenomena.
“I was irresistibly attracted to him”; “You can’t force me”; “We recognize the force of public opinion”; “I’m repelled by these policies.” We can’t measure any of these “social forces” in the way that we can measure gravity or magnetic force. But not only has physics-based thinking entered our language, it is also at the heart of many of our most important models of social behavior, from economics to psychology. The question is, do we want it there?
Language takes an astonishing variety of forms across the world—to such a huge extent that a long-standing debate rages around the question of whether all languages have even a single property in common. Well, there’s a new candidate for the elusive title of “language universal” according to a paper in this week’s issue of PNAS. All languages, the authors say, self-organise in such a way that related concepts stay as close together as possible within a sentence, making it easier to piece together the overall meaning.
Language universals are a big deal because they shed light on heavy questions about human cognition. The most famous proponent of the idea of language universals is Noam Chomsky, who suggested a “universal grammar” that underlies all languages. Finding a property that occurs in every single language would suggest that some element of language is genetically predetermined and perhaps that there is specific brain architecture dedicated to language.
However, other researchers argue that there are vanishingly few candidates for a true language universal. They say that there is enormous diversity at every possible level of linguistic structure from the sentence right down to the individual sounds we make with our mouths (that’s without including sign languages).
There are widespread tendencies across languages, they concede, but they argue that these patterns are just a signal that languages find common solutions to common problems. Without finding a true universal, it’s difficult to make the case that language is a specific cognitive package rather than a more general result of the remarkable capabilities of the human brain.
Il paradosso, sempre più evidente in questi ultimi anni, è che le strutture più robuste nei confronti dei pericoli prevedibili si dimostrano essere le più fragili di fronte a situazioni impreviste. John Doyle, scienziato del California Institute of Technology, ha coniato il termine “robust-yet-fragile” (“robusto-ma-fragile”) o “RYF” per descrivere l’architettura di sistemi di questo tipo, in grado di resistere di fronte ai pericoli previsti ma estremamente fragili rispetto a minacce impreviste. I sistemi RYF sono quelli più diffusi nel nostro ambiente economico e sociale: sono efficienti ed affidabili, spesso dando l’illusione di poter durare per sempre.”
Large-scale data from social media have a significant potential to describe complex phenomena in real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, we identify the dynamics of the volume, and show that this quantity has some information on the elections outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation over short terms. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion. Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.
Twitter-based analysis of the dynamics of collective attention to political parties Young-Ho Eom, Michelangelo Puliga, Jasmina Smailović, Igor Mozetič, Guido Caldarelli
We analyse a large mobile phone activity dataset provided by Telecom Italia for the Telecom Big Data Challenge contest. The dataset reports the international country codes of every call/SMS made and received by mobile phone users in Milan, Italy, between November and December 2013, with a spatial resolution of about 200 meters. We first show that the observed spatial distribution of international codes well matches the distribution of international communities reported by official statistics, confirming the value of mobile phone data for demographic research. Next, we define an entropy function to measure the heterogeneity of the international phone activity in space and time. By comparing the entropy function to empirical data, we show that it can be used to identify the city’s hotspots, defined by the presence of points of interests. Eventually, we use the entropy function to characterize the spatial distribution of international communities in the city. Adopting a topological data analysis approach, we find that international mobile phone users exhibit some robust clustering patterns that correlate with basic socio-economic variables. Our results suggest that mobile phone records can be used in conjunction with topological data analysis tools to study the geography of migrant communities in a global city.
Unveiling patterns of international communities in a global city using mobile phone data Paolo Bajardi, Matteo Delfino, André Panisson, Giovanni Petri and Michele Tizzoni
Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper.
Self-Organization, Emergence, and Constraint in Complex Natural Systems Jonathan Lawhead
Pourquoi parler d’effondrement et de collapse de notre civilisation ? Parce que le faisceau d’informations factuelles est très convergent, parce que cela a à voir avec les systèmes complexes, et parce que la résilience, individuelle et collective, commence par l’acceptation de la réalité telle qu’elle est.
Don't look now, but the pronoun "I" is becoming obsolete.
Recent microbiological research has shown that thinking of plants and animals, including humans, as autonomous individuals is a serious over-simplification.
A series of groundbreaking studies have revealed that what we have always thought of as individuals are actually "biomolecular networks" that consist of visible hosts plus millions of invisible microbes that have a significant effect on how the host develops, the diseases it catches, how it behaves and possibly even its social interactions.
