Abstract: The ability of scientific knowledge to contribute to public debate about societal risks depends on how the public assimilates information resulting from the scientific community. Bayesian decision theory assumes that people update a belief by allocating weights to a prior belief and new information to form a posterior belief. The purpose of this study was to determine the effects of prior beliefs on assimilation of scientific information and test several hypotheses about the manner in which people process scientific information on genetically modified food and global warming. Results indicated that assimilation of information is dependent on prior beliefs and that the failure to update beliefs in a Bayesian fashion is a result of several factors including: misinterpreting information, illusionary correlations, selectively scrutinizing information, information-processing problems, knowledge, political affiliation, and cognitive function.
Remember domino theory? One country going Communist was supposed to topple the next, and then the next, and the next. The metaphor drove much of United States foreign policy in the middle of the 20th century. But it had the wrong name. From a physical point of view, it should have been called the “sandpile theory.” Real-world political phase transitions tend to happen not in neat sequences, but in sudden coordinated fits, like the Arab Spring, or the collapse of the Eastern Bloc. These reflect quiet periods punctuated by crises—like a sandpile. You can add grains of sand to the top of a sandpile for a while, to no apparent effect. Then, all at once, an avalanche sweeps sand down from the top in an irregular pattern, possibly setting off little sub-avalanches as it goes.
The Intergovernmental Panel on Climate Change (IPCC) is becoming irrelevant to climate policy. By seeking consensus and avoiding controversy, the organization is suffering from the streetlight effect — focusing ever more attention on a well-lit pool of the brightest climate science. But the insights that matter are out in the darkness, far from the places that the natural sciences alone can illuminate.
Climate change: Embed the social sciences in climate policy David Victor
Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper.
Self-Organization, Emergence, and Constraint in Complex Natural Systems Jonathan Lawhead
Making toast doesn’t sound very complicated -- until someone asks you to draw the process, step by step. Tom Wujec loves asking people and teams to draw how they make toast, because the process reveals unexpected truths about how we can solve our biggest, most complicated problems at work. Learn how to run this exercise yourself, and hear Wujec’s surprising insights from watching thousands of people draw toast.
To maintain stability yet retain the flexibility to adapt to changing circumstances, social systems must strike a balance between the maintenance of a shared reality and the survival of minority opinion. A computational model is presented that investigates the interplay of two basic, oppositional social processes—conformity and anticonformity—in promoting the emergence of this balance. Computer simulations employing a cellular automata platform tested hypotheses concerning the survival of minority opinion and the maintenance of system stability for different proportions of anticonformity. Results revealed that a relatively small proportion of anticonformists facilitated the survival of a minority opinion held by a larger number of conformists who would otherwise succumb to pressures for social consensus. Beyond a critical threshold, however, increased proportions of anticonformists undermined social stability. Understanding the adaptive benefits of balanced oppositional forces has implications for optimal functioning in psychological and social processes in general.
The Critical Few: Anticonformists at the Crossroads of Minority Opinion Survival and Collapse by Matthew Jarman, Andrzej Nowak, Wojciech Borkowski, David Serfass, Alexander Wong and Robin Vallacher http://jasss.soc.surrey.ac.uk/18/1/6.html
Computers aren't best suited to visual object recognition. Our brains are hardwired to quickly see and match patterns in everything, with great leaps of intuition, while the processing center of a computer is more akin to a very powerful calculator. But that hasn't stopped neuroscientists and computer scientists from trying over the past 40 years to design computer networks that mimic our visual skills. Recent advances in computing power and deep learning algorithms have accelerated that process to the point where a group of MIT neuroscientists has found a network design that compares favorably to the brain of our primate cousins.
This is important beyond the needs of automated digital information processing like Google's image search. Computer-based neural networks that work like the human brain will further our understanding of how the brain works, and any attempts to create them will test that understanding. Essentially, the fact that these networks work to a level comparable to primates suggests that neuroscientists now have a solid grasp of how object recognition works in the brain.
