Diversification Across Mining Pools: Optimal Mining Strategies under PoW

Mining is a central operation of all proof-of-work (PoW) based cryptocurrencies. The vast majority of miners today participate in "mining pools" instead of "solo mining" in order to lower risk and achieve a more steady income. However, this rise of participation in mining pools negatively affects the decentralization levels of most cryptocurrencies. In this work, we look into mining pools from the point of view of a miner: We present an analytical model and implement a computational tool that allows miners to optimally distribute their computational power over multiple pools and PoW cryptocurrencies (i.e. build a mining portfolio), taking into account their risk aversion levels. Our tool allows miners to maximize their risk-adjusted earnings by diversifying across multiple mining pools which enhances PoW decentralization. Finally, we run an experiment in Bitcoin historical data and demonstrate that a miner diversifying over multiple pools, as instructed by our model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward over its standard deviation/volatility).Comment: 13 pages, 16 figures. Presented at WEIS 201

Design of Complex Engineered Systems Using Multiagent Coordination

This thesis is the combination of two research publications working toward a unified strategy in which the design of complex engineered systems can be completed using a multiagent coordination approach. Current engineered system modeling techniques segment large complex models into multiple groups to be simulated independently. These methods restrict the evaluations of such complex systems, as their failure properties are typically unknown until they are experienced in operation. In an effort to help engineers to design complex engineered systems, this research proposes that a distributed yet non-legislated approach can be used in the design processes by splitting up the overall system into specific teams. The approach specifically hypothesizes that multiagent credit assignment can be used to effectively determine how to properly incentivize subsystem designers so that the global set of system-level objectives can be achieved. The first publication presents a multiagent systems based approach for designing a self-replicating robotic manufacturing factory in space. The simulation in this work is able to present the coordination of the agents during the construction of the factory as the parameters of the learning algorithm are changed. The results show the advantage of using a learning algorithm to design a large system. The second publication presents a hybrid approach to design complex engineered systems, providing a method in which design decisions can be reconciled without the need for either detailed interaction models or external legislating mechanisms. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm can effectively design a complex engineered system. Each publication utilized a system model to illustrate and simulate the methods and potential results. By designing complex systems with a multiagent coordination approach, a design methodology can be developed in an effort to reduce design uncertainty and provide mechanisms through which the system level impact of decisions can be estimated without explicitly modeling such interactions

Comparative analysis of order allocation methods and intelligent systems for effective download of production capacities of manufacturing enterprise

The article compares the planning systems of the enterprise, determines their opportunities to use simulation for the process of distribution of production orders. A comparative review of the methods used in production planning systems and the multi-agent approach in solving the problem of the allocation of production orders is carried out. The possibility of approaches in cases when it is impossible to perform production tasks within the established time frame is considered, as well as to effectively load the available capacities of the units. In case it is not possible to produce the necessary quantity to distribute the part to another, similar to the type of equipment used, but different from it by the quantity and production capacity of the unit. Transmission must be carried out under the condition of sufficient capacity in the subdivision. The results of theoretical and experimental studies are presented. In the process of work, we used the BPsim. MAS dynamic modeling system. An automated system with the possibility of using simulation for the allocation of production orders is considered. © 2019 IOP Publishing Ltd. All rights reserved.The work was supported by Act 211 Government of the Russian Federation, contract № 02.A03.21.0006

Social Choice Optimization

Social choice is the theory about collective decision towards social welfare starting from individual opinions, preferences, interests or welfare. The field of Computational Social Welfare is somewhat recent and it is gaining impact in the Artificial Intelligence Community. Classical literature makes the assumption of single-peaked preferences, i.e. there exist a order in the preferences and there is a global maximum in this order. This year some theoretical results were published about Two-stage Approval Voting Systems (TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular Preferences (CPs). The purpose of this paper is three-fold: Firstly, I want to introduced Social Choice Optimisation as a generalisation of TAVs where there is a max stage and a min stage implementing thus a Minimax, well-known Artificial Intelligence decision-making rule to minimize hindering towards a (Social) Goal. Secondly, I want to introduce, following my Open Standardization and Open Integration Theory (in refinement process) put in practice in my dissertation, the Open Standardization of Social Inclusion, as a global social goal of Social Choice Optimization

Intelligent Agents and Their Potential for Future Design and Synthesis Environment

This document contains the proceedings of the Workshop on Intelligent Agents and Their Potential for Future Design and Synthesis Environment, held at NASA Langley Research Center, Hampton, VA, September 16-17, 1998. The workshop was jointly sponsored by the University of Virginia's Center for Advanced Computational Technology and NASA. Workshop attendees came from NASA, industry and universities. The objectives of the workshop were to assess the status of intelligent agents technology and to identify the potential of software agents for use in future design and synthesis environment. The presentations covered the current status of agent technology and several applications of intelligent software agents. Certain materials and products are identified in this publication in order to specify adequately the materials and products that were investigated in the research effort. In no case does such identification imply recommendation or endorsement of products by NASA, nor does it imply that the materials and products are the only ones or the best ones available for this purpose. In many cases equivalent materials and products are available and would probably produce equivalent results

A Mechanism Design Approach to Bandwidth Allocation in Tactical Data Networks

The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems\u27. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today\u27s software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest to the defense community. In this thesis, we consider a scenario where a number of military platforms have been tasked with the goal of detecting and tracking targets. The sensors onboard a military platform have a partial and inaccurate view of the operating picture and need to make use of data transmitted from neighboring sensors in order to improve the accuracy of their own measurements. The communication takes place over tactical data networks with scarce bandwidth. The problem is compounded by the possibility that the local goals of military platforms might not be aligned with the global system goal. Such a scenario might occur in multi-flag, multi-platform military exercises, where the military commanders of each platform are more concerned with the well-being of their own platform over others. Therefore there is a need to design a mechanism that efficiently allocates the flow of data within the network to ensure that the resulting global performance maximizes the information gain of the entire system, despite the self-interested actions of the individual actors. We propose a two-stage mechanism based on modified strictly-proper scoring rules, with unknown costs, whereby multiple sensor platforms can provide estimates of limited precisions and the center does not have to rely on knowledge of the actual outcome when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the uncertainty in the operating environment. We apply our robust optimization - based scoring rules algorithm to an agent-based model framework of the combat tactical data network, and analyze the results obtained. Through the work we hope to demonstrate how mechanism design, perched at the intersection of game theory and microeconomics, is aptly suited to address one set of challenges of the ULS system paradigm - challenges not amenable to traditional system engineering approaches

Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.Ingeniería, Industria y Construcció