Optimal law enforcement and criminal organization

In this paper, we take an organizational view of organized crime. In particular, we study the organizational consequences of product illegality attending at the following characteristics: (i) contracts are not enforceable in court, (ii) all participants are subject to the risk of being punished, (iii) employees present a major threat to the entrepreneur having the most detailed knowledge concerning participation, (iv) separation between ownership and management is difficult because record-keeping and auditing augments criminal evidence.

On the timing of balance of payments crises: Disaggregated information and interest rate policy

This paper proposes a dynamic framework to study the timing of balance of paymentscrises. The model incorporates two main ingredients: (i) investors have private information; (ii)investors interact in a dynamic setting, weighing the high returns on domestic assets against the incentives to pull out before the devaluation. The model shows that the presence of disaggregated information delays the onset of BOP crises, giving rise to discrete devaluations. It also shows that high interest rates can be eective in delaying and possibly avoiding the abandonment of the peg. The optimal policy is to raise interest rates sharply as fundamentals become very weak. However, this policy is time inconsistent, suggesting a role for commitment devices such as currency boards or IMF pressure.

Sequential screening

We present a model of price discrimination where a monopolistfaces a consumer who is privately informed about thedistribution of his valuation for an indivisible unit ofgood but has yet to learn privately the actual valuation.The monopolist sequentially screens the consumer with amenu of contracts:the consumer self-selects once by choosing a contract andthen self-selects again when he learns the actual valuation. A deterministic sequential mechanism is a menu of refundcontracts, each consisting of an advance payment and a refundamount in case of no consumption, but sequential mechanismsmay involve randomization.We characterize the optimal sequential mechanism when someconsumer types are more eager in the sense of first-orderstochastic dominance, and when some types face greatervaluation uncertainty in the sense of mean-preserving-spread.We show that it can be optimal to subsidize consumer typeswith smaller valuation uncertainty (through low refund, as inairplane ticket pricing) in order to reduce the rent to thosewith greater uncertainty. The size of distortion depends bothon the type distribution and on how informative the consumer'sinitial private knowledge is about his valuation, but noton how much he initially knows about the valuation per se.

The new Keynesian approach to monetary policy analysis: Lessons and new directions

I discuss several lessons regarding the design and conduct of monetary policy that have emerged out of the New Keynesian research program. Those lessons include the bene.ts of price stability, the gains from commitment about future policies, the importance of nat-ural variables as benchmarks for policy, and the bene.ts of a credible anti-inflationary stance. I also point to one challenge facing NK modelling efforts: the need to come up with relevant sources of policy tradeoffs. A potentially useful approach to meeting that challenge, based on the introduction of real imperfections, is presented.

The science of monetary policy: A new Keynesian perspective

This paper reviews the recent literature on monetary policy rules. We exposit the monetary policy design problem within a simple baselinetheoretical framework. We then consider the implications of adding various real world complications. Among other things, we show that the optimal policy implicitly incorporates inflation targeting. Wealso characterize the gains from making credible commitments to fightinflation. In contrast to conventional wisdom, we show that gains from commitment may emerge even in the central bank is not trying toinadvisedly push output above its natural level. We also consider theimplications of frictions such as imperfect information.

Candida bloodstream infections in intensive care units: analysis of the extended prevalence of infection in intensive care unit study.

OBJECTIVES: To provide a global, up-to-date picture of the prevalence, treatment, and outcomes of Candida bloodstream infections in intensive care unit patients and compare Candida with bacterial bloodstream infection. DESIGN: A retrospective analysis of the Extended Prevalence of Infection in the ICU Study (EPIC II). Demographic, physiological, infection-related and therapeutic data were collected. Patients were grouped as having Candida, Gram-positive, Gram-negative, and combined Candida/bacterial bloodstream infection. Outcome data were assessed at intensive care unit and hospital discharge. SETTING: EPIC II included 1265 intensive care units in 76 countries. PATIENTS: Patients in participating intensive care units on study day. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Of the 14,414 patients in EPIC II, 99 patients had Candida bloodstream infections for a prevalence of 6.9 per 1000 patients. Sixty-one patients had candidemia alone and 38 patients had combined bloodstream infections. Candida albicans (n = 70) was the predominant species. Primary therapy included monotherapy with fluconazole (n = 39), caspofungin (n = 16), and a polyene-based product (n = 12). Combination therapy was infrequently used (n = 10). Compared with patients with Gram-positive (n = 420) and Gram-negative (n = 264) bloodstream infections, patients with candidemia were more likely to have solid tumors (p < .05) and appeared to have been in an intensive care unit longer (14 days [range, 5-25 days], 8 days [range, 3-20 days], and 10 days [range, 2-23 days], respectively), but this difference was not statistically significant. Severity of illness and organ dysfunction scores were similar between groups. Patients with Candida bloodstream infections, compared with patients with Gram-positive and Gram-negative bloodstream infections, had the greatest crude intensive care unit mortality rates (42.6%, 25.3%, and 29.1%, respectively) and longer intensive care unit lengths of stay (median [interquartile range]) (33 days [18-44], 20 days [9-43], and 21 days [8-46], respectively); however, these differences were not statistically significant. CONCLUSION: Candidemia remains a significant problem in intensive care units patients. In the EPIC II population, Candida albicans was the most common organism and fluconazole remained the predominant antifungal agent used. Candida bloodstream infections are associated with high intensive care unit and hospital mortality rates and resource use.

