Social Simulation
Follow
Find
2.5K views | +0 today
Social Simulation
News about social simulation, social networks dynamics and complex systems
Your new post is loading...
Your new post is loading...
Scooped by Frédéric Amblard
Scoop.it!

Epidemics on a stochastic model of temporal network

Contacts between individuals serve as pathways where infections may propagate. These contact patterns can be represented by network structures. Static structures have been the common modeling paradigm but recent results suggest that temporal structures play different roles to regulate the spread of infections or infection-like dynamics. On temporal networks a vertex is active only at certain moments and inactive otherwise such that a contact is not continuously available. In several empirical networks, the time between two consecutive vertex-activation events typically follows heterogeneous activity (e.g. bursts). In this chapter, we present a simple and intuitive stochastic model of a temporal network and investigate how epidemics co-evolves with the temporal structures, focusing on the growth dynamics of the epidemics. The model assumes no underlying topological structure and is only constrained by the time between two consecutive events of vertex activation. The main observation is that the speed of the infection spread is different in case of heterogeneous and homogeneous temporal patterns but the differences depend on the stage of the epidemics. In comparison to the homogeneous scenario, the power law case results in a faster growth in the beginning but turns out to be slower after a certain time, taking several time steps to reach the whole network.

more...
No comment yet.
Rescooped by Frédéric Amblard from Talks
Scoop.it!

Frans de Waal: Moral behavior in animals

Frans de Waal: Moral behavior in animals | Social Simulation | Scoop.it

Empathy, cooperation, fairness and reciprocity -- caring about the well-being of others seems like a very human trait. But Frans de Waal shares some surprising videos of behavioral tests, on primates and other mammals, that show how many of these moral traits all of us share.


Via Complexity Digest
more...
No comment yet.
Rescooped by Frédéric Amblard from Social Foraging
Scoop.it!

The mechanics of stochastic slowdown in evolutionary games

We study the stochastic dynamics of evolutionary games, and focus on the so-called 'stochastic slowdown' effect, previously observed in (Altrock et. al, 2010) for simple evolutionary dynamics. Slowdown here refers to the fact that a beneficial mutation may take longer to fixate than a neutral one. More precisely, the fixation time conditioned on the mutant taking over can show a maximum at intermediate selection strength. We show that this phenomenon is present in the prisoner's dilemma, and also discuss counterintuitive slowdown and speedup in coexistence games. In order to establish the microscopic origins of these phenomena, we calculate the average sojourn times. This allows us to identify the transient states which contribute most to the slowdown effect, and enables us to provide an understanding of slowdown in the takeover of a small group of cooperators by defectors: Defection spreads quickly initially, but the final steps to takeover can be delayed significantly. The analysis of coexistence games reveals even more intricate behavior. In small populations, the conditional average fixation time can show multiple extrema as a function of the selection strength, e.g., slowdown, speedup, and slowdown again. We classify two-player games with respect to the possibility to observe non-monotonic behavior of the conditional average fixation time as a function of selection strength.


Via Ashish Umre
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Irving Wladawsky-Berger: Design Principles for Complex, Unpredictable, People Oriented Systems

Irving Wladawsky-Berger: Design Principles for Complex, Unpredictable, People Oriented Systems | Social Simulation | Scoop.it
An IBM Global CEO Study conducted in 2010 concluded that complexity was the primary challenge emerging out of its conversations with 1,500 CEOs and senior government officials.
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Modeling civil violence in Afghanistan: Ethnic geography, control, and collaboration

Modeling civil violence in Afghanistan: Ethnic geography, control, and collaboration | Social Simulation | Scoop.it

We develop a computational model to explore how ethnic geography shapes the distribution of violence in civil war. We seed the model with disaggregated data on ethnic settlement patterns in Afghanistan and calibrate the model parameters to fit empirically observed locations of violence against civilians. Our simulation suggests that (i) political actors are more likely to attack civilians in heterogeneous areas where members of one ethnic group are exposed to members of a rival group; (ii) violence directed at civilians occurs with greater frequency in locations where one political actor exercises hegemonic but incomplete territorial control (relative to areas of complete or mixed control); and (iii) geographically concentrated ethnic minorities face a higher risk of violence.

more...
No comment yet.
Rescooped by Frédéric Amblard from Papers
Scoop.it!

