Changing Monetary Policy Rules, Learning, and Real Exchange Rate Dynamics

When central banks set nominal interest rates according to an interest rate reaction function, such as the Taylor rule, and the exchange rate is priced by uncovered interest parity, the real exchange rate is determined by expected inflation differentials and output gap differentials. In this paper I examine the implications of these Taylor-rule fundamentals for real exchange rate determination in an environment where market participants are ignorant of the numerical values of the model's coefficients but attempt to acquire that information using least-squares learning rules. I find evidence that this simple learning environment provides a plausible framework for understanding real dollar--DM exchange rate dynamics from 1976 to 2003. The least-squares learning path for the real exchange rate implied by inflation and output gap data exhibits the real depreciation of the 70s, the great appreciation (1979.4-1985.1) and the subsequent great depreciation (1985.2-1991.1) observed in the data. An emphasis on Taylor-rule fundamentals may provide a resolution to the exchange rate disconnect puzzle.

Statistics of the Kolkata Paise Restaurant Problem

We study the dynamics of a few stochastic learning strategies for the 'Kolkata Paise Restaurant' problem, where N agents choose among N equally priced but differently ranked restaurants every evening such that each agent tries get to dinner in the best restaurant (each serving only one customer and the rest arriving there going without dinner that evening). We consider the learning strategies to be similar for all the agents and assume that each follow the same probabilistic or stochastic strategy dependent on the information of the past successes in the game. We show that some 'naive' strategies lead to much better utilization of the services than some relatively 'smarter' strategies. We also show that the service utilization fraction as high as 0.80 can result for a stochastic strategy, where each agent sticks to his past choice (independent of success achieved or not; with probability decreasing inversely in the past crowd size). The numerical results for utilization fraction of the services in some limiting cases are analytically examined.Comment: 10 pages, 3 figs; accepted in New J Phy

Developing Heuristic-Based Quality Judgements: Attention Blocking in Consumer Choice

Through a series of experiments we illustrate how the sequential order in which consumers receive information can influence the way this information is processed and affect consumers’ decisions. Specifically, when participants initially receive information regarding brand/quality or price/quality associations, these associations can block consumers’ attention to more relevant quality-determining physical attributes. Moreover, this process of attention blocking can carry-over to affect quality judgements pertaining to similarly branded or priced products beyond the product in which blocking was initiated. This implies that consumers judgements of quality may be heavily dependent on “first impressions” which develop into brand and price heuristics.Consumer Behavior; Consumer Learning; Marketing Strategy

Does Media Affect Learning: Where Are We Now?

It is time to extinguish the argument as to whether or not the media of 1983 could, should or would affect learning outcomes. The technological advances that have occurred in the 20 years since Clark sparked the debate and Kozma fanned the flames have made the question irrelevant. High-speed, portable, reasonably priced computers, the Internet, and the World Wide Web have changed the face of how, when, and where learning occurs. The media of 2004 does affect learning. The question is no longer if; the question is how

Combinatorial Assortment Optimization

Assortment optimization refers to the problem of designing a slate of products to offer potential customers, such as stocking the shelves in a convenience store. The price of each product is fixed in advance, and a probabilistic choice function describes which product a customer will choose from any given subset. We introduce the combinatorial assortment problem, where each customer may select a bundle of products. We consider a model of consumer choice where the relative value of different bundles is described by a valuation function, while individual customers may differ in their absolute willingness to pay, and study the complexity of the resulting optimization problem. We show that any sub-polynomial approximation to the problem requires exponentially many demand queries when the valuation function is XOS, and that no FPTAS exists even for succinctly-representable submodular valuations. On the positive side, we show how to obtain constant approximations under a "well-priced" condition, where each product's price is sufficiently high. We also provide an exact algorithm for $k$-additive valuations, and show how to extend our results to a learning setting where the seller must infer the customers' preferences from their purchasing behavior

Optimaztion of Fantasy Basketball Lineups via Machine Learning

Machine learning is providing a way to glean never before known insights from the data that gets recorded every day. This paper examines the application of machine learning to the novel field of Daily Fantasy Basketball. The particularities of the fantasy basketball ruleset and playstyle are discussed, and then the results of a data science case study are reviewed. The data set consists of player performance statistics as well as Fantasy Points, implied team total, DvP, and player status. The end goal is to evaluate how accurately the computer can predict a player’s fantasy performance based off a chosen feature set, selection algorithm, and probabilistic methods

Re-mining item associations: methodology and a case study in apparel retailing

Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques