Distinguishing Overconfidence from Rational Best-Response in Markets

This paper studies the causal effect of individuals' overconfidence and bounded rationality on asset markets. To do that, we combine a new market mechanism with an experimental design, where (1) players' interaction is centered on the inferences they make about each others' information, (2) overconfidence in private information is controlled by the experimenter (i.e., used as a treatment), and (3) natural analogs to prices, returns and volume exist. We find that in sessions where subjects are induced to be overconfident, volume and price error analogs are higher than predicted by the fully-rational model. However, qualitatively similar results are obtained in sessions where there is no aggregate overconfidence. To explain this, we suggest an alternative possibility: participants strategically respond to the errors contained in others' actions by rationally discounting the informativeness of these actions. Estimating a structural model of individuals' decisions that allows for both overconfidence and errors, we are able to separate these two channels. We find that a substantial fraction of excess volume and price error analogs is attributable to strategic response to errors, while the remaining is attributable to overconfidence. Further, we show that price analog exhibit serial autocorrelation only in the overconfidence-induced sessions.

Learning From The Skills Of Others: Experimental Evidence

This paper reports an experimental test of how, when observing others' actions, participants learn more than just information that the others have. We use a setting where all information is public and where subjects face two kinds of information sets: (1) the information that is necessary and su±cient for them to payoff-maximize and (2) the decisions of previous players. We show that by observing the second type of information subjects learn how to improve their own decision-making process. Specifically, the accurate players make small errors no matter what information set they face whereas the inaccurate players perform much better when the decisions of others are public.

Securities Auctions under Moral Hazard: An Experimental Study

We study, both theoretically and in the lab, the performance of open outcry debt and equity auctions in the presence of both private information and hidden e¤ort in an independent private value setting. We characterize symmetric equilibrium bidding strategies and show that these lead to e¢ cient allocation. More interestingly, the revenue ranking between the debt and equity auctions depends on the returns to en- trepreneurial e¤ort. When returns are either very low or vary high, the equity auction leads to higher expected revenues to the seller than does the debt auction. When the returns to e¤ort are intermediate, we show that debt auctions can outperform equity auctions. We then test these predictions in a controlled laboratory setting and …nd broad support for the comparative predictions of the model.

Towards Bypassing Lower Bounds for Graph Shortcuts

For a given (possibly directed) graph G, a hopset (a.k.a. shortcut set) is a (small) set of edges whose addition reduces the graph diameter while preserving desired properties from the given graph G, such as, reachability and shortest-path distances. The key objective is in optimizing the tradeoff between the achieved diameter and the size of the shortcut set (possibly also, the distance distortion). Despite the centrality of these objects and their thorough study over the years, there are still significant gaps between the known upper and lower bound results. A common property shared by almost all known shortcut lower bounds is that they hold for the seemingly simpler task of reducing the diameter of the given graph, D_G, by a constant additive term, in fact, even by just one! We denote such restricted structures by (D_G-1)-diameter hopsets. In this paper we show that this relaxation can be leveraged to narrow the current gaps, and in certain cases to also bypass the known lower bound results, when restricting to sparse graphs (with O(n) edges): - {Hopsets for Directed Weighted Sparse Graphs.} For every n-vertex directed and weighted sparse graph G with D_G ? n^{1/4}, one can compute an exact (D_G-1)-diameter hopset of linear size. Combining this with known lower bound results for dense graphs, we get a separation between dense and sparse graphs, hence shortcutting sparse graphs is provably easier. For reachability hopsets, we can provide (D_G-1)-diameter hopsets of linear size, for sparse DAGs, already for D_G ? n^{1/5}. This should be compared with the diameter bound of O?(n^{1/3}) [Kogan and Parter, SODA 2022], and the lower bound of D_G = n^{1/6} by [Huang and Pettie, {SIAM} J. Discret. Math. 2018]. - {Additive Hopsets for Undirected and Unweighted Graphs.} We show a construction of +24 additive (D_G-1)-diameter hopsets with linear number of edges for D_G ? n^{1/12} for sparse graphs. This bypasses the current lower bound of D_G = n^{1/6} obtained for exact (D_G-1)-diameter hopset by [HP\u2718]. For general graphs, the bound becomes D_G ? n^{1/6} which matches the lower bound of exact (D_G-1) hopsets implied by [HP\u2718]. We also provide new additive D-diameter hopsets with linear size, for any given diameter D. Altogether, we show that the current lower bounds can be bypassed by restricting to sparse graphs (with O(n) edges). Moreover, the gaps are narrowed significantly for any graph by allowing for a constant additive stretch