Physics and Astronomy

A model for opinion formation and anticipation based on the match between hidden personal "preferences" and product qualities is presented. We assume that products and individuals are represented by means of vectors in an L-dimensional "taste" space. The opinion of an individual on a given product is proportional to the overlap between the corresponding vectors. Assuming that both individual preferences and product qualities are hidden degrees of freedom, and that only the expressed opinion is observable, we use the correlations among individuals' opinions on products to extract information about the hidden quantities. In particular, the method can be used to anticipate the opinion of an individual on a given product, to study the overlaps of preferences of two individuals, and to extract the dimensionality (L) of the hidden taste space.

Using the concept of the Boolean derivative we study damage spreading for one dimensional elementary cellular automata and define their maximal Lyapunov exponent. A random matrix approximation describes quite well the behavior of ``chaotic'' rules and predicts a directed percolation-type phase transition. After the introduction of a small noise elementary cellular automata reveal the same type of transition.

We study also the maximum Lyapunov exponent for CA, defined in analogy with continuous dynamical systems as the exponential rate of expansion of the linear map induced by the evolution rule of CA, constructed with the aid of the Boolean derivatives. The synchronization threshold is strongly correlated to the maximum Lyapunov exponent and we propose approximate relations between these quantities. The value of this threshold can be used to parametrize the space time complexity of CA.

We study the phase diagram and the critical behavior of a one-dimensional radius-1 two-state totalistic probabilistic cellular automaton having two absorbing states. This system exhibits a first-order phase transition between the fully occupied state and the empty state, two second-order phase transitions between a partially occupied state and either the fully occupied state or the empty state, and a second-order damage-spreading phase transition. It is found that all the second-order phase transitions have the same critical behavior as the directed percolation model. The mean-field approximation gives a rather good qualitative description of all these phase transitions.

We explore a community-detection cellular automata algorithm inspired by human heuristics, based on information diffusion and a non-linear processing phase with a dynamics inspired by human heuristics. The main point of the methods is that of furnishing different "views" of the clustering levels from an individual point of view. We apply the method to networks with local connectivity and long-range rewiring.

We study the influence of global, local and community-level risk perception on the extinction probability of a disease in several models of social networks. In particular, we study the infection progression as a susceptible-infected-susceptible (SIS) model on several modular networks, formed by a certain number of random and scale-free communities. We find that in the scale-free networks the progression is faster than in random ones with the same average connectivity degree. For what concerns the role of perception, we find that the knowledge of the infection level in one's own neighborhood is the most effective property in stopping the spreading of a disease, but at the same time the more expensive one in terms of the quantity of required information, thus the cost/effectiveness optimum is a tradeoff between several parameters.

The emergence and the global adaptation of mobile devices has influenced human interactions at the individual, community, and social levels leading to the so called Cyber-Physical World (CPW) convergence scenario [1]. One of the most important features of CPW is the possibility of exploiting information about the structure of the social communities of users, revealed by joint movement patterns and frequency of physical co-location. Mobile devices of users that belong to the same social community are likely to "see" each other (and thus be able to communicate through ad-hoc networking techniques) more frequently and regularly than devices outside the community. In mobile opportunistic networks, this fact can be exploited, for example, to optimize networking operations such as forwarding and dissemination of messages. In this paper we present the application of a cognitive-inspired algorithm [2, 3, 4] for revealing the structure of these dynamic social networks (simulated by the HCMM model [5]) using information about physical encounters logged by the users' mobile devices. The main features of our algorithm are: (i) the capacity of detecting social communities induced by physical co-location of users through distributed algorithms; (ii) the capacity to detect users belonging to more communities (thus acting as bridges across them), and (iii) the capacity to detect the time evolution of communities.

Collective awareness platforms (CAPs) are internet and mobile tools for collaboration, sustainability and social innovation that can allows drastic improvement of our lifestyle, beyond the standard economic model. However, their development is often driven (and motivated) by technology, while their adoption and usage characteristics are determined by the social interactions and can be affected by many items, up to failure. We describe here our approach to CAPs modelling that includes elements from cognitive and evolutionary sciences, in the hope of providing instruments for the improvement and the assessment of CAPs.

