Category Archives: Algemeen

A Rapidly Changing Earth System.

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The arrival of the age when human activity has come to dominate and seriously compromise the stability of the Earth System poses fundamental questions for our cultural, social and economic institutions, our communities, and our systems of governance. Three decades of internationally coordinated research on the Earth system has led to the conclusion that Earth has entered a new geological epoch – the Anthropocene.

Humanity will face a turbulent road of rapid and profound changes and uncertainties on route to a politically, socially and ecologically resilient society. The Anthropocene changes our relationship with the planet and the stability and resilience of the Earth system as a whole.

The globally dominant industrial growth economies operate as if reality is about organizing inert matter in efficient ways to satisfy human needs and wants and generate surplus value. This cultural ‘operating system’ couples our everyday taken-for-granted assumptions about the world to an ideology of dead matter, human utility and perpetual growth. The fundamental challenge of the Anthropocene is to restore a commitment to the vitality of life in all its forms on this planet as the basis of our institutions and professions. This is the cultural renaissance the Anthropocene calls forth.

Earth’s entry into an anthropogenic era poses challenging questions for the long-term sustainability of global human civilization. It is, in fact, not clear if a planetary civilization as energy-intensive as ours can be sustained for centuries. While some aspects of this question rest within political science and sociology, a broader perspective is developing on the transition and transformation, that only a new collaboration among the physical, biological, and social sciences can address to illuminate and inform the choices we face.

In addition, the trajectory will almost surely be characterized by the activation of some tipping elements (Tipping Cascades) and by nonlinear dynamics and abrupt shifts at the level of critical biomes that support humanity. Current rates of change of important features of the Earth System already match or exceed those of abrupt geophysical events in the past. With these trends likely to continue for the next several decades at least, the contemporary way of guiding development founded on theories, tools, and beliefs of gradual or incremental change, with a focus on economy efficiency, will likely not be adequate to cope with this trajectory. Thus, in addition to adaptation, increasing resilience and anti-fragility will become a key strategy for navigating the future. There is significant evidence from a number of sources that the risk of a planetary threshold and thus, the need to create a divergent pathway should be taken seriously.

The Earth System may be approaching a planetary threshold that could lock in a continuing rapid pathway toward much warmer conditions. This pathway would be propelled by strong, intrinsic, biogeophysical feedbacks difficult to influence by human actions, a pathway that could not be reversed, steered, or substantially slowed.

The impacts of on human societies would likely be massive, sometimes abrupt, and undoubtedly disruptive. Avoiding this threshold by creating a stabilized pathway can only be achieved and maintained by a coordinated, deliberate effort by human societies to manage our relationship with the rest of the Earth System, recognizing that humanity is an integral, interacting component of the system. Humanity is now facing the need for critical decisions and actions that could influence our future for centuries, if not millennia .

Generic resilience-building and anti-fragility strategies include developing, buffers, redundancy, diversity, and other features of resilience and anti-fragility that are critical for transforming human systems in the face of global ecological and natural systems and possible surprise associated with tipping points. Such a strategy include maintenance of diversity, modularity, and redundancy; management of connectivity, openness, slow variables, and feedbacks;  understanding social–ecological systems as complex adaptive systems, especially at the level of the Earth System as a whole.


… the Earth has been pushed out of the pre-industrial Holocene norm by human activities. How will the Anthropocene evolve? Even with a rapid and decisive shift of contemporary human societies toward sustainable development, the Anthropocene will remain a distinctly different epoch from the Holocene.

The current trajectory of human societies would lead to an Anthropocene that is a much warmer and biotically different state of the Earth System, one that is no longer governed by the late Quaternary regime of glacial–interglacial cycles, and with far fewer species. Earth in a much warmer greenhouse state would be nothing new. However, it would be novel for Homo sapiens, which evolved only 200,000 years ago. Under  this scenario, the Earth System would be markedly different from the one humans now know, and from the state that supported the development of human civilization. Which trajectory the Anthropocene follows depends on the decisions and actions of global society today, and over the next few decades.

Human technology could be responsible for many more manifestations of ‘nature’ than the previous Earth was ever capable of sustaining.

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Humans are altering the planet, including long-term global geologic processes, at an increasing rate. Any formal recognition of an Anthropocene epoch in the geological time scale hinges on whether humans have changed the Earth system sufficiently to produce a stratigraphic signature in sediments and ice that is distinct from that of the Holocene epoch.

Biologically, there is nothing remarkable in the fact that humans are agents of ecological change and environmental upset. All species transform their surroundings. The dizzying complexity of landscapes on Earth is not just a happy accident of geology and climate, but the result of billions of years of organisms grazing, excavating, defecating, and decomposing. Nor is it unusual that certain lucky species are able to outcompete and eventually entirely displace other species. The Great American Interchange, when North American fauna crossed the newly formed isthmus of Panama to conquer South America three million years ago is just one among countless examples of swift, large-scale extinctions resulting from competition and predation.

The driving human forces responsible for many of the anthropogenic signatures are a product of the three linked force multipliers: accelerated technological development, rapid growth of the human population, and increased consumption of resources. These have combined to result in increased use of metals and minerals, fossil fuels, and agricultural fertilizers and increased transformation of land and nearshore marine ecosystems for human use. The net effect has been a loss of natural biomes to agriculture, cities, roads, and other human constructs and the replacement of wild animals and plants by domesticated species to meet growing demands for food. This increase in consumption of natural resources is closely linked to the growth of the human population. Anatomically modern Homo sapiens emerged ~200,000 years ago . Around the start of the Holocene, humans had colonized all of the continents except Antarctica and the South Pacific islands and had reached a total population estimated at 2 million. Up to this point, human influence on the Earth system was small relative to what has happened since the mid-20th century; even so, human impacts contributed to the extinction of Pleistocene megafauna. However, the key signals used to recognize the start of the Holocene epoch were not directly influenced by human forcing, which is a major distinction from the proposed Anthropocene epoch.

