Efficiency of Continuous Double Auctions under Individual Evolutionary Learning with Full or Limited Information

In this paper we explore how specific aspects of market transparency and agents' behavior affect the efficiency of the market outcome. In particular, we are interested whether learning behavior with and without information about actions of other participants improves market efficiency. We consider a simple market for a homogeneous good populated by buyers and sellers. The valuations of the buyers and the costs of the sellers are given exogenously. Agents are involved in consecutive trading sessions, which are organized as a continuous double auction with electronic book. Using Individual Evolutionary Learning agents submit price bids and offers, trying to learn the most profitable strategy by looking at their realized and counterfactual or "foregone" payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agents' orders tend to be similar, while under limited information agents tend to submit their valuations/costs. This behavioral outcome results in higher price volatility for the latter treatment. We also find that learning improves allocative efficiency when compared with to outcomes with Zero-Intelligent traders.

Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

Double auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings

Mutually Destructive Bidding: The FCC Auction Design Problem

Dissatisfaction with previous assignment mechanisms and the desire to raise revenue induced Congress to grant the FCC authority to auction radio licenses. The debate over an appropriate auction design was wide ranging with many imaginative proposals. Many of the arguments and their scientific support are unfortunately not publicly available. Here, we present our side of this debate for the record. Synergies across license valuations complicate the auction design process. Theory suggests that a “simple” (i.e., non-combinatorial) auction will have difficulty in assigning licenses efficiently in such an environment. This difficulty increases with increases in “fitting complexity.” In some environments, bidding may become “mutually destructive.” Experiments indicate that a combinatorial auction is superior to a simple auction in terms of economic efficiency and revenue generation in bidding environments with a low amount of fitting complexity. Concerns that a combinatorial auction will cause a “threshold” problem are not borne out when bidders for small packages can communicate

Rank-order competition in the voluntary provision of impure public goods

Publicly provided goods often create differential payoffs due to timely or spatial distances of group members. We design and test a provision mechanism which utilizes rank competition to mitigate free-riding in impure public goods. In our Rank-Order Voluntary Contribution Mechanism (Rank-Order-VCM) group members compete via observable contributions for a larger share of the public good; high contributors receive preferential access (a larger share), while low contributors receive restricted access (a lower share). In a laboratory experiment Rank-Order-VCM elicits median contributions equal to the full endowment throughout the finitely played games with constant groups. In the control treatment, with randomly assigned ranks, the contributions are significantly lower and decline over time. We thus provide evidence of rank competition, in situations where discriminatory access to public goods is possible, being efficiency enhancing

The martyrdom effect : when pain and effort increase prosocial contributions

Most theories of motivation and behavior (and lay intuitions alike) consider pain and effort to be deterrents. In contrast to this widely held view, we provide evidence that the prospect of enduring pain and exerting effort for a prosocial cause can promote contributions to the cause. Specifically, we show that willingness to contribute to a charitable or collective cause increases when the contribution process is expected to be painful and effortful rather than easy and enjoyable. Across five experiments, we document this “martyrdom effect,” show that the observed patterns defy standard economic and psychological accounts, and identify a mediator and moderator of the effect. Experiment 1 showed that people are willing to donate more to charity when they anticipate having to suffer to raise money. Experiment 2 extended these findings to a non-charity laboratory context that involved real money and actual pain. Experiment 3 demonstrated that the martyrdom effect is not the result of an attribute substitution strategy (whereby people use the amount of pain and effort involved in fundraising to determine donation worthiness). Experiment 4 showed that perceptions of meaningfulness partially mediate the martyrdom effect. Finally, Experiment 5 demonstrated that the nature of the prosocial cause moderates the martyrdom effect: the effect is strongest for causes associated with human suffering. We propose that anticipated pain and effort lead people to ascribe greater meaning to their contributions and to the experience of contributing, thereby motivating higher prosocial contributions. We conclude by considering some implications of this puzzling phenomenon. Copyright © 2011 John Wiley & Sons, Ltd