An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Evaluation of human movement qualities: A methodology based on transferable-utility games on graphs.

Abstract A novel computational method for the analysis of expressive full-body movement qualities is introduced, which exploits concepts and tools from graph theory and game theory. The human skeletal structure is modeled as an undirected graph, where the joints are the vertices and the edge set contains both physical and nonphysical links. Physical links correspond to connections between adjacent physical body joints (e.g., the forearm, which connects the elbow to the wrist). Nonphysical links act as \u201cbridges\u201d between parts of the body not directly connected by the skeletal structure, but sharing very similar feature values. The edge weights depend on features obtained by using Motion Capture data. Then, a mathematical game is constructed over the graph structure, where the vertices represent the players and the edges represent communication channels between them. Hence, the body movement is modeled in terms of a game built on the graph structure. Since the vertices and the edges contribute to the overall quality of the movement, the adopted game-theoretical model is of cooperative nature. A game-theoretical concept, called Shapley value, is exploited as a centrality index to estimate the contribution of each vertex to a shared goal (e.g., to the way a particular movement quality is transferred among the vertices). The proposed method is applied to a data set of Motion Capture data of subjects performing expressive movements, recorded in the framework of the H2020-ICT-2015 EU Project WhoLoDance, Project no. 688865. Results are presented: development of novel method, contribution to the scientific community with a new data corpus, application the discussed method to 100 movement recordings and creation of database archive of stimuli for further use in research studies in the framework of the WhoLoDance Project

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

Better Than Conscious? The Brain, the Psyche, Behavior, and Institutions

The title of this chapter is deliberately provocative. Intuitively, many will be inclined to see conscious control of mental process as a good thing. Yet control comes at a high price. The consciously not directly controlled, automatic, parallel processing of information is not only much faster, it also handles much more information, and it does so in a qualitatively different manner. This different mental machinery is not adequate for all tasks. The human ability to consciously deliberate has evolved for good reason. But on many more tasks than one might think at first sight, intuitive decision-making, or at least an intuitive component in a more complex mental process, does indeed improve performance. This chapter presents the issue, offers concepts to understand it, discusses the effects in terms of problem solving capacity, contrasts norms for saying when this is a good thing, and points to scientific and real world audiences for this work.

Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters