Diet is a major issue facing humanity. To combat malnourishment and diseases associated with over nutrition, both research and technological breakthroughs are needed. Can science help us develop better ways to feed ourselves? This, of course, is a complex question with many potential answers—from innovations in agricultural sciences and crop production, to changes in livestock farming, to implementing and enforcing broad changes in the sustainable use of land and marine resources.
In this brief Essay, I will consider three attractive opportunities in my own field that may help provide solutions to these challenges: (1) understanding our brain circuits controlling appetite for sweet; (2) developing ways of producing intrinsically palatable, novel protein-rich nutrients in a low cost, self-sustainable, renewable, high-capacity platform; and (3) elucidating the links between our diet, the microbiome, gut-brain circuits, and metabolism. Ultimately, it may be possible to prevent disease through our diet.
Play is learning. As Vygotsky noted, it “contains all developmental tendencies in a condensed form and is itself a major source of development.”
Virtually every child, the world over, plays. The drive to play is so intense that children will do so when they have no real toys, when parents do not actively encourage the behavior, and even in the middle of a war zone. In the eyes of a young child, running, pretending, and building are fun. Researchers and educators know that these playful activities benefit the development of the whole child across social, cognitive, physical, and emotional domains. Yet, while experts continue to expound a powerful argument for the importance of play in children’s lives, the actual time children spend playing continues to decrease. Today, children play eight hours less each week than their counterparts did two decades ago . Under pressure of rising academic standards, play is being replaced by test preparation in kindergartens and grade schools, and parents who aim to give their preschoolers a leg up are led to believe that flashcards and educational “toys” are the path to success. Our society has created a false dichotomy between play and learning.
This book presents a unique synthesis of the current neuroscience of cognition by one of the world’s authorities in the field. The guiding principle to this synthesis is the tenet that the entirety of our knowledge is encoded by relations, and thus by connections, in neuronal networks of our cerebral cortex. Cognitive networks develop by experience on a base of widely dispersed modular cell assemblies representing elementary sensations and movements. As they develop cognitive networks organize themselves hierarchically by order of complexity or abstraction of their content. Because networks intersect profusely, a neuronal assembly anywhere in the cortex can be part of many networks, and therefore many items of knowledge. All cognitive functions consist of neural transactions within and between cognitive networks. After reviewing the neurobiology and architecture of cortical networks (also named cognits), the author undertakes a systematic study of cortical dynamics in each of the major cognitive functions–perception, memory, attention, language, and intelligence. In this study, he makes use of a large body of evidence from a variety of methodologies, in the brain of the human as well as the nonhuman primate. The outcome of his interdisciplinary endeavor is the emergence of a structural and dynamic order in the cerebral cortex that, though still sketchy and fragmentary, mirrors with remarkable fidelity the order in the human mind.
Most accounts of human cognitive architectures have focused on computational accounts of cognition while making little contact with the study of anatomical structures and physiological processes. A renewed convergence between neurobiology and cognition is well under way. A promising area arises from the overlap between systems/cognitive neuroscience on the one side and the discipline of network science on the other. Neuroscience increasingly adopts network tools and concepts to describe the operation of collections of brain regions. Beyond just providing illustrative metaphors, network science offers a theoretical framework for approaching brain structure and function as a multi-scale system composed of networks of neurons, circuits, nuclei, cortical areas, and systems of areas. This paper views large-scale networks at the level of areas and systems, mostly on the basis of data from human neuroimaging, and how this view of network structure and function has begun to illuminate our understanding of the biological basis of cognitive architectures.
Big Data research is currently split on whether and to what extent Twitter can be characterized as an informational or social network. We contribute to this line of inquiry through an investigation of digital humanities (DH) scholars’ uses and gratifications of Twitter. Our findings show that Twitter is considered a critical tool for informal communication within DH invisible colleges, functioning at varying levels as both an information network (learning to ‘Twitter’ and maintaining awareness) and a social network (imagining audiences and engaging other digital humanists). We find that Twitter follow relationships reflect common academic interests and are closely tied to scholars’ pre-existing social ties and conference or event co-attendance. The concept of the invisible college continues to be relevant but requires revisiting. The invisible college formed on Twitter is messy, consisting of overlapping social contexts (professional, personal and public), scholars with different habits of engagement, and both formal and informal ties. Our research illustrates the value of using multiple methods to explore the complex questions arising from Big Data studies and points toward future research that could implement Big Data techniques on a small scale, focusing on subtopics or emerging fields, to expose the nature of scholars’ invisible colleges made visible on Twitter.
