Social dilemmas in an online social network: the structure and evolution of cooperation

We investigate two paradigms for studying the evolution of cooperation--Prisoner's Dilemma and Snowdrift game in an online friendship network obtained from a social networking site. We demonstrate that such social network has small-world property and degree distribution has a power-law tail. Besides, it has hierarchical organizations and exhibits disassortative mixing pattern. We study the evolutionary version of the two types of games on it. It is found that enhancement and sustainment of cooperative behaviors are attributable to the underlying network topological organization. It is also shown that cooperators can survive when confronted with the invasion of defectors throughout the entire ranges of parameters of both games. The evolution of cooperation on empirical networks is influenced by various network effects in a combined manner, compared with that on model networks. Our results can help understand the cooperative behaviors in human groups and society.Comment: 14 pages, 7 figure

An ecology of the net : message morphology and evolution in NetNews

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1996.Includes bibliographical references (p. 85-89).by Michael Lloyd Best.M.S

An Evolutionary Perspective of Radical Innovation and its implications for Management and Organizations

The thesis develops an evolutionary perspective of technological change based on a complex analogy between biological and technological evolution. The theoretical framework is based on a rich tradition of interdisciplinary research, integrating Herbert Simon\u2019s seminal theory on modular complex systems, artifact-centered evolutionary models of innovation (e.g. Basalla\u2019s), and fundamental evolutionary processes recently researched in microbiology \u2013 including in particular exaptation and horizontal transfer. The novel evolutionary perspective is supported by analytical narratives of paradigmatic historical and prehistorical cases \u2013 including the bow-and-arrow and the turbojet revolution \u2013 emphasizing its explanatory power in understanding presumptive anomalies and the inception of radical innovation. Finally, some implications for innovation management (managing creative radical engineering), organizations (rethinking the mirror hypothesis) are explored as promising implications of this novel perspective of technological change.The thesis develops an evolutionary perspective of technological change based on a complex analogy between biological and technological evolution. The theoretical framework is based on a rich tradition of interdisciplinary research, integrating Herbert Simon\u2019s seminal theory on modular complex systems, artifact-centered evolutionary models of innovation (e.g. Basalla\u2019s), and fundamental evolutionary processes recently researched in microbiology \u2013 including in particular exaptation and horizontal transfer. The novel evolutionary perspective is supported by analytical narratives of paradigmatic historical and prehistorical cases \u2013 including the bow-and-arrow and the turbojet revolution \u2013 emphasizing its explanatory power in understanding presumptive anomalies and the inception of radical innovation. Finally, some implications for innovation management (managing creative radical engineering), organizations (rethinking the mirror hypothesis) are explored as promising implications of this novel perspective of technological change

An Efficient Execution Model for Reactive Stream Programs

Stream programming is a paradigm where a program is structured by a set of computational nodes connected by streams. Focusing on data moving between computational nodes via streams, this programming model fits well for applications that process long sequences of data. We call such applications reactive stream programs (RSPs) to distinguish them from stream programs with rather small and finite input data. In stream programming, concurrency is expressed implicitly via communication streams. This helps to reduce the complexity of parallel programming. For this reason, stream programming has gained popularity as a programming model for parallel platforms. However, it is also challenging to analyse and improve the performance without an understanding of the program's internal behaviour. This thesis targets an effi cient execution model for deploying RSPs on parallel platforms. This execution model includes a monitoring framework to understand the internal behaviour of RSPs, scheduling strategies for RSPs on uniform shared-memory platforms; and mapping techniques for deploying RSPs on heterogeneous distributed platforms. The foundation of the execution model is based on a study of the performance of RSPs in terms of throughput and latency. This study includes quantitative formulae for throughput and latency; and the identification of factors that influence these performance metrics. Based on the study of RSP performance, this thesis exploits characteristics of RSPs to derive effective scheduling strategies on uniform shared-memory platforms. Aiming to optimise both throughput and latency, these scheduling strategies are implemented in two heuristic-based schedulers. Both of them are designed to be centralised to provide load balancing for RSPs with dynamic behaviour as well as dynamic structures. The first one uses the notion of positive and negative data demands on each stream to determine the scheduling priorities. This scheduler is independent from the runtime system. The second one requires the runtime system to provide the position information for each computational node in the RSP; and uses that to decide the scheduling priorities. Our experiments show that both schedulers provides similar performance while being significantly better than a reference implementation without dynamic load balancing. Also based on the study of RSP performance, we present in this thesis two new heuristic partitioning algorithms which are used to map RSPs onto heterogeneous distributed platforms. These are Kernighan-Lin Adaptation (KLA) and Congestion Avoidance (CA), where the main objective is to optimise the throughput. This is a multi-parameter optimisation problem where existing graph partitioning algorithms are not applicable. Compared to the generic meta-heuristic Simulated Annealing algorithm, both proposed algorithms achieve equally good or better results. KLA is faster for small benchmarks while slower for large ones. In contrast, CA is always orders of magnitudes faster even for very large benchmarks