Structural diversity in social contagion

The concept of contagion has steadily expanded from its original grounding in epidemic disease to describe a vast array of processes that spread across networks, notably social phenomena such as fads, political opinions, the adoption of new technologies, and financial decisions. (...) We find that the probability of contagion is tightly controlled by the number of connected components in an individual's contact neighborhood, rather than by the actual size of the neighborhood. Surprisingly, once this “structural diversity” is controlled for, the size of the contact neighborhood is in fact generally a negative predictor of contagion. More broadly, our analysis shows how data at the size and resolution of the Facebook network make possible the identification of subtle structural signals that go undetected at smaller scales yet hold pivotal predictive roles for the outcomes of social processes.

 

Structural diversity in social contagion
Johan Ugander, Lars Backstrom, Cameron Marlow, and Jon Kleinberg

PNAS

http://www.pnas.org/content/early/2012/03/27/1116502109.abstract


Via Complexity Digest
more...
No comment yet.
Rescooped by Frédéric Amblard from Papers
Scoop.it!

Graph fission in an evolving voter model

We consider a simplified model of a social network in which individuals have one of two opinions (called 0 and 1) and their opinions and the network connections coevolve. Edges are picked at random. If the two connected individuals hold different opinions then, with probability 1 - α, one imitates the opinion of the other; otherwise (i.e., with probability α), the link between them is broken and one of them makes a new connection to an individual chosen at random (i) from those with the same opinion or (ii) from the network as a whole. The evolution of the system stops when there are no longer any discordant edges connecting individuals with different opinions. Letting ρ be the fraction of voters holding the minority opinion after the evolution stops, we are interested in how ρ depends on α and the initial fraction u of voters with opinion 1. In case (i), there is a critical value αc which does not depend on u, with ρ ≈ u for α > αc and ρ ≈ 0 for α < αc. In case (ii), the transition point αc(u) depends on the initial density u. For α > αc(u), ρ ≈ u, but for α < αc(u), we have ρ(α,u) = ρ(α,1/2). Using simulations and approximate calculations, we explain why these two nearly identical models have such dramatically different phase transitions.

 

Graph fission in an evolving voter model
Richard Durrett et al.

PNAS March 6, 2012 vol. 109 no. 10 3682-3687

http://dx.doi.org/10.1073/pnas.1200709109


Via Complexity Digest
more...
No comment yet.
Rescooped by Frédéric Amblard from Papers
Scoop.it!

Suppressing cascades of load in interdependent networks

Understanding how interdependence among systems affects cascading behaviors is increasingly important across many fields of science and engineering. Inspired by cascades of load shedding in coupled electric grids and other infrastructure, we study the Bak–Tang–Wiesenfeld sandpile model on modular random graphs and on graphs based on actual, interdependent power grids. Starting from two isolated networks, adding some connectivity between them is beneficial, for it suppresses the largest cascades in each system. Too much interconnectivity, however, becomes detrimental for two reasons. First, interconnections open pathways for neighboring networks to inflict large cascades. Second, as in real infrastructure, new interconnections increase capacity and total possible load, which fuels even larger cascades. Using a multitype branching process and simulations we show these effects and estimate the optimal level of interconnectivity that balances their trade-offs. Such equilibria could allow, for example, power grid owners to minimize the largest cascades in their grid. We also show that asymmetric capacity among interdependent networks affects the optimal connectivity that each prefers and may lead to an arms race for greater capacity. Our multitype branching process framework provides building blocks for better prediction of cascading processes on modular random graphs and on multitype networks in general.

 

Suppressing cascades of load in interdependent networks
Charles D. Brummitt, Raissa M. D’Souza, and E. A. Leicht

http://dx.doi.org/10.1073/pnas.1110586109


Via Complexity Digest
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Computer Built Using Swarms Of Soldier Crabs

Computer Built Using Swarms Of Soldier Crabs | Social Simulation | Scoop.it
Computer scientists at Kobe University in Japan have built a computer that draws inspiration from the swarming behavior of soldier crabs.The...
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Evolving networks with bimodal degre

Evolving networks with bimodal degre | Social Simulation | Scoop.it

Networks with bimodal degree distribution are most robust to targeted and random attacks. We present a model for constructing a network with bimodal degree distribution. The procedure adopted is to add nodes to the network with a probability p and delete the links between nodes with probability (1 − p). We introduce an additional constraint in the process through an immunity score, which controls the dynamics of the growth process based on the feedback value of the last few time steps. This results in bimodal nature for the degree distribution. We study the standard quantities which characterize the networks, like average path length and clustering coefficient in the context of our growth process and show that the resultant network is in the small world family. It is interesting to note that bimodality in degree distribution is an emergent phenomenon.

more...
No comment yet.
Rescooped by Frédéric Amblard from Papers
Scoop.it!

