A number of social-ecological systems exhibit complex behavior associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviors is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance—measured, for example, by the maximum element of the covariance matrix of the network—is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.
Early Warning Signs in Social-Ecological Networks.
PLoS ONE 9(7): e101851. doi:10.1371/journal.pone.0101851 (2014)
Recently much attention has been paid to the study of the robustness of interdependent and multiplex networks and, in particular, the networks of networks. The robustness of interdependent networks can be evaluated by the size of a mutually connected component when a fraction of nodes have been removed from these networks. Here we characterize the emergence of the mutually connected component in a network of networks in which every node of a network (layer) alpha is connected with q_alpha its randomly chosen replicas in some other networks and is interdependent of these nodes with probability r. We find that when the superdegrees q_alpha of different layers in a network of networks are distributed heterogeneously, multiple percolation phase transition can occur. We show that, depending on the value of r, these transition are continuous or discontinuous.
It is time to move from innovation as an ideology to innovation as a process.
Liz Rykert's insight:
Important conversation about Innovation in the social sector. Three points highlighted:
"First, innovation is often perceived as a development shortcut; thus innovation becomes overrated. The tremendous value that is created by incremental improvements of the core, routine activities of social sector organizations gets sidelined. Therefore pushing innovation at the expense of strengthening more routine activities may actually destroy rather than create value.
Second, innovation in social sector organizations often has little external impact to show when it is enacted in unpredictable environments. Even proven innovations often fail when transferred to a different context. Yet the cumulative learning from failures may be tremendously valuable in understanding how a particular context ticks. This potentially builds and strengthens an organization’s capacity for productive innovation over time. In other words, if we evaluate innovation primarily by its outcome in the form of external impact, we may undervalue the positive internal organizational impact that comes from learning from failed innovation.
Third, the hoped-for success factors for innovation that researchers and consultants have identified ignore the power of negative organizational factors, such as bad leadership, dysfunctional teams, and overambitious production goals."
Complex systems have multilevel dynamics emerging from interactions between their parts. Networks have provided deep insights into those dynamics, but only represent relations between two things while the generality is relations between many things. Hypergraphs and their related Galois connections have long been used to model such relations, but their set theoretic nature has inadequate and inappropriate structure. Simplicial complexes can better represent relations between many things but they too have limitations. Hypersimplices, which are defined as simplices in which the relational structure is explicit, overcome these limitations. Hypernetworks, which in the simplest cases are sets of hypersimplices, have a multidimensional connectivity structure which constrains those dynamics represented by patterns of numbers over the hypersimplices and their vertices. The dynamics of hypernetwork also involve the formation and disintegration of hypersimplices, which are seen as structural events related to system time. Hypernetworks provide algebraic structure able to represent multilevel systems and combine their top-down and bottom-up micro, meso and macro-dynamics. Hypernetworks naturally generalise graphs, hypergraphs and networks. These ideas will be presented in a graphical way through examples which also show the relevance of hypernetworks to policy. It will be argued that hypernetworks are necessary if not sufficient for a science of complex systems and its applications. The talk will be aimed at a general audience and no prior knowledge will be assumed.
10th ECCO / GBI seminar series. Spring 2014
From networks to hypernetworks in complex systems science
Control: It’s the essence of management. We’re trained to measure inputs, throughputs, and outputs in hopes of increasing efficiency and producing desired results. In a world of linear processes, such as in the factories of the Industrial Age, that made sense. But in today’s knowledge economy, where enterprises are complex, adaptive systems, it’s counterproductive.
The real problem is confusion between control and order. Control implies centralized control and hierarchical relationships. The person with control tells others what to do and whether they are successful or not. Order, on the other hand, emerges from self-organization. There may not be anyone telling others what to do, yet things get done—often with great efficiency and effectiveness. People know what is expected of them and what they can expect of others.
But how can this be true? Mustn’t an orchestra have a conductor? A dance troupe, a choreographer? A company, a CEO?
Not necessarily. Nature abounds with examples of what is known as swarm intelligence. Termites build intricate dwellings without the benefit of set of plans or engineers with advanced degrees. Birds migrate thousands of miles in formations where the lead position rotates to optimize their collective capacity. There are no marching orders or hierarchies dictating who leads. Massive flocks of starlings engage in intricate maneuvers known as murmuration with neither collisions nor confusion. There is order without overarching control. Indeed, our obsession with control helps explain why human-designed organizations fail to achieve such beautiful synchronicity.
