Representing Preferences with a Unique Subjective State Space: A Corrigendum1

Dekel, Lipman and Rustichini (2001) (henceforth DLR) axiomatically characterized three representations of preferences that allow for a desire for flexibility and/or commitment. In one of these representations (ordinal expected utility), the independence axiom is stated in a weaker form than is necessary to obtain the representation; in another (additive expected utility), the continuity axiom is too weak. In this erratum we provide examples showing that the axioms used by DLR are not sufficient, and provide stronger versions of these axioms that, together with the other axioms used by DLR, are necessary and sufficient for these two representations.     

Representing Preferences with a Unique Subjective State Space

We extend Kreps' (1979) analysis of preference for flexibility, reinterpreted by Kreps (1992) as a model of unforeseen contingencies. We enrich the choice set, consequently obtaining uniqueness results that were not possible in Kreps' model. We consider several representations and allow the agent to prefer commitment in some contingencies. In the representations, the agent acts as if she had coherent beliefs about a set of possible future (ex post) preferences, each of which is an expected‐utility preference. We show that this set of ex post preferences, called the subjective state space, is essentially unique given the restriction that all ex post preferences are expected‐utility preferences and is minimal even without this restriction. Because the subjective state space is identified, the way ex post utilities are aggregated into an ex ante ranking is also essentially unique. Hence when a representation that is additive across states exists, the additivity is meaningful in the sense that all representations are intrinsically additive. Uniqueness enables us to show that the size of the subjective state space provides a measure of the agent's uncertainty about future contingencies and that the way the states are aggregated indicates whether these contingencies lead to a desire for flexibility or commitment.     

The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models

AStA Advances in Statistical Analysis - Passes are undoubtedly the more frequent events in football and other team sports. Passing networks and their structural features can be useful to evaluate...     

End‐of‐Life Inventory Decisions for Consumer Electronics Service Parts

We consider a consumer electronics manufacturer's problem of controlling the inventory of spare parts in the final phase of the service life cycle. The final phase starts when the part production is terminated and continues until the last service contract or warranty period expires. Placing final orders for service parts is considered to be a popular tactic to satisfy demand during this period and to mitigate the effect of part obsolescence at the end of the service life cycle. Previous research focuses on repairing defective products by replacing the defective parts with properly functioning spare ones. However, for consumer electronic products there typically is considerable price erosion while repair costs stay steady over time. As a consequence, there might be a point in time at which the unit price of the product drops below the repair costs. If so, it is more cost effective to adopt an alternative policy to meet service demands toward the end of the final phase, such as offering customers a new product of the similar type or a discount on a next generation product. This study examines the cost trade‐offs of implementing alternative policies for the repair policy and develops an exact expression for the expected total cost function. Using this expression, the optimal final order quantity and switching time from repair to an alternative policy can be determined simultaneously. Numerical analysis of a real world case sheds light on the cost benefits of these policies and also yields insights into the quantitative importance of the various cost parameters.