Are there computers in the classroom? Does it matter? Students, Computers and Learning: Making the Connection examines how students’ access to and use of information and communication technology (ICT) devices has evolved in recent years, and explores how education systems and schools are integrating ICT into students’ learning experiences. Based on results from PISA 2012, the report discusses differences in access to and use of ICT – what are collectively known as the “digital divide” – that are related to students’ socio-economic status, gender, geographic location, and the school a child attends. The report highlights the importance of bolstering students’ ability to navigate through digital texts. It also examines the relationship among computer access in schools, computer use in classrooms, and performance in the PISA assessment. As the report makes clear, all students first need to be equipped with basic literacy and numeracy skills so that they can participate fully in the hyper-connected, digitised societies of the 21st century.
At the University of Lincoln, the student as producer agenda is seeking to disrupt consumer-based learning relationships by reinventing the undergraduate curriculum along the lines of research-engaged teaching. The open education movement, with its emphasis on creative commons and collaborative working practices, also disrupts traditional and formal campus-based education. This paper looks at the linkages between the Student as Producer project and the processes of embedding open educational practice at Lincoln. Both reinforce the need for digital scholarship and the prerequisite digital literacies that are essential for learning in a digital age.
We examine the relationship between scholarly practice and participatory technologies and explore how such technologies invite and reflect the emergence of a new form of scholarship that we call Networked Participatory Scholarship: scholars’ participation in online social networks to share, reflect upon, critique, improve, validate, and otherwise develop their scholarship. We discuss emergent techno-cultural pressures that may influence higher education scholars to reconsider some of the foundational principles upon which scholarship has been established due to the limitations of a pre-digital world, and delineate how scholarship itself is changing with the emergence of certain tools, social behaviors, and cultural expectations associated with participatory technologies.
Evolutionary biology is not a slow-moving science. Just last month a new species of hominid (Homo naledi) was unveiled at a news conference in South Africa. When did modern humans branch off as an independent species? What have been our most important adaptations? And, most importantly, what is the next evolutionary step for humanity? We reached out and spoke to five of the foremost experts on human evolution, who shared their expertise and predictions.
Anatomically modern Homo sapiens (us), are thought to have emerged as a distinct species around 200,000 years ago in Africa. While we often imagine one species of hominid handing the baton to the next in a neat, linear “evolution of man” progression, Homo sapiens lived simultaneously with several other hominid species—Homo neanderthalensis, Homo floresiensis, and the much older Homo erectus, whose geographic and temporal boundaries remain fuzzy. They also had sex with each other, as evidenced by the amount of Neanderthal DNA in our genetic material (about 2.5% – 3% on average).
Using ants and other social insects as models, computer scientists have created software agents that cooperate to solve complex problems, such as the rerouting of traffic in a busy telecom network. Insects that live in colonies—ants, bees, wasps, termites—have long fascinated everyone from naturalists to artists. Maurice Maeterlinck, the Belgian poet, once wrote, “What is it that governs here? What is it that issues orders, foresees the future, elaborates plans and preserves equilibrium?” These, indeed, are puzzling questions. Each insect in a colony seems to have its own agenda, and yet the group as a whole appears to be highly organized. Apparently the seamless integration of all individual activities does not require any supervision. In fact, scientists who study the behavior of social insects have found that cooperation at the colony level is largely self-organized: in numerous situations the coordination arises from interactions among individuals. Although these interactions might be simple (one ant merely following the trail left by another), together they can solve difficult problems (finding the shortest route among countless possible paths to a food source). This collective behavior that emerges from a group of social insects has been dubbed “swarm intelligence.”
