Monthly Archives: november 2018

Intelligent Life

By | Algemeen | No Comments

With networks, we can organize and integrate information at different levels. On a biological level, our bodies are made up of many networks that are integrated at and communicating on multiple scales. From our genome to the molecules and cells that makeup the organs in our bodies all the way out to ourselves in our world: we are fundamentally a network of networks. Living organisms are, in essence, complex systems which process information using a combination of hardware and software. Over time, people figured this out and started to use natural systems as inspiration for efficient solutions. If we listen to what nature is telling to us, we can take better decisions, build more sustainable buildings, create systems that function where and when we need them to, all in one, assign nature solution to human problems. Nature is an ecosystem made from living organisms like us humans.

In thriving living systems, the entire process of divergence, relationship and convergence is self-organizing, set into motion by life itself. In the dynamic, moment-by-moment interplay of divergent parts coming together to create a sufficiently convergent whole, supported and connected by  a consistent yet adaptive relational and governing infrastructure, all animated and guided by the self-organizing spark of life. By this the living system is able to self-organize in order not only to persist but to adapt and ultimately to generate higher, more complex forms of life. Without the spark of life, these outcomes are impossible. With it, the paradox of diversity within unity is reconciled naturally and effortlessly in living systems, generating resilience, innovation and even beauty.

The generation of design (configuration, patterns, geometry, shape, structure, rhythm) in nature is a physical phenomenon that unites all animate and inanimate systems. Design in Nature always shows itself as systems that flow and improve themselves. Systems improve themselves because the movement from chaos to order takes many discrete steps. Nature creates and operates it’s systems efficiently. Natural processes and nature’s problem-solving methods emanate originality, precision, and incredible utilization of resources. It is no wonder why we always return to them when everything else fails or when we are in need of an excellent solution. The natural world is the most adaptable complex system ever known to humans. Evolution provides us with countless examples of systems performing various types of computations. We harnessed some of these ideas and created artificial systems comparable with natural ones, like optimization algorithms inspired by ant colonies This type of probabilistic models is useful for finding optimal solutions for situations encountered in operations management, like the shortest path problem, combinatorics problems in resource allocation, multi-objective optimization problems.

The natural world has always designed intelligent systems. Chemical networks, cells, our brain, or our societies are examples of adaptive and autonomous systems. Everywhere you look, the natural world bursts with examples of complex adaptive systems. However, nature has a significant advantage on its side: time. The majority of these systems are the result of years and years of evolution. Years during which they went from one configuration to the next until they found the best way to solve the task. Fair enough, sometimes constraints prevented a natural system from finding the best solution. Those situations had catastrophic consequences such as the extinction of a species or the loss of a large number of members of a population. Technology does not have the luxury of perfecting a solution over millions of years nor can we afford catastrophes. With all their differences, nature and technology should not be excluding each other. We should pay close attention to the natural world. Start by finding out if a biological system has not already solved the problem. If it has, then there is no point in reinventing the wheel, extract the fundamental principles and methods and transfer them to the problem we are trying to solve. Nature is a source of inspiration, while technology is the engine for creation. Artificial Intelligence (AI) is closely related to biology, neuroscience, or cognitive science. Scientists and practitioners borrowed many ideas from the natural world about computation. AI algorithms and in some instance entire fields derive from biological systems. For example, neural networks use elements from the architecture of the brain. Moreover, we have optimization algorithms inspired by natural evolution, ant colonies, or immune systems. Thus, AI and the natural world share many similarities.

So AI is closer to us then we might think as AI seeks to design and build computational systems that can reason, sense, and make decisions in complex environments and under much uncertainty. The connection between artificial intelligence and nature helps to create advanced technologies. Understanding the processes behind any form of intelligence occurring in the animated world will reshape business  and industrial processes.

Developments in artificial intelligence relate to biological and the natural world. Many algorithms and systems inspired by systems found in nature have been developed: evolutionary algorithms, artificial neural networks computational immunity systems, bio-robotics, swarm intelligence or optimization algorithms based on colonies, hives, or flocks. These ideas transformed how we develop new technologies and solve problems. Bio-inspired solutions benefit from the fact that nature has already refined a lot of the steps to make them as efficient as possible. Thus, by this we are able to develop disruptive technologies by combining engineering and natures fine-tuned solutions.

