Catgame: A Tool For Problem Solving In Complex Dynamic Systems Using Game Theoretic Knowledge Distribution In Cultural Algorithms, And Its Application (catneuro) To The Deep Learning Of Game Controller

Cultural Algorithms (CA) are knowledge-intensive, population-based stochastic optimization methods that are modeled after human cultures and are suited to solving problems in complex environments. The CA Belief Space stores knowledge harvested from prior generations and re-distributes it to future generations via a knowledge distribution (KD) mechanism. Each of the population individuals is then guided through the search space via the associated knowledge. Previously, CA implementations have used only competitive KD mechanisms that have performed well for problems embedded in static environments. Relatively recently, CA research has evolved to encompass dynamic problem environments. Given increasing environmental complexity, a natural question arises about whether KD mechanisms that also incorporate cooperation can perform better in such environments than purely competitive ones? Borrowing from game theory, game-based KD mechanisms are implemented and tested against the default competitive mechanism – Weighted Majority (WTD). Two different concepts of complexity are addressed – numerical optimization under dynamic environments and hierarchal, multi-objective optimization for evolving deep learning models. The former is addressed with the CATGame software system and the later with CATNeuro. CATGame implements three types of games that span both cooperation and competition for knowledge distribution, namely: Iterated Prisoner\u27s Dilemma (IPD), Stag-Hunt and Stackelberg. The performance of the three game mechanisms is compared with the aid of a dynamic problem generator called Cones World. Weighted Majority, aka “wisdom of the crowd”, the default CA competitive KD mechanism is used as the benchmark. It is shown that games that support both cooperation and competition do indeed perform better but not in all cases. The results shed light on what kinds of games are suited to problem solving in complex, dynamic environments. Specifically, games that balance exploration and exploitation using the local signal of ‘social’ rank – Stag-Hunt and IPD – perform better. Stag-Hunt which is also the most cooperative of the games tested, performed the best overall. Dynamic analysis of the ‘social’ aspects of the CA test runs shows that Stag-Hunt allocates compute resources more consistently than the others in response to environmental complexity changes. Stackelberg where the allocation decisions are centralized, like in a centrally planned economic system, is found to be the least adaptive. CATNeuro is for solving neural architecture search (NAS) problems. Contemporary ‘deep learning’ neural network models are proven effective. However, the network topologies may be complex and not immediately obvious for the problem at hand. This has given rise to the secondary field of neural architecture search. It is still nascent with many frameworks and approaches now becoming available. This paper describes a NAS method based on graph evolution pioneered by NEAT (Neuroevolution of Augmenting Topologies) but driven by the evolutionary mechanisms under Cultural Algorithms. Here CATNeuro is applied to find optimal network topologies to play a 2D fighting game called FightingICE (derived from “The Rumble Fish” video game). A policy-based, reinforcement learning method is used to create the training data for network optimization. CATNeuro is still evolving. To inform the development of CATNeuro, in this primary foray into NAS, we contrast the performance of CATNeuro with two different knowledge distribution mechanisms – the stalwart Weighted Majority and a new one based on the Stag-Hunt game from evolutionary game theory that performed the best in CATGame. The research shows that Stag-Hunt has a distinct edge over WTD in terms of game performance, model accuracy, and model size. It is therefore deemed to be the preferred mechanism for complex, hierarchical optimization tasks such as NAS and is planned to be used as the default KD mechanism in CATNeuro going forward

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

Project Fiasco: An Analysis of Ontario's Electronic Health Record Project

Policy failure is a recurring theme in large government technology projects. The Ontario Electronic Health Record (EHR) project is one of the most recent, and high profile, Canadian examples. The EHR project had two main phases – the design phase (in which the architecture of the system was determined) and an implementation phase (in which the operation of the system was carried out). This study has two objectives: first, to develop a set of frameworks that can be used to understand the design phase and the implementation phases; and second, to use these frameworks to describe and to understand why the EHR initiative was so unsuccessful. To facilitate an understanding of the implementation phase of a project, a game theoretic framework is employed that classifies technology solutions as either independent or interdependent. When solutions are interdependent, the framework suggests that, in order to obtain the greatest value, the government should exert its authority to ensure the coordination and cooperation of the actors in the system. To understand the design phase of a project, a framework is developed that links together the nature of the problem with the type of organization best suited to solving the problem. I argue that the complexity, or decomposability, of a problem directly affects the optimal method of a search for solutions, and the optimal means of organizing that search. These two frameworks are then applied to Ontario’s EHR project to analyze why the EHR project was so unsuccessful. I conclude that decision makers failed to consider the interdependent nature of EHR solutions; instead, they encouraged independent actors to develop their own ehealth solutions, effectively undermining the provincial goal of an interoperable system. I also conclude that decision makers misdiagnosed the nature of the EHR problem, resulting in an ineffective search procedure to locate an EHR solution. These two errors resulted in a policy fiasco that was manifested in almost total project failure and a resulting high degree of public outrage. We also speculate on why these errors were made

Social Operative System (sOS): The Use of Technology to Develop New Forms of Governance

[spa] La investigación analiza las formas de gobernanza actuales basadas en modelos centralizados, y desarrolla un modelo emergente, basado en a) los datos de los individuos, b) las formas de aprendizaje AI, y c) el control mediante una DAO

What to bid and when to stop

Negotiation is an important activity in human society, and is studied by various disciplines, ranging from economics and game theory, to electronic commerce, social psychology, and artificial intelligence. Traditionally, negotiation is a necessary, but also time-consuming and expensive activity. Therefore, in the last decades there has been a large interest in the automation of negotiation, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents eventually being able to negotiate on behalf of human negotiators.Every year, automated negotiation agents are improving in various ways, and there is now a large body of negotiation strategies available, all with their unique strengths and weaknesses. For example, some agents are able to predict the opponent's preferences very well, while others focus more on having a sophisticated bidding strategy. The problem however, is that there is little incremental improvement in agent design, as the agents are tested in varying negotiation settings, using a diverse set of performance measures. This makes it very difficult to meaningfully compare the agents, let alone their underlying techniques. As a result, we lack a reliable way to pinpoint the most effective components in a negotiating agent.There are two major advantages of distinguishing between the different components of a negotiating agent's strategy: first, it allows the study of the behavior and performance of the components in isolation. For example, it becomes possible to compare the preference learning component of all agents, and to identify the best among them. Second, we can proceed to mix and match different components to create new negotiation strategies., e.g.: replacing the preference learning technique of an agent and then examining whether this makes a difference. Such a procedure enables us to combine the individual components to systematically explore the space of possible negotiation strategies.To develop a compositional approach to evaluate and combine the components, we identify structure in most agent designs by introducing the BOA architecture, in which we can develop and integrate the different components of a negotiating agent. We identify three main components of a general negotiation strategy; namely a bidding strategy (B), possibly an opponent model (O), and an acceptance strategy (A). The bidding strategy considers what concessions it deems appropriate given its own preferences, and takes the opponent into account by using an opponent model. The acceptance strategy decides whether offers proposed by the opponent should be accepted.The BOA architecture is integrated into a generic negotiation environment called Genius, which is a software environment for designing and evaluating negotiation strategies. To explore the negotiation strategy space of the negotiation research community, we amend the Genius repository with various existing agents and scenarios from literature. Additionally, we organize a yearly international negotiation competition (ANAC) to harvest even more strategies and scenarios. ANAC also acts as an evaluation tool for negotiation strategies, and encourages the design of negotiation strategies and scenarios.We re-implement agents from literature and ANAC and decouple them to fit into the BOA architecture without introducing any changes in their behavior. For each of the three components, we manage to find and analyze the best ones for specific cases, as described below. We show that the BOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components.In every negotiation, one of the negotiating parties must accept an offer to reach an agreement. Therefore, it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When contemplating whether to accept an offer, the agent is faced with the acceptance dilemma: accepting the offer may be suboptimal, as better offers may still be presented before time runs out. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. We classify and compare state-of-the-art generic acceptance conditions. We propose new acceptance strategies and we demonstrate that they outperform the other conditions. We also provide insight into why some conditions work better than others and investigate correlations between the properties of the negotiation scenario and the efficacy of acceptance conditions.Later, we adopt a more principled approach by applying optimal stopping theory to calculate the optimal decision on the acceptance of an offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We show that the proposed approach is able to find the optimal time to accept, and improves upon all existing acceptance strategies.Another principal component of a negotiating agent's strategy is its ability to take the opponent's preferences into account. The quality of an opponent model can be measured in two different ways. One is to use the agent's performance as a benchmark for the model's quality. We evaluate and compare the performance of a selection of state-of-the-art opponent modeling techniques in negotiation. We provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. We identify a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature.The other way to measure the quality of an opponent model is to directly evaluate its accuracy by using similarity measures. We review all methods to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads to new insights concerning how to construct an opponent model, and what we need to measure when optimizing performance.Finally, we take two different approaches to gain more insight into effective bidding strategies. We present a new classification method for negotiation strategies, based on their pattern of concession making against different kinds of opponents. We apply this technique to classify some well-known negotiating strategies, and we formulate guidelines on how agents should bid in order to be successful, which gives insight into the bidding strategy space of negotiating agents. Furthermore, we apply optimal stopping theory again, this time to find the concessions that maximize utility for the bidder against particular opponents. We show there is an interesting connection between optimal bidding and optimal acceptance strategies, in the sense that they are mirrored versions of each other.Lastly, after analyzing all components separately, we put the pieces back together again. We take all BOA components accumulated so far, including the best ones, and combine them all together to explore the space of negotiation strategies.We compute the contribution of each component to the overall negotiation result, and we study the interaction between components. We find that combining the best agent components indeed makes the strongest agents. This shows that the component-based view of the BOA architecture not only provides a useful basis for developing negotiating agents but also provides a useful analytical tool. By varying the BOA components we are able to demonstrate the contribution of each component to the negotiation result, and thus analyze the significance of each. The bidding strategy is by far the most important to consider, followed by the acceptance conditions and finally followed by the opponent model.Our results validate the analytical approach of the BOA framework to first optimize the individual components, and then to recombine them into a negotiating agent

