计量经济分析 = Econometric analysis

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The power of feedback.

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

Research on so-called ‘Big Data’ has received a considerable momentum and is expected to grow in the future. One very interesting stream of research on Big Data analyzes online networks. Many online networks are known to have some typical macro-characteristics, such as ‘small world’ properties. Much less is known about underlying micro-processes leading to these properties. The models used by Big Data researchers usually are inspired by mathematical ease of exposition. We propose to follow in addition a different strategy that leads to knowledge about micro-processes that match with actual online behavior. This knowledge can then be used for the selection of mathematically-tractable models of online network formation and evolution. Insight from social and behavioral research is needed for pursuing this strategy of knowledge generation about micro-processes. Accordingly, our proposal points to a unique role that social scientists could play in Big Data research.

/pdf/big-data-big-gaps-of-knowledge-in-the-field-of-internet-54sgucfqpw.pdf

"Big Data" : big gaps of knowledge in the field of internet science

With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students’ online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables extracted from the LMS, which makes it hard to draw general conclusions about the mechanisms underlying student performance. We first provide an overview of the theoretical arguments used in learning analytics research and the typical predictors that have been used in recent studies. We then analyze 17 blended courses with 4,989 students in a single institution using Moodle LMS, in which we predict student performance from LMS predictor variables as used in the literature and from in-between assessment grades, using both multi-level and standard regressions. Our analyses show that the results of predictive modeling, notwithstanding the fact that they are collected within a single institution, strongly vary across courses. Thus, the portability of the prediction models across courses is low. In addition, we show that for the purpose of early intervention or when in-between assessment grades are taken into account, LMS data are of little (additional) value. We outline the implications of our findings and emphasize the need to include more specific theoretical argumentation and additional data sources other than just the LMS data.

Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS

Trust and Commitments

Previous research on reputation systems primarily focused on their trust-building function. The present research addresses their trust-rebuilding function-specifically, the role of the short text comments given in reaction to negative feedback. Rebuilding trust is often necessary because on-line markets are noisy environments. The results of two experimental studies of eBay users show that trustworthiness judgments are influenced by the text comments accompanying negative feedback and also by whether a trust violation is perceived as competence-based or morality-based. Plain apologies were more successful than denials in repairing trust. These effects were mediated by the perceived believability of the comments. Thus, to avoid the detrimental effect of noise on trust, operators of on-line marketplaces should encourage text feedback comments and reactions.

On-line Reputation Systems: The Effects of Feedback Comments and Reactions on Building and Rebuilding Trust in On-line Auctions

This chapter addresses social embeddedness effects on ex ante management of economic transactions. We focus on dyadic embeddedness, that is the history of prior transactions between business partners and the anticipation of future transactions. Ex ante management through, for example, contractual arrangements is costly but mitigates risks associated with the transaction, such as risks from strategic and opportunistic behavior. Dyadic embeddedness can reduce such risks and, hence, the need for ex ante management by, for instance, making reciprocity and conditional cooperation feasible. The chapter presents a novel theoretical model generating dyadic embeddedness effects, together with effects of transaction characteristics and management costs. We stress the interaction of the history of prior transactions and expectations of future business. Hypotheses are tested using new and primary data from an extensive survey of more than 900 purchases of information technology (IT) products (hard- and software) by almost 800 small- and medium-sized enterprises (SMEs). Results support, in particular, the hypotheses on effects of dyadic embeddedness.

Chris Snijders

Papers

"Big Data" : big gaps of knowledge in the field of internet science

Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS

Trust and Commitments

On-line Reputation Systems: The Effects of Feedback Comments and Reactions on Building and Rebuilding Trust in On-line Auctions

Contacts and contracts: dyadic embeddedness and the contractual behavior of firms