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I, Quantum Robot - Scientific American (blog)

I, Quantum Robot - Scientific American (blog) | Complex Networks Everywhere | Scoop.it
Scientific American (blog)
I, Quantum Robot
Scientific American (blog)
Artificial Intelligence is the ability of a computer system to operate in a manner similar to human intelligence.
Alejandro J. Alvarez S.'s insight:

Very interesting, I enjoyed reading it.

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luiy's curator insight, March 19, 2013 6:13 AM

The quantum robot is the idea of combining quantum theory with robot technology. In other words, it is a practical use of the combination of quantum computing and robot technology. Quantum computing involves using quantum systems and quantum states to do computations.

 

A robot is an automated machine that is capable of doing a set of complex tasks. In some applications of robots, the programming used to run the robots may be based on artificial intelligence. Artificial Intelligence is the ability of a computer system to operate in a manner similar to human intelligence. Think of artificial intelligence as if you were training a machine to act like a human. Essentially, quantum robots are complex quantum systems.They are mobile systems with on board quantum computers that interact with their environments. Several programs would be involved in the operation of the robot. These programs would be quantum searching algorithms and quantum reinforcement learning algorithms.

 

Quantum reinforcement learning is based on superposition of the quantum state and quantum parallelism. A quantum state is a system that is a set of quantum numbers. The four basic quantum numbers represent the energy level, angular momentum, spin, and magnetization. In the superposition of quantum states, the idea is to get one state to look like another.

 

Let’s say I have two dogs. One dog knows how to fetch a bone (energy level), sit up (angular momentum), give a high five (spin), and shake hands (magnetization). Now, let’s apply the superposition of quantum states. Since one dog has been trained and given the commands, the other dog must learn to mimic or copy what the first dog did. Each time a command is achieved, reinforcement is given. The reinforcement for the dog would be a bone (or no bone if the command is not achieved).

 

In quantum reinforcement learning, it is slightly different. The idea would be similar to an “If-Then” statement. An example would be if the quantum state has a certain energy level, then the angular momentum is certain value. This idea of “If-Then” statements in the quantum world leads to an idea which can be a topic of its own; Quantum Logic.

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Structural #Patterns of the Occupy Movement on Facebook | #socialchange #SNA

Structural #Patterns of the Occupy Movement on Facebook | #socialchange #SNA | Complex Networks Everywhere | Scoop.it

In this work we study a peculiar example of social organization on Facebook: the Occupy Movement -- i.e., an international protest movement against social and economic inequality organized online at a city level. We consider 179 US Facebook public pages during the time period between September 2011 and February 2013. The dataset includes 618K active users and 753K posts that received about 5.2M likes and 1.1M comments. By labeling user according to their interaction patterns on pages -- e.g., a user is considered to be polarized if she has at least the 95% of her likes on a specific page -- we find that activities are not locally coordinated by geographically close pages, but are driven by pages linked to major US cities that act as hubs within the various groups. Such a pattern is verified even by extracting the backbone structure -- i.e., filtering statistically relevant weight heterogeneities -- for both the pages-reshares and the pages-common users networks.

 

Structural Patterns of the Occupy Movement on Facebook
Michela Del Vicario, Qian Zhang, Alessandro Bessi, Fabiana Zollo, Antonio Scala, Guido Caldarelli, Walter Quattrociocchi

http://arxiv.org/abs/1501.07203


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Information - Function - Biology

Here you will find a description of the aims, activities and theoretical synthesis of the "Information - Function - Biology" project.

 

The broad aim is to advance a deep understanding of life and living which integrates concepts over all scales, all time and all forms of biological organisation. The key insight enabling this is to see that living is an information process, that living forms are concentrations of information engaged in storing, communicating, filtering and recombining information. There are several inspirations for this work, but perhaps the most prominent is the book by Erwin Schrodinger, which he called “What is Life?*”. That is why this website has the URL http://www.whatlifeis.info 


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The LinkedIn Economic Graph Challenge

The LinkedIn Economic Graph Challenge | Complex Networks Everywhere | Scoop.it

There are approximately 3 billion people in the global workforce. LinkedIn's vision is to create economic opportunity for every one of them. The development of the world's first Economic Graph will lead to making that vision a reality. This, of course, is no easy task. Our vision is grand, but it's not unattainable.

So, here's the challenge: Given the wealth of data that exists within LinkedIn, what research would you propose that has the potential to create greater economic opportunity?

We are launching the LinkedIn Economic Graph Challenge to encourage researchers, academics and data-driven thinkers to solve some of the most challenging economic problems of our times.

 

http://economicgraphchallenge.linkedin.com


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Postdoctoral and Student/Predoctoral Research Positions @NECSI

The New England Complex Systems Institute has funding for postdoctoral and predoctoral research appointments. We look for outstanding applicants with training in physics, mathematics or computer science. We value strong writing abilities. Candidates should be interested in contributing to a wide range of NECSI's research areas, including analysis and modeling of

Socio-economic systems relevant to:
- The food and economic crises,
- Conflicts, revolutions, and ethnic violence
- International development, and
- Pandemics

Fundamental mathematical advances such as:
- Multisc ale representations
- Network representations
To apply please visit: http://www.necsi.edu/education/postdoc/app.php


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Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness

Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness | Complex Networks Everywhere | Scoop.it
What are the neural signatures of consciousness? This is an elusive yet fascinating challenge to current cognitive neuroscience, but it takes on an immediate clinical and societal significance in patients diagnosed as vegetative and minimally conscious. In these patients, it leads us to ask whether we can test for the presence of these signatures in the absence of any external signs of awareness. Recent conceptual advances suggest that consciousness requires a dynamic balance between integrated and differentiated networks of information exchange between brain regions. Here we apply this insight to study such networks in patients and compare them to healthy adults. Using the science of graph theory, we show that the rich and diversely connected networks that support awareness are characteristically impaired in patients, lacking the ability to efficiently integrate information across disparate regions via well-connected hubs. We find that the quality of patients' networks also correlates well with their degree of behavioural responsiveness, and some vegetative patients who show signs of hidden awareness have remarkably well-preserved networks similar to healthy adults.

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How Information Theory Could Hold the Key to Quantifying Nature | WIRED

How Information Theory Could Hold the Key to Quantifying Nature | WIRED | Complex Networks Everywhere | Scoop.it
The Western Ghats in India rise like a wall between the Arabian Sea and the heart of the subcontinent to the east. The 1,000-mile-long chain of coastal mountains is dense with lush rainforest and grasslands, and each year, clouds bearing monsoon rains blow in from the southwest and break against the mountains’ flanks, unloading water…

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António F Fonseca's curator insight, October 22, 2014 5:51 AM

Predicting diversity is not an easy task.

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CASM: Thematic series on Modeling large-scale communication networks using complex networks and agent-based modeling techniques

Complex Adaptive Systems Modeling welcomes submissions to the new thematic series on Modeling large-scale communication networks using complex networks and agent-based modeling techniques.
This thematic series intends to publish high quality original research as well as review articles on case studies, models and methods for the modeling and simulation of large-scale computer communication networks using either of the following two approaches:

 Complex networks (such as modeled using tools such as Gephi, Network Workbench and others) Agent-based models (such as based on NetLogo, Repast, Mason, Swarm and others)

Potential topics include, but are not limited to:

Multiagent systemsCognitive Sensor NetworksWireless Sensor NetworksSensor Actuator NetworksCloud computing infra-structuresInternet of ThingsService-oriented architecturesPervasive/Mobile ComputingPeer-to-peer networks

 

http://www.casmodeling.com/about/update/COMM_NETS ;


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Multiple percolation transitions in a configuration model of a network of networks

Multiple percolation transitions in a configuration model of a network of networks | Complex Networks Everywhere | Scoop.it

Recently much attention has been paid to the study of the robustness of interdependent and multiplex networks and, in particular, the networks of networks. The robustness of interdependent networks can be evaluated by the size of a mutually connected component when a fraction of nodes have been removed from these networks. Here we characterize the emergence of the mutually connected component in a network of networks in which every node of a network (layer) alpha is connected with q_alpha its randomly chosen replicas in some other networks and is interdependent of these nodes with probability r. We find that when the superdegrees q_alpha of different layers in a network of networks are distributed heterogeneously, multiple percolation phase transition can occur. We show that, depending on the value of r, these transition are continuous or discontinuous.


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Transfer Entropy and Transient Limits of Computation

Transfer entropy is a recently introduced information-theoretic measure quantifying directed statistical coherence between spatiotemporal processes, and is widely used in diverse fields ranging from finance to neuroscience. However, its relationships to fundamental limits of computation, such as Landauer's limit, remain unknown. Here we show that in order to increase transfer entropy (predictability) by one bit, heat flow must match or exceed Landauer's limit. Importantly, we generalise Landauer's limit to bi-directional information dynamics for non-equilibrium processes, revealing that the limit applies to prediction, in addition to retrodiction (information erasure). Furthermore, the results are related to negentropy, and to Bremermann's limit and the Bekenstein bound, producing, perhaps surprisingly, lower bounds on the computational deceleration and information loss incurred during an increase in predictability about the process. The identified relationships set new computational limits in terms of fundamental physical quantities, and establish transfer entropy as a central measure connecting information theory, thermodynamics and theory of computation.

 

Transfer Entropy and Transient Limits of Computation
Mikhail Prokopenko and Joseph T. Lizier
Scientific Reports 4, 5394, doi:10.1038/srep05394
http://www.nature.com/srep/2014/140623/srep05394/full/srep05394.html


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Colbert Sesanker's curator insight, August 30, 2014 10:40 PM

combine with integrated information

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Grand challenges for Computational Intelligence

The intelligence phenomenon continues to fascinate scientists and engineers, remaining an elusive moving target. Following numerous past observations (e.g., Hofstadter, 1985, p. 585), it can be pointed out that several attempts to construct “artificial intelligence” have turned to designing programs with discriminative power. These programs would allow computers to discern between meaningful and meaningless in similar ways to how humans perform this task. Interestingly, as noted by de Looze (2006) among others, such discrimination is based on etymology of “intellect” derived from Latin “intellego” (inter-lego): to choose between, or to perceive/read (a core message) between (alternatives). In terms of computational intelligence, the ability to read between the lines, extracting some new essence, corresponds to mechanisms capable of generating computational novelty and choice, coupled with active perception, learning, prediction, and post-diction. When a robot demonstrates a stable control in presence of a priori unknown environmental perturbations, it exhibits intelligence. When a software agent generates and learns new behaviors in a self-organizing rather than a predefined way, it seems to be curiosity-driven. When an algorithm rapidly solves a hard computational problem, by efficiently exploring its search-space, it appears intelligent.

 

Prokopenko M (2014) Grand challenges for computational intelligence. Front. Robot. AI 1:2. http://journal.frontiersin.org/Journal/10.3389/frobt.2014.00002/full


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A draft map of the human proteome

The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org ) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

 

A draft map of the human proteome
• Min-Sik Kim, et al.

Nature 509, 575–581 (29 May 2014) http://dx.doi.org/10.1038/nature13302


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How population heterogeneity in susceptibility and infectivity influences epidemic dynamics

How population heterogeneity in susceptibility and infectivity influences epidemic dynamics | Complex Networks Everywhere | Scoop.it

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The Simple Rules of Social Contagion : Scientific Reports : Nature Publishing Group

The Simple Rules of Social Contagion : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.

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Entropy | Special Issue : Information Processing in Complex Systems

All systems in nature have one thing in common: they process information. Information is registered in the state of a system and its elements, implicitly and invisibly. As elements interact, information is transferred and modified. Indeed, bits of information about the state of one element will travel—imperfectly—to the state of the other element, forming its new state. This storage, transfer, and modification of information, possibly between levels of a multi level system, is imperfect due to randomness or noise. From this viewpoint, a system can be formalized as a collection of bits that is organized according to its rules of dynamics and its topology of interactions. Mapping out exactly how these bits of information percolate through the system could reveal new fundamental insights in how the parts orchestrate to produce the properties of the system. A theory of information processing would be capable of defining a set of universal properties of dynamical multi level complex systems, which describe and compare the dynamics of diverse complex systems ranging from social interaction to brain networks, from financial markets to biomedicine. Each possible combination of rules of dynamics and topology of interactions, with disparate semantics, would reduce to a single language of information processing.

 

Guest Editor: Dr. Rick Quax

 

Deadline for manuscript submissions: 28 February 2015


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Complexity, Governance & Networks

Complexity, Governance & Networks | Complex Networks Everywhere | Scoop.it

Complexity, Governance, and Networks aims to contribute to the philosophical, theoretical, methodological, and empirical developments in complexity, governance, and network studies in public administration, public policy, politics, and non-governmental organizations.  The journal publishes primarily theoretical essays and original research papers.  

 

http://www.cgnj.info

 

First Issue at http://www.cgnj.info/index.php?option=com_content&view=article&id=164%3Atoc-cgn-01-2014&catid=49%3Acomplexity-governance-networks&Itemid=213&lang=en ;


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2015 Complex System Summer School | Santa Fe Institute

2015 Complex System Summer School | Santa Fe Institute | Complex Networks Everywhere | Scoop.it

The Complex Systems Summer School offers an intensive four week introduction to complex behavior in mathematical, physical, living, and social systems for graduate students and postdoctoral fellows in the sciences and social sciences. The school is for participants who seek background and hands-on experience to help them prepare to conduct interdisciplinary research in areas related to complex systems.

 

http://santafe.edu/education/schools/complex-systems-summer-schools/2015-program-info/


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PhD Scholarships in Complex Systems @Sydney_Uni

Funding is available for applicants interested in carrying out fundamental and applied research in the field of complex systems. The research will involve theoretical work as well as computer simulations. It will aim to discover fundamental connections between information-theoretic and statistical-mechanical approaches to self-organisation, while investigating a variety of topics in nonlinear critical phenomena, with particular focus on information dynamics during phase transitions. 

The PhD will be supervised by Prof. Mikhail Prokopenko. The applicant will join the Complex Systems Research Group (CSRG) at The School of Civil Engineering – The University of Sydney. The CSRG group comprises ten academics, and has wide collaborations across the University, Australia, and internationally. It is a vibrant, world-leading group in the fields of guided self-organisation and critical phenomena forecasting.

...

The scholarship also includes covering the fees payable by international students.


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▶ Cities as complex adaptative systems. Luis Bettencourt - YouTube

Simposio Complejidad e Interdisciplina, efectuado del 4 al 6 de noviembre de 2013


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António F Fonseca's curator insight, October 22, 2014 5:38 AM

Nice talk by a portuguese cientist working at Santa Fe Institute.

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Introduction to System Dynamics

Introduction to System Dynamics | Complex Networks Everywhere | Scoop.it

Introduction to systems thinking and system dynamics modeling applied to strategy, organizational change, and policy design.


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ECCS WARM-UP: II School on Complex Networks, Sept 19-21, 2014, Lucca

Following last years successful edition, we have once more decided to organize a summer school coinciding with the European Conference on Complex Systems  thus profiting the opportunity offered by the presence of a wide variety of experts in different topics in Lucca. The projected school aims to offer young researchers the opportunity to learn new methods, present their work and meet fellow researchers, and it also represents a good opportunity for young researcher to prepare their participation to the main ECCS conference in an informal and relaxed environment.
Following our policy to display local talent, three renowned italian researchers will each present a different aspect of complex networks in three hour sessions. Names such as Dr. Roberta Sinatra, Dr. Ciro Catutto and Prof. Stefano Battiston should sound familiar to any interested student. Furthermore, we plan a meeting where each participant will have the possibility to share with the others his work, organized as a flash presentation workshop. Of course, a major social event is also included, to stimulate networking and “prepare” the official ECCS conference.

 

http://eccswarmup.wordpress.com


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Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group

Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.

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Sibout Nooteboom's curator insight, July 13, 2014 3:52 AM

Fascinating advances

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How languages evolve - Alex Gendler

How languages evolve - Alex Gendler | Complex Networks Everywhere | Scoop.it
Over the course of human history, thousands of languages have developed from what was once a much smaller number. How did we end up with so many? And how do we keep track of them all? Alex Gendler explains how linguists group languages into language families, demonstrating how these linguistic trees give us crucial insights into the past.

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The world after Big Data: What the digital revolution means for us

The world after Big Data: What the digital revolution means for us | Complex Networks Everywhere | Scoop.it

Never before were politicians, business leaders, and scientists more urgently needed to master the challenges ahead of us. We are in the middle of a third industrial revolution. While we see the symptoms, such as the financial and economic crisis, cybercrime and cyberwar, we haven't understood the implications well. But at the end of this socio-economic transformation, we will live in a digital society. This comes with breath-taking opportunities and challenges, as they occur only every 100 years.

 

http://futurict.blogspot.mx/2014/05/the-world-after-big-data-what-digital.html

 

See also What's the Next Big Thing after Big Data? https://www.youtube.com/watch?v=P5Y76UB080M&list=UUYrlsSzinJN42rKmFlOOYxA


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Gary Bamford's curator insight, May 30, 2014 2:37 AM

Come on, keep up!

Rick Frank's curator insight, May 30, 2014 9:38 AM

This is a bit idealistic but I like the thought process behind it.

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Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group

Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group | Complex Networks Everywhere | Scoop.it
Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics.

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António F Fonseca's curator insight, December 28, 2013 7:14 AM

Another paper on opinion dynamics.

Luciano Lampi's curator insight, January 11, 2014 5:45 PM

Humanrithms....

Claude Emond's curator insight, January 20, 2014 5:51 PM

Opinions are an unescapable part of sharing and influencing the direction of collective intelligence

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Origin of Peer Influence in Social Networks

Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be.
DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702

Origin of Peer Influence in Social Networks
Phys. Rev. Lett. 112, 098702 – Published 6 March 2014
Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco


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Eli Levine's curator insight, March 10, 2014 5:16 PM

Indeed, we are all interconnected in very profound and subtle ways, whether we accept it or not.


This one's for the Libertarians and conservatives out there, who don't seem to think that their actions effect the other, or that the other can effect them, or that the actions done onto the other will effect the actions that are done onto them by the other.

 

Kind of like how they blame the poor for being angry at the rich, after the poor produced the wealth that engorges the rich.

 

Silly people....

 

Think about it.