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

Introduction

On October 24, 2003, SUERF celebrated its 40th anniversary in the Galerie Dorée of the Banque de France with an especially high level and rich seminar. The memorable occasion was further elevated by Jean Claude Trichet giving the tenth SUERF Annual Lecture in his last public speech as Governor of the Banque de France, prior to taking over as President of the European Central Bank. This study brings together in slightly edited form the four papers presented at the seminar and the Annual Lecture.

Opinion Dynamics and Learning in Social Networks

We provide an overview of recent research on belief and opinion dynamics in social networks. We discuss both Bayesian and non-Bayesian models of social learning and focus on the implications of the form of learning (e.g., Bayesian vs. non-Bayesian), the sources of information (e.g., observation vs. communication), and the structure of social networks in which individuals are situated on three key questions: (1) whether social learning will lead to consensus, i.e., to agreement among individuals starting with different views; (2) whether social learning will effectively aggregate dispersed information and thus weed out incorrect beliefs; (3) whether media sources, prominent agents, politicians and the state will be able to manipulate beliefs and spread misinformation in a society

The role of sanctioning in the evolutionary dynamics of collective action

Tese de mestrado em Física, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012Preventing global warming requires overall cooperation. Contributions will depend on the risk of future losses, which plays a key role in decision making. Here, I discuss an evolutionary game theoretical model in which decisions within small groups under high risk and stringent requirements toward success significantly raise the chances of coordinating to save the planet’s climate, thus escaping the tragedy of the commons. I analyze both deterministic dynamics in infinite populations, and stochastic dynamics in finite populations. I also study the impact of different types of sanctioning mechanisms in deterring non-cooperative behavior in climate negotiations, towards the mitigation of the effects of climate change. To this end, I introduce punishment in the collective-risk dilemma and study the dynamics of collective action in finite populations. I show that a significant increase in cooperation is attained whenever individuals have the opportunity to contribute (or not) to institutions that punish free riders. I investigate the impact of having local instead of global sanctioning institutions, showing that the former – which are expected to require less financial resources and which involve agreements between a smaller number of individuals – are more conducive to the prevalence of an overall cooperative behavior. In the optics of evolutionary game theory, the system dynamics is solved by means of a multidimensional stochastic Markov process. The interaction between individuals is not pairwise but it occurs in n-person games; the individuals have perception of the risk and are allowed to explore unpopulated strategies.A prevenção do aquecimento global requer cooperação global. As atuais contribuições dependerão do risco das perdas futuras, desempenhando assim um papel fundamental na tomada de decisões. Nesta tese discuto um modelo teórico para um jogo evolutivo, no qual a tomada de decisões envolvendo grupos pequenos, com alto risco e requisitos rigorosos em direção ao sucesso aumenta significativamente as hipóteses de coordenação para a salvação do clima do planeta, evitando assim a tragédia dos comuns. Tanto a dinâmica determinística em populações infinitas como a dinâmica estocástica em populações finitas são analisadas. Além disto, estudo ainda o impacto de diferentes tipos de mecanismos de sanção para desencorajar o comportamento não cooperativo nas negociações climáticas, de forma a mitigar os efeitos das alterações climáticas. Para este fim, introduzo punição no jogo evolutivo e estudo a dinâmica da ação coletiva em populações finitas. Mostro que um aumento significativo na cooperação ´e alcançado quando os indivíduos têm a oportunidade de contribuir (ou não) para as instituições que punem os chamados “free riders”. Investigo o impacto da conceção de instituições fiscalizadoras locais em vez de instituições globais, mostrando que as primeiras – das quais se espera que exijam menos recursos financeiros e que envolvam acordos entre um número menor de indivíduos – são mais favoráveis para a prevalência de um comportamento global cooperativo. Na ótica da teoria dos jogos evolutiva, a dinâmica do sistema é resolvida por meio de um processo estocástico de Markov multidimensional. A interação entre indivíduos não é entre pares, mas ocorre em jogos de n pessoas, os indivíduos têm perceção do risco e têm a capacidade de explorar estratégias despovoadas

Technical Change and Industrial Dynamics as Evolutionary Processes

This work prepared for B. Hall and N. Rosenberg (eds.) Handbook of Innovation, Elsevier (2010), lays out the basic premises of this research and review and integrate much of what has been learned on the processes of technological evolution, their main features and their effects on the evolution of industries. First, we map and integrate the various pieces of evidence concerning the nature and structure of technological knowledge the sources of novel opportunities, the dynamics through which they are tapped and the revealed outcomes in terms of advances in production techniques and product characteristics. Explicit recognition of the evolutionary manners through which technological change proceed has also profound implications for the way economists theorize about and analyze a number of topics central to the discipline. One is the theory of the firm in industries where technological and organizational innovation is important. Indeed a large literature has grown up on this topic, addressing the nature of the technological and organizational capabilities which business firms embody and the ways they evolve over time. Another domain concerns the nature of competition in such industries, wherein innovation and diffusion affect growth and survival probabilities of heterogeneous firms, and, relatedly, the determinants of industrial structure. The processes of knowledge accumulation and diffusion involve winners and losers, changing distributions of competitive abilities across different firms, and, with that, changing industrial structures. Both the sector-specific characteristics of technologies and their degrees of maturity over their life cycles influence the patterns of industrial organization ? including of course size distributions, degrees of concentration, relative importance of incumbents and entrants, etc. This is the second set of topics which we address. Finally, in the conclusions, we briefly flag some fundamental aspects of economic growth and development as an innovation driven evolutionary process.Innovation, Technological paradigms, Technological regimes and trajectories, Evolution, Learning, Capability-based theories of the firm, Selection, Industrial dynamics, Emergent properties, Endogenous growth

Religion as a Seed Crystal for Altruistic Cooperation

The ability to solve problems of collective action is crucial for economic performance. Growth-fostering behavioral propensities such as respecting property, honoring contracts, or helping others are collectively beneficial but individually costly. The paradigmatic formalization of this rationality gap is provided by the non-iterated Prisoners’ Dilemma, where rational players are locked in at a state of mutual defection while mutual cooperation would be better for everyone. In sporadic, ex-ante anonymous interactions (like in modern large-scale societies), the ‘shadow of the future’ cannot sustain cooperation. Cooperation must be altruistic, in the sense that a cooperator enhances her opponent’s payoff at her own expense. In this piece of work another group selection mechanism is presented that generates and sustains altruism in ex-ante anonymous settings. Assuming that cooperative attitudes are coupled with a preference for participating in costly rituals (religious involvement is taken as an example), interactions take place within two endogenously separated groups. The signaling value of religion in the model derives not from differential costliness but from cooperators’ intrinsic nature of motivation. Noncooperative types have to learn about the matching gains from religious involvement while cooperative types need not. This induces an initial advantage to cooperative/religious types at the beginning of each generation, thereby sustaining altruism in the long run via religious participation.Altruism; Prisoners' Dilemma; Evolutionary Game Theory; Signaling; Religion

The forest through the trees:Making sense of an ecological dynamics approach to measuring and developing collective behaviour in football

In this book, we interpret the literature that has analysed football performance from a tactical standpoint using an ecological dynamics perspective. This approach focuses on the performer–environment relationship and provides a basis for understanding the dynamic nature of performance in collective team sports (1) and will be explained in detail throughout. The first section of this text will provide a brief description of association football as well as commonly used methods to analyse football performance. The next section will briefly introduce common theories and practices used to measure team behaviour, decision-making, and performance enhancement in team sport, which are then used to introduce the ecological dynamics framework. This framework will then be used to aid the application of these findings for tactical analysis in team sports such as football. Finally, we will introduce some of the scientific literature on improving team performance, particularly in reference to team coordination and decision-making. The following sections of this book will deal specifically with how small-sided games can be used to develop tactical behaviour in football. A small-sided games approach was chosen as these modified games allow for the simultaneous development of players’ technical skills, conditioning, and ability to solve and overcome tactical challenges through coordinative behaviour and effective decision-making (2-5). Small-sided games provide an environment that mimics the perception–action couplings of in situ performance, which should, in theory, improve the transferability of learned behaviours to in-game performance (4, 6). As a result, small-sided games are often used by coaches and form an integral part of this text. Finally, we conclude with some recommendations for future research, and some practical considerations for coaches interested in applying the research discussed in this book

Thought and Behavior Contagion in Capital Markets

Prevailing models of capital markets capture a limited form of social influence and information transmission, in which the beliefs and behavior of an investor affects others only through market price, information transmission and processing is simple (without thoughts and feelings), and there is no localization in the influence of an investor on others. In reality, individuals often process verbal arguments obtained in conversation or from media presentations, and observe the behavior of others. We review here evidence concerning how these activities cause beliefs and behaviors to spread, affect financial decisions, and affect market prices; and theoretical models of social influence and its effects on capital markets. Social influence is central to how information and investor sentiment are transmitted, so thought and behavior contagion should be incorporated into the theory of capital markets.capital markets; thought contagion; behavioral contagion; herd behavior; information cascades; social learning; investor psychology; accounting regulation; disclosure policy; behavioral finance; market efficiency; popular models; memes

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

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion