40 research outputs found
Price Prediction in a Trading Agent Competition
The 2002 Trading Agent Competition (TAC) presented a challenging market game
in the domain of travel shopping. One of the pivotal issues in this domain is
uncertainty about hotel prices, which have a significant influence on the
relative cost of alternative trip schedules. Thus, virtually all participants
employ some method for predicting hotel prices. We survey approaches employed
in the tournament, finding that agents apply an interesting diversity of
techniques, taking into account differing sources of evidence bearing on
prices. Based on data provided by entrants on their agents' actual predictions
in the TAC-02 finals and semifinals, we analyze the relative efficacy of these
approaches. The results show that taking into account game-specific information
about flight prices is a major distinguishing factor. Machine learning methods
effectively induce the relationship between flight and hotel prices from game
data, and a purely analytical approach based on competitive equilibrium
analysis achieves equal accuracy with no historical data. Employing a new
measure of prediction quality, we relate absolute accuracy to bottom-line
performance in the game
Behavioral dynamics and influence in networked coloring and consensus
We report on human-subject experiments on the problems of coloring (a social differentiation task) and consensus (a social agreement task) in a networked setting. Both tasks can be viewed as coordination games, and despite their cognitive similarity, we find that within a parameterized family of social networks, network structure elicits opposing behavioral effects in the two problems, with increased long-distance connectivity making consensus easier for subjects and coloring harder. We investigate the influence that subjects have on their network neighbors and the collective outcome, and find that it varies considerably, beyond what can be explained by network position alone. We also find strong correlations between influence and other features of individual subject behavior. In contrast to much of the recent research in network science, which often emphasizes network topology out of the context of any specific problem and places primacy on network position, our findings highlight the potential importance of the details of tasks and individuals in social networks
Hiding from centrality measures : a Stackelberg game perspective
202307 bcwwAccepted ManuscriptRGCEarly releas
Designing an Ad Auctions Game for the Trading Agent Competition
We introduce the TAC Ad Auctions game (TAC/AA), a new game for the Trading Agent Competition. The Ad Auctions game investigates complex strategic issues found in real sponsored search auctions that are not captured in current analytical models. We provide an overview of TAC/AA, introducing its key features and design rationale. TAC/AA will debut in summer 2009, with the final tournament commencing in conjunction with the TADA-09 workshop
Stochastic nash equilibrium problems: sample average approximation and applications
This paper presents a Nash equilibrium model where the underlying objective functions involve uncertainty and nonsmoothness. The well-known sample average approximation method is applied to solve the problem and the first order equilibrium conditions are characterized in terms of Clarke generalized gradients. Under some moderate conditions, it is shown that with probability one, a statistical estimator (a Nash equilibrium or a Nash-C-stationary point) obtained from sample average approximate equilibrium problem converges to its true counterpart. Moreover, under some calmness conditions of the Clarke generalized derivatives, it is shown that with probability approaching one exponentially fast by increasing sample size, the Nash-C-stationary point converges to a weak Nash-C-stationary point of the true problem. Finally, the model is applied to stochastic Nash equilibrium problem in the wholesale electricity market