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arXiv2013

Innovation networks

This paper advances a framework for modeling the component interactions between cognitive and social aspects of scientific creativity and technological innovation. Specifically, it aims to characterize Innovation Networks; those networks that involve the interplay of people, ideas and organizations to create new, technologically feasible, commercially-realizable products, processes and organizational structures. The tri-partite framework captures networks of ideas (Concept Level), people (Individual Level) and social structures (Social-Organizational Level) and the interactions between these levels. At the concept level, new ideas are the nodes that are created and linked, kept open for further investigation or closed if solved by actors at the individual or organizational levels. At the individual level, the nodes are actors linked by shared worldviews (based on shared professional, educational, experiential backgrounds) who are the builders of the concept level. At the social-organizational level, the nodes are organizations linked by common efforts on a given project (e.g., a company-university collaboration) that by virtue of their intellectual property or rules of governance constrain the actions of individuals (at the Individual Level) or ideas (at the Concept Level). After describing this framework and its implications we paint a number of scenarios to flesh out how it can be applied.

cs.AI↗

arXiv2011

Avalanche transmission and critical behavior in load bearing hierarchical networks

The strength and stability properties of hierarchical load bearing networks and their strengthened variants have been discussed in recent work. Here, we study the avalanche time distributions on these load bearing networks. The avalanche time distributions of the V- lattice, a unique realization of the networks, show power-law behavior when tested with certain fractions of its trunk weights. All other avalanche distributions show Gaussian peaked behavior. Thus the V- lattice is the critical case of the network. We discuss the implications of this result.

cond-mat.stat-mech↗

arXiv2014

Lurking in Social Networks: Topology-based Analysis and Ranking Methods

The massive presence of silent members in online communities, the so-called lurkers, has long attracted the attention of researchers in social science, cognitive psychology, and computer-human interaction. However, the study of lurking phenomena represents an unexplored opportunity of research in data mining, information retrieval and related fields. In this paper, we take a first step towards the formal specification and analysis of lurking in social networks. We address the new problem of lurker ranking and propose the first centrality methods specifically conceived for ranking lurkers in social networks. Our approach utilizes only the network topology without probing into text contents or user relationships related to media. Using Twitter, Flickr, FriendFeed and GooglePlus as cases in point, our methods' performance was evaluated against data-driven rankings as well as existing centrality methods, including the classic PageRank and alpha-centrality. Empirical evidence has shown the significance of our lurker ranking approach, and its uniqueness in effectively identifying and ranking lurkers in an online social network.

cs.SI↗

arXiv2018

Entropy-based approach to missing-links prediction

Link-prediction is an active research field within network theory, aiming at uncovering missing connections or predicting the emergence of future relationships from the observed network structure. This paper represents our contribution to the stream of research concerning missing links prediction. Here, we propose an entropy-based method to predict a given percentage of missing links, by identifying them with the most probable non-observed ones. The probability coefficients are computed by solving opportunely defined null-models over the accessible network structure. Upon comparing our likelihood-based, local method with the most popular algorithms over a set of economic, financial and food networks, we find ours to perform best, as pointed out by a number of statistical indicators (e.g. the precision, the area under the ROC curve, etc.). Moreover, the entropy-based formalism adopted in the present paper allows us to straightforwardly extend the link-prediction exercise to directed networks as well, thus overcoming one of the main limitations of current algorithms. The higher accuracy achievable by employing these methods - together with their larger flexibility - makes them strong competitors of available link-prediction algorithms.

physics.soc-ph↗

arXiv2024

On the implementation of zero-determinant strategies in repeated games

Zero-determinant strategies are a class of strategies in repeated games which unilaterally control payoffs. Zero-determinant strategies have attracted much attention in studies of social dilemma, particularly in the context of evolution of cooperation. So far, not only general properties of zero-determinant strategies have been investigated, but zero-determinant strategies have been applied to control in the fields of information and communications technology and analysis of imitation. Here, we further deepen our understanding on general mathematical properties of zero-determinant strategies. We first prove that zero-determinant strategies, if exist, can be implemented by some one-dimensional transition probability. Next, we prove that, if a two-player game has a non-trivial potential function, a zero-determinant strategy exists in its repeated version. These results assist us to implement zero-determinant strategies in broader situations.

cond-mat.stat-mech↗

arXiv2014

Climatic seasonality may affect ecological network structure: Food webs and mutualistic networks

Ecological networks exhibit non-random structural patterns, such as modularity and nestedness, which indicate ecosystem stability, species diversity, and connectance. Such structure-stability relationships are well known. However, another important perspective is less well understood: the relationship between the environment and structure. Inspired by theoretical studies that suggest that network structure can change due to environmental variability, we collected data on a number of empirical food webs and mutualistic networks and evaluated the effect of climatic seasonality on ecological network structure. As expected, we found that climatic seasonality affects ecological network structure. In particular, an increase in modularity due to climatic seasonality was observed in food webs; however, it is debatable whether this occurs in mutualistic networks. Interestingly, the type of climatic seasonality that affects network structure differs with ecosystem type. Rainfall and temperature seasonality influence freshwater food webs and mutualistic networks, respectively; food webs are smaller, and more modular, with increasing rainfall seasonality. Mutualistic networks exhibit a higher diversity (particularly of animals) with increasing temperature seasonality. These results confirm the theoretical prediction that stability increases with greater perturbation. Although these results are still debatable because of several limitations in the data analysis, they may enhance our understanding of environment-structure relationships.

q-bio.PE↗

arXiv2013

Collective behavior and evolutionary games - An introduction

This is an introduction to the special issue titled "Collective behavior and evolutionary games" that is in the making at Chaos, Solitons & Fractals. The term collective behavior covers many different phenomena in nature and society. From bird flocks and fish swarms to social movements and herding effects, it is the lack of a central planner that makes the spontaneous emergence of sometimes beautifully ordered and seemingly meticulously designed behavior all the more sensational and intriguing. The goal of the special issue is to attract submissions that identify unifying principles that describe the essential aspects of collective behavior, and which thus allow for a better interpretation and foster the understanding of the complexity arising in such systems. As the title of the special issue suggests, the later may come from the realm of evolutionary games, but this is certainly not a necessity, neither for this special issue, and certainly not in general. Interdisciplinary work on all aspects of collective behavior, regardless of background and motivation, and including synchronization and human cognition, is very welcome.

physics.soc-ph↗

arXiv2013

Evolutionary games with facilitators: When does selection favor cooperation?

We study the combined influence of selection and random fluctuations on the evolutionary dynamics of two-strategy ("cooperation" and "defection") games in populations comprising cooperation facilitators. The latter are individuals that support cooperation by enhancing the reproductive potential of cooperators relative to the fitness of defectors. By computing the fixation probability of a single cooperator in finite and well-mixed populations that include a fixed number of facilitators, and by using mean field analysis, we determine when selection promotes cooperation in the important classes of prisoner's dilemma, snowdrift and stag-hunt games. In particular, we identify the circumstances under which selection favors the replacement and invasion of defection by cooperation. Our findings, corroborated by stochastic simulations, show that the spread of cooperation can be promoted through various scenarios when the density of facilitators exceeds a critical value whose dependence on the population size and selection strength is analyzed. We also determine under which conditions cooperation is more likely to replace defection than vice versa. Keywords: Evolutionary games; dynamics of cooperation; social dilemmas; fixation; population dynamics.

q-bio.PE↗

arXiv2013

Effect of assessment error and private information on stern-judging in indirect reciprocity

Stern-judging is one of the best-known assessment rules in indirect reciprocity. Indirect reciprocity is a fundamental mechanism for the evolution of cooperation. It relies on mutual monitoring and assessments, i.e., individuals judge, following their own assessment rules, whether other individuals are "good" or "bad" according to information on their past behaviors. Among many assessment rules, stern-judging is known to provide stable cooperation in a population, as observed when all members in the population know all about others' behaviors (public information case) and when the members never commit an assessment error. In this paper, the effect of assessment error and private information on stern-judging is investigated. By analyzing the image matrix, which describes who is good in the eyes of whom in the population, we analytically show that private information and assessment error cause the collapse of stern-judging: all individuals assess other individuals as "good" at random with a probability of 1/2.

