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Control principles of complex systems

Control principles of complex systems | Complexity Science | Scoop.it

A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system’s components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in dynamical systems and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these questions here recent advances on the controllability and the control of complex networks are reviewed, exploring the intricate interplay between the network topology and dynamical laws. The pertinent mathematical results are matched with empirical findings and applications. Uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

 

Control principles of complex systems
Yang-Yu Liu and Albert-László Barabási
Rev. Mod. Phys. 88, 035006


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Anthropocene: The human age

Anthropocene: The human age | Complexity Science | Scoop.it
Momentum is building to establish a new geological epoch that recognizes humanity's impact on the planet. But there is fierce debate behind the scenes.

 

http://www.nature.com/news/anthropocene-the-human-age-1.17085


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ASundberg's curator insight, March 29, 2015 9:30 AM

Brief historicization of the anthropocene discussion. 

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Towards a Global Systems Science

Dirk Helbing, ETH Zurich


Talk given at the European Conference on Complex Systems 2014 in Lucca, Italy

 

http://youtu.be/UHp0lV6ppQQ

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Information - Function - Biology

Here you will find a description of the aims, activities and theoretical synthesis of the "Information - Function - Biology" project.

 

The broad aim is to advance a deep understanding of life and living which integrates concepts over all scales, all time and all forms of biological organisation. The key insight enabling this is to see that living is an information process, that living forms are concentrations of information engaged in storing, communicating, filtering and recombining information. There are several inspirations for this work, but perhaps the most prominent is the book by Erwin Schrodinger, which he called “What is Life?*”. That is why this website has the URL http://www.whatlifeis.info 


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Competitive Dynamics on Complex Networks

Competitive Dynamics on Complex Networks | Complexity Science | Scoop.it

We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.

 

Competitive Dynamics on Complex Networks

Jiuhua Zhao, Qipeng Liu, & Xiaofan Wang
Scientific Reports 4, Article number: 5858
http://dx.doi.org/10.1038/srep05858

 

 


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AleksBlumentals's curator insight, August 14, 2014 2:39 AM

How do you discover Caseworthiness? 

 


Tom Cockburn's curator insight, August 14, 2014 10:41 AM

Could be useful

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Early Warning Signs in Social-Ecological Networks

Early Warning Signs in Social-Ecological Networks | Complexity Science | Scoop.it

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)

Suweis Samir, D'Odorico Paolo


Code of the analysis available at https://github.com/suweis


http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0101851


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Tom Cockburn's curator insight, July 31, 2014 3:24 AM

Reliably unreliable systems interacting

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The biodiversity of species and their rates of extinction, distribution, and protection

Recent studies clarify where the most vulnerable species live, where and how humanity changes the planet, and how this drives extinctions. We assess key statistics about species, their distribution, and their status. Most are undescribed. Those we know best have large geographical ranges and are often common within them. Most known species have small ranges. The numbers of small-ranged species are increasing quickly, even in well-known taxa. They are geographically concentrated and are disproportionately likely to be threatened or already extinct. Current rates of extinction are about 1000 times the likely background rate of extinction. Future rates depend on many factors and are poised to increase. Although there has been rapid progress in developing protected areas, such efforts are not ecologically representative, nor do they optimally protect biodiversity.

 

The biodiversity of species and their rates of extinction, distribution, and protection
S. L. Pimm, C. N. Jenkins, R. Abell, T. M. Brooks, J. L. Gittleman, L. N. Joppa, P. H. Raven, C. M. Roberts, J. O. Sexton

Science 30 May 2014:
Vol. 344 no. 6187
http://dx.doi.org/10.1126/science.1246752


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Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agent-based simulations

Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theory approach to collective decision making, agent-based simulations were conducted to investigate how collective decision making would be affected by the agents' diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing non-trivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed that collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multi-level decision making are discussed.

 

Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agent-based simulations
Hiroki Sayama, Shelley D. Dionne, Francis J. Yammarino

http://arxiv.org/abs/1311.3674


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António F Fonseca's curator insight, December 28, 2013 7:15 AM

Some problems may only be solved through agent-based simulation.