To see how current networks hold up, the MIT scientists started by testing primates. They implanted arrays of electrodes in the inferior temporal (IT) cortex and area V4 (a part of the visual system that feeds into the IT cortex) of the primates' brains. This allowed them to see how neurons related to object recognition responded when the animals looked at various objects in 1,960 images. The viewing time per image was a mere 100 milliseconds, which is long enough for humans to recognize an object.
They then compared these results with those of the latest deep neural networks. These networks produce arrays of numbers when fed an image – different numbers for different images. If it groups similar objects into similar clusters in this number matrix representation, it's deemed accurate. "Through each of these computational transformations, through each of these layers of networks, certain objects or images get closer together, while others get further apart," explains lead author Charles Cadieu.
The best network, developed by researchers at New York University, classified objects as well as the macaque - a medium-sized Old World monkey - brain. That's the good news. The bad is that they don't know why. Neural networks are learning from massive datasets containing millions or billions of images, churning through the information with help from the high-performance graphical processing units that power the latest video games. But nobody knows quite what is going on in there as the networks refine their own algorithms.
Using algorithms based on the swarming behavior of ants and bees, the U.S. Navy is turning to driverless boats to protect its ships.
This August, on the James River in Virginia, the U.S. Navy staged the kind of scene you’d expect to see at the beginning of a James Bond movie. As a large ship moved through the water, a helicopter overhead spotted an unidentified boat approaching and sent a warning to a small fleet of escort boats. Some were armed with loudspeakers, others with flashing lights, another with a .50 caliber machine gun.
Once the fleet zeroed in on the threatening vessel with radar and infrared sensors, some of the escort boats broke away and quickly encircled it. They flashed lights and blasted warnings through loudspeakers. Threat resolved.
All of the escort boats were unmanned—and yet they moved together as a group, thanks to what’s known as “swarm intelligence.”
Himalayas and tropical regions likely next hotspots for language extinction. The world's roughly 7000 known languages are disappearing faster than species, with a different tongue dying approximately every 2 weeks. Now, by borrowing methods used in ecology to track endangered species, researchers have identified the primary threat to linguistic diversity: economic development. Though such growth has been shown to wipe out language in the past on a case-by-case basis, this is the first study to demonstrate that it is a global phenomenon, researchers say.
Many people know about the threatened polar bear and extinct passenger pigeon, but few have heard of endangered and extinct languages such as Eyak in Alaska, whose last speaker died in 2008, or Ubykh in Turkey, whose last fluent speaker died in 1992, says Tatsuya Amano, a zoologist at the University of Cambridge in the United Kingdom and lead author of the new study. It’s well known that economic growth or the desire to achieve it can drive language loss, he notes—dominant languages such as Mandarin Chinese and English are often required for upward mobility in education and business, and economic assistance often encourages recipients to speak dominant languages. Whereas specific case studies demonstrate such forces at work, such as the transition from Cornish to English in the United Kingdom and from Horom to English in Nigeria, this is the first study to examine losses worldwide and rank economic growth alongside other possible influences, he says.
Data on the number and location of surviving fluent speakers of endangered languages are scant, but Amano and colleagues used the most complete source available—an online repository called Ethnologue—for their analysis, he says. From the database, the group was able to calculate the geographical range, number of speakers, and rate of speaker decline for languages worldwide and map that data within square grid cells roughly 190 km across, spanning the entire globe. Although they were able to obtain information about the range and number of speakers for more than 90% of the world’s estimated 6909 languages, they could only glean details about the rate of decline or growth for 9%, or 649, of those languages, Amano notes.
Next, they looked for correlations between language loss and factors such as a country's gross domestic product and levels of globalization as calculated by an internationally recognized index. In addition, they examined environmental factors such as altitude, which might contribute to language loss by affecting how easily communities can communicate and travel.
Of all the variables tested, economic growth was most strongly linked to language loss, Amano says. Two types of language loss hotspots emerged from the study, published online today in the Proceedings of the Royal Society B. One was in economically well developed regions such as northwestern North America and northern Australia; a second was in economically developing regions such as the tropics and the Himalayas. Certain aspects of geography seemed to act as a buffer or threat, Amano says. For example, recent declines appear to occur faster in temperate climates than in the tropics or mountainous regions—perhaps because it is easier to travel in and out of temperate regions, Amano says. More research is necessary to determine precisely what it is about economic development that kills languages, he adds. Figuring out how growth interacts with other factors such as landscape is the next step, he says.
"This is the first really solid statistical study I've seen which shows principles about language decline that we've know about, but hadn't been able to put together in a sound way," says Leanne Hinton, a linguist at the University of California, Berkeley. Economics is far from the whole story, however, she says. In the United States, for example, current attitudes toward endangered tongues stem in large part from historical policies that forced young American Indians to eschew their native tongues in order to learn English, she says. Generations of disease, murder, and genocide—both historic and present, in some regions—have also played an important role and were not included in the new study's analysis, she says.
Although the study is silent on the subject of interventions to help preserve endangered languages, there is a range of revitalization efforts that can serve as examples, such as the incorporation of the Hawaiian language into school curricula and daily government operations, she says.
Memes were originally framed in relationship to genes. In The Selfish Gene, Dawkins claimed that humans are “survival machines” for our genes, the replicating molecules that emerged from the primordial soup and that, through mutation and natural selection, evolved to generate beings that were more effective as carriers and propagators of genes. Still, Dawkins explained, genes could not account for all of human behavior, particularly the evolution of cultures. So he identified a second replicator, a “unit of cultural transmission” that he believed was “leaping from brain to brain” through imitation. He named these units “memes,” an adaption of the Greek word mimene, “to imitate.” Dawkins’ memes include everything from ideas, songs, and religious ideals to pottery fads. Like genes, memes mutate and evolve, competing for a limited resource—namely, our attention. Memes are, in Dawkins’ view, viruses of the mind—infectious. The successful ones grow exponentially, like a super flu. While memes are sometimes malignant (hellfire and faith, for atheist Dawkins), sometimes benign (catchy songs), and sometimes terrible for our genes (abstinence), memes do not have conscious motives. But still, he claims, memes parasitize us and drive us.
In the seminal work 'An Evolutionary Approach to Norms', Axelrod identified internalization as one of the key mechanisms that supports the spreading and stabilization of norms. But how does this process work? This paper advocates a rich cognitive model of different types, degrees and factors of norm internalization. Rather than a none-or-all phenomenon, we claim that norm internalization is a dynamic process, whose deepest step occurs when norms are complied with thoughtlessly. In order to implement a theoretical model of internalization and check its effectiveness in sustaining social norms and promoting cooperation, a simulated web-service distributed market has been designed, where both services and agents' tasks are dynamically assigned. Internalizers are compared with agents whose behaviour is driven only by self-interested motivations. Simulation findings show that in dynamic unpredictable scenarios, internalizers prove more adaptive and achieve higher level of cooperation than agents whose decision-making is based only on utility calculation.
Self-Policing Through Norm Internalization: A Cognitive Solution to the Tragedy of the Digital Commons in Social Networks by Daniel Villatoro, Giulia Andrighetto, Rosaria Conte and Jordi Sabater-Mir http://jasss.soc.surrey.ac.uk/18/2/2.html
What will be the growth of the Gross Domestic Product (GDP) or the competitiveness of China, United States, and Vietnam in the next 3, 5 or 10 years? Despite this kind of questions has a large societal impact and an extreme value for economic policy making, providing a scientific basis for economic predictability is still a very challenging problem. Recent results of a new branch—Economic Complexity—have set the basis for a framework to approach such a challenge and to provide new perspectives to cast economic prediction into the conceptual scheme of forecasting the evolution of a dynamical system as in the case of weather dynamics. We argue that a recently introduced non-monetary metrics for country competitiveness (fitness) allows for quantifying the hidden growth potential of countries by the means of the comparison of this measure for intangible assets with monetary figures, such as GDP per capita . This comparison defines the fitness-income plane where we observe that country dynamics presents strongly heterogeneous patterns of evolution. The flow in some zones is found to be laminar while in others a chaotic behavior is instead observed. These two regimes correspond to very different predictability features for the evolution of countries: in the former regime, we find strong predictable pattern while the latter scenario exhibits a very low predictability. In such a framework, regressions, the usual tool used in economics, are no more the appropriate strategy to deal with such a heterogeneous scenario and new concepts, borrowed from dynamical systems theory, are mandatory. We therefore propose a data-driven method— the selective predictability scheme —in which we adopt a strategy similar to the methods of analogues , firstly introduced by Lorenz, to assess future evolution of countries.