In his seminal works on group dynamics Bion defined a specific therapeutic setting allowing psychoanalytic observations on group phenomena. In describing the setting he proposed that the group was where his voice arrived. This physical limit was later made operative by assuming that the natural dimension of a therapeutic group is around 12 people. Bion introduced a theory of the group aspects of the mind in which proto-mental individual states spontaneously evolve into shared psychological states that are characterized by a series of features: (1) they emerge as a consequence of the natural tendency of (both conscious and unconscious) emotions to combine into structured group patterns; (2) they have a certain degree of stability in time; (3) they tend to alternate so that the dissolution of one is rapidly followed by the emergence of another; (4) they can be described in qualitative terms according to the nature of the emotional mix that dominates the state, in structural terms by a kind of typical "leadership" pattern, and in "cognitive" terms by a set of implicit expectations that are helpful in explaining the group behavior (i.e., the group behaves "as if" it was assuming that). Here we adopt a formal approach derived from Socio-physics in order to explore some of the structural and dynamic properties of this small group dynamics. We will described data from an analytic DS model simulating small group interactions of agents endowed with a very simplified emotional and cognitive dynamic in order to assess the following main points: (1) are metastable collective states allowed to emerge in the model and if so, under which conditions in the parameter space? (2) can these states be differentiated in structural terms? (3) to what extent are the emergent dynamic features of the systems dependent of the system size? We will finally discuss possible future applications of the quantitative descriptions of the interaction structure in the small group clinical setting.

Our work stems from the consideration that the spreading of a disease is modulated by the individual's perception of the infected neighborhood and his/her strategy to avoid being infected as well. We introduced a general "cellular agent" model that accounts for a heterogeneous and variable network of connections. The probability of infection is assumed to depend on the perception that an individual has about the spreading of the disease in her local neighborhood and on broadcasting media. In the one-dimensional homogeneous case the model reduces to the DK one, while for long-range coupling the dynamics exhibits large fluctuations that may lead to the complete extinction of the disease. Also CSDC and INFN, sez. Firenze.

We investigate the problem of the formation of communities of users that selectively exchange messages among them in a simulated environment. This closed community can be seen as the prototype of the bubble effect, i.e., the isolation of individuals from other communities. We develop a computational model of a society, where each individual is represented as a simple neural network (a perceptron), under the influence of a recommendation system that honestly forward messages (posts) to other individuals that in the past appreciated previous messages from the sender, i.e., that showed a certain degree of affinity. This dynamical affinity database determines the interaction network. We start from a set of individuals with random preferences (factors), so that at the beginning, there is no community structure at all. We show that the simple effect of the recommendation system is not sufficient to induce the isolation of communities, even when the database of user–user affinity is based ...

In this paper, first we present the experimental results about a small group of people exchanging private and public messages in a virtual community. Our goal is to study the cognitive network that emerges during a chat seance. We used the Derrida coefficient and the triangle structure under the working assumption that moods and perceived mutual affinity can produce results complementary to a full semantic analysis. The most outstanding outcome is the difference between the network obtained considering publicly exchanged messages and the one considering only privately exchanged messages: In the former case, the network is very homogeneous, in the sense that each individual interacts in the same way with all the participants, whilst in the latter the interactions among different agents are very heterogeneous, and are based on "the enemy of my enemy is my friend" strategy. Finally, a recent characterization of the triangular cliques has been considered in order to describe t...

We present the WebTeach project, formed by a web interface to database for test management, a wiki site for the diffusion of teaching material and student forums, and a suite for the generation of multiple-choice mathematical quiz with automatic elaboration of forms. This system has been massively tested for the entrance test to the Engineering Faculty of the University of Florence, Italy.

The interaction between a fluid and a granular material plays a crucial role in a large class of phenomena such as landscape morphology and transport of sediments, aeolian sand dunes formation and ripples dynamics. Standard models involve deterministic continuum equations or, alternatively, Lattice Boltzmann and Lattice Gas Cellular Automata. We here introduce a toy-model to address the issue of aeolian ripple formation and evolution. Our simplified approach accounts for the basic physical mechanisms and enables to reproduce the observed phenomenology in the framework of an innovative statistical mechanics formulation.

We consider a model in which agents of different species move over a complex network, are subject to reproduction and compete for resources. The complementary roles of competition and diffusion produce a variety of fixed points, whose stability depends on the structure of the underlying complex network. The survival and death of species is influenced by the network degree distribution, clustering, degree-degree correlations and community structures. We found that the invasion of all the nodes by just one species is possible only in Erdös-Renyi and regular graphs, while networks with scale-free degree distribution, as those observed in real social, biological and technological systems, guarantee the coexistence of different species and therefore help enhancing species diversity.