Audiref Cía.'s curator insight, December 28, 2013 11:01 AM

Especialmente el grupo de base, la toma de decisiones colectiva, de gestión es fundamental en las organizaciones. El uso de un enfoque de la teoría evolutiva para la toma de decisiones colectiva, se llevaron a cabo simulaciones basadas en agentes para investigar cómo la toma de decisiones colectiva se vería afectada por la diversidad de los agentes en la comprensión y / o comportamiento en la discusión de problemas, así como por su estructura de red social. Resultados de la simulación indican que los grupos con entendimiento problema constante tienden a producir valores de utilidad más altos de las ideas y muestran una mejor toma de convergencia, pero sólo si no había ningún sesgo a nivel de grupo en la comprensión colectiva problema. 

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Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks : Scientific Reports : Nature Publishing Group

Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks : Scientific Reports : Nature Publishing Group | Complexity Science | Scoop.it
Recently, the impact of network structure on evolutionary dynamics has been at the center of attention when studying the evolutionary process of structured populations. This paper aims at finding out the key structural feature of network to capture its impact on evolutionary dynamics. To this end, a novel concept called heat heterogeneity is introduced to characterize the structural heterogeneity of network, and the correlation between heat heterogeneity of structure and outcome of evolutionary dynamics is further investigated on various networks. It is found that the heat heterogeneity mainly determines the impact of network structure on evolutionary dynamics on complex networks. In detail, the heat heterogeneity readjusts the selection effect on evolutionary dynamics. Networks with high heat heterogeneity amplify the selection effect on the birth-death process and suppress the selection effect on the death-birth process. Based on the above results, an effective algorithm is proposed to generate selection adjusters with desired size and average degree.

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The Strange New Science of Chaos - YouTube

A 1989 program, with Lorenz


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Vasileios Basios's curator insight, April 1, 2014 9:43 AM

Wow! such a rare delightful material .... Ralph Abraham and Lorenz who could imagine!

Luciano Lampi's curator insight, April 16, 2014 8:31 AM

to be watched by the new generations!  old certitudes and new doubts?

Liz Rykert's curator insight, April 19, 2014 9:56 PM

Great to hear Lorenz

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Overcoming structural uncertainty in computer models

Overcoming structural uncertainty in computer models | Complexity Science | Scoop.it
A computer model is a representation of the functional relationship between one set of parameters, which forms the model input, and a corresponding set of target parameters, which forms the model output. A true model for a particular problem can rarely be defined with certainty. The most we can do to mitigate error is to quantify the uncertainty in the model. Scientists have now offered a method to incorporate judgments into a model about structural uncertainty that results from building an 'incorrect' model.

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Migratory Animals Couple Biodiversity and Ecosystem Functioning Worldwide

Animal migrations span the globe, involving immense numbers of individuals from a wide range of taxa. Migrants transport nutrients, energy, and other organisms as they forage and are preyed upon throughout their journeys. These highly predictable, pulsed movements across large spatial scales render migration a potentially powerful yet underappreciated dimension of biodiversity that is intimately embedded within resident communities. We review examples from across the animal kingdom to distill fundamental processes by which migratory animals influence communities and ecosystems, demonstrating that they can uniquely alter energy flow, food-web topology and stability, trophic cascades, and the structure of metacommunities. Given the potential for migration to alter ecological networks worldwide, we suggest an integrative framework through which community dynamics and ecosystem functioning may explicitly consider animal migrations.

 

Migratory Animals Couple Biodiversity and Ecosystem Functioning Worldwide
S. Bauer, B. J. Hoye

Science 4 April 2014:
Vol. 344 no. 6179
http://dx.doi.org/10.1126/science.1242552 ;


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Contributions and challenges for network models in cognitive neuroscience

Contributions and challenges for network models in cognitive neuroscience | Complexity Science | Scoop.it

The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.

