Automated Mechanism Design

Mechanisms design has traditionally been a manual endeavor. In 2002, Conitzer and Sandholm introduced the automated mechanism design (AMD) approach, where the mechanism is computationally created for the specific problem instance at hand. This has several advantages: 1) it can yield better mechanisms than the ones known to date, 2) it applies beyond the problem classes studied manually to date, 3) it can circumvent seminal economic impossibility results that hold for classes of problems but not all instances, and 4) it shifts the burden of design from man to machine. In this talk I will overview our results on AMD to date. I will cover problem representations and the computational complexity of different variants of the design problem. Initial applications include revenue-maximizing combinatorial auctions and (combinatorial) public goods problems. Algorithms for AMD will be discussed. To reduce the computational complexity of designing optimal combinatorial auctions, I introduce an incentive compatible, individually rational subfamily called Virtual Valuations Combinatorial Auctions. The auction mechanism\u27s revenue can be boosted (started, for example, from the VCG) by hill-climbing in this subspace. I will also present computational complexity and communication complexity results that motivate multi-stage and non-truth-promoting mechanisms. Finally, I present our first steps toward automatically designing multi-stage mechanisms

A Grey-Box Approach to Automated Mechanism Design

Auctions play an important role in electronic commerce, and have been used to solve problems in distributed computing. Automated approaches to designing effective auction mechanisms are helpful in reducing the burden of traditional game theoretic, analytic approaches and in searching through the large space of possible auction mechanisms. This paper presents an approach to automated mechanism design (AMD) in the domain of double auctions. We describe a novel parametrized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.Comment: 18 pages, 2 figures, 2 tables, and 1 algorithm. Extended abstract to appear in the proceedings of AAMAS'201

Automated Auction Mechanism Design with Competing Markets

Resource allocation is a major issue in multiple areas of computer science. Despite the wide range of resource types across these areas, for example real commodities in e-commerce and computing resources in distributed computing, auctions are commonly used in solving the optimization problems involved in these areas, since well designed auctions achieve desirable economic outcomes. Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. 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. Following this line of work, we present what we call a grey-box approach to automated auction mechanism design using reinforcement learning and evolutionary computation methods. We first describe a new strategic game, called \cat, which were designed to run multiple markets that compete to attract traders and make profit. The CAT game enables us to address the imbalance between prior work in this field that studied auctions in an isolated environment and the actual competitive situation that markets face. We then define a novel, parameterized framework for auction mechanisms, and present a classification of auction rules with each as a building block fitting into the framework. Finally we evaluate the viability of building blocks, and acquire auction mechanisms by combining viable blocks through iterations of CAT games. We carried out experiments to examine the effectiveness of the grey-box approach. The best mechanisms we learnt were able to outperform the standard mechanisms against which learning took place and carefully hand-coded mechanisms which won tournaments based on the CAT game. These best mechanisms were also able to outperform mechanisms from the literature even when the evaluation did not take place in the context of CAT games. These results suggest that the grey-box approach can generate robust double auction mechanisms and, as a consequence, is an effective approach to automated mechanism design. The contributions of this work are two-fold. First, the grey-box approach helps to design better auction mechanisms which can play a central role in solutions to resource allocation problems in various application domains of computer science. Second, the parameterized view and the reinforcement learning-based search method can be used in other strategic, competitive situations where decision making processes are complex and difficult to design and evaluate manually

Does Resorting to Online Dispute Resolution Promote Agreements? Experimental Evidence

This paper presents the results of an experiment performed to test the properties of an innovative bargaining mechanism (called automated negotiation) used to resolve disputes arising from Internet-based transactions. Automated negotiation is an online sealed-bid process in which an automated algorithm evaluates bids from the parties and settles the case if the offers are within a prescribed range. The observed individual behavior, based on 40 rounds of bargaining, is shown to be drastically affected by the design of automated negotiation. The settlement rule encourages disputants to behave strategically by adopting aggressive bargaining positions, which implies that the mechanism is not able to promote agreements and generate efficiency. This conclusion is consistent with the experimental results on arbitration and the well-known chilling effect: Automated negotiation tends to "chill" bargaining as it creates incentives for individuals to misrepresent their true valuations and discourage them to converge on their own. However, this perverse effect induced by the settlement rule depends strongly on the conflict situation. When the threat that a disagreement occurs is more credible, the strategic effect is reduced since defendants are more interested in maximizing the efficiency of a settlement than their own expected profit.Online Dispute Resolution, Arbitration, Experimental Economics, Electronic Commerce, Bargaining

Automated Mechanism Design for Classification with Partial Verification

We study the problem of automated mechanism design with partial verification, where each type can (mis)report only a restricted set of types (rather than any other type), induced by the principal's limited verification power. We prove hardness results when the revelation principle does not necessarily hold, as well as when types have even minimally different preferences. In light of these hardness results, we focus on truthful mechanisms in the setting where all types share the same preference over outcomes, which is motivated by applications in, e.g., strategic classification. We present a number of algorithmic and structural results, including an efficient algorithm for finding optimal deterministic truthful mechanisms, which also implies a faster algorithm for finding optimal randomized truthful mechanisms via a characterization based on convexity. We then consider a more general setting, where the principal's cost is a function of the combination of outcomes assigned to each type. In particular, we focus on the case where the cost function is submodular, and give generalizations of essentially all our results in the classical setting where the cost function is additive. Our results provide a relatively complete picture for automated mechanism design with partial verification.Comment: AAAI'2

Automated mechanism design for B2B e-commerce models

Business-to-business electronic marketplaces (B2B e-Marketplaces) have been in the limelight since 1999 with the commercialisation of the Internet and subsequent “dot.com” boom [1]. Literature is indicative of the growth of the B2B sectors in all industries, and B2B e-Marketplace is one of the sectors that have witnessed a rapid increase. Consequently, the importance of developing the B2B e-Commerce Model for improved value chain in B2B exchanges is extremely important for SMEs to expose to the world marketplace. There are three research objectives (ROs) in this study; first (RO1) to critical review the concepts of the B2B e-Marketplace including their technologies, operations, business relationships and functionalities; second (RO2) to design an automated mechanism of B2B e-Marketplace for Small to Medium Sized Enterprises (SMEs); and third (RO3) to propose a conceptual B2B e-Commerce model for SMEs. The proposed model is constructed by the analytical findings obtained from the contemporary B2B e-Marketplace literature