But, we should seek to act as wise, compassionate stewards of life — our own lives and all life. Only with these intentions can we be trusted to self-govern. And only with this guidance we can proceed in our technological drive.

Understanding our cities: a fusion of complex system science and AI.

By | Algemeen | 7 Comments

The fascinating thing about cities is that different aspects of them allow us to think about them in many different ways. At the level of urban infrastructure, cities certainly have features of machines, with vast constructed networks involved in transporting people, water, electricity, and waste.

At the level of the economy, cities resemble complex ecosystems, with companies and individuals filling specific niches and all living and working in a symbiotic dance. And at the level of growth and change, cities also feel like living, breathing, constantly growing and changing organisms.

But ultimately, the fact that a city has features of both a machine, a societal ecosystem, as well as a living thing means that a city is truly its own category: a novel type of socio-ecological-technological system that humans have made, and is perhaps one of our more incredible inventions.

When something is complicated, it is intricate but often lacks the dynamics that makes a system hard to understand. On the other hand, a complex system implies feedback, a sensitive dependence on the initial conditions, and emergent phenomena that are hard to predict.

As our cities’ systems grow and increasingly become interconnected, we are finding ourselves in a realm of the entanglement at the level of our cities. All cities face challenges, decision-making and accountability in a complex ‘system of systems’, of both traditional systems, such as critical infrastructure, as well as new ones resulting from emerging technologies, such as virtualization, sensor networks, etc. All aspects of a city’s life are complex combinations of events in both the real world (and physical space) and digital world (of cyberspace) and many transactions and interactions take place in or between both. Wherever they take place, the outcomes are certainly felt in the real world of a city’s stakeholders. For dynamical models to be realistic, they need to have accurate initial conditions, exact causality between systems variables and defined kinetics. The other issue with dynamical models of complex systems is the nonlinearity characteristic of complex systems. Because of the complex relationships between the variables in complex systems, the dynamics of the system quickly become nonlinear and complex.

A productive response involves looking at methodologies to understand living organisms or ecosystems. Since cities do resemble living things, at least in certain ways, perhaps we can use these approaches for living things and apply them to our own constructions, specifically our cities.

Obviously, ‘biological thinking’ can help us to better understand our urban environment, but we might also use ideas from physics to understand how innovation and productivity scale with the population of a city, network science to understand the many different diffuse networks that serve our cities, and even the quantitative social sciences to see how information spreads within an urban population.

We should be careful when we observe regularities in the global behavior of such systems: those regularities should not be taken as a clue that a formal, analytic explanation is lurking beneath the surface. The term complex system is used to describe precisely those cases where the global behavior of the system shows interesting regularities, and is not completely random, but where the nature of the interactions between the components is such that we would normally expect the consequences of those interactions to be beyond the reach of analytic solutions. Methods based on self-organization featuring emerging behaviors are very suitable for facing the complexity of the system.

These kind of systems are typically tackled by drawing inspiration from natural systems, where an intrinsic self-organization exist by which the globally intended behavior emerges out of local interaction of individuals. Given the intrinsic complexity of these applications then, new innovative techniques and tools for their analysis, design and deployment, are to be conceived. They  have to support the designer in controlling the emergence of adaptive behavior and in making qualitative and quantitate predictions about how the system works and about possible design errors.

There is a relationship between the study of complex systems, and the techniques of multi-agent system modeling. Multi-agent system modeling is about generative modeling of social processes that is computable in the mathematical sense. Urban components, actors are relatively simple components in the complex system of the city. Or more specifically, each component’s behavior is relatively simple compared to what the overall urban system is doing. A city as a whole is capable of engaging in complex behaviors. In contrast, no one single component, actor will have the impulse or knowledge to undertake such collective tasks on its own. It’s these collective behaviors that arise unexpectedly that are called ’emergent’ behaviors.

Complexity describes the behavior — it captures the available information, sensory capabilities, interaction dynamics and the range of possible actions a system can take. Complexity captures these degrees of freedom and the information available, while emergent phenomena are the actual behaviors, the occurrence or the appearance of those behaviors.

