Prediction, Learning and Games
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Problem Solving Foraging Ants, Spiking Neural Networks and Double Pheromones

Problem Solving Foraging Ants, Spiking Neural Networks and Double Pheromones | Prediction, Learning and Games | Scoop.it
Abstract: A model of an Ant System where ants are controlled by a spiking neural circuit and a second order pheromone mechanism in a foraging task is presented. A neural circuit is trained for individual ants and subsequently the ants are exposed to a virtual environment where a swarm of ants performed a resource foraging task. The model comprises an associative and unsupervised learning strategy for the neural circuit of the ant. The neural circuit adapts to the environment by means of classical conditioning. The initially unknown environment includes different types of stimuli representing food (rewarding) and obstacles (harmful) which, when they come in direct contact with the ant, elicit a reflex response in the motor neural system of the ant: moving towards or away from the source of the stimulus. The spiking neural circuits of the ant is trained to identify food and obstacles and move towards the former and avoid the latter. The ants are released on a landscape with multiple food sources where one ant alone would have difficulty harvesting the landscape to maximum efficiency. In this case the introduction of a double pheromone mechanism (positive and negative reinforcement feedback) yields better results than traditional ant colony optimization strategies. Traditional ant systems include mainly a positive reinforcement pheromone. This approach uses a second pheromone that acts as a marker for forbidden paths (negative feedback). This blockade is not permanent and is controlled by the evaporation rate of the pheromones. The combined action of both pheromones acts as a collective stigmergic memory of the swarm, which reduces the search space of the problem. This paper explores how the adaptation and learning abilities observed in biologically inspired cognitive architectures is synergistically enhanced by swarm optimization strategies. The model portraits two forms of artificial intelligent behaviour: at the individual level the spiking neural network is the main controller and at the collective level the pheromone distribution is a map towards the solution emerged by the colony. The presented model is an important pedagogical tool as it is also an easy to use library that allows access to the spiking neural network paradigm from inside a Netlogo—a language used mostly in agent based modelling and experimentation with complex systems.

Via Alessandro Cerboni
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Unchaining Innovation: Could Bitcoin's Underlying Tech be a Powerful Tool for ... - Government Technology

Unchaining Innovation: Could Bitcoin's Underlying Tech be a Powerful Tool for ... - Government Technology | Prediction, Learning and Games | Scoop.it
The technology, known as the blockchain, is a sort of infinite running ledger, keeping exact track of every bitcoin transaction -- and it may have far-reaching impacts in government.

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Google's Artificial Intelligence Speaks, and She's a Woman

Google's Artificial Intelligence Speaks, and She's a Woman | Prediction, Learning and Games | Scoop.it
Stephen Hawking would say the development of artificial intelligence (AI) will end humanity, but there are those who would disagree - Google is one of them.

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Scientists create computational algorithm for fact-checking

Scientists create computational algorithm for fact-checking | Prediction, Learning and Games | Scoop.it
Network scientists at Indiana University have developed a new computational method that can leverage any body of knowledge to aid in the complex human task of fact-checking.

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Neuroscientists capture the moment a brain records an idea (Wired UK)

Neuroscientists capture the moment a brain records an idea (Wired UK) | Prediction, Learning and Games | Scoop.it
New research using cutting-edge brain imaging technology has offered the first glimpse into how new concepts develop in the human brainNew research using cutting-edge brain imaging technology has offered the first glimpse into how new concepts develop in the human brain -- to the point where it's possible to tell exactly what kind of object someone's thinking about
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Linking Economic Complexity, Institutions and Income Inequality

The mix of products that a country exports predicts that country's subsequent pattern of diversification and economic growth. But does this product mix also predict income inequality? Here we combine methods from econometrics, network science, and economic complexity to show that countries that export complex products - products that are exported by a few diversified countries - have lower levels of income inequality - at comparable levels of GDP per capita and education - than countries exporting simpler products. Using multivariate analysis we show that the connection between income inequality and economic complexity is stronger than what can be explained using aggregate measures of income, institutions, export concentration, and human capital, and also, that increases in economic complexity are accompanied by decreases in income inequality over long periods of time. Finally, we use the position of a country in the network of related products - or product space - to explain how changes in a country's export structure translate into changes in income inequality. We interpret these results by combining the literature in institutions with that on economic complexity and structural transformations. We argue that the connection between income inequality and economic complexity is also evidence of the co-evolution between institutions and productive activities.


