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

Convention: a philosophical study

Partnerships between companies receive a great deal of attention from top managers and researchers at the time of their formation. This attention results largely from the common perception that the initial structuring of partnerships and establishment of common goals determines partnership outcomes and success. In Alliance Advantage, Doz and Hamel shift the focus away from deal making to the internal processes within the partnership and the unfolding interactions among partners that play an important and relatively unexplored role in shaping outcomes. Focusing on the underlying reasons why companies enter alliances and the processes by which they continually learn from their interactions and re-evaluate common--and individual--goals, the authors paint a sophisticated picture of alliance dynamics over time. The authors challenge organizations to define their objectives for alliance formulation and consider whether their own corporate culture provides an "alliance ready" atmosphere.

Alliance advantage : the art of creating value through partnering

Planning with complexity: an introduction for collaborative rationality for public policy

Motivated by the growing importance of social media, this paper examines the relationship between new media, old media, and sales in the context of the music industry. In particular, we study the interplay between blog buzz, radio play, and music sales at both the album and song levels of analysis. We employ the panel vector autoregression (PVAR) methodology, an extension of vector autoregression to panel data. We find that radio play is consistently and positively related to future sales at both the song and album levels. Blog buzz, however, is not related to album sales and negatively related to song sales, suggesting that sales displacement due to free online sampling dominates any positive word-of-mouth effects of song buzz on sales. Further, the negative relationship between song buzz and sales is stronger for niche music relative to mainstream music, and for less popular songs within albums. We discuss the implications of these results for both research and practice regarding the role of new media in the music industry.

Social media, traditional media, and music sales

Yun-Ke Changa,∗, Miguel Angel Morales-Arroyob, Hla Thanc, Zarchi Tunc and Zhujun Wangc aWee Kim Wee School of Communication and Information, Nanyang Technological University, Jalan Bahar, Singapore bInstituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City, Mexico cSchool of Communication and Information, Nanyang Technological University, Jalan Bahar, Singapore

Collaborative learning in wikis

This study investigates the contribution of electronic word-of-mouth (eWOM) to the sales of music albums. We conducted an empirical investigation of twenty-two music albums for a period of eleven consecutive weeks. eWOM was identified as an uncertainty-reducing element in consumer decision-making. Generally, the research hypotheses were partially supported using a multi-variate linear regression model. We also found a stronger correlation between some eWOM channels and sales compared to other channels. eWOM has traditionally been considered an unstructured and ad-hoc source of sentiment. Our results suggest that eWOM generated in social networking when analysed appropriately is a fairly reliable predictor of market success. It is effective as a tacit suggestion, recommendation, or referral element with viral network effects. The intended contribution of this work is in identifying eWOM as a significant information contributor in the digital marketplace.

The Emergence of Electronic Word-of-Mouth as a Marketing Channel for the Digital Marketplace

Word-of-Mouth (WOM) is a popular communication mechanism. Web 2.0 tools such as blogs, user driven audio-video sharing platforms, and social media sites have provided a multitude of feedback channels. In the current study we explored the impact of electronic-word-of-mouth, eWOM, on music albums in the American market. We analyzed the impact of different eWOM dimensions on the sales over a period of 12 weeks. The results indicate that a combination of these dimensions impacts the sales more than the individual dimensions do.

Identification of critical eWOM dimensions for music albums

This paper presents a framework for the analyzing revenue distribution in the delivery of digital content such as music, movies, games, books, and news. In such content delivery networks, there are various roles played by producers, consumers, syndicators, aggregators and distributors in the marketplace. We outline a framework for business modeling known as VISOR and adapt some ideas from game theory in order to investigate notions of efficiency and fairness in the digital media eco-system. This framework suggests that the revenue distribution within a business model is determined by the range between producers’ cost of production and consumers’ willingness to pay. The allocation of these revenues among the players is in turn determined by their value capacity which is a function of the interface for content, service platform, organizing model and revenue streams. The game-theoretic notions of fairness and efficiency are introduced as a strategy for stability in the workplace. The paper concludes that stability is the key to derivative value in a digital media network.

Deriving Value in Digital Media Networks

Introduction The emerging digital marketplace, with the Internet as a key facilitator, is an amorphous web of connections among producers and consumers (Berman, Abraham, Battino, Shipnuck, & Neus, 2007). The Internet has also presented a synergistic opportunity for commentary, recommendation, suggestion, and referral through Wordof-Mouth WOM (Dellarocas, 2003). This seems particularly so for music and movies which elicit a following. Word-of-mouth is an alternative which rapidly creates awareness among users about a new album, products, services, and content (V. Dhar & Chang, 2007). Burson-Marsteller and Roper Starch coined the term 'e-fluentials' in 1999 (Burson-Marsteller, 2005). This finding asserted that in traditional, offline, word-of-mouth communication one person affects the attitude and behavior of approximately another two people, whereas in eWOM one person affects the behavior of eight other people. In 2001, it was found that the e-fluentials represented about 11 million Americans, each potentially influencing up to 14 people (BursonMarsteller, 2005). Since 1999, the number of online adults in the US has grown from about 9 percent to more than 80 percent-by more than 178 million people (Leggatt, 2007). Considering these numbers, higher computer literacy, and availability of more useful content, the e-fluentials may be expected to influence a much larger section of the society today. Formally stated, an eWOM unit is a relevant remark made by a potential, actual, or former customer about a product or service (Hennig-Thurau, Qwinner, Walsh, & Gremler, 2004). This remark is readily available to potential customers and partners through the Internet. eWOM involves text-based, recorded, traceable, organized, and reusable one-to- many consumer interactions among strangers in cyberspace. Several studies have examined consumer-created content from the perspective of information credibility. The owner-created information focuses more on the technical details. It does not describe a product's actual performance or user perspective. Dellarocas (2003) argued that the Internet's bidirectional communication structure and the ability to artificially create large-scale word-of-mouth networks at low cost can have implications on brand reputation, consumer retention, and quality assurance. In an early study, it was found that buyers pay a 20% premium on the basis of familiarity with the seller (Brynjolfsson & Smith, 2000). The credibility of the promotional messages in online chat rooms and the implication of those new information channels on sales has been studied (Mayzlin, 2006). Specific factors like identification, promotion, loyalty, satisfaction, and participation have been studied in free-software virtual communities (Casalo, Flavian, & Guinaliu, 2010). Hence, eWOM has also been described as empowering consumers, and adding value to sales (Fiona, 2005). It is an integral aspect of the digital market worthy of continued research attention. This study investigates the impact of electronic word-of-mouth (eWOM) on sales and revenues for digital products such as music albums. To better understand the word-of-mouth phenomenon, we refer to the terminology framework designed by the word-of-mouth Marketing Association, womMA (WOMMA, 2005). We adopt this womMA framework from an Object-Oriented perspective so that distinct eWOM functionalities, their attributes, relationships, behavior such as hits, download, streaming, sales and revenues may be derived. Table 1 illustrates the eWOM ecosystem at a high level. This framework has four main classes. The objects for these classes are: * Participants are consumers-Senders or Receivers. * Actions are the activities that a participant performs on WOM units, such as reading a blog post, replying or composing a comment, or recommending or suggesting content to others. * A WOM unit is a consumer generated message found in channels such as posts, comments, and downloads. …

/pdf/the-emergence-of-electronic-word-of-mouth-as-a-marketing-60krcfvjtz.pdf

Miguel A. Morales-Arroyo

Papers

Collaborative learning in wikis

The Emergence of Electronic Word-of-Mouth as a Marketing Channel for the Digital Marketplace

Identification of critical eWOM dimensions for music albums

Deriving Value in Digital Media Networks

The Emergence of Electronic Word-of-Mouth as a Marketing Channel for the Digital Marketplace