What is remarkable, however, is the stunning speed of human adaptation relative to other species, and that our adaptation is self-directed. From sonar and flight to disease immunity, humans can ‘evolve’ exquisite new traits in a single generation. The Anthropocene represents a catastrophic mismatch between the pace of human technological evolution and the genetic evolution of nearly every other species on Earth. As with many other geological epochs, the Anthropocene has been heralded with a mass extinction, one which is generally accepted to be the sixth great one to occur on Earth.

The Anthropocene represents a catastrophic mismatch

Mass extinctions, however, have always been succeeded by a recovery of biodiversity. The Permian mass extinction made room for dinosaurs to flourish; the Cretaceous extinction gave rise to the marvellous diversification of mammals and birds. These massive adaptive radiation events, where surviving populations swiftly speciate, take anywhere from tens of thousands to tens of millions of years, depending on the degree of the initial extinction and the stability of the Earth’s climate.

Population extinctions today are orders of magnitude more frequent than species extinctions. Population extinctions, however, are a prelude to species extinctions, so Earth’s sixth mass extinction episode has proceeded further than most assume. The massive loss of populations is already damaging the services ecosystems provide to civilization. When considering this frightening assault on the foundations of human civilization, one must never forget that Earth’s capacity to support life, including human life, has been shaped by life itself . When public mention is made of the extinction crisis, it usually focuses on a few animal species (hundreds out of millions) known to have gone extinct, and projecting many more extinctions in the future. But a glance at our maps presents a much more realistic picture: they suggest that as much as 50% of the number of animal individuals that once shared Earth with us are already gone, as are billions of populations. Given the increasing trajectories of the drivers of extinction and their synergistic effects. Future losses easily may amount to a further rapid defaunation of the globe and comparable losses in the diversity of plants, including the local (and eventually global) defaunation-driven coextinction of plants. The likelihood of this rapid defaunation lies in the proximate causes of population extinctions: habitat conversion, climate disruption, overexploitation, toxification, species invasions, disease, and (potentially) large-scale war— all tied to one another in complex patterns and usually reinforcing each other’s impacts. Much less frequently mentioned are, however, the ultimate drivers of those immediate causes of biotic destruction, namely, human overpopulation and continued population growth, and overconsumption. These drivers, all of which trace to the fiction that perpetual growth can occur on a finite planet, are themselves increasing rapidly. Thus, to emphasize, the sixth mass extinction is already here and the window for effective action is very short, probably two or three decades at most. All signs point to ever more powerful assaults on biodiversity in the next two decades, painting a dismal picture of the future of life, including human life.

No matter the severity of the extinction, however, vacant ecological niches are eventually filled and new ones are created as life adapts to a newly empty Earth. Keeping this in mind, it’s possible to argue that not all human activity is antithetical to biodiversity. Our destructive tendencies might actually be a form of creative destruction, clearing the playing field so marginalized actors can dominate. More controversially, human activity may actually create new species and modes of being, just as the Cambrian explosion 530 million years ago was marked by biological breakthroughs such as active predation, hard exoskeletons, and the beginning of the vertebrate body plan.

Our destructive tendencies might be a form of creative destruction

What if we already are in the midst of a previously unnoticed adaptive radiation phase, an ‘Anthropocene explosion’, that has so far gone unnoticed?  Where should we look for this evolutionary event? Three groups of entities are at the cusp of notable speciation events: human-associated animals, genetically engineered organisms, and manmade technologies, both physical and digital.

Firstly, the most obvious beneficiaries of the Anthropocene explosion are those that have been the sole actors in past adaptive radiations, that is, living organisms. Synanthropes—organisms that associate with human settlements—have adopted the human environment as their native habitat, and therefore likely have a bright future ahead of them. Our cultural evolution is mirrored in their genetic evolution. Pests and pathogens, for instance, evolve in concert with pesticides and medicines. Many city animals already show specific adaptations to the loud, hectic and artificially bright urban wilderness. As the Anthropocene marches onwards, the speculative naturalist may be tempted to hope that rats, cats and cockroaches will diversify into new and splendid forms. In the realm of ‘true’ wilderness, certain creatures are thriving as the human machine decimates others. In this area, the ocean is perhaps the starkest example. Scraped clean by long lines and bottom-trawlers, and acidified by a carbon-heavy atmosphere, the oceans face a ‘gelatinous future’ dominated by jellyfish and microbes, which will flourish in the ecological niches vacated by fish. Only the most nihilistic observer, however, would argue that an ocean dominated by jellyfish and microbes has the same value as one teeming with corals, sharks, and whales, or that a rat-and-trash filled alley is as ecologically productive or philosophically inspiring as a forested valley.

Human activity may create new species and modes of being

Although breeding domesticated species for selected traits speeds up genetic change, it will not gift the Anthropocene with fantastic new species. We’ve pushed the genetic envelope in terms of how much milk a cow can produce, or how small a chihuahua can shrink while still remaining a functioning organism. Despite their extravagant appearances, these animals are not distinct species from their wild counterparts. It will be thousands or even millions of years before truly novel species emerge from the diversification of synanthropes and other tenacious clingers-on.

It therefore secondly falls to genetic engineering to add truly novel organisms to the ‘Anthropocene Explosion’. The transgenic GlowFish, for instance, is one of the most well-known and appealing ‘charismatic microfauna’ of the GMO world, a creature which is in many ways as wonderful as the common zebrafish. The GlowFish is a first step towards creating a new, valid species. A creature even more marvellously engineered, perhaps even pieced together gene by gene to construct an organism from the ground up, would be equally valid and worthy of our appreciation and protection as a species that arose through natural selection.

Thirdly, we need to look at technology to see a potential for a new type of evolutionary event. There is something poetic in the fact that the widespread acceptance of the Anthropocene coincides with the moment that our technologies are poised to become as complex and autonomous as organic life. The sphere of human thought, culture, and technology—sometimes called the noosphere or the technium—is not just dependent upon the biosphere but intimately bound up with it, and vice versa. Though we have maintained a stubbornly mechanical conception of technology, in truth the technosphere may be, or be becoming, a valid form of nature, populated by actors that are ‘species’ in everything but name.