Emergence happens when the system has evolved to some critical point. In self-organized systems, critical states act as a kind of attractor. Once it reaches that critical state, the system seems to flip a switch and become resilient to future disruptions — the same disruptions that drove them to criticality in the first place. A collective then emerges, whose behavior as a whole is no longer correlated to the behavior of individual components. In this way, the system maintains its decentralized character, yet can act as a single entity. Thus, in tying the concepts to computational systems, the expression of any algorithms of these individual components must necessarily be simple, distributed and scalable. So while any system may begin with a simple set of components, under the right conditions, it will nevertheless be enough to generate a diverse range of differently-scaled systems, whether in nature or computing.

Ultimately, any sort of model that describes a complex system can be useful in providing insight into how cities operate, they must always be used with a certain amount of humility, in recognition of the complex reality that is a city.

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.

Understanding and modelling cites, defined as 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.

Developments in artificial intelligence relate to biological and the natural world. Many algorithms and systems have been developed inspired by systems found in nature. Examples are evolutionary algorithms, artificial neural networks computational immunity systems, bio-robotics, swarm intelligence or optimization algorithms based on colonies, hives, or flocks. These ideas transformed how we develop new technologies and solve problems. Bio-inspired solutions benefit from the fact that nature has already refined a lot of the steps to make them as efficient as possible. Thus, we will develop disruptive technologies by combining engineering and natures fine-tuned solutions.

Can we, based on this knowledge, experiences, develop intelligent models to visualize urban dynamic relations, feedbacks with the ability to process relevant (!) amounts of critical data? Urban modeling supported by artificial intelligence, with its ability to analyze scores of information from varied sources, can tease out the interactions between attributes and let us understand and predict the levers across the system we need to activate to enact change. Could the increasingly complex systems needed to manage the next generation of megacities become our first true artificial intelligence?  As we already have the models to understand and describe urban systems, the next step is to build artificial intelligence capabilities into our urban system modelling. A fusion of AI and complex system science will be vital to fully understand the urban web of life and maximize social-economic and ecological assets and values and catalyze a myriad of innovations. A real-time digital ‘dashboard’ for urban system management that would enable the monitoring, modelling probabilistic programming and an array of statistical machine-learning techniques. A decision support system for the management of these complex systems at a scale and speed never before possible. We have our urban system modelling and AI methods to do this. The challenge is to build something truly transformational, easy to use in real-time, open-access and data-dense, initiative uses machine learning and simulation modelling (urban design & architecture) to create a 3-4D living model of a (specific) city. This will require collaboration among partners and so we are building a platform (we still have some hurdles to take) that can bring the breakthrough: prototyping a Digital Urban Twin in its full form.

We have a once-in-our-lifetime opportunity

By | Algemeen | No Comments

The Holocene epoch of the last 10,000 years or so is defined by highly unusual stability in the Earth system. In particular, the climate system shows little variability compared to the preceding late Pleistocene. The Holocene is now giving way to the Anthropocene, in which human influences introduce instability in the Earth system of a degree unprecedented in human history – but common in geological time. The consequences for all political institutions, not just those parts of government normally classified as environmental, are profound.

This unusually stable Earth system of the Holocene epoch of the past 10,000 years, in which human civilization arose, is yielding to a more dynamic and unstable Anthropocene driven by human practices. The consequences for key institutions such as states, markets, and global governance, are profound. Path dependency in institutions complicit in destabilizing the Earth system constrains response to this emerging epoch. Institutional analysis can highlight reflexivity as the antidote to problematic path dependency. A more ecological discourse stresses resilience, foresight and state shifts in the Earth system. Ecosystemic reflexivity can be located as the first virtue of political institutions in the Anthropocene. Undermining all normative institutional models, this analysis enables re-thinking of political institutions in dynamic social-ecological terms.

The domains  variening from the nitrogen and carbon cycles to ocean acidification, urbanization, and climate change, energy and material use etc– might at first appear vastly different. Yet there are also significant similarities. Importantly, all  domains exhibit key manifestations of the changed role of humankind in the planetary system, as it is captured in the notion of the Anthropocene. Each displays different dimensions, but all  are inevitably entangled in the complexities of the Anthropocene. Moreover, analysis manifests the global links through numerous interdependencies and teleconnections. The interdependencies, inequalities and disparities that are uncovered in exploring the domains have important consequences for the governance challenge of the Anthropocene and the underlying need for fundamental changes in social values and development pathways.

