Contents: Volume I Acknowledgements Introduction Patrick Bolton PART I ADVERSE SELECTION 1 JA Mirrlees (1971), 'An Exploration in the Theory of Optimum Income Taxation' 2 Michael Mussa and Sherwin Rosen (1978), 'Monopoly and Product Quality' 3 Michael Spence (1973), 'Job Market Signaling' 4 Eric Maskin and Jean Tirole (1992), 'The Principal-Agent Relationship with an Informed Principal, II: Common Values' 5 Michael Rothschild and Joseph Stiglitz (1976), 'Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information' 6 B Douglas Bernheim and Michael D Whinston (1986), 'Menu Auctions, Resource Allocation, and Economic Influence' 7 Robert M Townsend (1979), 'Optimal Contracts and Competitive Markets with Costly State Verification' 8 Philippe Aghion and Patrick Bolton (1987), 'Contracts as a Barrier to Entry' PART II MORAL HAZARD 9 JA Mirrlees (1999), 'The Theory of Moral Hazard and Unobservable Behaviour: Part I' 10 Bengt Holmstroem (1979), 'Moral Hazard and Observability' 11 Sanford J Grossman and Oliver D Hart (1983), 'An Analysis of the Principal-Agent Problem' 12 Bengt Holmstrom and Paul Milgrom (1990), 'Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design' 13 Jean-Jacques Laffont and Jean Tirole (1986), 'Using Cost Observation to Regulate Firms' PART III MECHANISM DESIGN AND OPTIMAL AUCTIONS 14 Eric Maskin ([1977] 1999), 'Nash Equilibrium and Welfare Optimality' 15 Claude d'Aspremont and Louis-Andre Gerard-Varet (1979), 'Incentives and Incomplete Information' 16 Roger B Myerson (1981), 'Optimal Auction Design' 17 Roger B Myerson and Mark A Satterthwaite (1983), 'Efficient Mechanisms for Bilateral Trading' 18 Jacques Cremer and Richard P McLean (1985), 'Optimal Selling Strategies under Uncertainty for a Discriminating Monopolist when Demands are Interdependent' 19 John Moore and Rafael Repullo (1988), 'Subgame-Perfect Implementation' Name Index Volume II Acknowledgements An introduction by the editor to both volumes appears in Volume I PART I OPTIMAL CONTRACTS AND ORGANIZATIONS 1 Bengt Holmstrom (1982), 'Moral Hazard in Teams' 2 Guillermo A Calvo and Stanislaw Wellisz (1978), 'Supervision, Loss of Control, and the Optimal Size of the Firm' 3 Jerry R Green and Nancy L Stokey (1983), 'A Comparison of Tournaments and Contracts' 4 Jean Tirole (1986), 'Hierarchies and Bureaucracies: On the Role of Collusion in Organizations' PART II CONTRACT DYNAMICS, SELF-ENFORCING CONTRACTS AND RENEGOTIATION 5 Mathias Dewatripont (1989), 'Renegotiation and Information Revelation over Time: The Case of Optimal Labor Contracts' 6 Oliver D Hart and Jean Tirole (1988), 'Contract Renegotiation and Coasian Dynamics' 7 M Dewatripont and E Maskin (1995), 'Credit and Efficiency in Centralized and Decentralized Economies' 8 Robert M Townsend (1982), 'Optimal Multiperiod Contracts and the Gain from Enduring Relationships under Private Information' 9 Edward J Green (1987), 'Lending and the Smoothing of Uninsurable Income' 10 William P Rogerson (1985), 'Repeated Moral Hazard' 11 Bengt Holmstrom and Paul Milgrom (1987), 'Aggregation and Linearity in the Provision of Intertemporal Incentives' 12 W Bentley MacLeod and James M Malcomson (1989), 'Implicit Contracts, Incentive Compatibility, and Involuntary Unemployment' 13 Jonathan Levin (2003), 'Relational Incentive Contracts' 14 Bengt Holmstroem (1999), 'Managerial Incentive Problems: A Dynamic Perspective' PART III INCOMPLETE CONTRACTS AND THE THEORY OF THE FIRM 15 Sanford J Grossman and Oliver D Hart (1986), 'The Costs and Benefits of Ownership: A Theory of Vertical and Lateral Integration' 16 Oliver Hart and John Moore (1990), 'Property Rights and the Nature of the Firm' 17 Patrick Bolton and Michel D Whinston (1993), 'Incomplete Contracts, Vertical Integration, and Supply Assurance' 18 Philippe Aghion and Patrick Bolton (1992), 'An Incomplete Contracts Approach to Financial Contracting' 19 Philippe Aghion and Jean Tirole (1997), 'Formal and Real Authority in Organizations' 20 Patrick Bolton and David S Scharfstein (1990), 'A Theory of Predation Based on Agency Problems in Financial Contracting' Name Index

The economics of contracts

Assessing web sites quality

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An Introduction To Auction Theory

Public online social network services have achieved dazzling success in recent years. As a result, vertical social network services for universities' are expected warmly by campus users. As the majority of activities in university campus knowledge-intensive, one of the core functions of campus social network system is to facilitate knowledge sharing. In the cyberspace of universities, knowledge is stored in various kinds of digital resources such as documents, photos, videos etc. In this paper, we discuss the design and implementation of our campus social network system, concentrating on knowledge sharing mechanism in the system. The knowledge sharing mechanism has three features including the utilization of users' personal social network facilitate the dissemination of digital resources, the use of a six-tuple model based tagging to realize the unified labeling for digital resources and the adoption of a multi-scale evaluation method for digital resources. With all these considerations, we expect to improve the effectiveness of knowledge by enlarging the dissemination scope of digital resources carrying it in cyberspace of universities.

