Control of Diffusion Processes in Multi-agent Networks

Diffusion processes are instrumental in describing the movement of a continuous quantity in a generic network of interacting agents. Here, we present a probabilistic framework for diffusion in networks and propose to classify agent interactions according to two protocols where the total network quantity is conserved or variable. For both protocols, our focus is on asymmetric interactions between agents involving directed graphs. Specifically, we define how the dynamics of conservative and non-conservative networks relate to the weighted in-degree Laplacian and the weighted out-degree Laplacian. We show how network diffusion can be externally manipulated by applying time-varying input functions at individual nodes. The network control and design schemes enable flow modifications that allow the alteration of the dynamic and stationary behavior of the network in conservative and non-conservative networks. The proposed framework is relevant in the context of group coordination, herding behavior, distributed algorithms, and network control

Assessing the time intervals between economic recessions

Economic recessions occur with varying duration and intensity and may entail substantial losses in terms of GDP, employment, household income, and investment spending. In this work, we propose a statistical model for the time intervals between recessions that accounts for the state of the economy and the impact of market adjustments and regulatory changes. The model uses a generalized renewal process based on the Gumbel distribution (GuGRP) in which times between consecutive events are conditionally independent. We also present a novel goodness of fit test tailored to the GuGRP that validates the use of the statistical model for the analysis of recessions. Analyzing recessions in the U.S. and Europe, we demonstrate that the statistical model characterizes well recession inter-arrival times and that the model performs better than simpler, commonly used distributions. In addition, the presented statistical model enables us to compare the adjustment processes in different economies and to forecast the occurrence of future recessions

A collaborative expert system for group decision making in public policy

In the policy arena, there is high pressure to provide right and quick decisions for problems that are often poorly defined. There is hence an urgent need to support stakeholders in establishing a shared understanding of policy problems and to assist them in the design of potential solutions. Here we propose a formal methodology based on the construction and analysis of system maps, i.e., a graphical representation of the complex interdependencies of all relevant factors that affect the problem under study. Owing to their collaborative design, system maps provide a transparent tool with broad stakeholder acceptance to analyze ill-defined problems in a formal way. The construction of system maps involves expert elicitation to define system components, system boundaries, and interactions between system components, whereas the dynamical system behavior can be approximated by means of system dynamics. Although there is great value in the construction of the system map to enhance the understanding of the problem scenario, we consider this as an intermediate step. The final target is to present the full life-cycle of system maps and assist decision-makers in the entire decision-making process through the construction and analysis of system maps, i.e., from the understanding of the system behavior, to the definition of objectives and constraints, and finally the presentation of feasible solutions. System maps provides us with an effective framework to collect information dispersed over the experts, facilitate mediation, and analyze formally potential pathway solutions, meeting different criteria of optimality

Energy Efficiency of Distributed Signal Processing in Wireless Networks: A Cross-Layer Analysis

In order to meet the growing mobile data demand, future wireless networks will be equipped with a mulitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: 1) hybrid, where the signal processing operations are shared between nodes and APs; 2) centralized, where signal processing is entirely implemented at the APs; and 3) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches

Successful Treatment of Early Endometrial Carcinoma by Local Delivery of Levonorgestrel: A Case Report

We describe a case of a 67-year-old Caucasian woman with an early, moderately-differentiated adenocarcinoma of the endometrium. A levonorgestrel-releasing intrauterine system was inserted, which she tolerated well. A full D&C, following removal of the device, was performed after 9 months, confirming absence of tumoral tissue. Examination after 24 months showed a very thin endometrium, indicating complete remission

Supporting strategy selection in multiobjective decision problems under uncertainty and hidden requirements