Does it falls to genetic engineering to add truly novel organisms to the ‘Anthropocene Explosion’?

Up until now, what has prevented humans from viewing individual technologies in more organismal terms are their predictability and simplicity. What we are beginning to see are the very early stages of man-made technologies that may one day become as richly complex as DNA-based organisms. If technologies should someday exhibit true autonomy, able to gather energy on their own, repair themselves, and reproduce, it would be difficult to argue in good faith that their existence is any less ‘natural’ than that of a grasshopper or anemone. A technological species does not need to mimic an organic one in order to be viable. In fact, a digital or genetically engineered copy of the original is bound to pale in comparison. Rather than creating artificial sentience that matches that of an animal or human, it may be far more interesting to foster new, unprecedented forms of mind and embodiment.

Do we need to look at technology to see a potential for a new type of evolutionary event?

However, individuals of any species in isolation cannot exactly be said to constitute a nature. ‘Nature’ is a highly complex, unpredictable assemblage composed of interacting individuals. No matter how majestic a reminder of the wilderness, a polar bear in a zoo is more a cultural artefact than an aspect of nature; no matter how much it reminds us of a living dog, the Big Dog robot is still more cultural than natural.

Though humans often wrongfully categorize the natural world through the lens of technology—the body is a machine, a forest is a factory—there is a strong resonance between the digital and the genetic. An organism’s ‘hardware’ is encoded in the software of DNA and RNA. Life, in all its apparent glory, exists solely for the propagation of genetic information. Our bodies are elaborate (and disposable) vehicles for our genomes, which have been upgrading from body to body, species to species over the last four billion years.

From this perspective, all of nature is merely the interaction of billions of genetic programs. If this is true, then interacting man-made digital technologies might be the equivalents of physical ecosystems. It’s therefore arguable that the sum total of earth’s information flow has not diminished during the Anthropocene, but rather that biodiversity has merely switched media from nucleotides to electronic circuits. It may be that one day we be able of  perfectly simulating entire ecosystems, from an entire redwood down to the last neuron in a snail’s brain, and that we could run this simulation many times over. Human technology, then, could be responsible for many more manifestations of ‘nature’ than the previous Earth was ever capable of sustaining.

But … is this what we want?

Energy Transformation

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Targets on greenhouse gas emissions and transition towards renewable energy are ambitious. The generation of energy will be increasingly from variable renewables like wind and solar in all sizes, ranging from big offshore wind farms and large solar fields down to urban wind turbines on high rise buildings and rooftop solar panels at individual homes.

Not only the diversity in generation capacity will increase but also the diversity in ownership from a few owners of big power plants in the past to a mix of numerous owners of smaller and dispersed generating plants today and tomorrow.

The prospect of massively distributed clean renewable power generation is becoming a reality more than ever before. The present combination of technology improvements and market-scale developments is soon to be followed by a second wave of more capable and lower cost storage solutions.

Alternating Current (AC) power systems have been in dominant position for over 100 years due to the inherent characteristic from fossil energy driven rotating machines. The high-voltage, high-power grid today is based on AC technology. The large conventional generators connected to this grid are responsible for supplying power, keeping the frequency within limits, and maintaining the voltage within boundaries throughout the nodes on the grid. This has been predominantly unidirectional; i.e., from these large conventional generators to the consumers through the transmission and distribution system. The power supply, demand balancing, and voltage control in such grids have been relatively simple, mainly because of the availability and predictability of the generators.

The gradual changes of load types and distributed renewable generation in AC local distribution systems provide food for consideration of adding Direct Current (DC) networks. In the early stage, power systems were designed to supply the lighting, heating, and motor driving loads which are mainly AC type. However, load evolution in AC local distribution systems have been occurring quietly with the development of power electronics techniques and new lighting equipment for high efficiency of energy utilization and control flexibility.

Recently, two converging factors have renewed interest in DC power distribution. First, there are better alternatives for decentralized power generation, the most significant of these being solar PV panels. Because solar panels can be located right where energy demand is, long distance power transmission isn’t a requirement. Furthermore, solar panels naturally produce DC power, and so do chemical batteries, which are the most practical storage technology for PV systems.

AC power is in many cases converted back to DC power by the adapters of DC-internal appliances like computers, LEDs and microwaves. These energy conversions imply power losses, which could be avoided if a solar powered building would be equipped with DC distribution. In other words, a DC electrical system could make a solar PV system more energy efficient.

Secondly a growing share of our electrical appliances operate internally on DC power.  Traditional AC motors as direct drivers for washing machines, refrigerators, air conditioners and various industrial machines are being gradually replaced by power electronics based AC motors in order to control the motor speed and to save energy. Within the next 10-20 years, we can expect an expansion of the total loads in households being made up of DC consumption. In, for example, a building that generates solar PV power but distributes it indoors over an AC electrical system, a double energy conversion is required.


Because the operational energy use and costs of a solar PV system are nil, a higher energy efficiency translates into lower capital costs, as fewer solar panels are needed to generate a given amount of electricity. Furthermore, there is no need to install an inverter, which is a costly device that has to be replaced at least once during the life of a solar PV system. Lower capital costs also imply lower embodied energy: if fewer solar panels and no inverter are required, it takes less energy to produce the solar PV installation, which is crucial to improve the sustainability of the technology.

A similar advantage would apply to electrical devices. In, for example, a building with DC power distribution, DC-internal electric devices can do away with all the components that are necessary for AC to DC conversion. This would make them simpler, cheaper, more reliable, and less energy-intensive to produce. The AC/DC adapters (which can be housed in an external power supply or in the device itself) are often the life-limiting component of DC-internal devices, and they are quite substantial in size.

Large advantage is possible in data centers, where computers are the main load. Some data centers have already switched to DC systems, even if they’re not powered by solar energy. Because a large adapter is more efficient than a multitude of small adapters, converting AC to DC at a local level (using a bulk rectifier) rather than at the individual servers, can generate significant energy savings.


At the moment, when we are ‘discussing’ the energy transition, there is a giant window of opportunity as similar in scope, scale, and character to the data/telecommunication industry’s disruptive migration to solid state computers, microprocessor-based electronics, and the Internet. DC power has come back as an increasingly strong opportunity, thanks to the technology advancements in power conversion, generation, transmission, and consumption. However, in spite of significant advantages in many applications, there are still several challenges to overcome and the DC technology should be integrated into the system through a smooth and step-by-step process. The DC technology has already started to be integrated into the existing AC system step by step. This is leading to the emergence of hybrid AC/DC systems in which AC and DC buses are connected through interlinking, bidirectional converters. Control of the interlinking converter, as the energy bridge between the AC and DC sides, is a critical issue for ensuring stability and utilizing the system potential to improve the quality of service. Microgrids, which are characterized by a combination of dispersed generation units, storage systems and loads, are one key application where hybrid AC/DC systems may offer significant benefits.

Microgrids, as a promising building block of future smart distribution systems, are one of the main areas where the DC technologies are expected to prevail. In particular, hybrid AC/ DC Microgrids may facilitate the integration process of DC power technologies into the existing AC systems.

What’s needed is an electrical energy network of power that can deliver the same systemic virtues to power systems that the Internet produced for communications: the concept of interconnected domains of smaller, more self-reliant grids. These grids should be equally capable of distributing both centrally and widely deployed distributed electricity generation. The present power grids do not get power from distributed sources; they are still highly centralized with little storage capability. Engineering marvels that they are, they have essentially been designed to distribute power generated at large central generation stations in one direction to loads where it is consumed.



Urban System Engineering

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Urban complexity implies multiple dimensions of interactions over a vast range of phenomena governed for example by economic, physical, ecological and environmental aspects and political, health and educational systems. And of social aspects, cognitive and ethical intelligence like social economic status, equality, demographics, psychological and cognitive factors such as ideology, sexual identity. Ethical intelligence defined as the structural logic to survive, earn value, add value, acquire and manage knowledge and deal with the nature of reality. Revealing these full range of interactions between sets of these variables is difficult. Complex systems, at least theoretically could be a better way of showing such multi-layered interactions. The difficulty is that the way key factors are nested into a depiction or model of a complex system is often reductive or very restrictive, being for the most part much less than dynamic. This is so of autonomous agent systems as well. Network analysis, a common method for analyzing complex systems, is also reductive, missing these factors as well even though much light is thrown on how the complex system connectivity influences it. In a nut shell, complex system need to include temporal or heterochronic relationships (chronocomlexity) between multiple social variables nested nodes and edges.

Urban complexity and resilience, begins with two radical premises. The first is that humans and nature are strongly coupled and co-evolving, and should therefore be conceived of as one ‘social-ecological’ system. The second is that the long-held assumption that systems respond to change in a linear, predictable fashion is simply wrong. According to resilience thinking, systems are in constant flux; they are highly unpredictable and self-organizing, with feedbacks across time and space. In the jargon of theorists, they are complex adaptive systems, exhibiting the hallmarks of complexity. A key feature of complex adaptive systems is that they can settle into a number of different equilibria. The concept of resilience upends old ideas about sustainability: instead of embracing stasis, resilience emphasizes volatility, flexibility, and de-centralization.

Change, from a resilience perspective, has the potential to create opportunity for development, novelty, and innovation. Resilience is not a condition nor a passive state, it is a truly dynamic and societal process, progressive and in flux all the time. So, resilience is neither the mere fact of persistence; nor does the latter reliably imply the former. Resilience is a quality: a capacity to negotiate change through creative responses, including the prospect of transformation to a radically different form when conditions demand. In their current form, cities inherently lack resilience. By pushing Earth’s climate and biosphere out of the dynamics of the Holocene humanity is at risk of moving our planet outside a safe operating space for humanity by altering important feedback loops, potentially producing abrupt and irreversible systemic changes with impacts on current and future generations.

From the start human agency, global social and economic networks and important feedback interactions between human systems and planetary and urban processes – have not been dynamically represented or otherwise resolved in existing and integrated assessment models.

Capturing these dynamics in a new generation of Urban Dynamic Models should allow us to address a number of critical questions about socio-economic-ecological turbulence in our cities.

The biggest challenge in answering such questions is to understand human activities and social structures as the least predictable, but at present also the most influential component of cities in the Anthropocene. Understanding and modelling cites, the tightly intertwined social-economic-environmental system that humanity now inhabits, requires addressing human agency, system-level effects of networks and complex coevolutionary dynamics. Analyzing and understanding these dynamics sheds light on a coevolutionary view of urban dynamics in the Anthropocene, including multiple development pathways, obstacles, dangerous domains and the sought-after safe and just space for humanity.

Theory and models of biogeophysical dynamics are well established, and our efforts developing  an adaptive network and flexible framework for modelling social-economic-environmental urban dynamics, regime shifts and transformations in an emergent and dynamic way, are offering interesting perspectives.  Dynamic prescription of scenarios, including phenomena such as social learning, segregation, norm and value change, and group dynamics such as coalition formation are very promising.

Our existing Urban System Engineering is an exciting approach/model to study such phenomena. Such analysis offers significant potential to augment existing models and methodologies. But we are not there yet, further research, experiments, support is necessary.

Rethinking the fundamental objective of our urban future and their destinies.

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Humanity is making tremendous progress. It’s the best time ever to be alive. Why does no one know it?

Cities are a dense network of interconnected systems of increasing complexity, all of which use feedback information to exist in dynamic equilibrium. A new era of innovation for our urban future. A moment of recognition/realization

The city as the general form of human settlement, as the ecological niche of our species, belongs to the world of antifragility. The city as a system has proven through history to be capable to adapt, self-organize, improve and take advantage of the unpredictable, in short to prosper in disorder. Not only does the city exist for ten thousands years, not only is our culture predominantly a product of city, but also the majority of the human population lives in cities and the trends are that of a further consistent and rapid increase of urban population.