Does It Compute?

A discussion of computational biology has to start with a pioneer of the field, Alan Turing, especially in this centennial year of his birth. He introduced us to the digital computer and proposed that much biology could be described by mathematical equations—the number of spirals in a sunflower is a Fibonacci number and pattern formation in animal skins can be described by a reaction diffusion model. Turing lacked the data and the computing power to substantiate his models. Today, the availability of vast quantities of new data, together with striking advances in computing power, is promising to give us new insights into the mechanisms of life. This special section, together with related content in Science Signaling and Science Careers, highlights recent advances and outstanding challenges.

 

Does It Compute?
Valda Vinson, Beverly A. Purnell, Laura M. Zahn, John Travis

Science 13 April 2012: Vol. 336 no. 6078 p. 171
http://dx.doi.org/10.1126/science.336.6078.171


Via Complexity Digest
more...
No comment yet.
Rescooped by Frédéric Amblard from Papers
Scoop.it!

Beyond Turing's Machines

In marking Alan Turing's centenary, it's worth asking what was his most fundamental achievement and what he left for future science to take up when he took his own life in 1954. His success in World War II, as the chief scientific figure in the British cryptographic effort, with hands-on responsibility for the Atlantic naval conflict, had a great and immediate impact. But in its ever-growing influence since that time, the principle of the universal machine, which Turing published in 1937, beats even this.

 

Beyond Turing's Machines
Andrew Hodges

Science 13 April 2012: Vol. 336 no. 6078 pp. 163-164
http://dx.doi.org/10.1126/science.1218417


Via Complexity Digest
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Artificial Intelligence Could Be on Brink of Passing Turing Test

Artificial Intelligence Could Be on Brink of Passing Turing Test | Social Simulation | Scoop.it
One hundred years after Alan Turing was born, his eponymous test remains an elusive benchmark for artificial intelligence. Now, for the firs...
more...
No comment yet.
Rescooped by Frédéric Amblard from Papers
Scoop.it!

Network Science of the Game of Go

Network Science of the Game of Go | Social Simulation | Scoop.it
You can make networks from pretty much anything. Connect people based on friendships or phone calls, proteins based on interaction, words ba...

Via Complexity Digest
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Effective trapping of random walkers in complex networks

Effective trapping of random walkers in complex networks | Social Simulation | Scoop.it

Exploring the World Wide Web has become one of the key issues in information science, specifically in view of its application to the PageRank-like algorithms used in search engines. The random walk approach has been employed to study such a problem. The probability of return to the origin (RTO) of random walks is inversely related to how information can be accessed during random surfing.We find analytically that the RTO probability for a given starting node shows a crossover from a slow to a fast decay behavior with time and the crossover time increases with the degree of the starting node. We remark that the RTO probability becomes almost constant in the early-time regime as the degree exponent approaches two. This result indicates that a random surfer can be effectively trapped at the hub and supports the necessity of the random jump strategy empirically used in the Google’s search engine.

 

Effective trapping of random walkers in complex networks S. Hwang, D.-S. Lee, and B. Kahng Physical Review E 85, 046110 (2012) [pdf] Exploring the World Wide Web has become one of the key issues i...

more...
No comment yet.
Rescooped by Frédéric Amblard from Papers
Scoop.it!

Sex differences in intimate relationships

Sex differences in intimate relationships | Social Simulation | Scoop.it

Social networks based on dyadic relationships are fundamentally important for understanding of human sociality. However, we have little understanding of the dynamics of close relationships and how these change over time. Evolutionary theory suggests that, even in monogamous mating systems, the pattern of investment in close relationships should vary across the lifespan when post-weaning investment plays an important role in maximising fitness. Mobile phone data sets provide a unique window into the structure and dynamics of relationships. We here use data from a large mobile phone dataset to demonstrate striking sex differences in the gender-bias of preferred relationships that reflect the way the reproductive investment strategies of both sexes change across the lifespan, i.e. women's shifting patterns of investment in reproduction and parental care. These results suggest that human social strategies may have more complex dynamics than previously assumed and a life-history perspective is crucial for understanding them.

 

Sex differences in intimate relationships

Vasyl Palchykov, Kimmo Kaski, Janos Kertész, Albert-László Barabási & Robin I. M. Dunbar
Scientific Reports 2, Article number: 370 http://dx.doi.org/10.1038/srep00370


Via Complexity Digest
more...
No comment yet.
Rescooped by Frédéric Amblard from Papers
Scoop.it!