 

Contributions and challenges for network models in cognitive neuroscience
• Olaf Sporns
Nature Neuroscience (2014) http://dx.doi.org/10.1038/nn.3690


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Predicting Regional Economic Indices using Big Data of Individual Bank Card Transactions

For centuries quality of life was a subject of studies across different disciplines. However, only with the emergence of a digital era, it became possible to investigate this topic on a larger scale. Over time it became clear that quality of life not only depends on one, but on three relatively different parameters: social, economic and well-being measures. In this study we focus only on the first two, since the last one is often very subjective and consequently hard to measure. Using a complete set of bank card transactions recorded by Banco Bilbao Vizcaya Argentaria (BBVA) during 2011 in Spain, we first create a feature space by defining various meaningful characteristics of a particular area performance through activity of its businesses, residents and visitors. We then evaluate those quantities by considering available official statistics for Spanish provinces (e.g., housing prices, unemployment rate, life expectancy) and investigate whether they can be predicted based on our feature space. For the purpose of prediction, our study proposes a supervised machine learning approach. Our finding is that there is a clear correlation between individual spending behavior and official socioeconomic indexes denoting quality of life. Moreover, we believe that this modus operandi is useful to understand, predict and analyze the impact of human activity on the wellness of our society on scales for which there is no consistent official statistics available (e.g., cities and towns, districts or smaller neighborhoods).

 

Predicting Regional Economic Indices using Big Data of Individual Bank Card Transactions
Stanislav Sobolevsky, Emanuele Massaro, Iva Bojic, Juan Murillo Arias, Carlo Ratti

http://arxiv.org/abs/1506.00036 ;


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Scientists: practice doesn't necessarily make perfect. Sorry.

Scientists: practice doesn't necessarily make perfect. Sorry. | Complexity Science | Scoop.it
There's a long-standing myth that, in order to master a skill, all it takes is roughly 10,000 hours of practice. The idea was first popularised in Malcolm Gladwell 's 2008 book Outliers , based on a study of musicians by psychologist K. Anders...

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Olgy Gary's curator insight, March 14, 2015 11:18 PM

Here's a good article debunking the myth that practice makes perfect. Practice is one of the factors, but so are natural talent and genetic ability.

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▶ Cities as complex adaptative systems. Luis Bettencourt

http://youtu.be/vp6eKjQHNl0

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Systems Thinking and the Future of Cities

Systems Thinking and the Future of Cities | Complexity Science | Scoop.it
The idea that nothing exists in isolation−but only as part of a system−has long been embedded in folklore, religious scriptures, and common sense.

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Josie Gibson's curator insight, September 14, 2014 7:11 PM

Timely focus on the critical role of thinking systemically as a leader...

Jason Leong's curator insight, September 29, 2014 4:15 AM

"Despite the inherent logic of systems thinking, governments, corporations, foundations, universities, and non-profit organizations still work mostly by breaking issues and problems into their separate parts and dealing with each in isolation. Separate agencies, departments, and organizations specialize in energy, land, food, air, water, wildlife, economy, finance, building regulations, urban policy, technology, health, and transportation−as if each were unrelated to the others. So, one agency pushes hard to grow the economy while another is charged to clean up the resulting mess and so forth, which is to say that the right hand and left hand seldom knows−or cares−what the other is doing. The results are often counter-productive, overly expensive, risky, sometimes disastrous, and most always ironic."

Miklos Szilagyi's curator insight, September 29, 2014 4:57 AM

Very comprehensive and interesting.... and not only about the cities... Good...

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Connecting Core Percolation and Controllability of Complex Networks

Connecting Core Percolation and Controllability of Complex Networks | Complexity Science | Scoop.it

Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.

 

Connecting Core Percolation and Controllability of Complex Networks
• Tao Jia & Márton Pósfai

Scientific Reports 4, Article number: 5379 http://dx.doi.org/10.1038/srep05379


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Sibout Nooteboom's curator insight, July 13, 2014 3:52 AM

Fascinating advances

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Navigability of interconnected networks under random failures

Network theory has been exploited in the last decades to deepen our comprehension of complex systems. However, real-world complex systems exhibit multiple levels of relationships and require modeling by interconnected networks, characterizing interactions on several levels simultaneously. Questions such as “what is the efficiency of exploration of a city using the multiple transportation layers, like subway and bus?” and “what is its resilience to failures?” have to be answered using the multiplex framework. Here, we introduce fundamental mechanisms to perform such exploration, using random walks on multilayer networks, and we show how the topological structure, together with the navigation strategy, influences the efficiency in exploring the whole structure.

