Simple threshold rules solve explore/exploit trade‐offs in a resource accumulation search task

How, and how well, do people switch between exploration and exploitation to search for and accumulate resources? We study the decision processes underlying such exploration/exploitation trade‐offs using a novel card selection task that captures the common situation of searching among multiple resources (e.g., jobs) that can be exploited without depleting. With experience, participants learn to switch appropriately between exploration and exploitation and approach optimal performance. We model participants' behavior on this task with random, threshold, and sampling strategies, and find that a linear decreasing threshold rule best fits participants' results. Further evidence that participants use decreasing threshold‐based strategies comes from reaction time differences between exploration and exploitation; however, participants themselves report non‐decreasing thresholds. Decreasing threshold strategies that “front‐load” exploration and switch quickly to exploitation are particularly effective in resource accumulation tasks, in contrast to optimal stopping problems like the Secretary Problem requiring longer exploration

A new model of information seeking stopping behavior

textWeb search engines play an important role in peoples daily life. Widespread usage of search engine poses continuous challenges for designing information search systems that can bring people best user experience. To address this challenges, it is particularly important to understand how people seek information. In spite of a large number of studies on human information seeking, the reasons of when and why users terminate information seeking are uncertain and many proposed theories have a limited capability for predicting this type of behavior. In our study, we conducted lab-based experiments, where participants performed assigned information search tasks on Wikipedia pages. Inspired by theories and methods from cognitive science, we captured participants information search behavior such as query usage, search engine result page visits, Wikipedia page visits, and task duration. Additionally, we used eye-tracking techniques to examine the number of people's eye fixations. Using exploratory factor analysis (EFA), we have confirmed exploratory and validation processes can be distinguished based on different types of costs associated with each of them. Based on the findings of the regression tree model, evaluating the cost and gain in the validation process provide important feedback to people for controlling and monitoring their information search.Informatio

Sample Efficient Policy Search for Optimal Stopping Domains

Optimal stopping problems consider the question of deciding when to stop an observation-generating process in order to maximize a return. We examine the problem of simultaneously learning and planning in such domains, when data is collected directly from the environment. We propose GFSE, a simple and flexible model-free policy search method that reuses data for sample efficiency by leveraging problem structure. We bound the sample complexity of our approach to guarantee uniform convergence of policy value estimates, tightening existing PAC bounds to achieve logarithmic dependence on horizon length for our setting. We also examine the benefit of our method against prevalent model-based and model-free approaches on 3 domains taken from diverse fields.Comment: To appear in IJCAI-201

Competing with asking prices

In many markets, sellers advertise their good with an asking price. This is a price at which the seller will take his good off the market and trade immediately, though it is understood that a buyer can submit an offer below the asking price and that this offer may be accepted if the seller receives no better offers. Despite their prevalence in a variety of real world markets, asking prices have received little attention in the academic literature. We construct an environment with a few simple, realistic ingredients and demonstrate that using an asking price is optimal: it is the pricing mechanism that maximizes sellers’ revenues and it implements the efficient outcome in equilibrium. We provide a complete characterization of this equilibrium and use it to explore the positive implications of this pricing mechanism for transaction prices and allocations.Ludo Visschers gratefully acknowledges financial support from the Juan de la Cierva Grant; project grant ECO2010- 20614 (Dirección general de investigación científica y técnica), and the Bank of Spain’s Programa de Investigación de Excelencia