Secure Abstractions for Trusted Cloud Computation

Cloud computing is adopted by most organizations due to its characteristics, namely offering on-demand resources and services that can quickly be provisioned with minimal management effort and maintenance expenses for its users. However it still suffers from security incidents which have lead to many data security concerns and reluctance in further adherence. With the advent of these incidents, cryptographic technologies such as homomorphic and searchable encryption schemes were leveraged to provide solutions that mitigated data security concerns. The goal of this thesis is to provide a set of secure abstractions to serve as a tool for programmers to develop their own distributed applications. Furthermore, these abstractions can also be used to support trusted cloud computations in the context of NoSQL data stores. For this purpose we leveraged conflict-free replicated data types (CRDTs) as they provide a mechanism to ensure data consistency when replicated that has no need for synchronization, which aligns well with the distributed and replicated nature of the cloud, and the aforementioned cryptographic technologies to comply with the security requirements. The main challenge of this thesis consisted in combining the cryptographic technologies with the CRDTs in such way that it was possible to support all of the data structures functionalities over ciphertext while striving to attain the best security and performance possible. To evaluate our abstractions we conducted an experiment to compare each secure abstraction with their non secure counterpart performance wise. Additionally, we also analysed the security level provided by each of the structures in light of the cryptographic scheme used to support it. The results of our experiment shows that our abstractions provide the intended data security with an acceptable performance overhead, showing that it has potential to be used to build solutions for trusted cloud computation