"It's a case of the whole being greater than the sum of its parts," said Seth Bordenstein, associate professor of biological sciences at Vanderbilt University, who has contributed to the body of scientific knowledge that is pointing to the conclusion that symbiotic microbes play a fundamental role in virtually all aspects of plant and animal biology, including the origin of new species.
In this case, the parts are the host and its genome plus the thousands of different species of bacteria living in or on the host, along with all their genomes, collectively known as the microbiome.
(The host is something like the tip of the iceberg while the bacteria are like the part of the iceberg that is underwater: Nine out of every 10 cells in plant and animal bodies are bacterial. But bacterial cells are so much smaller than host cells that they have generally gone unnoticed.)
“As I watched films of these ant colonies, it looked like what was happening at the synapse of neurons. Both of these systems accumulate evidence about their inputs—returning ants or incoming voltage pulses—to make their decisions about whether to generate an output—an outgoing forager or a packet of neurotransmitter,” Goldman said. On his next trip to Stanford, he extended his stay. An unusual research collaboration had begun to coalesce: Ants would be used to study the brain, and the brain, to study ants.
The outcome of the British General Election to be held in just over one week's time is widely regarded as the most difficult in living memory to predict. Current polls suggest that the two main parties are neck and neck but that there will be a landslide to the Scottish Nationalist Party with that party taking most of the constituencies in Scotland. The Liberal Democrats are forecast to loose more than half their seats and the fringe parties of whom the UK Independence Party is the biggest are simply unknown quantities. Much of this volatility relates to long-standing and deeply rooted cultural and nationalist attitudes that relate to geographical fault lines that have been present for 500 years or more but occasionally reveal themselves, at times like this. In this paper our purpose is to raise the notion that these fault lines are critical to thinking about regionalism, nationalism and the hierarchy of cities in Great Britain (excluding Northern Ireland). We use a percolation method (Arcaute et al. 2015) to reveal them that treats Britain as a giant cluster of related places each defined from the intersections of the road network at a very fine spatial scale. We break this giant cluster into a detailed hierarchy of sub-clusters by successively reducing a distance threshold which first breaks off some of the Scottish Islands and then reveals the very distinct nations and regions that make up Britain, all the way down to the definition of the largest cities that appear when the threshold reaches 300m. We use these percolation clusters to apportion the 2010 voting pattern to a new hierarchy of constituencies based on these clusters, and this gives us a picture of how Britain might vote on purely geographical lines. We then examine this voting pattern which provides us with some sense of how important the new configuration of political parties might be to the election next week.
The Fractured Nature of British Politics Carlos Molinero, Elsa Arcaute, Duncan Smith, Michael Batty
Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations, which are the outcome of geographical, political and historical constraints. Using percolation theory on the street intersections and on the road network of Britain, we obtain hierarchies at different scales that are independent of administrative arrangements. Natural boundaries, such as islands and National Parks, consistently emerge at the largest/regional scales. Cities are devised through recursive percolations on each of the emerging clusters, but the system does not undergo a phase transition at the distance threshold at which cities can be defined. This specific distance is obtained by computing the fractal dimension of the clusters extracted at each distance threshold. We observe that the fractal dimension presents a maximum over all the different distance thresholds. The clusters obtained at this maximum are in very good correspondence to the morphological definition of cities given by satellite images, and by other methods previously developed by the authors (Arcaute et al. 2015).
Hierarchical organisation of Britain through percolation theory Elsa Arcaute, Carlos Molinero, Erez Hatna, Roberto Murcio, Camilo Vargas-Ruiz, Paolo Masucci, Jiaqiu Wang, Michael Batty
The question What is Complexity? has occupied a great deal of time and paper over the last 20 or so years. There are a myriad different perspectives and definitions but still no consensus. In this paper I take a phenomenological approach, identifying several factors that discriminate well between systems that would be consensually agreed to be simple versus others that would be consensually agreed to be complex - biological systems and human languages. I argue that a crucial component is that of structural building block hierarchies that, in the case of complex systems, correspond also to a functional hierarchy. I argue that complexity is an emergent property of this structural/functional hierarchy, induced by a property - fitness in the case of biological systems and meaning in the case of languages - that links the elements of this hierarchy across multiple scales. Additionally, I argue that non-complex systems "are" while complex systems "do" so that the latter, in distinction to physical systems, must be described not only in a space of states but also in a space of update rules (strategies) which we do not know how to specify. Further, the existence of structural/functional building block hierarchies allows for the functional specialisation of structural modules as amply observed in nature. Finally, we argue that there is at least one measuring apparatus capable of measuring complexity as characterised in the paper - the human brain itself.
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