4,188 research outputs found
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
Early warning signs for saddle-escape transitions in complex networks
Many real world systems are at risk of undergoing critical transitions,
leading to sudden qualitative and sometimes irreversible regime shifts. The
development of early warning signals is recognized as a major challenge. Recent
progress builds on a mathematical framework in which a real-world system is
described by a low-dimensional equation system with a small number of key
variables, where the critical transition often corresponds to a bifurcation.
Here we show that in high-dimensional systems, containing many variables, we
frequently encounter an additional non-bifurcative saddle-type mechanism
leading to critical transitions. This generic class of transitions has been
missed in the search for early-warnings up to now. In fact, the saddle-type
mechanism also applies to low-dimensional systems with saddle-dynamics. Near a
saddle a system moves slowly and the state may be perceived as stable over
substantial time periods. We develop an early warning sign for the saddle-type
transition. We illustrate our results in two network models and epidemiological
data. This work thus establishes a connection from critical transitions to
networks and an early warning sign for a new type of critical transition. In
complex models and big data we anticipate that saddle-transitions will be
encountered frequently in the future.Comment: revised versio
Networks based on collisions among mobile agents
We investigate in detail a recent model of colliding mobile agents [Phys.
Rev. Lett.~96, 088702], used as an alternative approach to construct evolving
networks of interactions formed by the collisions governed by suitable
dynamical rules. The system of mobile agents evolves towards a quasi-stationary
state which is, apart small fluctuations, well characterized by the density of
the system and the residence time of the agents. The residence time defines a
collision rate and by varying the collision rate, the system percolates at a
critical value, with the emergence of a giant cluster whose critical exponents
are the ones of two-dimensional percolation. Further, the degree and clustering
coefficient distributions and the average path length show that the network
associated with such a system presents non-trivial features which, depending on
the collision rule, enables one not only to recover the main properties of
standard networks, such as exponential, random and scale-free networks, but
also to obtain other topological structures. Namely, we show a specific example
where the obtained structure has topological features which characterize
accurately the structure and evolution of social networks in different
contexts, ranging from networks of acquaintances to networks of sexual
contacts.Comment: 12 pages, 17 figure
Dynamical patterns of epidemic outbreaks in complex heterogeneous networks
We present a thorough inspection of the dynamical behavior of epidemic
phenomena in populations with complex and heterogeneous connectivity patterns.
We show that the growth of the epidemic prevalence is virtually instantaneous
in all networks characterized by diverging degree fluctuations, independently
of the structure of the connectivity correlation functions characterizing the
population network. By means of analytical and numerical results, we show that
the outbreak time evolution follows a precise hierarchical dynamics. Once
reached the most highly connected hubs, the infection pervades the network in a
progressive cascade across smaller degree classes. Finally, we show the
influence of the initial conditions and the relevance of statistical results in
single case studies concerning heterogeneous networks. The emerging theoretical
framework appears of general interest in view of the recently observed
abundance of natural networks with complex topological features and might
provide useful insights for the development of adaptive strategies aimed at
epidemic containment.Comment: 13 pages, 11 figure
Opinion and community formation in coevolving networks
In human societies opinion formation is mediated by social interactions,
consequently taking place on a network of relationships and at the same time
influencing the structure of the network and its evolution. To investigate this
coevolution of opinions and social interaction structure we develop a dynamic
agent-based network model, by taking into account short range interactions like
discussions between individuals, long range interactions like a sense for
overall mood modulated by the attitudes of individuals, and external field
corresponding to outside influence. Moreover, individual biases can be
naturally taken into account. In addition the model includes the opinion
dependent link-rewiring scheme to describe network topology coevolution with a
slower time scale than that of the opinion formation. With this model
comprehensive numerical simulations and mean field calculations have been
carried out and they show the importance of the separation between fast and
slow time scales resulting in the network to organize as well-connected small
communities of agents with the same opinion.Comment: 10 pages, 5 figures. New inset for Fig. 1 and references added.
Submitted to Physical Review
Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes
One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts. In particular, in networks of identical nodes it has been shown that network heterogeneity, i.e. a broad degree distribution, can lower the epidemic threshold at which epidemics can invade the system. Network heterogeneity can thus allow diseases with lower transmission probabilities to persist and spread. However, it has been pointed out that networks in which the properties of nodes are intrinsically heterogeneous can be very resilient to disease spreading. Heterogeneity in structure can enhance or diminish the resilience of networks with heterogeneous nodes, depending on the correlations between the topological and intrinsic properties. Here, we consider a plausible scenario where people have intrinsic differences in susceptibility and adapt their social network structure to the presence of the disease. We show that the resilience of networks with heterogeneous connectivity can surpass those of networks with homogeneous connectivity. For epidemiology, this implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks
On The Application Of Computational Modeling To Complex Food Systems Issues
Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section.
Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity.
Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena.
Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool
- …