Heterogeneity-promoting optimal procurement

When procurement takes place in the presence of horizontally differentiated contractors, the design of the object being procured affects the resulting degree of competition. This paper highlights the interaction between theoptimal procurement mechanism and the design choice. Contrary to conventional wisdom, the sponsor's design choice, instead of homogenizingthe market to generate competition, promotes heterogeneity.

Nominal debt as a burden on monetary policy

We study the effects of nominal debt on the optimal sequential choice of monetary policy. When the stock of debt is nominal, the incentive to generate unanticipated inflation increases the cost of the outstanding debt even if no unanticipated inflation episodes occur in equilibrium. Without full commitment, the optimal sequential policy is to deplete the outstanding stock of debt progressively until these extra costs disappear. Nominal debt is therefore a burden on monetary policy, not only because it must be serviced, but also because it creates a time inconsistency problem that distorts interest rates. The introduction of alternative forms of taxation may lessen this burden, if there is enough commtiment to fiscal policy. If there is full commitment to an optimal fiscal policy, then the resulting monetary policy is the Friedman rule of zero nominal interest rates.

Dynamic optimal law enforcement with learning

We incorporate the process of enforcement learning by assuming that the agency's current marginal cost is a decreasing function of its past experience of detecting and convicting. The agency accumulates data and information (on criminals, on opportunities of crime) enhancing the ability to apprehend in the future at a lower marginal cost.We focus on the impact of enforcement learning on optimal stationary compliance rules. In particular, we show that the optimal stationary fine could be less-than-maximal and the optimal stationary probability of detection could be higher-than-otherwise.

Which inflation to target? A small open economy with sticky wages indexed to past inflation

In a closed economy context there is common agreement on price inflation stabilization being one of the objects of monetary policy. Moving to an open economy context gives rise to the coexistence of two measures of inflation: domestic inflation (DI) and consumer price inflation (CPI). Which one of the two measures should be the target variable? This is the question addressed in this paper. In particular, I use a small open economy model to show that once sticky wages indexed to past CPI inflation are introduced, a complete inward looking monetary policy is no more optimal. I first, derive a loss function from a secondorder approximation of the utility function and then, I compute the fully optimalmonetary policy under commitment. Then, I use the optimal monetary policy as a benchmark to compare the performance of different monetary policy rules. The main result is that once a positive degree of indexation is introduced in the model the rule performing better (among the Taylor type rules considered) is the one targeting wage inflation and CPI inflation. Moreover this rule delivers results very close to the one obtained under the fully optimal monetary policy with commitment.

Optimal law enforcement under asymmetric information

In this paper, we focus on the problem created by asymmetric informationabout the enforcer's (agent's) costs associated to enforcement expenditure. This adverse selection problem affects optimal law enforcement because a low cost enforcer may conceal its information by imitating a high cost enforcer, and must then be given a compensation to be induced to reveal its true costs. The government faces a trade-off between minimizing the enforcer's compensation and maximizing the net surplus of harmful acts. As a consequence, the probability of apprehension and punishment is usually reduced leading to more offenses being committed. We show that asymmetry of information does not affect law enforcement as long as raising public funds is costless. The consideration of costly raising of public funds permits to establish the positive correlation between asymmetry of information between government and enforcers andthe crime rate.

Identification of optimal structural connectivity using functional connectivity and neural modeling.

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.

Which is the optimal sampling strategy for habitat suitability modelling

Designing an efficient sampling strategy is of crucial importance for habitat suitability modelling. This paper compares four such strategies, namely, 'random', 'regular', 'proportional-stratified' and 'equal -stratified'- to investigate (1) how they affect prediction accuracy and (2) how sensitive they are to sample size. In order to compare them, a virtual species approach (Ecol. Model. 145 (2001) 111) in a real landscape, based on reliable data, was chosen. The distribution of the virtual species was sampled 300 times using each of the four strategies in four sample sizes. The sampled data were then fed into a GLM to make two types of prediction: (1) habitat suitability and (2) presence/ absence. Comparing the predictions to the known distribution of the virtual species allows model accuracy to be assessed. Habitat suitability predictions were assessed by Pearson's correlation coefficient and presence/absence predictions by Cohen's K agreement coefficient. The results show the 'regular' and 'equal-stratified' sampling strategies to be the most accurate and most robust. We propose the following characteristics to improve sample design: (1) increase sample size, (2) prefer systematic to random sampling and (3) include environmental information in the design'