I outline a possible logical path from the formulation of physics of classical mechanics to "abstract" systems like cellular automata. The goal of this article is that of illustrating why physicists often study extremely simplified models, instead of just numerically integrating the fundamental equations of physics. This exposition is obviously only partial and based on my expertise and my interests. A similar version of this text appeared under the title Interaction Based Computing in Physics in the Encyclopedia of Complexity and System Science, Springer, New York 2009 p. 4902. 2 Physics and computers Some sixty years ago, shortly after the end of Second World War, computers become available to scientists. Computers were used during the last years of the war for performing computations about the atomic bomb [1, 2]. Up to then, the only available tool, except experiments, was paper and pencil. Starting with Newton and Leibnitz, humans discovered that continuous mathematics (i.e., differential and integral calculus) allowed to derive many consequences of the a given hypothesis just by the manipulation of symbols. It seemed natural to express all quantities (e.g., time, space, mass) as continuous variables. Notice however that the idea of a continuous number is not at all "natural": one has to learn how to deal with it, while (small) integer numbers can be used and manipulated (added, subtracted) by illiterate humans and also by many animals. A point which is worth to be stressed is that any computation refers to a model of certain aspects of reality, considered most important, while others are assumed to be not important Unfortunately most of human-accessible explorations in physics are limited to almost-linear systems, or systems whose effective number of variables is quite small. On the other hand, most of naturally occurring phenomena can be "successfully" modeled only using nonlinear elements. Therefore, most of pre-computer physics is essentially linear physics, although astronomers (like other scientists) used to integrate numerically, by hand, the non-linear equations of gravitation, in order to compute the trajectories of planets. This computation, however, was so cumbersome that no "playing" with trajectories was possible. While analog computers have been used for integrating differential equations, the much more flexible digital computers are deterministic discrete systems. The way of working of a (serial) computer is that of a very fast automaton, that manipulates data following a program. In order to use computers as fast calculators, scientists ported and adapted existing numerical algorithms, and developed new ones. This implied the development of techniques able to approximate the computations of continuous mathematics using computer algebra. However, numbers in computers are not exactly the same as human numbers, in particular they have finite (and varying) precision. This intrinsic precision limit has deep consequences in the simulations of nonlinear systems, in particular of chaotic ones. Indeed, chaos was "numerically discovered" by Lorenz [3] after the observation that a simple approximation, a number that was retyped with fewer decimals, caused a macroscopic change in the trajectory under study. With all their limits, computers can be fruitfully used just to speed-up computations that could eventually be performed by humans. However, since the increase in velocity is of several order of magnitude, it becomes possible to include more and more details into the basic model of the phenomenon under investigation, well beyond what would be possible with an army of "human computers". The idea of exploiting the brute power of fast computers has originated a fruitful line of investigation in numerical physics especially in the field of chemistry, biological molecules, structure of matter. The power of computers has allowed for instance to include quantum mechanical effects in the computation of the structure of biomolecules [4], and although these techniques may be targeted as "brute force", the algorithms developed are actually quite sophisticated. However, a completely different usage of computers is possible: instead of exploiting them for performing computations on models that already proved to approximate the reality, one can use computers as "experimental" apparatus to investigate the patterns of theoretical models, generally non-linear. This is what Lorenz did after having found the first examample of chaos in computational physics. He started simplifying his equations in order to enucleate the minimal ingredients of what would be called the butterfly effect. Much earlier than Lorenz, Fermi, Pasta and Ulam (and the programmer Tsingou [5]) used one on the very first available computers to investigate the basis of statistical mechanics: how energy distributes among the oscillation modes of a chain of nonlinear oscillators [6]. Also in this case the model is simplified at its maximum, in order to put into evidence what are the fundamental ingredients of the observed pattern, and also to use all the available power of computers to increase the precision, the duration and the size of the simulation. This simplification is even more fruitful in the study of systems with many degrees of freedom, that we may denote generically as extended systems. We humans are not prepared to manipulate more that a few symbols at once. So, unless there is a way of grouping together many parts (using averages, like for instance when considering the pressure of a gas as an average over extremely many particle collisions), we are in difficulties in understanding such systems. They may nevertheless be studied performing "experiments" on computers. Again, the idea is that of simplifying at most the original model, in order to isolate the fundamental ingredients of the observed behavior. It is therefore natural to explore systems whose physics is different from the usual one. These artificial worlds are preferably formulated in discrete terms, more suitable to be implemented in computers (see Sect. 4).