 

Navigability of interconnected networks under random failures
Manlio De Domenico, Albert Solé-Ribalta, Sergio Gómez, and Alex Arenas

http://dx.doi.org/10.1073/pnas.1318469111


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Characterizing the effect of network structure on evolutionary dynamics via a novel measure of structural heterogeneity

Characterizing the effect of network structure on evolutionary dynamics via a novel measure of structural heterogeneity | Complexity Science | Scoop.it
Recently, the study of evolutionary dynamics on structured population has attracted an increasing attention in various fields. This paper aims at investigating the effect of network structure on evolutionary dynamics.

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Shaolin Tan's curator insight, July 22, 2013 9:07 PM

An effective algorithm is proposed to generate selection amplifiers with desired size and average degree

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Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group

Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group | Complexity Science | Scoop.it
Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics.

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António F Fonseca's curator insight, December 28, 2013 7:14 AM

Another paper on opinion dynamics.

Luciano Lampi's curator insight, January 11, 2014 5:45 PM

Humanrithms....

Claude Emond's curator insight, January 20, 2014 5:51 PM

Opinions are an unescapable part of sharing and influencing the direction of collective intelligence

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Shock waves on complex networks : Scientific Reports : Nature Publishing Group

Shock waves on complex networks : Scientific Reports : Nature Publishing Group | Complexity Science | Scoop.it
Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node.

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Eli Levine's curator insight, May 20, 2014 8:19 AM

Indeed, this is intuitive enough without the mathematics to back it up.  This could be mapped out and used for prioritizing the defense or attack of various points within the network, either in the digital or analog worlds.

 

Way cool science!

 

Think about it.

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Resilience of modular complex networks

Complex networks often have a modular structure, where a number of tightly- connected groups of nodes (modules) have relatively few interconnections. Modularity had been shown to have an important effect on the evolution and stability of biological networks, on the scalability and efficiency of large-scale infrastructure, and the development of economic and social systems. An analytical framework for understanding modularity and its effects on network vulnerability is still missing. Through recent advances in the understanding of multilayer networks, however, it is now possible to develop a theoretical framework to systematically study this critical issue. Here we study, analytically and numerically, the resilience of modular networks under attacks on interconnected nodes, which exhibit high betweenness values and are often more exposed to failure. Our model provides new understandings into the feedback between structure and function in real world systems, and consequently has important implications as diverse as developing efficient immunization strategies, designing robust large-scale infrastructure, and understanding brain function.

 

Resilience of modular complex networks
Saray Shai, Dror Y. Kenett, Yoed N. Kenett, Miriam Faust, Simon Dobson, Shlomo Havlin

http://arxiv.org/abs/1404.4748


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Surprising material could play huge role in saving energy: Tin selenide is best at converting waste heat to electricity

Surprising material could play huge role in saving energy: Tin selenide is best at converting waste heat to electricity | Complexity Science | Scoop.it
One strategy for addressing the world's energy crisis is to stop wasting so much energy when producing and using it, such as in coal-fired power plants or transportation. Nearly two-thirds of energy input is lost as waste heat. Now scientists have discovered a surprising material that is the best in the world at converting waste heat to useful electricity. This outstanding property could be exploited in solid-state thermoelectric devices, with potentially enormous energy savings.

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Modeling social-ecological problems in coastal ecosystems: A case study

Complex social-ecological systems (SES) are not amenable to simple mathematical modeling. However, to address critical issues in SES (e.g., understanding ecological resilience/amelioration of poverty) it is necessary to describe such systems in their entirety. Based on empirical knowledge of local stakeholders and experts, we mapped their conceptions of one SES. Modelers codified what actors told us into two models: a local-level model and an overarching multiple-entity description of the system. Looking at these two representations together helps us understand links between the locally specific and other levels of decision taking and vice-versa. This “bimodeling” approach is investigated in one SES in coastal Kenya.

 


Modeling social-ecological problems in coastal ecosystems: A case study
John Forrester, Richard Greaves, Howard Noble and Richard Taylor
Complexity

http://dx.doi.org/10.1002/cplx.21524


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