A Class of Automata Networks for Diffusion of Innovations Driven by Riccati Equations : Automata Networks for Diffusion of Innovations.

Innovation diffusion processes are generally described at aggregate level with models like the Bass model (1969) and the Generalized Bass Model (1994). However, the recognized importance of communication channels between agents has recently suggested the use of agent-based models, like Cellular Automata. We argue that an adoption process is nested in a communication network that evolves dynamically and implicitly generates a non-constant potential market. Using Cellular Automata we propose a two- phase model of an innovation diffusion process. First we describe the Communication Network necessary for the awareness of an innovation. Then, we model a nested process representing the proper adoption dynamics. Through a "Mean Field Approximation" we propose a continuous representation of the discrete time equations derived by our Automata Network. This constitutes a special non autonomous Riccati equation, not yet described in well-known international catalogues. The main results refer to the closed form solution of this equation and to the corresponding statistical analysis for identification and inference. We discuss an application in the field of bank services

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

An attraction-based cellular automaton model for generating spatiotemporal population maps in urban areas

We develop a cellular automaton (CA) model to produce spatiotemporal population maps that estimate population distributions in an urban area during a random working day. The resulting population maps are at 50 m and 5 minutes spatiotemporal resolution, showing clearly how the distribution of population varies throughout a 24-hour period. The maps indicate that some areas of the city, which are sparsely populated during the night, can be densely populated during the day. The developed CA model assumes that the population transition trends follow dynamics and propagation patterns similar to a contagious disease. Thus, our model designed to change the states of each grid cell (stable or dynamic) in a way that is similar to changes in the condition of individuals who are exposed to an infectious disease (susceptible or infected). In addition, the modeling space is informed by several geographic features, such as the transport routes, land-use categories, and population attraction points. The model is geosimulated for the city of Trondheim in Norway, where the synthetic day population could be validated using an estimated day-population map based on the registered workplace addresses and employee statistics. The generated maps can be used to estimate a value for the population-at-risk in the wake of a major disaster that occurs in an urban area at any time of a day. In addition to assessing exposure to hazards, the resulting maps also reveal movement patterns, transition trends, peak hours, and activity levels. Possible applications range from public safety, disaster management, transport modeling, and urban growth studies to strategic energy distribution planning.acceptedVersio

Data-driven & Theory-driven Science : Artificial Realities and Applications to Savings Groups

Paper I and Paper II is not published yet. They are excluded from the dissertation until they will be published.The scientific process is neither unique nor nomic. Two processes of scientific inquiry are theory-driven and data-driven science. This dissertation analyzes savings groups using theory-driven and data-driven methods. Simulated realities-based on data-driven theory-are used to understand the emerging dynamics of savings groups. Savings groups are grassroots, community-based organizations composed of 15 to 30 members. These organizations-usually supported by international development agencies-have weekly meetings during a cycle of operations that typically lasts a year. In the groups, savings are kept in two funds: a fund for loans and a social welfare fund that covers life-cycle events. The findings of Papers A to D in this dissertation provide new large-sample evidence about savings groups, their dynamics, and the factors affecting their financial performance. In practice, the results of Paper A to D shed light on the best policies to promote sustainable development with informal finance in a cost-effective way. A theory-driven approach indicates that the social fund in savings groups stimulates loan allocation among risk-sharing members, while implicitly covering idiosyncratic risks (Paper A). A data-driven approach based on Bayesian data-mining reveals that the macroeconomic environment and the facilitation model of development agencies have a strong influence on the profit-generating capacity of savings groups (Paper B). Machine-learning methods further show that business training is not the most frequent program implemented by development agencies, but it is in fact the most powerful intervention to encourage profits, particularly when a development agency stops working with a group and leaves a community (Paper C). Finally, the simulation of a village with artificial agents indicates that the businesses of savings groups can have higher profits due to the consolidation of social capital and the competitive advantage created through a process of homophily (Paper D). Metatheoretically, the theory-driven and data-driven approaches of this dissertation-and the complementarity between these approaches-contribute to the epistemology of data-intensive science. The dissertation concludes that the gelstaltic and quasi-teleological explanations of the data-driven approach help to the formulation of theories through inductive and abductive reasoning.publishedVersio

Estimation and stability of nonlinear control systems under intermittent information with applications to multi-agent robotics

This dissertation investigates the role of intermittent information in estimation and control problems and applies the obtained results to multi-agent tasks in robotics. First, we develop a stochastic hybrid model of mobile networks able to capture a large variety of heterogeneous multi-agent problems and phenomena. This model is applied to a case study where a heterogeneous mobile sensor network cooperatively detects and tracks mobile targets based on intermittent observations. When these observations form a satisfactory target trajectory, a mobile sensor is switched to the pursuit mode and deployed to capture the target. The cost of operating the sensors is determined from the geometric properties of the network, environment and probability of target detection. The above case study is motivated by the Marco Polo game played by children in swimming pools. Second, we develop adaptive sampling of targets positions in order to minimize energy consumption, while satisfying performance guarantees such as increased probability of detection over time, and no-escape conditions. A parsimonious predictor-corrector tracking filter, that uses geometrical properties of targets\u27 tracks to estimate their positions using imperfect and intermittent measurements, is presented. It is shown that this filter requires substantially less information and processing power than the Unscented Kalman Filter and Sampling Importance Resampling Particle Filter, while providing comparable estimation performance in the presence of intermittent information. Third, we investigate stability of nonlinear control systems under intermittent information. We replace the traditional periodic paradigm, where the up-to-date information is transmitted and control laws are executed in a periodic fashion, with the event-triggered paradigm. Building on the small gain theorem, we develop input-output triggered control algorithms yielding stable closed-loop systems. In other words, based on the currently available (but outdated) measurements of the outputs and external inputs of a plant, a mechanism triggering when to obtain new measurements and update the control inputs is provided. Depending on the noise environment, the developed algorithm yields stable, asymptotically stable, and Lp-stable (with bias) closed-loop systems. Control loops are modeled as interconnections of hybrid systems for which novel results on Lp-stability are presented. Prediction of a triggering event is achieved by employing Lp-gains over a finite horizon in the small gain theorem. By resorting to convex programming, a method to compute Lp-gains over a finite horizon is devised. Next, we investigate optimal intermittent feedback for nonlinear control systems. Using the currently available measurements from a plant, we develop a methodology that outputs when to update the control law with new measurements such that a given cost function is minimized. Our cost function captures trade-offs between the performance and energy consumption of the control system. The optimization problem is formulated as a Dynamic Programming problem, and Approximate Dynamic Programming is employed to solve it. Instead of advocating a particular approximation architecture for Approximate Dynamic Programming, we formulate properties that successful approximation architectures satisfy. In addition, we consider problems with partially observable states, and propose Particle Filtering to deal with partially observable states and intermittent feedback. Finally, we investigate a decentralized output synchronization problem of heterogeneous linear systems. We develop a self-triggered output broadcasting policy for the interconnected systems. Broadcasting time instants adapt to the current communication topology. For a fixed topology, our broadcasting policy yields global exponential output synchronization, and Lp-stable output synchronization in the presence of disturbances. Employing a converse Lyapunov theorem for impulsive systems, we provide an average dwell time condition that yields disturbance-to-state stable output synchronization in case of switching topology. Our approach is applicable to directed and unbalanced communication topologies.\u2