Contributions of the complexity paradigm to the understanding of Cerrado’s organization and dynamics

The Brazilian Cerrado is a vegetation mosaic composed of different physiognomies. Discussions remain open regarding the factors and processes responsible for the dynamic and spatial organization of the Cerrado - in its different physiognomies. The contributions of the complexity paradigm in this context are still less exploited, despite its great potential for explanations and predictions presented in previous diverse dynamic systems of complex behavior researches, a category in which the Cerrado can be included. This article has the intention of contributing to the construction of this new perspective, discussing - from theoretical concepts - the paradigm of complexity for the understanding of the organization and the dynamics of the Cerrado.The Brazilian Cerrado is a vegetation mosaic composed of different physiognomies. Discussions remain open regarding the factors and processes responsible for the dynamic and spatial organization of the Cerrado - in its different physiognomies. The contributions of the complexity paradigm in this context are still less exploited, despite its great potential for explanations and predictions presented in previous diverse dynamic systems of complex behavior researches, a category in which the Cerrado can be included. This article has the intention of contributing to the construction of this new perspective, discussing - from theoretical concepts - the paradigm of complexity for the understanding of the organization and the dynamics of the Cerrado8842417242

Collaborative prognostics in Social Asset Networks

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the fi eld of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must nd a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, de ned as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares t of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.EU H202

LCL problems on grids

Peer reviewe

Black-box Bayesian inference for agent-based models

Simulation models, in particular agent-based models, are gaining popularity in economics and the social sciences. The considerable flexibility they offer, as well as their capacity to reproduce a variety of empirically observed behaviours of complex systems, give them broad appeal, and the increasing availability of cheap computing power has made their use feasible. Yet a widespread adoption in real-world modelling and decision-making scenarios has been hindered by the difficulty of performing parameter estimation for such models. In general, simulation models lack a tractable likelihood function, which precludes a straightforward application of standard statistical inference techniques. A number of recent works have sought to address this problem through the application of likelihood-free inference techniques, in which parameter estimates are determined by performing some form of comparison between the observed data and simulation output. However, these approaches are (a) founded on restrictive assumptions, and/or (b) typically require many hundreds of thousands of simulations. These qualities make them unsuitable for large-scale simulations in economics and the social sciences, and can cast doubt on the validity of these inference methods in such scenarios. In this paper, we investigate the efficacy of two classes of simulation-efficient black-box approximate Bayesian inference methods that have recently drawn significant attention within the probabilistic machine learning community: neural posterior estimation and neural density ratio estimation. We present a number of benchmarking experiments in which we demonstrate that neural network-based black-box methods provide state of the art parameter inference for economic simulation models, and crucially are compatible with generic multivariate or even non-Euclidean time-series data. In addition, we suggest appropriate assessment criteria for use in future benchmarking of approximate Bayesian inference procedures for simulation models in economics and the social sciences

The Origin of Consciousness in a Biological Framework for a Mathematical Universe (23 Pages)

This essay explores the creation and evolution of life and consciousness through the lens of a biological framework for understanding the universe. The theory posits that the patterns inherent in biological systems mirror the underlying mathematical principles of the cosmos. Thus, every pattern that manifests from the universe’s “parent-pattern” contains a fundamental biological-pattern inherent to its function, revealing the objective nature and purpose of that thing. Examples include the way ocean currents resemble a circulatory system and how socioeconomic phenomena mimic cellular order. These correspondences suggest that life and consciousness are products of the universe’s biologically-patterned processes, and understanding these patterns is crucial for humanity's survival, especially as human society and environment becomes more complex—requiring a truer understanding of reality reality and how to organize themselves within it. The paper further argues that historical and philosophical concepts, such as Atman and Brahman in the Upanishads, Pnimiyut and Chitzoniyut in Kabbalah (Judaism), Batin and Zahir in Sufism (Islam), and many more align with this framework. The essay emphasizes that aligning human society with these biological patterns, as seen in biomimicry, is necessary for continued survival and harmony with the universe. The essay provides scientific studies and analogies supporting these ideas, illustrating how biological patterns are reflected across different domains of knowledge