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University campus social network system for knowledge sharing

Public online social network services have achieved dazzling success in
 recent years. As a result, vertical social network services for universities
 are expected warmly by campus users. As the majority of activities in
 university campus are knowledge and social interaction intensive, one of the
 core functions of campus social network system is to facilitate knowledge
 sharing on campus. In the cyberspace of universities, knowledge is stored in
 various kinds of digital resources such as documents, photos, videos etc. In
 this paper, we discuss the design and implementation of our campus social
 network system, concentrating on knowledge sharing mechanism in the system.
 The knowledge sharing mechanism has five features including the utilization
 of users’ personal social network to facilitate the dissemination of digital
 resources, the use of a six-tuple model based tagging to realize the unified
 labeling for digital resources, the fine-grained access control based on
 friend lists for safer knowledge sharing, the adoption of a multiscale
 evaluation method for digital resources and personalized recommendation for
 digital resource with social graph based collaborative filtering as its core
 idea. With all these considerations, we expect to improve the efficiency and
 effectiveness of knowledge while enlarging the dissemination scope of digital
 resources carrying it in cyberspace of universities.

In most of e-commerce site, evaluation systems are employed to evaluate each user after the tradings. Normally, seller's evaluation score is shown in e-commerce site that is computed based on score given by buyers. In recent e-commerce site, the evaluation is based on multiple attributes and buyers give their thinking for each attribute. In this viewpoint, the seller evaluation in e-commerce is collected knowledge from buyers and synthetic score is computational intelligence because each buyer makes his/her decision whether he/she trades with the seller. In this paper, we discuss the computational intelligence of evaluation system in e-commerce. Then, to avoid asymmetric and incomplete information in tradings, we design a mechanism of trader evaluation. After that, we give some experiments to show the rate of successful trade in our proposed mechanism. Contributions of this paper are showing theoretical discussion of e-commerce computational intelligence, design on evaluation mechanism and showing the effectiveness of the proposed mechanism.

Computational collective intelligence in electronic commerce: Incentive design in evaluation system

This paper shows the effects of traders evaluation based computational mechanism design in electronic commerce. The proposes a new user evaluation methods based on multi-criteria based rating system. In the existing trading systems, most of them have user evaluation to help buyers/sellers decision making to trade. However, a user can never know where the potential trading partners did trade successfully based on his/her evaluation criteria. And also, some traders show the information, which are advantage on their trade on the ecommerce website. In some cases, sellers lose a chance to sell the item because of lack of their and their items information. Contrary, buyers sometimes cannot make a decision to choose trading partner because of poor webpage structure even though the seller is truthful trader. To solve these problems, we propose a new evaluation system based on multi-criteria evaluation and sellers trading history. We show 6 experiment results using our proposed mechanism the rest of paper.

New Trading Evaluation Mechanism on Incentive Design

In this paper, we show theoretical properties and mechanism design based on information disclosure incentives. Normally, in existing electronic commerce, user evaluation system in electronic commerce provides rough attributes of evaluation scoring given by buyers who finished all trading procedures. Also, related work in user evaluations are assumed simple situations and conditions when they analyze and discuss the effects of evaluations. In this paper, we describe the detailed evaluation factors and new evaluation mechanism when users choose items in the trade. Also, we characterize the theoretical effects using our proposed mechanism in the user evaluation. In our proposed mechanism, sellers can provide some additional evaluation attributes to help buyers’ decision-making on items selection to purchase. The electronic commerce manager adds extra points based on the amount of information disclosure and credit history of sellers.

Reassuring Electronic Commerce Mechanism based on User Evaluation Controlling

In recent years, commerce over the internet has been widely developing with enhancement convenience of trading on the web. However, troubles on trading also increase. This paper proposes a new evaluation mechanism to decrease the information incompleteness and asymmetry by using multi-attribute evaluation. Buyers can precisely evaluate due to the multi-criteria chosen by sellers. Based on the number of selected criteria, the system determines the incentive rate of synthetic rating for sellers. Thus, sellers have an incentive to disclose a lot of evaluation attributes for buyers. Our experiment shows the operation given by the incentive rating based on number of criteria. Advantages of our proposed model include incomplete and asymmetric information decreases on the e-commerce site.

An Incentive Mechanism Design in E-Trade Evaluation Systems

In most of e-commerce sites evaluation systems are employed to evaluate each user after trades. Normally, seller's evaluation score is shown in e-commerce site and computed based on the score given by buyers. In recent e-commerce sites, the evaluation is based on multiple attributes and buyers give their thinking for each attribute. In this viewpoint, the seller evaluation in e-commerce is collected knowledge from buyers and the synthetic score is computational intelligence because each buyer makes his/her decision whether he/she trades with the seller. In this paper, we discuss the computational intelligence of the evaluation system in e-commerce. Then, to avoid asymmetric and incomplete information in trades, we design a mechanism of the trader evaluation. After that, we present some experiments to show the rate of successful trades in our proposed mechanism. Contributions of this paper are showing the theoretical discussion of e-commerce computational intelligence, design of evaluation mechanism and the effectiveness of the proposed mechanism.

Koki Murakata

Papers

Computational collective intelligence in electronic commerce: Incentive design in evaluation system

New Trading Evaluation Mechanism on Incentive Design

Reassuring Electronic Commerce Mechanism based on User Evaluation Controlling

An Incentive Mechanism Design in E-Trade Evaluation Systems

Analysis of Successful Trades Information Disclosure Mechanism