Computer Simulation on the Cooperation of Functional Molecules during the Early Stages of Evolution

Computer Simulation on the Cooperation of Functional Molecules during the Early Stages of Evolution | Social Simulation | Scoop.it

It is very likely that life began with some RNA (or RNA-like) molecules, self-replicating by base-pairing and exhibiting enzyme-like functions that favored the self-replication. Different functional molecules may have emerged by favoring their own self-replication at different aspects. Then, a direct route towards complexity/efficiency may have been through the coexistence/cooperation of these molecules.

Ma W, Hu J (2012) Computer Simulation on the Cooperation of Functional Molecules during the Early Stages of Evolution. PLoS ONE 7(4): e35454. doi:10.1371/journal.pone.0035454


Via Complexity Digest
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Scientists and bankers — a new model army

Scientists and bankers — a new model army | Social Simulation | Scoop.it
Bankers must now surrender more information on their activities. Scientists should use it to build better system-wide financial models, says John Liechty.
more...
No comment yet.
Rescooped by Frédéric Amblard from Complex Systems
Scoop.it!

Emergent Change | Rethinking Complexity

Emergent Change | Rethinking Complexity | Social Simulation | Scoop.it

When we look at change, we can easily distinguish between planned and unplanned change. In simple terms, planned change is change that we seek. Conversely, unplanned change is the type of change we are forced to accept and integrate. This latter type of change may have been planned by others and we are just the unsuspecting recipients of it; or the unplanned change may be totally unexpected by everyone as in the visit of a tornado and its resulting devastation.


Via Alessandro Cerboni, David Rodrigues
more...
No comment yet.
Rescooped by Frédéric Amblard from Complex Systems
Scoop.it!

Corporate structure, Darwinism and random selection | Synthesis

Corporate structure, Darwinism and random selection | Synthesis | Social Simulation | Scoop.it

By Paul Ormerod

 

The corporate world exhibits a wide variety of structures. Co-operatives and partnerships have been around for a long time and have some well known examples. The Co-op, for example, was founded in Rochdale as long ago as 1844 and now is represented worldwide. Goldman Sachs was a partnership for most of its existence. There are more exotic forms of the corporate beast, such as companies limited by guarantee, industrial and provident societies, friendly societies and, recently made possible by legislation in the UK, community interest companies.

 

But by far the dominant form of corporate organisation is that of the joint stock company with limited liability. In other words, companies ultimately controlled by shareholders. These can range from one person bands to the world’s largest firms such as Google. (...)


Via David Rodrigues
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Effects of Social Influence on the Wisdom of Crowds

Wisdom of crowds refers to the phenomenon that the aggregate prediction or forecast of a group of individuals can be surprisingly more accurate than most individuals in the group, and sometimes - than any of the individuals comprising it. This article models the impact of social influence on the wisdom of crowds. We build a minimalistic representation of individuals as Brownian particles coupled by means of social influence. We demonstrate that the model can reproduce results of a previous empirical study. This allows us to draw more fundamental conclusions about the role of social influence: In particular, we show that the question of whether social influence has a positive or negative net effect on the wisdom of crowds is ill-defined. Instead, it is the starting configuration of the population, in terms of its diversity and accuracy, that directly determines how beneficial social influence actually is. The article further examines the scenarios under which social influence promotes or impairs the wisdom of crowds.

more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Decoding the organizing principles of economy

Decoding the organizing principles of economy | Social Simulation | Scoop.it
It sounds paradoxical, but today it appears that we understand more about the universe than our society...
more...
No comment yet.
Scooped by Frédéric Amblard
Scoop.it!

Networks in motion

Networks that govern communication, growth, herd behavior, and other key processes in nature and society are becoming increasingly amenable to modeling, forecast, and control.

more...
No comment yet.
Rescooped by Frédéric Amblard from Global Brain
Scoop.it!

The Intelligent Complex Adaptive System Model for Organizations

The Intelligent Complex Adaptive System Model for Organizations | Social Simulation | Scoop.it

This paper proposes a new model for organizations that live in a dynamic, complex environment.


Via Spaceweaver
more...
No comment yet.
Rescooped by Frédéric Amblard from Global Brain
Scoop.it!

Cooperation and the evolution of intelligence

Cooperation and the evolution of intelligence | Social Simulation | Scoop.it

The emergence of intelligent strategies. Shown are the dynamics during 10,000 generation subsets of simulations for the prisoner's dilemma and snowdrift games. (Credit: Luke McNally, Sam P. Brown, and Andrew L.


Via Spaceweaver
more...
No comment yet.