Reconstruction, mobility, and synchronization in complex networks

During the last decades, it has become clear that systems formed by many interacting parts show emergent dynamical properties which are inherently related to the topology of the underlying pattern of connections among the constituent parts. Such systems, usually known as complex systems, are in general suitably described through their networks of contacts, that is, in terms of nodes (representing the system's components) and edges (standing for their interactions), which allows to catch their essential features in a simple and general representation. In recent years, increasing interest on this approach, thanks also to a favorable technological progress, led to the accumulation of an increasing amount of data. This situation has allowed the arising of new questions and, therefore, the diversification of the scientific work. Among them, we can point out three general issues that have been receiving a lot of interest: (i) is the available information always reliable and complete? (ii) how does a complex interaction pattern affect the emergence of collective behavior in complex systems? And (iii) which is the role of mobility within the framework of complex networks? This thesis has been developed along these three lines, which are strictly interrelated. We expand on three case-studies, each one of which deals with two the above mentioned macro-issues. We consider the issue of the incompleteness of the available information both in the case of natural (Chapter 2) and artificial (Chapter 3) networks. As a paradigmatic emergent behavior, we focus on the synchronization of coupled phase oscillators (Chapter 2 and Chapter 4), deeply investigating how different patterns of connections can affect the achievement of a globally coherent state. Finally, we include moving agents in two different frameworks, using them as explorers of unknown networks (Chapter 3) and considering them as interacting units able to establish connections with their neighbors (Chapter 4). In Chapter 2, we study the problem of the reconstruction of an unknown interaction network, whose nodes are Kuramoto oscillators. Our aim is to extract topological features of the connectivity pattern from purely dynamical measures, based on the fact that in a heterogeneous network the global dynamics is not only affected by the distribution of the natural frequencies but also by the location of the different values. The gathered topological information ranges from local features, such as the single node connectivity, to the hierarchical structure of functional clusters, and even to the entire adjacency matrix. In Chapter 4, instead, we present a model of integrate and fire oscillators that are moving agents, freely displacing on a plane. The phase of the oscillators evolves linearly in time and when it reaches a threshold value they fire at their neighbors. In this way, the interaction network is a dynamical object by itself since it is re-created at each time step by the motion of the units. Depending on the velocity of the motion, the average number of neighbors, the coupling strength and the size of the agents population, we identify different regimes. Moving agents are employed also in Chapter 3 where they play the role of explorers of unknown artificial networks, having the mission to recover information about their structures. We propose a model in which random walkers with previously assigned home nodes navigate through the network during a fixed amount of time. We consider that the exploration is successful if the walker gets the information gathered back home, otherwise, no data is retrieved. We show that there is an optimal solution to this problem in terms of the average information retrieved and the degree of the home nodes and design an adaptive strategy based on the behavior of the random walker.Durante las últimas décadas, se ha empezado a poner de manifiesto que sistemas formados por muchos elementos en interacción pueden mostrar propiedades dinámicas emergentes relacionadas con la topología del patrón de conexiones entre las partes constituyentes. Estos sistemas, generalmente conocidos como sistemas complejos, en muchos casos pueden ser descritos a través de sus redes de contactos, es decir, en términos de nodos (que representan los componentes del sistema) y de enlaces (sus interacciones). De esta manera es posible capturar sus características esenciales en una representación simple y general. En esta última década, el creciente interés en este enfoque, gracias también a un progreso tecnológico favorable, ha llevado a la acumulación de una cantidad ingente de datos. Eso, a su vez, ha permitido el surgimiento de nuevas preguntas y, por lo tanto, la diversificación de la actividad científica. Entre ellas, podemos destacar tres cuestiones generales que son objeto de mucho interés: (i) ¿la información disponible es siempre fiable y completa? (ii) ¿cómo un patrón de interacción complejo puede afectar el surgimiento de comportamientos colectivos? Y (iii) ¿cual es el papel de la movilidad en el marco de las redes complejas? Esta tesis se ha desarrollado siguiendo estas tres líneas, que están íntimamente relacionadas entre sí. Hemos profundizado en tres casos de estudio, cada uno de los cuales se ocupa de dos de los macro-temas mencionados. Consideramos la cuestión del carácter incompleto de la información disponible tanto en el caso de redes naturales (Capítulo 2) como de redes artificiales (Capítulo 3). Nos centramos en la sincronización de los osciladores de fase acoplados (Capítulos 2 y 4) en cuanto comportamiento emergente paradigmático, investigando en profundidad cómo los diferentes patrones de conexión puedan afectar la consecución de un estado coherente a nivel global. Por último, analizamos el rol de la movilidad incluyendo agentes móviles en dos marcos diferentes. En un caso, los utilizamos como exploradores de redes desconocidas (Capítulo 3), mientras que en otro los consideramos como unidades que interaccionan y son capaces de establecer conexiones con sus vecinos (Capítulo 4)

Entrepreneurial Action and Entrepreneurial Rents

This dissertation is comprised of three independently standing research papers (chapters 2, 3 and 4) that come together in the common theme of investigating the relationship between entrepreneurial action and performance. The introduction chapter argues that this theme is the main agenda of an entrepreneurial approach to strategy, and provides some background and context for the core chapters. The entrepreneurial approach to strategy falls in line with an array of action-based theories of strategy that trace their economic foundations to the Austrian school of economics. Chapters 2 and 3 take a game theoretical modeling and computer simulation approach and represent one of the first attempts at formal analysis of the Austrian economic foundations of action-based strategy theory. These chapters attempt to demonstrate ways in which formal analysis can begin to approach compatibility with the central tenets of Austrian economics (i.e., subjectivism, dynamism, and methodological individualism). The simulation results shed light on our understanding of the relationship between opportunity creation and discovery, and economic rents in the process of moving towards and away from equilibrium. Chapter 4 operationalizes creation and discovery as exploration and exploitation in an empirical study using data from the Kauffman Firm Survey and highlights the trade-offs faced by start-ups in linking action to different dimensions of performance (i.e., survival, profitability, and getting acquired). Using multinomial logistic regression for competing risks analysis and random effects panel data regression, we find that high technology start-ups face a trade-off between acquisition likelihood and profitability-given-survival while low and medium technology start-ups face a trade-off between survival and profitability-given-survival. Chapter 5 concludes the dissertation by highlighting some of the overall contributions and suggesting avenues for future research

Three Essays on Methodologies for Dynamic Modeling of Emerging Socio-technical Systems:The Case of Smart Grid Development

Socio-technical energy transitions are long-term and major transformations in incumbent energy infrastructures. They include fundamental changes in technologies as well as institutions and social patterns. Transition studies are primarily focused on frameworks for analyzing the entire transition process by investigating the historical cases of transitions. A multi-phase approach to transition posits this process begins with a pre-development phase characterized by technological and institutional lock-ins, and resistance from incumbent actors. This period is critical for a forward-looking approach to transitions, since early developments shape path-dependent and irreversible processes leading to the emergence of new transition pathways. However, our understanding about the mechanisms and dynamics of this phase is still very limited. This is mainly due to lack of data, weak conceptualization and the necessity of developing new methods proper to deal with these limitations. This dissertation develops methodologies for investigating some complex questions arising in the pre-development phase, by focusing on the case of smart grid development. The first essay uses insights from modeling interventions in complex systems and builds a System Dynamics model to investigate the cost allocation problem of smart metering roll-out. The second essay takes ideas from Technological Innovation System approach and develops a method to analyze the emergence of spatial diversity in smart grid development by combining Social Network Analysis and Agent-Based Modeling. The third essay builds on ideas from network theory and evolutionary modeling to develop a method for identifying the main path of knowledge development and analyzing knowledge trajectories in smart grid initiatives

The Biological Framework for a Mathematical Universe

The mathematical universe hypothesis is a theory that the physical universe is not merely described by mathematics, but is mathematics, specifically a mathematical structure. Our research provides evidence that the mathematical structure of the universe is biological in nature and all systems, processes, and objects within the universe function in harmony with biological patterns. Living organisms are the result of the universe’s biological pattern and are embedded within their physiology the patterns of this biological universe. Therefore physiological patterns in living organisms can be used as models to structurally map analogies from the biological domain to any target domain to reveal and understand the biological nature of the target domain. Our paper explores various analogies, structurally mapping a red blood cell to a cup; proteins produced from ribosomes to music produced from instruments; a beating heart to the melting and freezing of Antartica; cells, tissue, organs and blood, to people, organizations, industries, and money, and; bio-economic concepts in cellular society to socioeconomic concepts in human society. It also discusses how phenomena in cellular mitosis can help explain phenomena in the universe, such as black holes, dark matter, dark energy, and the structure of the universe. Building upon the concept of perennial wisdom, our research has provided evidence that the ideas of a biological universe were expressed across many past cultures and historical periods. The implications of this theory are vast, encompassing fields such as physics, science, philosophy, religion, law, economics, politics, and engineering, thus serving as a unifying theory for all knowledge. Our theory is supported by meta-analysis of scientific, historic, and religious literature, observations and first principles logic

Language Choice in a Multilingual Society: A View from Complexity Science

Complexity Science, which can be defined as the science which studies complex systems and emergent phenomena through the construction of computational models, has for decades proved to be useful in the understanding and description of quite diverse scientific phenomena, such as flocks of birds or fish, behaviour of the brain as a aggregate of neurons and traffic jams (Mitchell 2009). Those working in the social sciences, from Thomas Hobbes (17th c.) onwards (Ball 2004), have felt attracted to the shaping of a statistical view of social phenomena. Yet despite these early attempts, it is only recently that Complexity Science has begun to be used as a framework for the study of social phenomena. Indeed, its relevance is now sometimes said to be greater than any other scientific perspective, given that the boundaries between disciplines are fuzzier than ever (Ball 2012: VII “The major challenges of the twenty-first century are not ones that can be understood, let alone solved, from a particular academic perspective”).In addition, for financial support, Lucía Loureiro-Porto thanks the Spanish Ministry for Science and Innovation and the European Regional Development Fund (grant FFI2011-26693-C02-02), while Maxi San Miguel acknowledges financial support from FEDER and MINECO (Spain) under project FIS2012-30634.Peer reviewe