The modern world may be obsessed with speed and productivity, but twenty-first-century humans actually have much to learn from the ancient instincts of swarms. A fascinating new take on the concept of collective intelligence and its colorful manifestations in some of our most complex problems, The Smart Swarm introduces a compelling new understanding of the real experts on solving our own complex problems relating to such topics as business, politics, and technology. Based on extensive globe-trotting research, this lively tour from National Geographic reporter Peter Miller introduces thriving throngs of ant colonies, which have inspired computer programs for streamlining factory processes, telephone networks, and truck routes; termites, used in recent studies for climate-control solutions; schools of fish, on which the U.S. military modeled a team of robots; and many other examples of the wisdom to be gleaned about the behavior of crowds-among critters and corporations alike. In the tradition of James Surowiecki’s The Wisdom of Crowds and the innovative works of Malcolm Gladwell, The Smart Swarm is an entertaining yet enlightening look at small-scale phenomena with big implications for us all.
This article applies complexity theory to urban governance. It is argued that expert-based, hierarchical-instrumental policy making encounters insurmountable obstacles in modern liberal democracies. One of the root causes of this erosion of output legitimacy is the complexity of social systems. Complexity is defined as the density and dynamism of the interactions between the elements of a system. Complexity makes system outcomes unpredictable and hard to control and, for this reason, defies such well-known policy strategies as coordination from the center, model building, and reduction of the problem to a limited number of controllable variables. It is argued that participatory and deliberative models of governance are more effective in harnessing complexity because they increase interaction within systems and thereby system diversity and creativity. Using empirical data from research on citizen participation in disadvantaged neighborhoods in the Netherlands, the author shows (a) that neighborhoods can fruitfully be seen as complex social systems and (b) the different ways in which citizen participation is effective in harnessing this complexity.
This chapter addresses the recent discovery of ‘governance’ as the complex art of steering multiple agencies, institutions, and systems which are both operationally autonomous from one another and structurally coupled through various forms of reciprocal interdependence. This discovery could well reflect the dramatic intensification of societal complexity which flows from growing functional differentiation of institutional orders within an increasingly global society with all that this implies for the widening and deepening of systemic interdependencies across various social, spatial, and temporal horizons of action. Whilst recognizing that a governance bandwagon now seems to be rolling, I am reluctant to leap onto it — and certainly not in uncritical fashion. Instead I argue that governing complexity is far from simple and, indeed, that governance failure is routine. In developing this argument, some abstract claims are presented about ‘contingent necessity’ in order to problematize the notion of social complexity before considering problems of governance.
Unexpected epidemics, abrupt catastrophic shifts in biophysical systems, and economic crises that cascade across national borders and regions are events that challenge the steering capacity of governance at all political levels. This article seeks to extend the applicability of governance theory by developing hypotheses about how different governance types can be expected to handle processes of change characterized by nonlinear dynamics, threshold effects, cascades, and limited predictability. The ﬁrst part of the article argues the relevance of a complex adaptive system approach and goes on to review how well governance theory acknowledges the intriguing behavior of complex adaptive systems. In the second part, we develop a typology of governance systems based on their adaptive capacities. Finally, we investigate how combinations of governance systems on different levels buffer or weaken the capacity to govern complex adaptive systems.
The literature on governance that has emerged in the past 15 years indicates that the position of government in society has changed substantially. This development is most often looked at as the increased complexity of Western societies and its problems, the rise of network forms of policymaking, and the decline of possibilities for hierarchical steering through public agencies. The first aim of this paper is to take two central concepts from complexity theory, self-organization and co evolution, to develop a typology of relationships between government and society. Governance can then be defined as the way in which the activities of actors are coordinated around collective problems. A second aim of the paper is to use this typology to provide a historical account of the evolution of the government-society relationship. Rather than taking the hierarchical state as our starting point, we reflect on the emergence of this governance arrangement in Western Europe.