Matthieu Cristelli , Andrea Tacchella, Luciano Pietronero
The question What is Complexity? has occupied a great deal of time and paper over the last 20 or so years. There are a myriad different perspectives and definitions but still no consensus. In this paper I take a phenomenological approach, identifying several factors that discriminate well between systems that would be consensually agreed to be simple versus others that would be consensually agreed to be complex - biological systems and human languages. I argue that a crucial component is that of structural building block hierarchies that, in the case of complex systems, correspond also to a functional hierarchy. I argue that complexity is an emergent property of this structural/functional hierarchy, induced by a property - fitness in the case of biological systems and meaning in the case of languages - that links the elements of this hierarchy across multiple scales. Additionally, I argue that non-complex systems "are" while complex systems "do" so that the latter, in distinction to physical systems, must be described not only in a space of states but also in a space of update rules (strategies) which we do not know how to specify. Further, the existence of structural/functional building block hierarchies allows for the functional specialisation of structural modules as amply observed in nature. Finally, we argue that there is at least one measuring apparatus capable of measuring complexity as characterised in the paper - the human brain itself.
Though they started at opposite ends of the socioeconomic spectrum, McCulloch and Pitts were destined to live, work, and die together. Along the way, they would create the first mechanistic theory of the mind, the first computational approach to neuroscience, the logical design of modern computers, and the pillars of artificial intelligence. But this is more than a story about a fruitful research collaboration. It is also about the bonds of friendship, the fragility of the mind, and the limits of logic’s ability to redeem a messy and imperfect world.
People have wanted to understand our motivations, thoughts and behaviors since the ancient Greeks inscribed “know thyself” on the Temple of Apollo at Delphi. And understanding the brain’s place in health and disease is one of this century’s greatest challenges – take Alzheimer’s, dementia and depression for example.
There are many exciting contributions from neuroscience that have given insight into our thoughts and actions. Three neuroscientists have just been awarded the 2014 Nobel Prize for their discoveries of cells that act as a positioning system in the brain – in other words, the mechanism that allows us to navigate spaces using spatial information and memory at a cellular level.
There are many exciting contributions from neuroscience that have given insight into our thoughts and actions. For example, the neural basis of how we make fast and slow decisions and decision-making under conditions of uncertainty. There is also an understanding how the brain is affected by stress and how these stresses might switch our brains into habit mode, for example operating on “automatic pilot” and forgetting to carry out planned tasks, or the opposite goal-directed system, which would see you going out of your usual routine, for example, popping into a different supermarket to get special ingredients for a recipe.
Disruption in the balance between the two is evident in neuro-psychiatric disorders, such as obsessive compulsive disorder, and recent evidence suggests that lower grey matter volumes in the brain can bias towards habit formation. Neuroscience is also demonstrating commonalities in disorders of compulsivity, methamphetamine abuse and obese subjects with eating disorders.
Neuroscience can challenge previously accepted views. For example, major abnormalities in dopamine function were thought the main cause of adult attention deficit hyperactivity disorder (ADHD). However, recent work suggests that the main cause of the disorder may instead be associated with structural differences in grey matter in the brain.
What neuroscience has made evidently clear is that changes in the brain cause changes in your thinking and actions, but the relationship is two-way. Environmental stressors, including psychological and substance abuse, can also change the brain. We also now know our brains continue developing into late adolescence or early young adulthood, it is not surprising that these environmental influences are particularly potent in a number of disorders during childhood and adolescence including autism.
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