Characterization of self-organization processes in complex networks

Programa Doutoral em Física (MAP-fis)A estrutura de interações sociais numa população é muitas vezes modelada através de uma rede complexa que representa os indivíduos e respetivas relações sociais. Estas estruturas são conhecidas por afetarem de forma fundamental os processos dinâmicos que suportam. A caracterização desse efeito é, no entanto, uma tarefa complicada pois o tratamento matemático destes sistemas requer o estudo de um espaço de estados de grande dimensão, limitando a aplicabilidade de abordagens analíticas e numéricas. Esta tese teve como objetivo desenvolver métodos, inspirados na Física Estatística dos Sistemas Fora do Equilíbrio, com o fim de caracterizar processos dinâmicos em redes complexas. Nesta tese demonstramos que a estrutura de uma população naturalmente induz a emergência de padrões de correlações entre indivíduos que partilham traços semelhantes, um fenómeno também identificado em estudos empíricos. Estes padrões de correlações são independentes do tipo de processo dinâmico considerado, do tipo de informação que se propaga sendo observados numa classe alargada de redes complexas. Mostramos também que propriedades como o clustering e a densidade de ligações da rede têm um papel fundamental nos padrões de correlações emergentes. Outra questão fundamental diz respeito à relação entre as dinâmicas local e a global em redes sociais. De facto, as redes sociais afetam de forma tão fundamental os processos dinâmicos que suportam que em muitas situações o comportamento coletivo observado não tem qualquer relação aparente com a dinâmica local na sua génese. Este é um problema comum a muitos sistemas complexos e tipicamente associado a fenómenos emergentes e de auto-organização. Neste trabalho esta questão é explorada no contexto do problema da Cooperação e no âmbito da Teoria de Jogos Evolutiva. Para esse fim introduzimos uma quantidade que é estimada numericamente e a que damos o nome de Average Gradient of Selection (AGOS). Esta quantidade, relaciona de forma efetiva as dinâmicas local e global, possibilitando a descrição do processo de auto-organização em populações estruturadas. Através do AGOS mostramos que quando as interações entre indivíduos são descritas através do Dilema do Prisioneiro, uma metáfora popular no estudo da cooperação, a dinâmica coletiva emergente é sensível à forma da rede de interações entre os indivíduos. Em particular, demonstramos que quando a rede é homogénea (heterogénea) no que respeita à distribuição de grau o Dilema do Prisioneiro é transformado numa dinâmica coletiva de coexistência (coordenação). Mostramos ainda que esta transformação depende da pressão de seleção (associada ao grau de determinismo no processo de decisão dos indivíduos) e de taxa de mutações (a adoção espontânea de um novo comportamento por parte de um individuo) consideradas. A relação entre estas duas varáveis pode também resultar em alterações de regimes dinâmicos cujo o resultado pode, em casos particulares, resultar no desfecho drástico para a evolução da cooperação. Finalmente, fazemos uso do AGOS para caracterizar a dinâmica evolutiva da cooperação no caso em que a estrutura co-evolve. Demonstramos que na presença de uma estrutura social a dinâmica global é semelhante à de um jogo de coordenação entre N-pessoas, cujas características dependem de forma sensível das escalas de tempo relativas entre a evolução de comportamentos e a evolução da estrutura. Uma vez mais, a dinâmica global emergente contrasta com o Dilema do Prisioneiro que caracteriza as interações locais entre os indivíduos. Acreditamos que o AGOS, que pode ser facilmente aplicado no estudo de outros processos dinâmicos, proporciona uma contribuição significativa para o melhor entendimento de Sistemas Complexos, em particular aqueles em que as interações entre os elementos constituintes são bem definidos através uma rede complexa.The structure of social interactions in a population is often modeled by means of a complex network representing individuals and their social ties. These structures are known to fundamentally impact the processes they support. However, the characterization of how structure impacts a dynamical process is by no means an easy task. Indeed, the large configuration space spanned tends to limit the systematic applicability of numerical methods, while analytical treatments have failed to provide a good description of the system dynamics. The aim of this thesis was to develop methods inspired in the Statistical Physics of Systems far from equilibrium to characterize dynamical processes on complex networks. In this thesis we show how the structure of a population naturally induces the emergence of correlations between individuals that share similar traits, which is in accordance empirical evidence. These, so called, ’peer-influence” correlation patterns are independent of the type of dynamical process under consideration, the type of information being spread while being ubiquitous among a wide variety of network topologies. We have also find evidence that central to the ’peer-influence” patterns are topological features such as the clustering and the sparsity of the underlying network of interactions. Another fundamental problem concerns the relationship between local and global dynamics in social networks. Indeed, social networks affect in such a fundamental way the dynamics of the population they support that the collective, population-wide behavior that one observes often bears no relation to the individual processes it stems from. This is in fact a common problem among many Complex Systems typically associated with self-organization and emerging phenomena. Here we study this issue in the context of the problem of Cooperation and in the realm of Evolutionary Game Theory. To that end we introduce a numerically estimated mean-field quantity that we call the Average Gradient of Selection (AGOS). This quantity is able to effectively connect the local and global dynamics, providing a way to track the self-organization of cooperators and defectors in networked populations. With the AGOS we show that when individuals engage in a Prisoner’s Dilemma, a popular cooperation metaphor, the emerging collective dynamics depends on the shape of the underlying network of interactions. In particular, we show that degree homogeneous (heterogeneous) networks the Prisoner’s Dilemma is transformed into a collective coexistence (coordination) dynamics, contrasting with the defector dominance of the local dynamics. We further show that the extent to which these emergent phenomena are observed in structured populations is conditional on the selection pressure (the uncertainty associated with the decision making) and the rate of mutations (the spontaneously adoption of new behaviors by individuals) under consideration. Interestingly, the interplay between selection pressure and mutation rates can lead to drastic regime shifts in the evolution of cooperation. Finally, we make use of the AGOS to characterize the evolutionary dynamics of cooperation in the case of a co-evolving social structure. We demonstrate that in an adaptive social structure the population-wide dynamics resembles that of a N-person coordination game, whose characteristics depend sensitively on the relative time-scales between behavioral and network co-evolution. Once more, the resulting collective dynamics contrasts with the two-person Prisoner’s Dilemma that characterizes how individuals interact locally. We argue that the AGOS, which can be readily applied to other dynamical contexts and processes, provides a significant contribution to a better understanding of Complex Systems involving populations in which who interacts with whom is well-defined by a complex network

Microevolutionary language theory

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2000.Includes bibliographical references (p. 219-245).A new microevolutionary theory of complex design within language is proposed. Experiments were carried out that support the theory that complex functional design - adaptive complexity - accumulates due to the evolutionary algorithm at the simplest levels within human natural language. A large software system was developed which identifies and tracks evolutionary dynamics within text discourse. With this system hundreds of examples of activity suggesting evolutionary significance were distilled from a text collection of many millions of words. Research contributions include: (1) An active replicator model of microevolutionary dynamics within natural language, (2) methods to distill active replicators offering evidence of evolutionary processes in action and at multiple linguistic levels (lexical, lexical co-occurrence, lexico-syntactic, and syntactic), (3) a demonstration that language evolution and organic evolution are both examples of a single over-arching evolutionary algorithm, (4) a set of tools to comparatively study language over time, and (5) methods to materially improve text retrieval.by Michael Lloyd Best.Ph.D