Linking Economic Complexity, Institutions and Income Inequality
D. Hartmann, M. Guevara, C. Jara-Figueroa, M. Aristarán, C.A. Hidalgo

http://arxiv.org/abs/1505.07907


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Information-Theoretic Inference of Common Ancestors

A directed acyclic graph (DAG) partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of conditional independence relations. We are interested in properties of this class that can be derived from observations of a subsystem only. To this end, we prove an information-theoretic inequality that allows for the inference of common ancestors of observed parts in any DAG representing some unknown larger system. More explicitly, we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information. Within the causal interpretation of DAGs, our result can be seen as a quantitative extension of Reichenbach’s principle of common cause to more than two variables. Our conclusions are valid also for non-probabilistic observations, such as binary strings, since we state the proof for an axiomatized notion of “mutual information” that includes the stochastic as well as the algorithmic version.

 

Information-Theoretic Inference of Common Ancestors
Bastian Steudel and Nihat Ay

Entropy 2015, 17(4), 2304-2327; http://dx.doi.org/10.3390/e17042304 ;


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Neural Computations Mediating One-Shot Learning in the Human Brain

There are at least two distinct learning strategies for identifying the relationship between a cause and its consequence: (1) incremental learning, in which we gradually acquire knowledge through trial and error, and (2) one-shot learning, in which we rapidly learn from only a single pairing of a potential cause and a consequence. Little is known about how the brain switches between these two forms of learning. In this study, we provide evidence that the amount of uncertainty about the relationship between cause and consequence mediates the transition between incremental and one-shot learning. Specifically, the more uncertainty there is about the causal relationship, the higher the learning rate that is assigned to that stimulus. By imaging the brain while participants were performing the learning task, we also found that uncertainty about the causal association is encoded in the ventrolateral prefrontal cortex and that the degree of coupling between this region and the hippocampus increases during one-shot learning. We speculate that this prefrontal region may act as a “switch,” turning on and off one-shot learning as required.

 

Lee SW, O’Doherty JP, Shimojo S (2015) Neural Computations Mediating One-Shot Learning in the Human Brain. PLoS Biol 13(4): e1002137. http://dx.doi.org/10.1371/journal.pbio.1002137 ;


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Uncovering the structure and temporal dynamics of information propagation

Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

 

Uncovering the structure and temporal dynamics of information propagation
MANUEL GOMEZ RODRIGUEZ, JURE LESKOVEC, DAVID BALDUZZI, BERNHARD SCHÖLKOPF
Network Science , Volume 2 , Issue 01 , April 2014, pp 26 - 65
http://dx.doi.org/10.1017/nws.2014.3 ;


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Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines

The evaluation of the complexity of finite sequences is key in many areas of science. For example, the notions of structure, simplicity and randomness are common currency in biological systems epitomized by a sequence of fundamental nature and utmost importance: the DNA. Nevertheless, researchers have for a long time avoided any practical use of the current accepted mathematical theory of randomness, mainly because it has been considered to be useless in practice [8]. Despite this belief, related notions such as lossless uncompressibility tests have proven relative success, in areas such as sequence pattern detection [21] and have motivated distance measures and classification methods [9] in several areas (see [19] for a survey), to mention but two examples among many others of even more practical use. The method presented in this paper aims to provide sound directions to explore the feasibility and stability of the evaluation of the complexity of strings by means different to that of lossless compressibility, particularly useful for short strings. The authors known of only two similar attempts to compute the uncomputable, one related to the estimation of a Chaitin Omega number [4], and of another seminal related measure of complexity, Bennett's Logical Depth [23], [27]. This paper provides an approximation to the output frequency distribution of all Turing machines with 5 states and 2 symbols which in turn allow us to apply a central theorem in the theory of algorithmic complexity based in the notion of algorithmic probability (also known as Solomonoff's theory of inductive inference) that relates frequency of production of a string and its Kolmogorov complexity hence providing, upon application of the theorem, numerical estimations of Kolmogorov complexity by a method different to lossless compression algorithms.

 

Soler-Toscano F, Zenil H, Delahaye J-P, Gauvrit N (2014) Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines. PLoS ONE 9(5): e96223. http://dx.doi.org/10.1371/journal.pone.0096223


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Hector Zenil's curator insight, February 15, 2015 3:23 AM

Published in PLoS ONE

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▶ Seth Lloyd: Quantum Machine Learning

Machine learning algorithms find patterns in big data sets. This talk presents quantum machine learning algorithms that give exponential speed-ups over their best existing classical counterparts. The algorithms work by mapping the data set into a quantum state (big quantum data) that contains the data in quantum superposition. Quantum coherence is then used to reveal patterns in the data. The quantum algorithms scale as the logarithm of the size of the database.

 

Seth Lloyd visited the Quantum AI Lab at Google LA to give a tech talk on "Quantum Machine Learning." This talk took place on January 29, 2014.

https://www.youtube.com/watch?v=wkBPp9UovVU


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Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism

Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics.


Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism
Surya G. Nurzaman , Xiaoxiang Yu, Yongjae Kim and Fumiya Iida

Entropy 2014, 16(5), 2592-2610

http://www.mdpi.com/1099-4300/16/5/2592


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Eli Levine's curator insight, May 14, 2014 8:18 AM

This ties in with the concept of changing the software that runs on society's particular hardware.  Government is the control mechanism in a given society and it must obey the natural laws of the society in order to get the responses and effects that its members wish to have on the society.  This is similar to an airplane, in that the only way to get an airplane safely, reliably and consistently off the ground is to obey the natural laws of physics in the world that the airplane is also apart of.

 

It should be noted here that only benevolence, care, honesty, cost effectiveness and genuine action for the sake of the general public, however those are done, are the only ways for a government and its members to stay in power.  Underhanded techniques or the imposition of brute force will not work, especially in the context of an American society.  Such is how things work in our world.  And it's unfortunate that so many people who actually are holding political power in our society are so apparently clueless and unwilling to accept these principles in their daily courses of action.

 

Think about it.

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Why We Keep Getting the Same Old Ideas

Why We Keep Getting the Same Old Ideas | Prediction, Learning and Games | Scoop.it
When you change your thinking patterns, your brain makes new connections which give you different things to focus on and different ways to interpret what you are focusing on.
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'Expansion entropy': A new litmus test for chaos?

'Expansion entropy': A new litmus test for chaos? | Prediction, Learning and Games | Scoop.it
Can the flap of a butterfly's wings in Brazil set off a tornado in Texas? This intriguing hypothetical scenario, commonly called "the butterfly effect," has come to embody the popular conception of a chaotic system, in which a small difference in initial conditions will cascade toward a vastly different outcome in the future.


Understanding and modeling chaos can help address a variety of scientific and engineering questions, and so researchers have worked to develop better mathematical definitions of chaos. These definitions, in turn, will aid the construction of models that more accurately represent real-world chaotic systems.


Now, researchers from the University of Maryland have described a new definition of chaos that applies more broadly than previous definitions. This new definition is compact, can be easily approximated by numerical methods and works for a wide variety of chaotic systems. The discovery could one day help advance computer modeling across a wide variety of disciplines, from medicine to meteorology and beyond. The researchers present their new definition in the July 28, 2015 issue of the journal Chaos.


"Our definition of chaos identifies chaotic behavior even when it lurks in the dark corners of a model," said Brian Hunt, a professor of mathematics with a joint appointment in the Institute for Physical Science and Technology (IPST) at UMD. Hunt co-authored the paper with Edward Ott, a Distinguished University Professor of Physics and Electrical and Computer Engineering with a joint appointment in the Institute for Research in Electronics and Applied Physics (IREAP) at UMD.


The study of chaos is relatively young. MIT meteorologist Edward Lorenz, whose work gave rise to the term "the butterfly effect," first noticed chaotic characteristics in weather models in the mid-20th century. In 1963, he published a set of differential equations to describe atmospheric airflow and noted that tiny variations in initial conditions could drastically alter the solution to the equations over time, making it difficult to predict the weather in the long term.


Mathematically, extreme sensitivity to initial conditions can be represented by a quantity called a Lyapunov exponent. This number is positive if two infinitesimally close starting points diverge exponentially as time progresses. Yet, Lyapunov exponents have limitations as a definition of chaos: they only test for chaos in particular solutions of a model, not in the model itself, and they can be positive even when the underlying model is considered too straightforward to be deemed chaotic.


Via Dr. Stefan Gruenwald
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The man who created the world's first self aware robot says the next big test will change the human-robot relationship forever

The man who created the world's first self aware robot says the next big test will change the human-robot relationship forever | Prediction, Learning and Games | Scoop.it
Luciano Floridi issued a challenge to...

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TJ Allard's curator insight, July 26, 2015 2:41 PM

ok and......when? Its like I've been reading articles like this for a few years now. 

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The Humans Who Dream Of Companies That Won't Need Us

The Humans Who Dream Of Companies That Won't Need Us | Prediction, Learning and Games | Scoop.it
How would Ethereum's network autonomously run transportation apps, delivery services, and other companies? And would we even want that?

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Spaceweaver's curator insight, July 19, 2015 7:18 AM

This is very relevant to short and long term Global Brain technologies.

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Does Creativity Share a Genetic Root With Schizophrenia and Bipolar Disorder?

Does Creativity Share a Genetic Root With Schizophrenia and Bipolar Disorder? | Prediction, Learning and Games | Scoop.it
According to a new study, genes linked to creativity could also increase the risk of developing some psychiatric disorders.
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Robots walk, climb, drive (and fall) in race for $2m world prize (Wired UK)

Robots walk, climb, drive (and fall) in race for $2m world prize (Wired UK) | Prediction, Learning and Games | Scoop.it
A South Korean robot has won the 2015 Darpa Robotics Challenge, defeating 22 others to claim a $2m (£1.3m) prize from the US department of defence
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Googling Gives Illusion of Knowledge Even When The Search Reveals Nothing! - PsyBlog

Googling Gives Illusion of Knowledge Even When The Search Reveals Nothing! - PsyBlog | Prediction, Learning and Games | Scoop.it

Even when an internet search is unsuccessful, people feel they know more.

 

Searching the internet makes people feel they know more than they really do, a new study finds.

And it doesn’t seem to matter much that people don’t actually find the information for which they were searching.

 


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Thermodynamics of firms' growth

The distribution of firms' growth and firms' sizes is a topic under intense scrutiny. In this paper we show that a thermodynamic model based on the Maximum Entropy Principle, with dynamical prior information, can be constructed that adequately describes the dynamics and distribution of firms' growth. Our theoretical framework is tested against a comprehensive data-base of Spanish firms, which covers to a very large extent Spain's economic activity with a total of 1,155,142 firms evolving along a full decade. We show that the empirical exponent of Pareto's law, a rule often observed in the rank distribution of large-size firms, is explained by the capacity of the economic system for creating/destroying firms, and can be used to measure the health of a capitalist-based economy. Indeed, our model predicts that when the exponent is larger that 1, creation of firms is favored; when it is smaller that 1, destruction of firms is favored instead; and when it equals 1 (matching Zipf's law), the system is in a full macroeconomic equilibrium, entailing "free" creation and/or destruction of firms. For medium and smaller firm-sizes, the dynamical regime changes; the whole distribution can no longer be fitted to a single simple analytic form and numerical prediction is required. Our model constitutes the basis of a full predictive framework for the economic evolution of an ensemble of firms that can be potentially used to develop simulations and test hypothetical scenarios, as economic crisis or the response to specific policy measures.

 

Thermodynamics of firms' growth
Eduardo Zambrano, Alberto Hernando, Aurelio Fernandez-Bariviera, Ricardo Hernando, Angelo Plastino

http://arxiv.org/abs/1504.07666


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Ants Swarm Like Brains Think

Ants Swarm Like Brains Think | Prediction, Learning and Games | Scoop.it
“As I watched films of these ant colonies, it looked like what was happening at the synapse of neurons. Both of these systems accumulate evidence about their inputs—returning ants or incoming voltage pulses—to make their decisions about whether to generate an output—an outgoing forager or a packet of neurotransmitter,” Goldman said. On his next trip to Stanford, he extended his stay. An unusual research collaboration had begun to coalesce: Ants would be used to study the brain, and the brain, to study ants.

 

http://nautil.us/issue/23/dominoes/ants-swarm-like-brains-think-rp


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The Neuroscience Of Imagination

The Neuroscience Of Imagination | Prediction, Learning and Games | Scoop.it
Understanding how imagination works could be the key to daydreaming yourself into a sharper, more creative person.

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Eli Levine's curator insight, March 31, 2014 1:26 PM

On one hand, the imagination is a world of hallucinations and opinions that have no grounding in reality and no basis in facts.

 

On the other hand, it is the design space in our minds to create a much wider array of stuff than is in the actual universe, such that we can figure out new ways of operating on and in this plane of existence.

 

It must always be checked out against the long and short term effects of those actions in the real world.  It is useful to have this portable design space in order to problem solve and bring about new innovations for our world.  The trouble comes when people live in their imaginations rather than try to be aware of how their imaginative thoughts are only just thoughts until they are proven (or disproven) to be otherwise.

 

Think about it.

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How Attention Works: The Brain’s Anti-Distraction System Discovered — PsyBlog

How Attention Works: The Brain’s Anti-Distraction System Discovered — PsyBlog | Prediction, Learning and Games | Scoop.it

Attention is only partly about what we focus on, but also about what we manage to ignore. Neuroscientists have pinpointed the neural activity involved in avoiding distraction, a new study reports. This is the first study showing that our brains rely on an active suppression system to help us focus on the task at hand (Gaspar & McDonald, 2014).

The study’s lead author, John Gaspar, explained the traditional view of attentional control: This is an important discovery for neuroscientists and psychologists because most contemporary ideas of attention highlight brain processes that are involved in picking out relevant objects from the visual field. It’s like finding Waldo in a Where’s Waldo illustration.”

While this process is important, it doesn’t tell the whole story of how attention works.


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On the Edge of Chaos: Where Creativity Flourishes

On the Edge of Chaos: Where Creativity Flourishes | Prediction, Learning and Games | Scoop.it
Scientists have come a long way in understanding how the brain generates creative ideas. Their work can inform classroom structures if educators want to inspire more creativity in students.

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David McGavock's curator insight, May 11, 2014 7:26 PM

"To develop ideas that could be considered creative, the brain has to be both stable and flexible at the same time. Brains perform just this type of balancing act every second of every day. “The brain maintains a duality of systems that are constantly introducing flexibility into our thinking and then trying to stabilize our thinking,” Bilder said. The brain evaluates a new stimuli, compares it the plan originally set and then decides on the optimal degree of flexibility or stability to pursue. This cycle happens three times per second."

Miklos Szilagyi's curator insight, May 12, 2014 8:44 AM

Children are limitlessly creative... most of them... later we together, parents, school, society, we kill this in them... and then later on we find out, we need it and try to built it (back...).

 

Now, the first logical step would be not to kill it on the first place... I know it needs such a broad, longterm view which is difficult to achieve  our quarterly philosophy... 

Bodil Hernesvold's curator insight, May 14, 2014 4:23 PM

Is there chaos or not in my classroom? Does it count if the teacher is in a state of chaos? How do we "manage" or "encourage" chaos, then?

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Jonas Eliasson: How to solve traffic jams

It’s an unfortunate reality in nearly every major city—road congestion, especially during rush hours. Jonas Eliasson reveals how subtly nudging just a small percentage of drivers to stay off major roads can make traffic jams a thing of the past.


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