Walrasian tâtonnement has been a fundamental assumption in economics ever since Walras’ general equilibrium theory was introduced in 1874. Nearly a century after its introduction, Vernon Smith relaxed the Walrasian tâtonnement assumption by showing that neoclassical competitive market theory explains the equilibrating forces in “double‐auction” markets. I make a next step in this evolution by exploring the predictive power of neoclassical theory in decentralized naturally occurring markets. Using data gathered from two distinct markets—the sports card and collector pin markets—I find a tendency for exchange prices to approach the neoclassical competitive model prediction after a few market periods.

An important class of investment decisions is characterized by unrecoverable sunk costs, resolution of uncertainty through time, and the ability to invest in the future as an alternative to investing today. The options model provides guidance in such settings, including an investment decision rule called the "bad news principle": the downside investment state influences the investment decision whereas the upside investment state is ignored. This study takes a new approach to examining predictions of the options model by using the tools of experimental economics. Our evidence, which is drawn from student and professional trader subject pools, is broadly consonant with the options model.

Behavioral economics has become an important and integrated component of modern economics. Behavioral economists embrace the core principles of economics—optimization and equilibrium—and seek to develop and extend those ideas to make them more empirically accurate. Behavioral models assume that economic actors try to pick the best feasible option and those actors sometimes make mistakes. Behavioral ideas should be incorporated throughout the first-year undergraduate course. Instructors should also considering allocating a lecture (or more) to a focused discussion of behavioral concepts. We describe our approach to such a lecture, highlighting six modular principles and empirical examples that support them.

The gold standard in the sciences is uncovering causal relationships. A growing literature in economics utilizes field experiments as a methodology to establish causality between variables. Taking lessons from the economics literature, this study provides an “A‐to‐Z” description of how to conduct field experiments in accounting and finance. We begin by providing a user's guide into what a field experiment is, what behavioral parameters field experiments identify, and how to efficiently generate and analyze experimental data. We then provide a discussion of extant field experiments that touch on important issues in accounting and finance, and we also review areas that have ample opportunities for future field experimental explorations. We conclude that the time is ripe for field experimentation to deepen our understanding of important issues in accounting and finance.

A commonly held view is that laboratory experiments provide researchers with more “control” than natural field experiments. This paper explores how natural field experiments can provide researchers with more control than laboratory experiments. While laboratory experiments provide researchers with a high degree of control in the environment which participants agree to be experimental subjects, when participants systematically opt out of laboratory experiments, the researcher's ability to manipulate certain variables is limited. In contrast, natural field experiments bypass the participation decision altogether due to their covertness, and they allow for a potentially more diverse participant pool within the market of interest.

Demand Reduction in Multiunit Auctions: Evidence from a Sportscard Field Experiment by John A. List and David Lucking-Reiley. Published in volume 90, issue 4, pages 961-972 of American Economic Review, September 2000

Two behavioral concepts, loss aversion and mental accounting, have been combined to provide a theoretical explanation of the equity premium puzzle. Recent experimental evidence supports the theory, as students' behavior has been found to be consistent with myopic loss aversion (MLA). Yet, much like certain anomalies in the realm of riskless decision‐making, these behavioral tendencies may be attenuated among professionals. Using traders recruited from the CBOT, we do indeed find behavioral differences between professionals and students, but rather than discovering that the anomaly is muted, we find that traders exhibit behavior consistent with MLA to a greater extent than students.

We design a field experiment to test two theories of fund-raising for threshold public goods: Andreoni predicts that publicly announced "seed money" will increase charitable donations, whereas Bagnoli and Lipman predict a similar increase for a refund policy. Experimentally manipulating a solicitation of 3,000 households for a university capital campaign produced data confirming both predictions. Increasing seed money from 10 percent to 67 percent of the campaign goal produced a nearly sixfold increase in contributions, with significant effects on both participation rates and average gift size. Imposing a refund increased contributions by a more modest 20 percent, with significant effects on average gift size.

Experimental economists are leaving the reservation. They are recruiting subjects in the field rather than in the classroom, using field goods rather than induced valuations, and using field context rather than abstract terminology in instructions. We argue that there is something methodologically fundamental behind this trend. Field experiments differ from laboratory experiments in many ways. Although it is tempting to view field experiments as simply less controlled variants of laboratory experiments, we argue that to do so would be to seriously mischaracterize them. What passes for “control” in laboratory experiments might in fact be precisely the opposite if it is artificial to the subject or context of the task. We propose six factors that can be used to determine the field context of an experiment: the nature of the subject pool, the nature of the information that the subjects bring to the task, the nature of the commodity, the nature of the task or trading rules applied, the nature of the stakes, and the environment that subjects operate in.