RETHINKING …… cities accelerate time – space compression associated with globalization and expand consequences and interactions of interrelated phenomena, creating new configurations, redefying concepts of cities as major engines of global economy and redefying boundaries and cooperation. Converges of mega poles and emerge of mega regions expand the edges of urban socio ecological dynamics beyond the individual city boundaries and accelerate changes in multiple scales.

Cities are under constant pressure to adapt and this makes our build environment fragile. This pressure is applied by stressors that lead to change in areas such as economy, technology, society, ecology etc. and cannot be immediately neutralized by standard design, engineering and architectural concepts. Solutions that are being put forward today must therefore also be considered with a view to their -shortening- expiry date. Cities futures must be able to react to change by deploying different strategies. During the conception phase, the challenges of dealing with uncertainties and acknowledging the unknown are fundamental for development strategies.

These development strategies and concepts are not static, there is no absolute definition of a city, no end point, but rather a process, or series of steps, by which cities become more liveable and resilient and, hence, able to respond quicker to new challenges, shocks and continuous stress. The current urban and natural systems are seldom capable of dealing with sudden shocks, which are bound to occur at an increasing rate.

This are elements that recognize the important fact that cities are complex adaptive, hybrid systems -as systems of systems (natural, social, economical, geographical, ecological,…)- have always had the capability to adapt and to improve, due to external and internal stressors, due to the variety and plurality of needs and desires of their inhabitants, users, social and economic subjects, using available technologies and information.

THE REAL-TIME CITY IS REAL! As layers of networks and intelligent technology blanket urban space, new approaches to the study of cities are emerging. The way we describe and understand cities is being radically transformed—as are the tools we use to design them.

The view of the city as an ecosystem has changed the way we look at urban change and design. More recently, the city is seen as a complex adaptive hybrid system and this implies that adaptation, resilience and anti-fragility of the city can be discussed as core characteristics. A development dependent on accurate predictions is fragile, since such forecasting on complex system is, strictly speaking, impossible. This is in particular true for social systems, which are twice complex: besides their objective complexity due to numerous non-linear interactions among its constitutive elements, they also contain agents capable of choice and—within limits—free to chose.

But a development that does not tend towards a future and does not aim at producing a future, is a contradiction in terms. And it is not unreasonable to hold that a community wants and ought to think about its future, at least within an imaginable time horizon of three to four generations, and to try to avoid undesirable futures. In order to adapt to new circumstances urban systems need to become agile. Rather than only responding to change by coping with it, urban environments can actually become stronger than before through their response to stresses and unpredicted events (shocks). This concept is called anti-fragility. Anti-fragility is defined as a convex response to a stressor or source of harm (for some range of variation), leading to a positive sensitivity to increase in volatility (or variability, stress, dispersion of outcomes, or uncertainty, what is grouped under the designation disorder cluster, and offers interesting opportunities.

However, most of current urban futures and developments are based on a technological paradigm in which the quantification of elements such as housing, jobs and parking spaces, standards, and regulations seem more important than achieving resilience and anti-fragility strategies. These kinds of urbanism are strongly single issue driven and linear simple system-based. Recent developments, with a focus on data, often deepen the technological paradigm, hence adding vulnerability to urban systems. In this sense, not only does it appear inappropriate to call a city smart, especially in the case of the smartness of things and not people, but it also appears to us insufficient to promote the mere resilience and anti-fragility of cities.

A complex, adaptive and anti-fragile system such as city cannot only limit itself to absorb or ward off blows: it ought to do more than just adapt, it needs to evolve, transform: redundancies, duplications, plasticity, exaptation’s, are all elements of an evolution which has enabled the city to survive and thrive, and to evermore become the ecological niche of the human species.

A future avenue to increase the strength of the city is to create anti-fragile environments, which grow under influence of external impacts. Applying stressors in the conception of antifragile strategies necessitates a systemic view of the built environment. The entire built environment consists not only of constructional and technical systems, but also includes living space with complex spatial, social and economic interaction and its comprehension calls for a systemic approach. A systemic view includes an understanding of the environment that assumes interacting systems with dynamic relationships to everyday reality.

The different subsystems in the city, such as the transportation system, the energy system, and others, are increasingly seen as complex systems as such, but these feed in to the complexity of the city as a whole. The adaptivity of the city can be influenced through strategic design interventions supporting self organisation. Adaptation, anti-fragility requires creating space to adapt, hence this concept advocates redundancy (‘space for the unknown’) in the urban realm.

The complexity of the entire city is difficult -but not impossible- to grasp, as the interrelations, dependencies, and connectivity between all subsystems in the city are complex by nature. The metabolism and design of flows and modelling by Urban System Engineering made it possible to discuss the sustainability and anti-fragility of the city as a whole, and close the cycles and dynamic interactions of energy, water, materials, social, cultural, economic, ecological and technology.

These methodologies, including emergism and anti-fragilism, focus on the city as a responsive system, in which self-organisation and the adaptive capacities of complex systems determine urban processes. Emergism takes complexity as the input for the design of cities . Self-organisation and emergence are key concepts, and are used to design interventions in the system to achieve certain changes. These concepts are common in nature and can be used in designing future cities and landscapes that are more adaptable and anti-fragile. They exist in nature, between humans and nature, between humans and humans, between human-created structures and people in cities, societies, states, supply chains, social services, the globalized world. The create patterns of mutually supportive and reinforcing properties creating feedback-loops of communication in relational interdependency. We need to understand which patterns are creating a sense of liveness in our cities, the conditions for aliveness in natural settings, architectural, geographical space and social as well as economic systems.

Modelling by Urban System Engineering acts as a ‘facilitator’ of the process of change, intervening at specific structures, places or times to initiate a change in the system. Approaches such as eco-acupuncture and Swarm Intelligence (collective intelligence as a more flexible way of thinking about how to plan, design for and respond to challenges , based on the behavior of swarms in nature), aim to design interventions in an existing urban system to transform to become more resilient and anti-fragile; ‘fluid’ and capable of responding to different paces of change that might occur in the city: fast, slow, or sudden.

To support the processes which make cities resilient and antifragile is the fundamental objective of our urban future and of their destinies.

From Urban Complexity to predictive models.

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Over the past few decades an extensive literature has been published on the study of complex physical, biological and social systems. As complex systems tend to be generic and pervasive they differ from complicated  systems that are distinctive and specialized. Complex systems have some  generally accepted properties. Their structure spans several scales, constituents are interdependent and interact in  nonlinear ways. These interactions give rise to novel and emergent dynamics. The  combination of structure and emergence is viewed as self-organization.

Complex Systems Properties.

  • Many (relatively simple) components
  • Nonlinear interactions (including feedback loops)
  • No centralized control
  • Emergent behavior
  • Evolution and adaptation
  • Complexity arises from variables and processes that operate over different scales in space and time. Boundaries in time and space in non-equilibrium systems which separate alternative stable states

Ecosystems and social systems are complex adaptive systems: complex because they have many parts and many connections between the parts; adaptive because their feedback structure gives them the ability to change in ways that promote survival in a fluctuating environment.

How can we understand human – ecosystem interaction when social systems and ecosystems are so overwhelmingly complex? The answer lies in emergent properties: the distinctive features and behavior that ‘emerge’ from the way that complex adaptive systems are organized. Once aware of emergent properties, it is easier to ‘see’ what is really happening. Emergent properties are cornerstones for comprehending human – ecosystem interactions in ways that provide insights for sustainable development.

Emergent properties are the most often observed real world phenomenon in a complex system. Emergent properties are patterns and regularities arising through interactions  among smaller or simpler entities in a system that themselves do not exhibit such  properties. In biological systems interactions at lower levels emerge as objects expressing  their properties at a higher level. Emergent properties tend to arise  as new objects from one scale to another. Emergent properties are a key generic property  of complex adaptive economic system; it is what makes economies become complex.

A city is not just a large scale artificial built environment composed of smaller scale artifacts such as buildings, roads, bridges … each of which is composed of still smaller artifacts and so on. And, artifacts are essentially simple systems. They might be very complicated such as super computers but essentially they are simple system. So what is it that makes the simple system and artifact ‘city’ a complex system?

 Cities as dually complex systems

Complex systems studies range from detailed studies of specific systems, to studies of the mechanisms by which patterns of collective behaviors arise, to general studies of the principles of description and representation of complex systems. These studies are designed to enable us to understand and modify complex systems, design new ones for new functions or create contexts in which they self-organize to serve our needs without direct design or specification. The need for applications to biological, cognitive, social, information, and other engineered systems is apparent.

A city is to be understood as a human – ecosystem interaction, as dual complex adaptive systems because they are composed of material components and human components. As a set of material components alone, the city is an artifact and as such a simple system; as a set of human components – the urban agents – the city is a complex system. It is the urban agents that by means of their interaction – among themselves, with the city’s material components and with the environment – transform the artifact city into the complex artificial system city. In order for a complex -biological- system to survive and evolve there must be interplay between competition  and  co-operation  at  different  scales. Complex biological/social systems are called adaptive systems because they can  adapt to a changing environment. A small subset of adaptive complex systems are self reproducing and experience birth, growth and death.

As a dual complex adaptive system the city emerges out of the interactional activities of its agents, but once it emerges, it affects the behavior of its agents and so on in circular causality. Furthermore, because of its size, the city is a large-scale collective and complex artifact that on the one hand interacts with its environment, while on the other it is an environment for the millions of people that live and act in cities.

Artifacts are not just the outcome of human interaction; rather they are also the media of interaction. The process involves internal representations in the form of ideas, intentions, memories thoughts that originate and reside in the mind/brain of urban agents, and, on the other, external representations, that is to say artifacts such as texts, cities, buildings or roads that reside in the world.

They interact by means of the externally represented artifacts; roads, bridges, parks … buildings, neighborhoods and whole cities and metropoles.

The city is a dual complex adaptive system also in the sense that the city as a whole is a complex system and each of its agents is also a complex system. The implication is that we have to include the cognitive capability of the urban agents in the dynamics of cities.

Urgent need for a qualitative and quantitative understanding of cities.

Human civilization, its various parts, including its technology, and its environmental context, are all complex. The increasing complexity suggests that there will be a growing need for widespread understanding of complex systems as a counter point to the increasing specialization of professions and professional knowledge. The insights of complex systems research and its methodologies may become pervasive in guiding what we build, how we build it, and how we use and live with it. Possibly the most visible outcome of these developments will be an improved ability of human beings aided by technology to address complex global social and ecological/ environmental problems, third world development, poverty in developed countries, war and natural disasters.

These dual complex systems are dynamic and far from equilibrium. It is not possible to plan for an optimal future state.

For this we have to develop the ability to capture and represent specific systems, rather than just accumulate data about them. In this context: to describe relationships, know key behaviors, recognize relevance of properties to function, and to simulate dynamics and response. Furthermore to understand the interplay of behaviors at multiple scales, and between the system and its environment, connected or integrated within and across levels.

Describing and understand system behavior and the relations and boundaries between subsystems, and what are the relevant parameters for description or for affecting the behavior of the system. And by this dealing with complexity, with strategies that relate the complexity of the challenge to the complexity of the system that must respond to them.

In dual complex systems it is possible to predict most preferable futures -the desired system conditions- to some degree and for some time-scales. It is important to know what qualitative structure could emerge and discuss the merits and demerits of these, since these are the choices open to the system at present. Without models that can explore the possible future structures and morphologies of the system, planning and interventions can have no predictable outcomes.

Understanding complex systems does not mean that we can predict their behavior exactly, it is not just about massive databases, or massive simulations, even though these are important tools of research in complex systems. Understanding complexity is neither about prediction or lack of predictability, but rather a qualitative and quantitative knowledge of how well we can predict, and only within this constraint, what the prediction is.

