Intimidation or Impatience? Jump Bidding in On-line Ascending Automobile Auctions

We run a large field experiment with an online company specializing in selling used automobiles via ascending auctions. We manipulate experimentally the maximum amount which bidders can bid above the current standing price, thus affecting the ease with which bidders can engage in jump bidding. We test between the intimidation vs. costly bidding hypotheses of jump bidding by looking at the effect of these jump-bidding restrictions on average seller revenue. We find evidence consistent with costly bidding in one market (Texas), but intimidation in the other market (New York). This difference in findings between the two markets appears partly attributable to the more prominent presence of sellers who are car dealers in the Texas market.

Smoothing sparse and unevenly sampled curves using semiparametric mixed models: An application to online auctions

Functional data analysis can be challenging when the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data which can be difficult if the data is irregularly distributed. In this paper we present a new approach that can overcome this challenge. The approach is based on the ideas of mixed models. Specifically, we propose a semiparametric mixed model with boosting to recover the functional object. While the model can handle sparse and unevenly distributed data, it also results in conceptually more meaningful functional objects. In particular, we motivate our method within the framework of eBay's online auctions. Online auctions produce monotonic increasing price curves that are often correlated across two auctions. The semiparametric mixed model accounts for this correlation in a parsimonious way. It also estimates the underlying increasing trend from the data without imposing model-constraints. Our application shows that the resulting functional objects are conceptually more appealing. Moreover, when used to forecast the outcome of an online auction, our approach also results in more accurate price predictions compared to standard approaches. We illustrate our model on a set of 183 closed auctions for Palm M515 personal digital assistants

The Dynamics of Seller Reputation: Theory and Evidence from eBay

We propose a basic theoretical model of eBay's reputation mechanism, derive a series of implications and empirically test their validity. Our theoretical model features both adverse selection and moral hazard. We show that when a seller receives a negative rating for the first time his reputation decreases and so does his effort level. This implies a decline in sales and price; and an increase in the rate of arrival of subsequent negative feedback. Our model also suggests that sellers with worse records are more likely to exit (and possibly re-enter under a new identity), whereas better sellers have more to gain from buying a reputation' by building up a record of favorable feedback through purchases rather than sales. Our empirical evidence, based on a panel data set of seller feedback histories and cross-sectional data on transaction prices collected from eBay is broadly consistent with all of these predictions. An important conclusion of our results is that eBay's reputation system gives way to strategic responses from both buyers and sellers.

Risk- & Regret-Averse Bidders in Sealed-Bid Auctions

Overbidding, bidding more than risk-neutral Bayesian Nash Equilibrium, is a widely observed phenomenon in virtually all experimental auctions. The scholars within the auction literature propose the risk-averse preference model to explain overbidding structurally. However, the risk-averse preference model predicts underbidding in such important classes of auctions as all-pay auctions. To solve this discrepancy, we construct a structural model of bidding behavior in sealed-bid auctions, one in which bidders may regret their decisions. Our model nests both risk-averse and regret-averse attitudes and aims to explain overbidding in a wider class of auctions. We first derive equilibrium first-order conditions, which are used for estimation and calibration analyses, and show monotonic increasing properties of equilibrium bidding functions. Second, we carry out structural estimation and calibration analyses based on experimental data from Kagel and Levin (1993) and Noussair and Silver (2006). With these structurally estimated parameters, we test the significance of bidders’ risk-averse and regret-averse attitudes. The estimation results show that bidders exhibit weak risk-averse (close to risk-neutral) and strong regret-averse attitudes. Furthermore, regret-averse attitudes are significant when bidders anticipate losing. Calibration results demonstrate that our risk- & regret-averse model can explain overbidding across all of the above IPV auctions. Third, we simulate our model with the estimated parameters and obtain revenue rankings numerically. This allows us to confirm the revenue supremacy in all-pay auctions reported in experimental auction literature. We discuss extensions to asymmetric and Common-Value (CV) auctions in our online appendix

A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

The Bidder's Curse

We employ a novel approach to identify overbidding in the field. We compare auction prices to fixed prices for the same item on the same webpage. In detailed board-game data, 42 percent of auctions exceed the simultaneous fixed price. The result replicates in a broad cross-section of auctions (48 percent). A small fraction of overbidders, 17 percent, suffices to generate the overbidding. The observed behavior is inconsistent with rational behavior, even allowing for uncertainty and switching costs, since also the expected auction price exceeds the fixed price. Limited attention to outside options is most consistent with our results.

Is the ’Linkage Principle’ Valid?: Evidence from the Field

revenue comparison, auction choice, linkage principle, used-car auctions

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