Percolation in networks with local homeostatic plasticity

Percolation is a process that impairs network connectedness by deactivating links or nodes. This process features a phase transition that resembles paradigmatic critical transitions in epidemic spreading, biological networks, traffic and transportation systems. Some biological systems, such as networks of neural cells, actively respond to percolation-like damage, which enables these structures to maintain their function after degradation and aging. Here we study percolation in networks that actively respond to link damage by adopting a mechanism resembling synaptic scaling in neurons. We explain critical transitions in such active networks and show that these structures are more resilient to damage as they are able to maintain a stronger connectedness and ability to spread information. Moreover, we uncover the role of local rescaling strategies in biological networks and indicate a possibility of designing smart infrastructures with improved robustness to perturbations.

Critical Transitions in Lake Ecosystem State May Be Driven by Coupled Feedback Mechanisms: A Case Study from Lake Erhai, China

Critical transitions between ecosystem states can be triggered by relatively small external forces or internal perturbations and may show time-lagged or hysteretic recovery. Understanding the precise mechanisms of a transition is important for ecosystem management, but it is hampered by a lack of information about the preceding interactions and associated feedback between different components in an ecosystem. This paper employs a range of data, including paleolimnological, environmental monitoring and documentary sources from lake Erhai and its catchment, to investigate the ecosystem structure and dynamics across multiple trophic levels through the process of eutrophication. A long-term perspective shows the growth and decline of two distinct, but coupled, positive feedback loops: a macrophyte-loop and a phosphorus-recycling-loop. The macrophyte-loop became weaker, and the phosphorus-recycling-loop became stronger during the process of lake eutrophication, indicating that the critical transition was propelled by the interaction of two positive feedback loops with different strengths. For lake restoration, future weakening of the phosphorus-recycling loop or a reduction in external pressures is expected to trigger macrophyte growth and eventually produce clear water conditions, but the speed of recovery will probably depend on the rates of feedback loops and the strength of their coupling.

Bayesian on-line anticipation of critical transitions

The design of reliable indicators to anticipate critical transitions in complex systems is an important task in order to detect a coming sudden regime shift and to take action in order to either prevent it or mitigate its consequences. We present a data-driven method based on the estimation of a parameterized nonlinear stochastic differential equation that allows for a robust anticipation of critical transitions even in the presence of strong noise levels like they are present in many real world systems. Since the parameter estimation is done by a Markov Chain Monte Carlo approach we have access to credibility bands allowing for a better interpretation of the reliability of the results. By introducing a Bayesian linear segment fit it is possible to give an estimate for the time horizon in which the transition will probably occur based on the current state of information. This approach is also able to handle nonlinear time dependencies of the parameter controlling the transition. In general the method could be used as a tool for on-line analysis to detect changes in the resilience of the system and to provide information on the probability of the occurrence of a critical transition in future.

Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track

Objective Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. Background Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. Method Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. Results Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants’ distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. Conclusion The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. Application Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers’ situation awareness. Précis This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.

Structure-based identification of sensor species for anticipating critical transitions

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure—that is, the network topology of plant–animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of “sensor species,” whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant–pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.

The geometry of decision-making in individuals and collectives

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process, we reveal the occurrence of spontaneous and abrupt “critical” transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one among, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Thus, we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space–time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological contexts, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

Universality of noise-induced resilience restoration in spatially-extended ecological systems

Many systems may switch to an undesired state due to internal failures or external perturbations, of which critical transitions toward degraded ecosystem states are prominent examples. Resilience restoration focuses on the ability of spatially-extended systems and the required time to recover to their desired states under stochastic environmental conditions. The difficulty is rooted in the lack of mathematical tools to analyze systems with high dimensionality, nonlinearity, and stochastic effects. Here we show that nucleation theory can be employed to advance resilience restoration in spatially-embedded ecological systems. We find that systems may exhibit single-cluster or multi-cluster phases depending on their sizes and noise strengths. We also discover a scaling law governing the restoration time for arbitrary system sizes and noise strengths in two-dimensional systems. This approach is not limited to ecosystems and has applications in various dynamical systems, from biology to infrastructural systems.

Early warning signal reliability varies with COVID-19 waves

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policymakers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation and skewness can predict nonlinear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policymakers to improve the accuracy of urgent intervention decisions but best characterize hypothesized critical transitions.

Social accountability - Its position and potential in the development of Vietnam

<p>Civil society organizations in Vietnam are experiencing some critical transitions. As the nation is no longer on the list of low income countries, an increasing number of such organizations are changing their missions from alleviating poverty to promoting more democratic governance. ‘Social accountability’, as one of their most common employed approaches, is often the combination of civic engagement, evidence-based monitoring, and advocacy. Carrying with it the expectation of improving accountability in Vietnam, the approach is still a new, foreign-imported concept which will challenge and be challenged by particular contextual factors in the country. This study examines the practices of social accountability in Vietnam to find out its position and potential in terms of development of the country. Promoting social accountability in Vietnam is often based on the assumption that the approach will improve government’s accountability, strengthening the state – citizen relationship. It is envisaged that the country will be eventually more open as a result. It is as yet an optimistic vision and will take time for practitioners to put in place. This study aims to analyse how early adoption of social accountability is affected by Vietnam’s contextual factors, to what extent it is affecting governance and increasing people’s participation, and what organizations can actually expect of social accountability. The research aims to fill a gap in the literature regarding social accountability in Vietnam. As a new concept, social accountability is often introduced via materials provided by international organizations like World Bank and UNICEF. Most of the documents present successful cases of applying social accountability in other countries like India and Bangladesh, and countries in Latin America. Thus, a critical analysis of adopting social accountability in the Vietnam context is necessary to provide more insights for both practitioners and scholars on the topic. Employing interviews as the key method, the study seeks input from key informants who are involved in the adoption of social accountability in Vietnam. From perspectives of government officials, development practitioners, and community members, the reality of practicing social accountability and how it is interacting and negotiating with other factors in society should be more clearly revealed. Practical expectations and recommendations to conceive of and practice social accountability in Vietnam are also suggested.</p>