Under construction

By | Algemeen | No Comments

In my lectures on robotics, AI and bioscience we not only focus on the ‘science’, but lately we deliberate on the range, growth and convergence of emerging technologies that unlock solutions to the most intractable problems, fueling new industries, and enabling massive disruption. The  convergence of artificial intelligence, robotics, AR/VR, synthetic biology, etc. are discussed and debated  and we are painting the implications and potential impact of these technologies across a wide range of disciplines, economy and industries. A multi-disciplinary picture of the future driven by these exponential technologies, as we are at an intersection in time where globalization, entrepreneurship, innovation and sciences are blending.

One of the latest discussion was on how these developments could generate new models of an innovation-driven inclusive economy characterized by a range of new technologies that fuse physical, digital and biological worlds. Embracing entrepreneurship to seize opportunities of this new development paradigm by nurturing an environment that encourages new ideas, tolerates mistakes and supports new businesses.

We are still in the midst of Industry 4.0, where manufacturing has taken on the label of ‘smart’ through the integration of the IoT, AI, cyber-physical systems, cloud and cognitive computing. The basic principle behind this industrial revolution is that by chaining machines, intelligent devices and systems manufacturers are creating smart networks throughout the value chain (from materials to production) that can control each other.

As mentioned above, the scientific and technological advancements continue to grow at an incredible speed—so much that we already see the next step on the horizon, one which will bring an increased human touch back to manufacturing. Consumers high-demand of individualization in the products they buy, preferring a degree of ‘hands-on’ personalization and customization with their products. Therefore, where at the moment technology is at the forefront of manufacturing, we will see an increased collaboration between humans and intelligent systems. The merge between the high-speed intelligent systems and machines with the cognitive, critical thinking skills of humans. Estimates vary, but artificial intelligence and automation will probably affect about half of jobs within the next two decades. It is striking that all jobs have aspects that are routine, repetitive and ripe for machine learning. The key question is whether the new, emerging jobs are ones in which humans have a comparative advantage over machines, or if they will require human skills as a complement. The only certainty is that most workers will have to adjust. Their ability to work and contribute to society will depend on that adjustment being successful.

This accelerating pace of change and the widespread disruption enabled by technology feels extremely uncomfortable. Our brains have been hard-wired to think linearly for hundreds of thousands of years. Learning to think and anticipate on the forthcoming future is incredibly hard…but critical. That is one thing. We also need to meaningfully evolve policy, ethics, law, economic and social structures, etc.

The discussion continue.

AI in the city

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By definition, complex systems have many agents, but what makes it different from simply a large or complicated system is that the behavior of the system is not solely determined by the behavior of the agents, but also of the relationships between the agents. It has suites of interactivity, feedback loops and tipping points and emergent properties. These interdependencies increase the overall value (emergent value), may decrease the required investments. Complexity is the result of the interactions among the various agents, it can not be simplified without losing its essence.

As a complex system the city emerges out of the interactional activities of its agents, but once it emerges, it affects the behavior of its agents and so on in circular causality. The city in this respect is a complex evolving system. The city is a dual complex system as each of its agents is also a complex system. The implication is that we have to include the cognitive capability of the urban agents in the dynamics of cities. The city is an hybrid, co-evolving system.

Understanding these complex hybrid co-evolving systems takes intricate study, testing agents and their relation to each other in nuanced detail. Cities are comprehensive complex systems and many models have been developed that mimic its behavior. We can quite accurately replicate past behavior, but there are many aspects, feedback systems, emergence in urban systems that we don’t understand.

As post-industrial societies change into information and knowledge societies, gradually arizes the digital city of ubiquitous computers that are so prevalent that they are invisible, effectively melding into the background while having a profound effect in our everyday lives. Although the digital city is often referred to as a smart city – a label widely adopted by marketing departments of corporations and cities alike – its scope is much greater. Beyond a reductionist view of discrete solutions centered on digital technologies aiming at improving urban efficiencies, the digital city encompasses a deeper evolution: transforming them into systems capable of dynamically mediating the interactions between humans and their environments.

Improvements and convergences in machine learning and neurosciences combined with the availability of massive datasets and the ubiquity of high-performance scalable computing are propelling us into a new age of Artificial Intelligence (AI). The ability of advanced machine-learning algorithms to mine the growing stocks and flows of data related to the planning and operations of complex systems at the micro or macro levels is likely to trigger a wave of optimization across domains.

The impact that artificial intelligence (AI) has and will continue to have on our cities and the way we live and work in them over the next couple of decades, can be tremendous. AI presents a complex set of considerations for cities. As with any new technology, the possibilities are exciting but also raise deep policy (and philosophical) questions about their impact across several areas of urban life. But can artificial intelligence also support us to observe, understand, measure the interaction between agents, view this complex evolving urban system holistically, encourage anticipation of surprises and thresholds, encourage reflection and adaptive management?

Artificial Intelligence, with its ability to analyze scores of information from varied sources, can tease out the interactions between agents and let us understand the levers across the system we need to activate to enact change. AI can provide an virtual nervous model that employs a number of purpose-built AI programs and machine-learning algorithms to process the vast amounts of incoming data. A model that reflects the real social-physical-ecological city committed to support a new form of urban modelling that is capable of self-correction, predictive analytics and decision support.









City Intelligence

By | Algemeen | No Comments

Deep uncertainties veil our view of the future of our urban planet after 2050. Within this timeframe, the planet will face changes in migration, climate change, disruptions in financial systems, shifting energy supplies, and pandemics – these and more are complex enough on their own, let alone together.

The cities of the future will be huge,  dense and the statistics paint a bleak future.

The urban sphere is central to building sustainability for people, planet, and prosperity. Because of its importance, we need expanded and flexible scientific research and knowledge generation about urban systems. Creating this new urban knowledge requires interdisciplinary efforts for greater understanding of complex urbanization processes and their multi-scale interactions and feedbacks with the Earth system.

Cities are highly complex, socio-ecological and technical in nature and are difficult to analyze. By definition complex adaptive system are systems in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system’s behavior. The study of complex adaptive systems, a subset of nonlinear dynamical systems, is highly interdisciplinary and blends insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior.

We were largely unable to view this complex system holistically. Existing modeling and design tools invoke static ‘playbook’ concepts that don’t adequately represent the complexity with its constantly changing variables. Simple measures have proven to not work terribly well, the influences across the system are more powerful.

The understanding of adaptive and evolving complex systems and the ability to process relevant (!) amounts of data supported by artificial intelligence, with its ability to analyze scores of information from varied sources, can tease out the interactions between agents and let us understand the levers across the system we need to activate to enact change.

These programs will leverage rapid improvements in computing and neural networks in the coming decades. The challenges to design an intelligent analytic, predictive and prescriptive model to support decision making are certainly formidable. A model that will employ a number of purpose-built AI programs and machine-learning algorithms to process and monitor the vast amounts of incoming sensory data.

To keep such a system real time, sensors — whether cameras, acoustic networks, swarm drones and other wireless systems — will communicate information about the health and status of the city and its infrastructure. Geostationary and other satellites and orbital platforms will monitor the city’s atmosphere, pollution levels, weather systems, and local environment. Could the increasingly complex systems needed to manage the next generation of megacities become our first true artificial intelligence? The challenge of city intelligence is, to employ a biological analogy, more like genetic engineering than mechanical engineering, and part of the solution will require rewriting a city’s DNA

Dynamic systems such as these with novel AI and mathematical techniques generate capabilities to exploit and provide a deeper understanding of urban system component interactions and a unified view of the urban system behaviors. In fact it could be the first truly human-scale AI system capable of reactive and independent cognition.

Autonomous and Intelligent

By | Algemeen | No Comments

Although the burgeoning impact of robotics and autonomy has been evident for some time, we are now recognizing the rapidly accelerating emergence of robotics, artificial intelligence, and autonomy in our daily lives.

Fossil records demonstrate the sudden appearance – about 542 million years ago – of complex animals with mineralized skeletal remains. Some describe this Cambrian Explosion as the most significant event in Earth’s evolutionary history, one that irreversibly changed the biosphere and led to a stunning diversity of body forms and types. Today, the impacts of robotics, artificial intelligence and autonomy – together with their derivatives, e.g., machine learning — are about to induce a metaphorical Cambrian Explosion of transformative capabilities and applications. However this will confront us with unanticipated, high-order consequences from external factors principally outside our control.

The original Cambrian Explosion’s transformative evolutionary developments were triggered by a complex interplay of relatively small environmental changes, and the emergence of nervous networks, and the ability to move and interact with the world. A similarly complex interplay is clearly at work across the topics of robotics, artificial intelligence and autonomy as sensors, actuators, and processors get both cheaper and better. The fields of robotics, artificial intelligence and autonomy share a number of enabling technologies, research challenges, and future; it is difficult to discuss one in the absence of the others. Autonomous, for example, is a quality of a robotic system; autonomous swarms are typically multi-robotic configurations. Autonomous systems are inherently, and irreducibly, artificially intelligent robots.

Robotics, artificial intelligence, and autonomy – far from narrow topics – are closely linked to a broad range of enabling / adjunct technologies. Although much of the technology development will occur in the very fragmented labs and innovation centers, there is a need to view robotics, artificial intelligence and autonomy as a holistic, seamless system. Technology will alter our fundamental thinking about science because of the exponential convergence of key technologies including …

… Nanoscience and nanotechnology

… Biotechnology and Biomedicine

… Information Technology

… Cognitive Science and Neuroscience

… Quantum Science

Autonomy can evolve from solutions that are reactive, single platform, point solutions under minimal human control to solutions that are flexible, multi-modal, and goal-oriented featuring trusted man-machine collaboration, distributed autonomy and continuous learning.

  • Fully : Human Out of the Loop: no ability for human to intervene in real time
  • Supervised Autonomous: Human on the Loop: humans can intervene in real time
  • Semi-Autonomous: Human in the Loop: machines wait for human input before taking action. Augmented Teleoperation. A mode of operation wherein the human operator leverages video or other sensory feedback to directly control the actuators on a continuous basis
  • Non-Autonomous (Remote Control): human in the loop via remote controls; no autonomy in system.

A future that features ever more advanced human-robot-AI collaboration, a collaboration that in turn will accelerate the development of improved robotics and Artificial General Intelligence through rapid machine learning, adaptive controls, rapid algorithm development, and custom motion control systems. Novel mechanisms and high performance actuators will emerge as new construction paradigms are merging component design to generate compact multi-function systems that are both highly capable and energy efficient. Human-robotic system interaction will include conversational assistants, intent and emotion recognition, augmented reality, self-aware explainable systems, and multi-modal communications. Intelligent networks will develop to distributed, interconnected entities that have goals, sense their environment, identify constraints and threats, and plan and execute adaptive actions through the leverage of autonomy, artificial intelligence and robotics

Clearly humans and these technologies are destined to co-evolve. No aspect of our current existence will remain untouched. Humans will be augmented in many ways: physically, via exoskeletons, perceptionally, via direct sensor inputs, genetically, via AI-enabled gene-editing technologies such as CRISPR, and cognitively via AI.  Human reality will be a blended one in which physical and digital environments, media and interactions are woven together in a seamless integration of the virtual and the physical. As daunting – and worrisome – as these technological developments might seem, there will be an equally daunting challenge in the co-evolution between man and machine: the co-evolution of trust. We can expect autonomy to evolve from solutions that are reactive, single platform, point solutions under minimal human control to solutions that are flexible, multi-modal, and goal-oriented featuring trusted man-machine collaboration, distributed autonomy and continuous learning. Trusted man-machine collaboration will require validation of system competence, a process that will take our legacy test and verification procedures far beyond their current limitations. Humans will expect autonomy to be nonetheless directable, and will expect autonomous systems to be able to explain the logic for their behavior, regardless of the complexity of the deep neural networks that motivate it. These technologies in turn must be able to adapt to user abilities and preferences, and attain some level of human awareness (e.g., cognitive, physiological, emotional state, situational knowledge, intent recognition). Another consideration is how we should treat intelligent machines. Do humans have to empathize with robotic systems, especially when the systems resemble humans or pets?