Cascades of Failure and Extinction in Evolving Complex Systems

There is empirical evidence from a range of disciplines that as the connectivity of a network increases, we observe an increase in the average fitness of the system. But at the same time, there is an increase in the proportion of failure/extinction events which are extremely large. The probability of observing an extreme event remains very low, but it is markedly higher than in the system with lower degrees of connectivity. We are therefore concerned with systems whose properties are not static but which evolve dynamically over time. The focus in this paper, motivated by the empirical examples, is on networks in which the robustness or fragility of the vertices is not given, but which themselves evolve over time We give examples from complex systems such as outages in the US power grid, the robustness properties of cell biology networks, and trade links and the propagation of both currency crises and disease. We consider systems which are populated by agents which are heterogeneous in terms of their fitness for survival. The agents are connected on a network, which evolves over time. In each period agents take self-interested decisions to increase their fitness for survival to form alliances which increase the connectivity of the network. The network is subjected to external negative shocks both with respect to the size of the shock and the spatial impact of the shock. We examine the size/frequency distribution of extinctions and how this distribution evolves as the connectivity of the network grows. The results are robust with respect to the choice of statistical distribution of the shocks. The model is deliberately kept as parsimonious and simple as possible, and refrains from incorporating features such as increasing returns and externalities arising from preferential attachment which might bias the model in the direction of having the empirically observed features of many real world networks. The model still generates results consistent with the empirical evidence: increasing the number of connections causes an increase in the average fitness of agents, yet at the same time makes the system as whole more vulnerable to catastrophic failure/extinction events on an near-global scale.Agent-Based Model; Connectivity; Complex Systems; Networks

Cascades of Failure and Extinction in Evolving Complex Systems

There is empirical evidence from a range of disciplines that as the connectivity of a network increases, we observe an increase in the average fitness of the system. But at the same time, there is an increase in the proportion of failure/extinction events which are extremely large. The probability of observing an extreme event remains very low, but it is markedly higher than in the system with lower degrees of connectivity. We give examples from complex systems such as outages in the US power grid, the robustness properties of cell biology networks, and trade links and the propagation of both currency crises and disease. We consider networks which are populated by agents which are heterogeneous in terms of their fitness for survival. The network evolves over time, and in each period agents take self-interested decisions to increase their fitness for survival to form alliances which increase the connectivity of the network. The network is subjected to external negative shocks both with respect to the size of the shock and the spatial impact of the shock. We examine the size/frequency distribution of extinctions and how this distribution evolves as the connectivity of the network grows. The results are robust with respect to the choice of statistical distribution of the shocks. We find that increasing the number of connections causes an increase in the average fitness of agents, yet at the same time makes the system as whole more vulnerable to catastrophic failure/extinction events on an near-global scale.Comment: 15 pages, 5 figure

Learning to Personalize Medicine from Aggregate Data

AbstractThere is great interest in personalized medicine, in which treatment is tailored to the individual characteristics of patients. Achieving the objectives of precision healthcare will require clinically-grounded, evidence-based approaches, which in turn demands rigorous, scalable predictive analytics. Standard strategies for deriving prediction models for medicine involve acquiring ‘training’ data for large numbers of patients, labeling each patient according to the outcome of interest, and then using the labeled examples to learn to predict the outcome for new patients. Unfortunately, labeling individuals is time-consuming and expertise-intensive in medical applications and thus represents a major impediment to practical personalized medicine. We overcome this obstacle with a novel machine learning algorithm that enables individual-level prediction models to be induced from aggregate-level labeled data, which is readily-available in many health domains. The utility of the proposed learning methodology is demonstrated by: i.) leveraging US county-level mental health statistics to create a screening tool which detects individuals suffering from depression based upon their Twitter activity; ii.) designing a decision-support system that exploits aggregate clinical trials data on multiple sclerosis (MS) treatment to predict which therapy would work best for the presenting patient; iii.) employing group-level clinical trials data to induce a model able to find those MS patients likely to be helped by an experimental therapy.</jats:p

Finding Rare Disease Patients in EHR Databases via Lightly-Supervised Learning

AbstractThere is considerable interest in developing computational models capable of detecting rare disease patients in population-scale databases such as electronic health records (EHRs). Deriving these models is challenging for several reasons, perhaps the most daunting being the limited number of already-diagnosed, ‘labeled’ patients from which to learn. We overcome this obstacle with a novel lightly-supervised algorithm that leverages unlabeled and/or unreliably-labeled patient data – which is typically plentiful – to facilitate model induction. Importantly, we prove the algorithm issafe:adding unlabeled/unreliably-labeled data to the learning procedure produces models which are usually more accurate, and guaranteed never to be less accurate, than models learned from reliably-labeled data alone. The proposed method is shown to substantially outperform state-of-the-art models in patient-finding experiments involving two different rare diseases and a country-scale EHR database. Additionally, we demonstrate feasibility of transforming high-performance models generated through light supervision into simpler models which, while still accurate, are readily-interpretable by non-experts.</jats:p

Detecting and Monitoring Brain Disorders Using Smartphones and Machine Learning

AbstractThe ubiquity of smartphones in modern life suggests the possibility to use them to continuously monitor patients, for instance to detect undiagnosed diseases or track treatment progress. Such data collection and analysis may be especially beneficial to patients with i.) mental disorders, as these individuals can experience intermittent symptoms and impaired decision-making, which may impede diagnosis and care-seeking, and ii.) progressive neurological diseases, as real-time monitoring could facilitate earlier diagnosis and more effective treatment. This paper presents a new method of leveraging passively-collected smartphone data and machine learning to detect and monitor brain disorders such as depression and Parkinson’s disease. Crucially, the algorithm is able learn accurate, interpretable models from small numbers of labeled examples (i.e., smartphone users for whom sensor data has been gathered and disease status has been determined). Predictive modeling is achieved by learning from both real patient data and ‘synthetic’ patients constructed via adversarial learning. The proposed approach is shown to outperform state-of-the-art techniques in experiments involving disparate brain disorders and multiple patient datasets.</jats:p