physics.soc-ph↗

arXiv2014

Theoretical investigation on the Schelling's critical neighborhood demand

We derived the critical neighborhood demand in the Schelling's segregation model by studying the conditions for which a chain reaction of migrations of unsatisfied agents occurs. The essence of Schelling dynamics was approximated in two simplified models: (1) a random walk model for the initial stage of the migrations to illustrate the power-law behavior of chain reaction lengths under critical conditions, and (2) a two-room model for the whole process to represent a non-spatial version of segregation dynamics in the Schelling model. Our theoretical results showed good agreements with numerical results obtained from agent-based simulations.

nlin.AO↗

arXiv2013

Contagion spreading on complex networks with local deterministic dynamics

Typically, contagion strength is modeled by a transmission rate $λ$, whereby all nodes in a network are treated uniformly in a mean-field approximation. However, local agents react differently to the same contagion based on their local characteristics. Following our recent work [EPL \textbf{99}, 58002 (2012)], we investigate contagion spreading models with local dynamics on complex networks. We therefore quantify contagions by their quality, $0 \leq α\leq 1$, and follow their spreading as their transmission condition (fitness) is evaluated by local agents. We choose various deterministic local rules. Initial spreading with exponential quality-dependent time scales is followed by a stationary state with a prevalence depending on the quality of the contagion. We also observe various interesting phenomena, for example, high prevalence without the participation of the hubs. This is in sharp contrast with the general belief that hubs play a central role in a typical spreading process. We further study the role of network topology in various models and find that as long as small-world effect exists, the underlying topology does not contribute to the final stationary state but only affects the initial spreading velocity.

physics.soc-ph↗

arXiv2022

Epistemic Phase Transitions in Mathematical Proofs

Mathematical proofs are both paradigms of certainty and some of the most explicitly-justified arguments that we have in the cultural record. Their very explicitness, however, leads to a paradox, because the probability of error grows exponentially as the argument expands. When a mathematician encounters a proof, how does she come to believe it? Here we show that, under a cognitively-plausible belief formation mechanism combining deductive and abductive reasoning, belief in mathematical arguments can undergo what we call an epistemic phase transition: a dramatic and rapidly-propagating jump from uncertainty to near-complete confidence at reasonable levels of claim-to-claim error rates. To show this, we analyze an unusual dataset of forty-eight machine-aided proofs from the formalized reasoning system Coq, including major theorems ranging from ancient to 21st Century mathematics, along with five hand-constructed cases including Euclid, Apollonius, Hernstein's Topics in Algebra, and Andrew Wiles's proof of Fermat's Last Theorem. Our results bear both on recent work in the history and philosophy of mathematics on how we understand proofs, and on a question, basic to cognitive science, of how we justify complex beliefs.

cs.SC↗

arXiv2018

Data on the annual aggregated income taxes of the Italian municipalities over the quinquennium 2007-2011

This dataset contains the annual aggregated income taxes of all the Italian municipalities over the years 2007-2011. Data are clustered over the Italian regions and provinces. The source of the data is the Italian Ministry of Economics and Finance. The administrative variations in Italy over the quinquennium have been taken into account. Data are useful to understand the economic structure of Italy at the microscopic level of municipalities. They can serve also for making comparisons between economical aspects and other features of the Italian cities.

q-fin.ST↗

arXiv2013

Transmission grid extensions during the build-up of a fully renewable pan-European electricity supply

Spatio-temporal generation patterns for wind and solar photovoltaic power in Europe are used to investigate the future rise in transmission needs with an increasing penetration of these variable renewable energy sources (VRES) on the pan-European electricity system. VRES growth predictions according to the official National Renewable Energy Action Plans of the EU countries are used and extrapolated logistically up to a fully VRES-supplied power system. We find that keeping today's international net transfer capacities (NTCs) fixed over the next forty years reduces the final need for backup energy by 13% when compared to the situation with no NTCs. An overall doubling of today's NTCs will lead to a 26% reduction, and an overall quadrupling to a 33% reduction. The remaining need for backup energy is due to correlations in the generation patterns, and cannot be further reduced by transmission. The main investments in transmission lines are due during the ramp-up of VRES from 15% (as planned for 2020) to 80%. Additionally, our results show how the optimal mix between wind and solar energy shifts from about 70% to 80% wind share as the transmission grid is enhanced. Finally, we exemplify how reinforced transmission affects the import and export opportunities of single countries during the VRES ramp-up.

physics.soc-ph↗

arXiv2018

Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system

There are two competing concepts in the literature for the integration of high shares of renewable energy: the coupling of electricity to other energy sectors, such as transport and heating, and the reinforcement of continent-wide transmission networks. In this paper both cross-sector and cross-border integration are considered in the model PyPSA-Eur-Sec-30, the first open, spatially-resolved, temporally-resolved and sector-coupled energy model of Europe. Using a simplified network with one node per country, the cost-optimal system is calculated for a 95% reduction in carbon dioxide emissions compared to 1990, incorporating electricity, transport and heat demand. Flexibility from battery electric vehicles (BEV), power-to-gas units (P2G) and long-term thermal energy storage (LTES) make a significant contribution to the smoothing of variability from wind and solar and to the reduction of total system costs. The cost-minimising integration of BEV pairs well with the daily variations of solar power, while P2G and LTES balance the synoptic and seasonal variations of demand and renewables. In all scenarios, an expansion of cross-border transmission reduces system costs, but the more tightly the energy sectors are coupled, the weaker the benefit of transmission reinforcement becomes.

physics.soc-ph↗

arXiv2013

Indirect transmission and the effect of seasonal pathogen inactivation on infectious disease periodicity

The annual occurrence of many infectious diseases remains a constant burden to public health systems. The seasonal patterns in respiratory disease incidence observed in temperate regions have been attributed to the impact of environmental conditions on pathogen survival. A model describing the transmission of an infectious disease by means of a pathogenic state capable of surviving in an environmental reservoir outside of its host organism is presented in this paper. The ratio of pathogen lifespan to the duration of the infectious disease state is found to be a critical parameter in determining disease dynamics. The introduction of a seasonally forced pathogen inactivation rate identifies a time delay between peak pathogen survival and peak disease incidence. The delay is dependent on specific disease parameters and, for influenza, decreases with increasing reproduction number. The observed seasonal oscillations are found to have a period identical to that of the seasonally forced inactivation rate and which is independent of the duration of infection acquired immunity

q-bio.PE↗

arXiv2018

On the economics of electrical storage for variable renewable energy sources

The use of renewable energy sources is a major strategy to mitigate climate change. Yet Sinn (2017) argues that excessive electrical storage requirements limit the further expansion of variable wind and solar energy. We question, and alter, strong implicit assumptions of Sinn's approach and find that storage needs are considerably lower, up to two orders of magnitude. First, we move away from corner solutions by allowing for combinations of storage and renewable curtailment. Second, we specify a parsimonious optimization model that explicitly considers an economic efficiency perspective. We conclude that electrical storage is unlikely to limit the transition to renewable energy.

physics.soc-ph↗

arXiv2013

Mixing local and global information for community detection in large networks

The problem of clustering large complex networks plays a key role in several scientific fields ranging from Biology to Sociology and Computer Science. Many approaches to clustering complex networks are based on the idea of maximizing a network modularity function. Some of these approaches can be classified as global because they exploit knowledge about the whole network topology to find clusters. Other approaches, instead, can be interpreted as local because they require only a partial knowledge of the network topology, e.g., the neighbors of a vertex. Global approaches are able to achieve high values of modularity but they do not scale well on large networks and, therefore, they cannot be applied to analyze on-line social networks like Facebook or YouTube. In contrast, local approaches are fast and scale up to large, real-life networks, at the cost of poorer results than those achieved by local methods. In this article we propose a glocal method to maximizing modularity, i.e., our method uses information at the global level, yet its scalability on large networks is comparable to that of local methods. The proposed method is called COmplex Network CLUster DEtection (or, shortly, CONCLUDE.) It works in two stages: in the first stage it uses an information-propagation model, based on random and non-backtracking walks of finite length, to compute the importance of each edge in keeping the network connected (called edge centrality.) Then, edge centrality is used to map network vertices onto points of an Euclidean space and to compute distances between all pairs of connected vertices. In the second stage, CONCLUDE uses the distances computed in the first stage to partition the network into clusters. CONCLUDE is computationally efficient since in the average case its cost is roughly linear in the number of edges of the network.

cs.SI↗