Leading-effect vs. Risk-taking in Dynamic Tournaments: Evidence from a Real-life Randomized Experiment

Two 'order effects' may emerge in dynamic tournaments with information feedback. First, participants adjust effort across stages, which could advantage the leading participant who faces a larger 'effective prize' after an initial victory (leading-effect). Second, participants lagging behind may increase risk at the final stage as they have 'nothing to lose' (risk-taking). We use a randomized natural experiment in professional two-game soccer tournaments where the treatment (order of a stage-specific advantage) and team characteristics, e.g. ability, are independent. We develop an identification strategy to test for leading-effects controlling for risk-taking. We find no evidence of leading-effects and negligible risk-taking effects

Economics of intelligent selection of wireless access networks in a market-based framework : a game-theoretic approach

The Digital Marketplace is a market-based framework where network operators offer communications services with competition at the call level. It strives to address a tussle between the actors involved in a heterogeneous wireless access network. However, as with any market-like institution, it is vital to analyze the Digital Marketplace from the strategic perspective to ensure that all shortcomings are removed prior to implementation. In this paper, we analyze the selling mechanism proposed in the Digital Marketplace. The mechanism is based on a procurement first-price sealed-bid auction where the network operators represent the sellers/bidders, and the end-user of a wireless service is the buyer. However, this auction format is somewhat unusual as the winning bid is a composition of both the network operator’s monetary bid and their reputation rating. We create a simple economic model of the auction, and we show that it is mathematically intractable to derive the equilibrium bidding behavior when there are N network operators, and we make only generic assumptions about the structure of the bidding strategies. We then move on to consider a scenario with only two network operators, and assume that network operators use bidding strategies which are linear functions of their costs. This results in the derivation of the equilibrium bidding behavior in that scenario

Suitable task allocation in intelligent systems for assistive environments

The growing need of technological assistance to provide support to people with special needs demands for systems more and more efficient and with better performances. With this aim, this work tries to advance in a multirobot platform that allows the coordinated control of different agents and other elements in the environment to achieve an autonomous behavior based on the user’s needs or will. Therefore, this environment is structured according to the potentiality of each agent and elements of this environment and of the dynamic context, to generate the adequate actuation plans and the coordination of their execution.Peer ReviewedPostprint (author's final draft

How can innovation economics benefit from complex network analysis?

There is a deficit in economics of theories and empirical data on complex networks, though mathematicians, physicists, biologists, computer scientists, and sociologists are actively engaged in their study. This paper offers a focused review of prominent concepts in contemporary thinking in network research that may motivate further theoretical research and stimulate interest of economists. Possible avenues for modelling innovation, considered the driving force behind economic change, have been explored. A transition is needed from the analysis in economics of the transaction to the explicit examination of market structure and how it processes, or is processed by, innovation.Network; statistics; economy; innovation; modelling

Features for Killer Apps from a Semantic Web Perspective

There are certain features that that distinguish killer apps from other ordinary applications. This chapter examines those features in the context of the semantic web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing semantic web applications. Killer apps are highly tranformative technologies that create new e-commerce venues and widespread patterns of behaviour. Information technology, generally, and the Web, in particular, have benefited from killer apps to create new networks of users and increase its value. The semantic web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. The authors hope that this chapter will help to highlight some of the common ingredients of killer apps in e-commerce, and discuss how such applications might emerge in the semantic web

Optimal Tournament Contracts for Heterogeneous Workers

We analyze the optimal design of rank-order tournaments with heterogeneous workers. Iftournament prizes do not differ between the workers(uniform prizes), as in the previous tournament literature, the outcome will be ineffcient. In the case of limited liability, the employer may benefit from implementing more than first-best effort. We show that the employer can use individual prizes that satisfy a self-commitment condition and induce effcient incentives at the same time, thus solving a fundamental dilemma in tournament theory. Individual prizes exhibit two major advantages - they allow the extraction of worker rents and the adjustment of individual incentives, which will be important for the employer if he cannot rely on handicaps

How to Win Twice at an Auction. On the Incidence of Commissions in Auction Markets

We analyze the welfare consequences of an increase in the commissions charged by the organizer of an auction. Commissions are similar to taxes imposed on buyers and sellers and the economic problem that results looks similar to the question of tax incidence in consumer economics. We argue, however, that auction markets deserve a separate treatment. Indeed we show that an increase in commissions makes sellers worse off, but some (or all) buyers may gain. The results are therefore strikingly different from the standard result that all consumers lose after a tax or a commission increase. We apply our results to comment on the class action against Christie’s and Sotheby’s and argue that the method used to distribute compensations was misguided.Auction, Intermediation, Commissions, Welfare

Evolutionary Tournament-Based Comparison of Learning and Non-Learning Algorithms for Iterated Games

Evolutionary tournaments have been used effectively as a tool for comparing game-playing algorithms. For instance, in the late 1970's, Axelrod organized tournaments to compare algorithms for playing the iterated prisoner's dilemma (PD) game. These tournaments capture the dynamics in a population of agents that periodically adopt relatively successful algorithms in the environment. While these tournaments have provided us with a better understanding of the relative merits of algorithms for iterated PD, our understanding is less clear about algorithms for playing iterated versions of arbitrary single-stage games in an environment of heterogeneous agents. While the Nash equilibrium solution concept has been used to recommend using Nash equilibrium strategies for rational players playing general-sum games, learning algorithms like fictitious play may be preferred for playing against sub-rational players. In this paper, we study the relative performance of learning and non-learning algorithms in an evolutionary tournament where agents periodically adopt relatively successful algorithms in the population. The tournament is played over a testbed composed of all possible structurally distinct 2×2 conflicted games with ordinal payoffs: a baseline, neutral testbed for comparing algorithms. Before analyzing results from the evolutionary tournament, we discuss the testbed, our choice of representative learning and non-learning algorithms and relative rankings of these algorithms in a round-robin competition. The results from the tournament highlight the advantage of learning algorithms over players using static equilibrium strategies for repeated plays of arbitrary single-stage games. The results are likely to be of more benefit compared to work on static analysis of equilibrium strategies for choosing decision procedures for open, adapting agent society consisting of a variety of competitors.Repeated Games, Evolution, Simulation

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