The diagnosticity of feelings in judgment depends not only on their representativeness and relevance, but also on people's trust in their feelings in general. Trust in feelings is the degree to which individuals believe that their feelings generally point toward the "right" direction in judgments and decisions.
We introduce the pathwise optimization (PO) method, a new convex optimization procedure to produce upper and lower bounds on the optimal value (the “price”) of a high-dimensional optimal stopping problem. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the martingale duality approach. We demonstrate via numerical experiments that the PO method produces upper bounds of a quality comparable with state-of-the-art approaches, but in a fraction of the time required for those approaches.
We consider the problem of producing lower bounds on the optimal cost-to-go function of a Markov decision problem. We present two approaches to this problem: one based on the methodology of approximate linear programming (ALP) and another based on the so-called martingale duality approach. We show that these two approaches are intimately connected. Exploring this connection leads us to the problem of finding "optimal" martingale penalties within the martingale duality approach which we dub the pathwise optimization (PO) problem.
We develop an analytically-tractable model integrating the dynamic theory of investment with dynamic optimal incentive contracting, thereby endogenizing financing constraints. Incentive contracting generates a history-dependent wedge between marginal and average q, and both vary over time as good (bad) performance relaxes (tightens) financing constraints. Financial slack, not cash flow, is the appropriate proxy for financing constraints.
Over the course of a repeated game, players often exhibit learning in selecting their best response. Research in economics and marketing has identified two key types of learning rules: belief and reinforcement. It has been shown that players use either one of these learning rules or a combination of them, as in the Experience-Weighted Attraction (EWA) model. Accounting for such learning may help in understanding and predicting the outcomes of games.
We consider a trader who aims to liquidate a large position in the presence of an arbitrageur who hopes to profit from the trader's activity. The arbitrageur is uncertain about the trader's position and learns from observed price fluctuations. This is a dynamic game with asymmetric information. We present an algorithm for computing perfect Bayesian equilibrium behavior and conduct numerical experiments. Our results demonstrate that the trader's strategy differs significantly from one that would be optimal in the absence of the arbitrageur.
The performance of organizations depends partly on the reputations of their industries. Such reputations are "intangible commons." Interest in protecting mutual welfare motivates members of an industry to engage in self-regulation. However, the current literature tends to have a pessimistic view of the efficacy of self-regulation in solving the problem of reputational commons. We argue that the obstacles forecasted by such pessimistic reasoning are context-bound and can be overcome if industry self-regulation includes effective sanctions and exclusion strategies.
This paper presents a novel and practical non-parametric approximate dynamic programming (ADP) algorithm that enjoys graceful, dimension-independent approximation and sample complexity guarantees. In particular, we establish both theoretically and computationally that our proposal can serve as a viable replacement to state of the art parametric ADP algorithms, freeing the designer from carefully specifying an approximation architecture. We accomplish this by "kernelizing" a recent mathematical program for ADP (the "smoothed" approximate LP) proposed by Desai et al. (2011).
Seven studies show that compared to people with lower trust in their feelings, those with higher trust in their feelings were better able to predict the outcome of a wide variety of future events, including (a) future movie successes, (b) the 2008 U.S. Democratic Presidential nominee, (c) the winner of <em>American Idol</em>, (d) movements of the Dow Jones Index, and even (e) the weather.