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia

Engineering coordination : eine Methodologie für die Koordination von Planungssystemen

Planning problems, like real-world planning and scheduling problems, are complex tasks. As an efficient strategy for handing such problems is the ‘divide and conquer’ strategy has been identified. Each sub problem is then solved independently. Typically the sub problems are solved in a linear way. This approach enables the generation of sub-optimal plans for a number of real world problems. Today, this approach is widely accepted and has been established e.g. in the organizational structure of companies. But existing interdependencies between the sub problems are not sufficiently regarded, as each problem are solved sequentially and no feedback information is given. The field of coordination has been covered by a number of academic fields, like the distributed artificial intelligence, economics or game theory. An important result is, that there exist no method that leads to optimal results in any given coordination problem. Consequently, a suitable coordination mechanism has to be identified for each single coordination problem. Up to now, there exists no process for the selection of a coordination mechanism, neither in the engineering of distributed systems nor in agent oriented software engineering. Within the scope of this work the ECo process is presented, that address exactly this selection problem. The Eco process contains the following five steps. • Modeling of the coordination problem • Defining the coordination requirements • Selection / Design of the coordination mechanism • Implementation • Evaluation Each of these steps is detailed in the thesis. The modeling has to be done to enable a systemic analysis of the coordination problem. Coordination mechanisms have to respect the given situation and the context in which the coordination has to be done. The requirements imposed by the context of the coordination problem are formalized in the coordination requirements. The selection process is driven by these coordination requirements. Using the requirements as a distinction for the selection of a coordination mechanism is a central aspect of this thesis. Additionally these requirements can be used for documentation of design decisions. Therefore, it is reasonable to annotate the coordination mechanisms with the coordination requirements they fulfill and fail to ease the selection process, for a given situation. For that reason we present a new classification scheme for coordination methods within this thesis that classifies existing coordination methods according to a set of criteria that has been identified as important for the distinction between different coordination methods. The implementation phase of the ECo process is supported by the CoPS process and CoPS framework that has been developed within this thesis, as well. The CoPS process structures the design making that has to be done during the implementation phase. The CoPS framework provides a set of basic features software agents need for realizing the selected coordination method. Within the CoPS process techniques are presented for the design and implementation of conversations between agents that can be applied not only within the context of the coordination of planning systems, but for multiagent systems in general. The ECo-CoPS approach has been successfully validated in two case studies from the logistic domain.Reale Planungsprobleme, wie etwa die Produktionsplanung in einer Supply Chain, sind komplex Planungsprobleme. Eine übliche Strategie derart komplexen Problemen zu lösen, ist es diese Probleme in einfachere Teilprobleme zu zerlegen und diese dann separat, meist sequentiell, zu lösen (divide-and-conquer Strategie). Dieser Ansatz erlaubt die Erstellung von (suboptimalen) Plänen für eine Reihe von realen Anwendungen, und ist heute in den Organisationsstrukturen von größeren Unternehmen institutionalisiert worden. Allerdings werden Abhängigkeiten zwischen den Teilproblemen nicht ausreichend berücksichtigt, da die Partialprobleme sequentiell ohne Feedback gelöst werden. Die erstellten Teillösungen müssen deswegen oft nachträglich koordiniert werden. Das Gebiet der Koordination wird in verschiedenen Forschungsgebieten, wie etwa der verteilten Künstlichen Intelligenz, den Wirtschaftswissenschaften oder der Spieltheorie untersucht. Ein zentrales Ergebnis dieser Forschung ist, dass es keinen für alle Situationen geeigneten Koordinationsmechanismus gibt. Es stellt sich also die Aufgabe aus den zahlreichen vorgeschlagenen Koordinationsmechanismen eine Auswahl zu treffen, die für die aktuelle Situation den geeigneten Mechanismus identifiziert. Für die Auswahl eines solchen Mechanismus existiert bisher jedoch kein strukturiertes Verfahren für die Entwicklung von verteilten Systems und insbesondere im Bereich der Agenten orientierter Softwareentwicklung. Im Rahmen dieser Arbeit wird genau hierfür ein Verfahren vorgestellt, der ECo-Prozess. Mit Hilfe dieses Prozesses wird der Auswahlprozess in die folgenden Schritte eingeteilt: • Modellierung der Problemstellung und des relevante Kontextes • Formulierung von Anforderungen an einen Koordinationsmechanismus (coordination requirements) • Auswahl/Entwurf eines Koordinationsmechanismuses • Implementierung des Koordinationsverfahrens • Evaluation des Koordinationsverfahrens Diese Schritte werden im Rahmen der vorliegenden Arbeit detailliert beschrieben. Die Modellierung der Problemstellung stellt dabei den ersten Schritt dar, um die Problemstellung analytisch zugänglich zu machen. Koordinationsverfahren müssen die Gegebenheiten, den Kontext und die Domäne, in der sie angewendet werden sollen hinreichend berücksichtigen um anwendbar zu sein. Dieses kann über Anforderungen an den Koordinationsprozess formalisiert werden. Der von den Anforderungen getrieben Auswahlprozess ist ein Kernstück der hier vorgestellten Arbeit. Durch die Formulierung der Anforderungen und der Annotation eines Koordinationsmechanismus bezüglich der erfüllten und nicht erfüllten Anforderungen werden die Motive für Designentscheidungen dieses Verfahren expliziert. Wenn Koordinationsverfahren anhand dieser Anforderungen klassifiziert werden können, ist es weiterhin möglich den Auswahlprozess (unabhängig vom ECo-Ansatz) zu vereinfachen und zu beschleunigen. Im Rahmen dieser Arbeit wird eine Klassifikation von Koordinationsansätzen anhand von allgemeinen Kriterien vorgestellt, die die Identifikation von geeigneten Kandidaten erleichtern. Diese Kandidaten können dann detaillierter untersucht werden. Dies wurde in den vorgestellten Fallstudien erfolgreich demonstriert. Für die Unterstützung der Implementierung eines Koordinationsansatzes wird in dieser Arbeit zusätzlich der CoPS Prozess vorgeschlagen. Der CoPS Prozess erlaubt einen ganzheitlichen systematischen Ansatz für den Entwurf und die Implementierung eines Koordinationsverfahrens. Unterstürzt wird der CoPS Prozess durch das CoPS Framework, das die Implementierung erleichtert, indem es als eine Plattform mit Basisfunktionalität eines Agenten bereitstellt, der für die Koordination von Planungssystemen verantwortlich ist. Im Rahmen des CoPS Verfahrens werden Techniken für den Entwurf und die Implementierung von Konversation im Kontext des agenten-orientiertem Software Engineerings ausführlich behandelt. Der Entwurf von Konversationen geht dabei weit über Fragestellung der Formatierung von Nachrichten hinaus, wie dies etwa in den FIPA Standards geregelt ist, und ist für die Implementierung von agentenbasierten Systemen im Allgemeinen von Bedeutung. Die Funktionsweise des ECo-CoPS Ansatzes wird anhand von zweierfolgreich durchgeführten Fallstudien aus dem betriebswirtschaftlichen Kontext vorgestellt

Recent Evolutionary Theorizing About Economic Change

The paper provides a broad overview of evolutionary thinking, and in particular, of evolutionary interpretations of socioeconomic change. The work assesses the state of the art in fields such as technological change, innovation-driven growth, and cultural and institutional evolution. It is also an important reference for all students of evolutionary change

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page