Decision-makers are often faced with multi-faceted problems that require making trade-offs between multiple, conflicting objectives under various uncertainties. The task is even more difficult when considering dynamic, non-linear processes and when the decisions themselves are complex, for instance in the case of selecting trajectories for multiple decision variables. These types of problems are often solved using multiobjective optimization (MOO). A typical problem in MOO is that the number of Pareto optimal solutions can be very large, whereby the selection process of a single preferred solution is cumbersome. Moreover, preference between model-based solutions may not be determined only by their objective function values, but also in terms of how robust and implementable these solutions are. In this paper, we develop a methodological framework to support the identification of a small but diverse set of robust Pareto optimal solutions. In particular, we eliminate non-robust solutions from the Pareto front and cluster the remaining solutions based on their similarity in the decision variable space. This enables a manageable visual inspection of the remaining solutions to compare them in terms of practical implementability. We illustrate the framework and its benefits by means of an epidemic control problem that minimizes deaths and economic impacts, and a screening program for colorectal cancer that minimizes cancer prevalence and costs. These examples highlight the general applicability of the framework for disparate types of decision problems and process models

Protecting Food Supply and Farmer Livelihoods in West Africa: Strategies for Risk Reduction

Weather extremes and high population growth are challenging the achievement of SDG 2 Zero Hunger in West Africa. It is essential to understand how crop production decisions by farmers affect the reliability of food production and the stability of their livelihoods. • Future food security scenarios are often based on models that ignore annual weather variability and weather extremes. As a result, this approach also disregards the risk of having lower than expected yields, with adverse consequences for food security and farmer livelihoods. • We propose a stochastic modelling framework that allows to study the reliability of food production under crop yield uncertainty, and explore different strategies to increase this reliability at a minimum cost

Risk transfer policies and climate-induced immobility among smallholder farmers

Climate change is anticipated to impact smallholder farmer livelihoods substantially. However, empirical evidence is inconclusive regarding how increased climate stress affects smallholder farmers’ deployment of various livelihood strategies, including rural–urban migration. Here we use an agent-based model to show that in a South Asian agricultural community experiencing a 1.5 oC temperature increase by 2050, climate impacts are likely to decrease household income in 2050 by an average of 28%, with fewer households investing in both economic migration and cash crops, relative to a stationary climate. Pairing a small cash transfer with risk transfer mechanisms significantly increases the adoption of migration and cash crops, improves community incomes and reduces community inequality. While specific results depend on contextual factors such as risk preferences and climate risk exposure, these interventions are robust in improving adaptation outcomes and alleviating immobility, by addressing the intersection of risk aversion, financial constraints and climate impacts

Packet Reception Probabilities in Vehicular Communications Close to Intersections

Vehicular networks allow vehicles to share information and are expected to be an integral part of future intelligent transportation systems (ITS). To guide and validate the design process, analytical expressions of key performance metrics such as packet reception probabilities and throughput are necessary, in particular for accident-prone scenarios such as intersections. In this paper, we present a procedure to analytically determine the packet reception probability and throughput of a selected link, taking into account the relative increase in the number of vehicles (i.e., possible interferers) close to an intersection. We consider both slotted Aloha and CSMA/CA MAC protocols, and show how the procedure can be used to model different propagation environments of practical relevance. The procedure is validated for a selected set of case studies at low traffic densities

Characterization and Control of Conservative and Nonconservative Network Dynamics

Diffusion processes are instrumental to describe the movement of a continuous quantity in a generic network of interacting agents. Here, we present a probabilistic framework for diffusion in networks and study in particular two classes of agent interactions depending on whether the total network quantity follows a conservation law. Focusing on asymmetric interactions between agents, we define how the dynamics of conservative and non-conservative networks relate to the weighted in-degree and out-degree Laplacians. For uncontrolled networks, we define the convergence behavior of our framework, including the case of variable network topologies, as a function of the eigenvalues and eigenvectors of the weighted graph Laplacian. In addition, we study the control of the network dynamics by means of external controls and alterations in the network topology. For networks with exogenous controls, we analyze convergence and provide a method to measure the difference between conservative and non-conservative network dynamics based on the comparison of their respective attainability domains. In order to construct a network topology tailored for a desired behavior, we propose a Markov decision process (MDP) that learns specific network adjustments through a reinforcement learning algorithm. The presented network control and design schemes enable the alteration of the dynamic and stationary network behavior in conservative and non-conservative networks