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.
The outstanding problem of controlling complex networks is relevant to many areas of science and engineering, and has the potential to generate technological breakthroughs as well. We address the physically important issue of the energy required for achieving control by deriving and validating scaling laws for the lower and upper energy bounds. These bounds represent a reasonable estimate of the energy cost associated with control, and provide a step forward from the current research on controllability toward ultimate control of complex networked dynamical systems.
We introduce a future orientation index to quantify the degree to which Internet users worldwide seek more information about years in the future than years in the past. We analyse Google logs and find a striking correlation between the country's GDP and the predisposition of its inhabitants to look forward.
Quantifying the Advantage of Looking Forward
Tobias Preis, Helen Susannah Moat, H. Eugene Stanley & Steven R. Bishop Scientific Reports 2, Article number: 350 doi:10.1038/srep00350
The default approach to most complex problems is to engineer a complex solution. We see this in IT, generally, and in cloud computing specifically. Experience has taught us, however, that large-scale systems belie this tendency: Simpler solutions are best for solving complex problems. When developers write code, they talk about "elegant code," meaning they are able to come up with a concise, simple, solution to a complex coding problem.
Often groups need to meet repeatedly before a decision is reached. Hence, most individual decisions will be contingent on decisions taken previously by others. In particular, the decision to cooperate or not will depend on one’s own assessment of what constitutes a fair group outcome. Making use of a repeated N-person prisoner’s dilemma, we show that reciprocation towards groups opens a window of opportunity for cooperation to thrive, leading populations to engage in dynamics involving both coordination and coexistence, and characterized by cycles of cooperation and defection. Furthermore, we show that this process leads to the emergence of fairness, whose level will depend on the dilemma at stake.
MATSim provides a toolbox to implement large-scale agent-based transport simulations. The toolbox consists of severel modules which can be combined or used stand-alone. Modules can be replaced by own implementations to test single aspects of your own work. Currently, MATSim offers a toolbox for demand-modeling, agent-based mobility-simulation (traffic flow simulation), re-planning, a controler to iteratively run simulations as well as methods to analyze the output generated by the modules.
Be aware of the just-so data stories that sound reasonable but cannot be conclusively proven.
A lot of great pieces have been written about the relatively recent surge in interest in big data and data science, but in this piece I want to address the importance of deep data analysis: what we can learn from the statistical outliers by drilling down and asking, "What's different here? What's special about these outliers and what do they tell us about our models and assumptions?”
We study the dynamics of the Naming Game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the Naming Game dynamics. We investigate the outcomes of the dynamics on two different types of time-varying data - (i) the networks vary across days and (ii) the networks vary within very short intervals of time (20 seconds). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the Naming Game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.
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.
This paper illustrates the use of the nonparametric Wald-Wolfowitz test to detect stationarity and ergodicity in agent-based models. A nonparametric test is needed due to the practical impossibility to understand how the random component influences the emergent properties of the model in many agent-based models. Nonparametric tests on real data often lack power and this problem is addressed by applying the Wald-Wolfowitz test to the simulated data. The performance of the tests is evaluated using Monte Carlo simulations of a stochastic process with known properties. It is shown that with appropriate settings the tests can detect non-stationarity and non-ergodicity. Knowing whether a model is ergodic and stationary is essential in order to understand its behavior and the real system it is intended to represent; quantitative analysis of the artificial data helps to acquire such knowledge.
When does a social group reach agreement by imitation processes? I will discuss how we answer this question by considering the voter model, a paradigmatic example of simple model of social behavior. Aspects to be addressed include the role of tie heterogeneity and non persistent ties in social networks, as well as the heterogeneity in the timing of interactions and the coexistence of imitation and rational behavior. I will also discuss the competition between self-organization and external messages or mass media in models of social consensus.
The past decade has witnessed a momentous transformation in the way people interact and exchange information with each other. Content is now co-produced, shared, classified and rated on the Web by millions of people, while attention has become the ephemeral and valuable resource that everyone seeks to acquire.
This talk will focus on how social attention is allocated among all media and how it decays as novelty fades and new content is created. This will be followed by a description of the role that attention plays in the production and consumption of content within social media, how its dynamics can be used to predict future trends, and its connection with the emergence of a public agenda.
Are there any well-known computational models in the literature that you would like to use, but the code isn't readily available? CoMSES Net is soliciting nominations for models that should be prioritized for submission to the CoMSES Computational Model Library (CML), and implementation on up-to-date modeling platforms is needed.
We will be initiating a Model Recovery and Replication Drive, by contacting the original authors to recover their model code and documentation and providing them to encourage their replication by researchers and university class projects. We hope to meet the goal of having well-documented and well-implemented versions of the highest priority models available in the CML by the end of 2012.
To help prioritize models for submission and replication, CoMSES Net is soliciting nominations for the models that members would most like to see in the CML.
To nominate a model, enter the name and citation for that model in the form below before May 1, 2012. Once we have collected model nominations, we will organize an open vote to determine the Top 10 Most-Desired Models for the CML. We will then contact the original model authors for documentation and code from their original model, and make them available to interested researchers and instructors.
The original authors, related publications, and the names of individuals who replicate the model will be appropriately cited in the CML listing for all models submitted.
Social networks are not static but, rather, constantly evolve in time. One of the elements thought to drive the evolution of social network structure is homophily—the need for individuals to connect with others who are similar to them. In this paper, we study how the spread of a new opinion, idea, or behavior on such a homophily-driven social network is affected by the changing network structure. In particular, using simulations, we study a variant of the Axelrod model on a network with a homophily-driven rewiring rule imposed. First, we find that the presence of rewiring within the network, in general, impedes the reaching of consensus in opinion, as the time to reach consensus diverges exponentially with network size N. We then investigate whether the introduction of committed individuals who are rigid in their opinion on a particular issue can speed up the convergence to consensus on that issue. We demonstrate that as committed agents are added, beyond a critical value of the committed fraction, the consensus time growth becomes logarithmic in network size N. Furthermore, we show that slight changes in the interaction rule can produce strikingly different results in the scaling behavior of consensus time, Tc. However, the benefit gained by introducing committed agents is qualitatively preserved across all the interaction rules we consider.
Climate change is the popular environmental problem of today’s generation. In the 70s it was concern over pesticides (thanks to Rachel Carson), in the 80s sustainable development entered our lexicon, and today it is climate change.
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