Physical non-linear systems, societal complexity, co-evolution of socio-epistemic formations, intricate feedback loops between the material and the mental, econophysics, city planning, ……. Complexity is, without a doubt, a more than appropriate term for the Anthropocene. The interconnection of entities, places, agencies, and times is a strong conviction across the disciplinary board when it comes to the world today. Thus, it has become difficult to imagine a system that is, indeed, non-complex. Problems tend to become ever more wicked, solutions ever more tentative and short-lived. There seems to be a general limit not only to understanding but also to the forms of representation itself.

Understanding the impact of the Anthropocene is understanding the Earth System as being influenced by biogeophysical feedbacks within the system that can maintain it in a given state (negative feedbacks) and those that can amplify a perturbation and drive a transition to a different state (positive feedbacks). Some of the key negative feedbacks that could maintain the Earth System in Holocene-like conditions— notably, carbon uptake by land and ocean systems—are weakening relative to human forcing, increasing the risk that positive feedbacks could play an important role in determining the Earth System’s trajectory. Most of the feedbacks can show both continuous responses and tipping point behavior in which the feedback process becomes self-perpetuating after a critical threshold is crossed; subsystems exhibiting this behavior are often called tipping elements. The type of behavior—continuous response or tipping point/abrupt change—can depend on the magnitude or the rate of forcing, or both. Many feedbacks will show some gradual change before the tipping point is reached.

Human feedbacks in the Earth System are an external force driving change to the Earth System in a largely linear, deterministic way; the higher the forcing in terms of anthropogenic greenhouse gas emissions, the higher the global average temperature. However, analysis argue that human societies and our activities need to be recast as an integral, interacting component of a complex, adaptive Earth System. This framing puts the focus not only on human system dynamics that reduce greenhouse gas emissions but also, on those that create or enhance negative feedbacks that reduce the risk that the Earth System will cross a planetary threshold and lock into a -as example Hothouse Earth pathway.

The Anthropocene holds out the potential to transcend the silently assumed dualism which haunts much of environmental thought; clearly demonstrating that Humanity is imbedded within, entwined with, and vulnerable to nature. Arguing that as Humanity is undoubtedly contained within and impacting nature through its activities and as nature can be seen to be responding reciprocally to those human activities, that both Humanity and Nature must be understood as existing as a singular interconnected system.

At its core, the Anthropocene commits the practice and understanding of human ethics to the unprecedented proportions and dynamics of the epoch. The entire physical scale of the planet—from the individual to the global—is compressed down to questions of conscience, responsibility, and empathy. This ethical reorientation extends not only for and towards one’s immediate neighbor, including the next proximity along the scale, but also to the very remote human, or non-human, entity. Modernity seems to have interrupted long-held principles of spatiotemporal ethics, defined by an integral continuity between past and future generations, as well as a clear positioning within an immediate environment.

Recognition of the Anthropocene connotes a powerful challenge to human institutions, as the non-human world becomes impossible to ignore as a central player in human history. This challenge merits more than response from environmental governance conceived as a niche area to be consigned to a government department or an academic sub-discipline, or even the ‘mainstreaming’ of ecological concerns into all areas of government. By confirming the causal force of human social processes in driving the character of the Earth system, whose instability in turn becomes a larger force, the Anthropocene forces a re-think of social-ecological systems and the place of political institutions therein (along with deep commitments about what constitutes rationality in these institutions and beyond). The depth, novelty, dynamism, and complexity of the challenge call to the ecosystemic dimension of reflexivity effectively understanding the active Earth system, the capacity to reconsider core values such as justice in this light, and ability to seek, receive, and respond to  potential ecological state shifts. This framework can be applied in institutional analysis, evaluation, and design in a way that is true to the dynamic nature of the Anthropocene, and so avoids the temptation to think in terms of static institutional models. Taking the Anthropocene seriously suggests an evolving institutionalism joining inquiry and practice, in the face of existing dominant institutions that fall so far short of the requirements of this emerging epoch.