Facebook has become more than just a central component of online life; its a fixture of modern culture. Facebook is the world’s second most frequented website, with nearly 1.19 billion monthly active users, nearly 80% of which come from outside the US and Canada. In the US, 71% of online adults use Facebook, 63% of whom…
The Institute for Applied Economic Research (Ipea) – a Brazilian think-tank linked to the government – is making a request for proposals for eight IDB consultants to contribute with chapters to a seminal book on Complex Systems applied to Public Policies. On one hand, the project aims at pushing forward the modeling frontier, its methodologies and applications for the case of Brazil. On the other hand, the project pursues actual improvement on the understanding of public policies’ mechanisms and effects, through complex systems’ tools and concepts. The book encompasses five broad themes: (1) concepts and methods; (2) computational tools; (3) public policy phenomena as complex systems (specifically: society, economics, ecology and the cities); (4) applied examples in the world and its emergence in Brazil; and (5) possibilities of prognosis, scenarios and policy-effect analysis using complex systems tools. The consultant is expected to deliver a proposed extended summary, a preliminary version to be discussed in a seminar in Brazil (July-September 2014) and the final version of the chapter.
Signals and Boundaries: Building Blocks for Complex Adaptive Systems [John H. Holland] on Amazon.com. *FREE* shipping on qualifying offers. Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems
The importance of complexity is well-captured by Hawking's comment: "Complexity is the science of the 21st century". From the movement of flocks of birds to the Internet, environmental sustainability, and market regulation, the study and understanding of complex non-linear systems has become highly influential over the last 30 years.
In this Very Short Introduction, one of the leading figures in the field, John Holland, introduces the key elements and conceptual framework of complexity. From complex physical systems such as fluid flow and the difficulties of predicting weather, to complex adaptive systems such as the highly diverse and interdependent ecosystems of rainforests, he combines simple, well-known examples -- Adam Smith's pin factory, Darwin's comet orchid, and Simon's 'watchmaker' -- with an account of the approaches, involving agents and urn models, taken by complexity theory.
Over the past thirty years, a new systemic conception of life has emerged at the forefront of science. New emphasis has been given to complexity, networks, and patterns of organisation leading to a novel kind of 'systemic' thinking. This volume integrates the ideas, models, and theories underlying the systems view of life into a single coherent framework. Taking a broad sweep through history and across scientific disciplines, the authors examine the appearance of key concepts such as autopoiesis, dissipative structures, social networks, and a systemic understanding of evolution. The implications of the systems view of life for health care, management, and our global ecological and economic crises are also discussed. Written primarily for undergraduates, it is also essential reading for graduate students and researchers interested in understanding the new systemic conception of life and its implications for a broad range of professions - from economics and politics to medicine, psychology and law.
With his book “Capital in the Twenty-First Century,” Thomas Piketty has written a blockbuster in the world of economics.
Liz Rykert's insight:
Exciting new book by Thomas Picketty on Economic Inequality - the article form the NYTimes summarizes it well and includes some key steps for action. The essence is that economies and wages grow more slowly than the rates of return on investments and capital. Here is a quote from the article:
"A higher than normal rate of population and economic growth helped reduce inequality, along with higher taxes on the wealthy. But the professional and political assumption of the 1950s and 1960s, that inequality would stabilize and diminish on its own, proved to be an illusion. We are now back to a traditional pattern of returns on capital of 4 percent to 5 percent a year and rates of economic growth of around 1.5 percent a year.
So inequality has been quickly gathering pace, aided to some degree by the Reagan and Thatcher doctrines of tax cuts for the wealthy. “Trickle-down economics could have been true,” Mr. Piketty said simply. “It just happened to be wrong."
The generality of network properties allows the utilization of the ‘wisdom’ of biological systems surviving crisis events for many millions of years. Yeast protein-protein interaction network shows a decrease in community-overlap (an increase in community cohesion) in stress. Community rearrangement seems to be a cost-efficient, general crisis-management response of complex systems. Inter-community bridges, such as the highly dynamic ‘creative nodes’ emerge as crucial determinants helping crisis survival.
Crisis Responses and Crisis Management: what can we learn from Biological Networks? Péter Csermely, Agoston Mihalik, Zsolt Vassy, András London
Systema: connecting matter, life, culture and technology
Vimeo is the home for high-quality videos and the people who love them.
Liz Rykert's insight:
This is one of a series of short nicely produced videos on complexity concepts. This one deals with the shift from certainty to uncertainty - from Newtons laws to a more ambiguous emergent understanding of the world.