This paper uses complexity theory as a means towards clarifying some of Gilles Deleuze’s conceptualisations in communication and the philosophy of language. His neologisms and post-structuralist tropes are often complicated and appear to be merely metaphorical. However their meanings may be clarified and enriched provided they are grounded in the science of complexity and self-organising dynamics. Reconceptualizing communication in a manner consistent with Deleuze’s philosophy enriches our understanding of the complexity involved in the process of learning and the whole of educational experience. The paper explores education as “becoming,” that is, a process of growth and becoming-other enabled by creative communication. While the mathematics of complexity is beyond the scope of this paper, some of its conjunctions with Deleuze’s philosophy will be examined for the purpose of addressing such problematic areas in education as, for example, specialisation and the breadth of curriculum. Finally, the paper moves to a practical level so as to construct an image of a self-organised classroom. Self-organising dynamics are posited as consistent with what Noddings called an excellent system of education. Education proceeds without any reference to an external aim. Rather, the “aim” is implicit in the experiential process of self-organisation and, as such, is conducive to students’ learning, creation of meanings, and eliciting broad curricula.
Recently, Bechtel has been experimenting with Self-Organized Learning Environments, or SOLEs, in her elementary school classes. SOLEs are short forays into the kind of self-organized learning that Sugata Mitra found to be so powerful. In a classroom SOLE, Bechtel asks her students a “messy question,” something that doesn’t have just one right answer, then sets them loose to research the question in small groups. Students choose who they work with, find their own information, draw their own conclusions and present their findings to the whole class. It can be a bit chaotic, but Bechtel says that’s often good. “There’s chaos and then there’s learning and you can tell the difference,” Bechtel said. She’s excited about SOLE because the method has students asking questions and taking ownership in a whole new way. The IB program already emphasizes inquiry and finding information for oneself, but Bechtel says the total freedom of the SOLE has actually pushed students to go deeper, come up with more varied results and to help one another collaboratively.
How Complex Patterns Emerge from Simple Rules in Physical and Living Systems. Studies on this aspect of swarm behavior have provided valuable information about our behavior in the human swarm, from working our way through crowds to the design of collision avoidance systems for cars. There are many other lessons that we can also learn from the behavior of animals in groups, such as swarms of locusts, flocks of birds, and schools of fish. This book is about how we can use such lessons to make better group decisions and better decisions for ourselves as individuals within a group. The individual animals in a swarm, flock, or school follow rules that help them to get the most from the group. Some of these rules help them to stay together as a unit. Others allow them to act as if they were components of a superorganism, which has no individual leader, and where the whole becomes greater than the sum of its parts as the group develops swarm intelligence and uses it to make collective decisions. The modern science of complexity has shown that collective behavior in animal groups (especially those of insects such as locusts, bees, and ants) emerges from a set of very simple rules for interaction between neighbors. It has also revealed that many of the complex patterns in human society arise from similarly simple rules of social interaction between individuals. My ultimate aim in this book is to explore how the process works and, more importantly, to help find simple rules that might guide us through the fog of complexity that so often seems to enshroud our lives.
Petabytes of data about human movements, transactions, and communication patterns are continuously being generated by everyday technologies such as mobile phones and credit cards. In collaboration with the mobile phone, internet, and credit card industries, Eagle and colleagues are aggregating and analyzing behavioral data from over 250 million people from North and South America, Europe, Asia and Africa. Eagle discusses projects arising from these collaborations that involve inferring behavioral dynamics on a broad spectrum of scales from risky behavior in a group of MIT freshman to population-level behavioral signatures, including cholera outbreaks in Rwanda and wealth in the UK. The research group is developing a range of large-scale network analysis and machine learning algorithms that will provide deeper insight into human behavior.
The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of new data collection technologies, advanced data mining and analytics support, there seems to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts include social networks and blogs, political discourse, corporate announcements, digital journalism, mobile telephony, home entertainment, online gaming, financial services, online shopping, social advertising, and social commerce. The changing costs of data collection and the new capabilities that researchers have to conduct research that leverages micro-level, meso-level and macro-level data suggest the possibility of a scientific paradigm shift toward computational social science. The new thinking related to empirical regularities analysis, experimental design, and longitudinal empirical research further suggests that these approaches can be tailored for rapid acquisition of big data sets. This will allow business analysts and researchers to achieve frequent, controlled and meaningful observations of real-world phenomena. We discuss how our philosophy of science should be changing in step with the times, and illustrate our perspective with comparisons between earlier and current research inquiry. We argue against the assertion that theory no longer matters and offer some new research directions.