Detecting and Describing Dynamic Equilibria in Adaptive Networks

We review modeling attempts for the paradigmatic contact process (or SIS model) on adaptive networks. Elaborating on one particular proposed mechanism of topology change (rewiring) and its mean field analysis, we obtain a coarse-grained view of coevolving network topology in the stationary active phase of the system. Introducing an alternative framework applicable to a wide class of adaptive networks, active stationary states are detected, and an extended description of the resulting steady-state statistics is given for three different rewiring schemes. We find that slight modifications of the standard rewiring rule can result in either minuscule or drastic change of steady-state network topologies.Comment: 14 pages, 10 figures; typo in the third of Eqs. (1) correcte

Complex networks analysis in socioeconomic models

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared for Complexity and Geographical Economics - Topics and Tools, P. Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published

Dynamics and steady-state properties of adaptive networks

Tese de doutoramento, Física, Universidade de Lisboa, Faculdade de Ciências, 2013Collective phenomena often arise through structured interactions among a system's constituents. In the subclass of adaptive networks, the interaction structure coevolves with the dynamics it supports, yielding a feedback loop that is common in a variety of complex systems. To understand and steer such systems, modeling their asymptotic regimes is an essential prerequisite. In the particular case of a dynamic equilibrium, each node in the adaptive network experiences a perpetual change in connections and state, while a comprehensive set of measures characterizing the node ensemble are stationary. Furthermore, the dynamic equilibria of a wide class of adaptive networks appear to be unique, as their characteristic measures are insensitive to initial conditions in both state and topology. This work focuses on dynamic equilibria in adaptive networks, and while it does so in the context of two paradigmatic coevolutionary processes, obtained results easily generalize to other dynamics. In the rst part, a low-dimensional framework is elaborated on using the adaptive contact process. A tentative description of the phase diagram and the steady state is obtained, and a parameter region identi ed where asymmetric microscopic dynamics yield a symmetry between node subensembles. This symmetry is accounted for by novel recurrence relations, which predict it for a wide range of adaptive networks. Furthermore, stationary nodeensemble distributions are analytically generated by these relations from one free parameter. Secondly, another analytic framework is put forward that detects and describes dynamic equilibria, while assigning to them general properties that must hold for a variety of adaptive networks. Modeling a single node's evolution in state and connections as a random walk, the ergodic properties of the network process are used to extract node-ensemble statistics from the node's long-term behavior. These statistical measures are composed of a variety of stationary distributions that are related to one another through simple transformations. Applying this fully self-su cient framework, the dynamic equilibria of three di erent avors of the adaptive contact process are subsequently described and compared. Lastly, an asymmetric variant of the coevolutionary voter model is motivated and proposed, and as for the adaptive contact process, a low-dimensional description is given. In a parameter region where a dynamic equilibrium lets the in nite system display perpetual dynamics, this description can be further reduced to a one-dimensional random walk. For nite system sizes, this allows to analytically characterize longevity of the dynamic equilibrium, with results being compared to the symmetric variant of the process. A nontrivial parameter combination is identi ed for which, in the low-dimensional description of the process, the asymmetric coevolutionary model emulates symmetric voter dynamics without topological coevolution. This emerging symmetry is partially con rmed for the full system and subsequently elaborated on. Slightly varying the original asymmetric model, an additional asymptotic regime is shown to occur that coexists with all others and complicates system description.A estrutura das interacções entre os constituintes elementares de um sistema está frequentemente na origem de comportamentos colectivos não triviais. Em redes adaptativas, esta estrutura de interacção evolui a par com a dinâaica que nela assenta, traduzindo uma retroacção que de comum encontrar em vários sistemas complexos. Resultados analíticos sobre os estados assimptóticos destes sistemas são uma peça essencial para a sua compreensão e controlo. O equilíbrio dinâmico de um caso particular de estado assimptótico em que cada nodo da rede adaptativa vai sempre mudando o seu estado e as suas ligações a outros nodos, enquanto que um conjunto de medidas que caracterizam estatisticamente o ensemble dos nodos mantêm valores fixos. Alémm disso, uma classe muito geral de redes adaptativas apresenta equilíbrios dinâmicos que parecem ser únicos, no sentido em que aqueles valores estacionários não dependem das condições iniciais, quer em termos do estados dos nodos quer em termos da topologia da rede.Este trabalho incide no estudo do equilíbrio dinâmiico de redes adaptativas no contexto particular de dois modelos paradigmáticos de coevolação, mas os principais resultados podem ser facilmente generalizados a outros processos. Na primeira parte, revisita-se e desenvolve-se uma abordagem da variante adaptativa do processo de contacto baseada num modelo de baixa dimensão. Obtem-se uma descrição aproximada do diagrama de fases do sistema e do equilíbrio dinâmico, e identifica-se nessa fase uma combinação de parâmetros para a qual a dinâmica microscópica, que de assimétrica nos estados dos nodos, da origem a uma simetria entre os dois subconjuntos de nodos. Esta simetria é explicada através da derivação de relações de recorrência para as distribuições de grau, que a preveêm para uma ampla classe de redes adaptativas. Estas relações permitem também gerar analiticamente as distribuições de grau estacionárias de cada subconjunto de nodos a partir de um parâmetro livre.Na segunda parte, desenvolve-se uma outra abordagem analítica que permite detectar e descrever o equilíbrio dinâmico, a partir de propriedades gerais que se têm que verificar em muitas redes adaptativas. Na base desta abordagem está a descrição do processo estocástico associado à evolução do estado e das ligações de cada nó, e as propriedades ergódicas que permitem obter as estatísticas de ensemble na rede a partir do comportamento a longo termo de um nó. Estas medidas estatísticas podem ser calculadas a partir de várias distribuições estacionárias que se relacionam umas com as outras através de transformações simples. Como aplicação desta abordagem completa, os equilíbrios dinâmicos de três diferentes variantes do processo de contacto adaptativo são descritos e comparados. Finalmente, motiva-se e propõe-se uma variante assimétrica do voter model coevolutivo. A fase activa metastável é tentativamente descrita como uma random walk ao longo de uma variedade lenta, à semelhan ca do que foi feito na literatura para o modelo simétrico, e os resultados para os dois casos são comparados.É identicada uma combinação de parâmetros particular para a qual este modelo assim etrico emula o modelo simétrico em rede fixa, o que é mais um exemplo da simetria emergente prevista pelas relações de recorrência estabelecidas na primeira parte. Considera-se ainda uma outra variante assimétrica, mais complexa, do voter model co-evolutivo, que apresenta um diagrama de fases essencialmente diferente, e cuja descrição se mostra requerer novas abordagens.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/45179/2008

An Interview with Thomas J. Sargent

The rational expectations hypothesis swept through macroeconomics during the 1970’s and permanently altered the landscape. It remains the prevailing paradigm in macroeconomics, and rational expectations is routinely used as the standard solution concept in both theoretical and applied macroeconomic modelling. The rational expectations hypothesis was initially formulated by John F. Muth Jr. in the early 1960s. Together with Robert Lucas Jr., Thomas (Tom) Sargent pioneered the rational expectations revolution in macroeconomics in the 1970s. We interviewed Tom Sargent for Macroeconomic Dynamics .

Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure