Flipping and ex post aggregation

We show that Bayesian ex post aggregation is unstable with respect to refinements. Suppose a group of Bayesians use ex post aggregation. Since it is a joint problem, each agent's problem is captured by the same model, but probabilities and utilities may vary. If they analyze the same situation in more detail, their refined analysis should preserve their preferences among acts. However, ex post aggregation could bring about a preference reversal on the group level. Ex post aggregation thus depends on how much information is used and may keep oscillating (“flipping”) as one keeps adding more information. Received: 16 April 2002/Accepted: 27 May 2002     

On strategy-proofness and essentially single-valued cores: A converse result

In a general model of indivisible good allocation, S?nmez (1999) established that, whenever the core is nonempty for each preference profile, if an allocation rule is strategy-proof, individually rational and Pareto optimal, then the rule is a selection from the core correspondence, and the core correspondence must be essentially single-valued. This paper studies the converse claim of this result. I demonstrate that whenever the preference domain satisfies a certain condition of `richness', if the core correspondence is essentially single-valued, then any selection from the core correspondence is strategy-proof (even weakly coalition strategy-proof, in fact). In particular, on the domain of preferences in which each individual has strict preferences over his own assignments and there is no consumption externality, such an allocation rule is coalition strategy-proof. And on this domain, coalition strategy-proofness is equivalent to Maskin monotonicity, an important property in implementation theory. Received: 22 February 2000/Accepted: 22 January 2002 I am grateful to Ryo-ichi Nagahisa, Shinji Ohseto, Hiroshi Ono, Tomoichi Shinotsuka and Shigehiro Serizawa for valuable comments. And I am indebted to two anonymous referees for useful suggestions. Especially, I owe the present proof of Lemma 2 to one referee. I also thank Yukihiko Funaki, Atsushi Kajii, Mamoru Kaneko, Eiichi Miyagawa, Tatsuyoshi Saijo, Manimay Sengupta, Yves Sprumont, Yoshikatsu Tatamitani, Manabu Toda, Takashi Ui, Takehiko Yamato, Naoki Yoshihara and the participants of the seminars in Hokkaido University, Kansai University, ISER (Osaka University), Otaru University of Commerce and Tsukuba University. All errors are my own responsiblity.     

Measuring Substance Abuse Treatment Need Among Adults in Florida: A Social Indicators Approach

Determining the size and demographiccharacteristics of substance abuse populationsis extremely important for implementing publicpolicies aimed at the control of substanceabuse. Such information not only assists in theallocation of limited treatment resources bythe state, but also in the monitoring ofsubstance abuse trends over time and in theevaluation of innovative policy initiatives. Inthis study, we develop three composite measuresof treatment need. We then use these measuresto estimate treatment need for alcohol abuseand for controlled substance abuse within eachof Florida's 67 counties. This study providesan important empirical component of communityplanning, quantifying and, to a limited degree,specifying the level of need for the substanceabuse treatment of community residents. Anadditional benefit is the development of a costeffective and unobtrusive methodology fordetermining empirically when levels of need arechanging so that treatment levels can beadjusted accordingly. With proper use,policymakers can readily employ the methodologydeveloped in this study in Florida andelsewhere to make better-informed decisions inthe allocation of finite substance abusetreatment resources.     

The analysis of incomplete data using stochastic covariates

In the development of many diseases there are often associated random variables which continuously reflect the progress of a subject towards the final expression of the disease (failure). At any given time these processes, which we call stochastic covariates, may provide information about the current hazard and the remaining time to failure. Likewise, in situations when the specific times of key prior events are not known, such as the time of onset of an occult tumour or the time of infection with HIV-1, it may be possible to identify a stochastic covariate which reveals, indirectly, when the event of interest occurred. The analysis of carcinogenicity trials which involve occult tumours is usually based on the time of death or sacrifice and an indicator of tumour presence for each animal in the experiment. However, the size of an occult tumour observed at the endpoint represents data concerning tumour development which may convey additional information concerning both the tumour incidence rate and the rate of death to which tumour-bearing animals are subject. We develop a stochastic model for tumour growth and suggest different ways in which the effect of this growth on the hazard of failure might be modelled. Using a combined model for tumour growth and additive competing risks of death, we show that if this tumour size information is used, assumptions concerning tumour lethality, the context of observation or multiple sacrifice times are no longer necessary in order to estimate the tumour incidence rate. Parametric estimation based on the method of maximum likelihood is outlined and is applied to simulated data from the combined model. The results of this limited study confirm that use of the stochastic covariate tumour size results in more precise estimation of the incidence rate for occult tumours.