Is it really good to annuitize?

Although rational consumers without bequest motives are better off investing exclusively with annuitized instruments in partial equilibrium, we demonstrate the welfare effect of annuitization is ambiguous in general equilibrium on account of pecuniary externalities. Absent institutional constraints like prices and budgets, the optimal consumption rule would have marginal utility increase at the preferential discount rate. In a rational competitive equilibrium where households fully annuitize, the growth rate of marginal utility will be the discount rate minus the interest rate, resulting in a consumption profile that is too flat. Accidental bequests transfer wealth from the old to the young, steepening the consumption profile and yielding a better equilibrium. If households are restricted to Keynesian consumption functions, the optimal irrational equilibrium with standard preferences can replicate observed consumption and macroeconomic behavior, and the equilibrium without annuities delivers higher utility than the equilibrium with annuities. Whereas preceding papers have merely hypothesized that households might engage in socially optimal, yet irrational behavior, the failure of households to annuitize is a real-world example of this. Policymakers should not take steps to encourage more annuitization by the public.

The maximum tolerated dose of walking for people with severe osteoarthritis of the knee: a phase I trial

OBJECTIVE: To determine how much physical activity, in the form of walking, can be safely and feasibly tolerated for people with severe knee osteoarthritis (OA). DESIGN: Phase I dose response trial with escalating walking doses of 10, 20, 35, 50, 70, and 95 min over 1 week, were prescribed non-randomly to people with severe knee OA. The primary stopping rule was a substantial increase in knee pain. The primary outcomes were an estimation of the maximum tolerated dose of walking; and the proportion of people who did not complete the dose for feasibility reasons. The secondary outcomes were pain, stiffness and activity limitation Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: Twenty-four participants (13 women) aged 53-83 years, and average body mass index (BMI) of 34 kg/m(2) (SD 9) were recruited. Three participants were assigned to each dose between 10 and 70 min, and nine participants assigned to the 95-min dose. The trial was stopped at 95 min due to the maximum number of adverse events occurring at this dose. Therefore, the maximum tolerated dose was 70 min. No participant stopped due to reasons related to feasibility. There was a moderate association between dose and increased activity (linear R(2) = 0.31, cubic R(2) = 0.69) and reduced stiffness (linear R(2) = 0.20, cubic R(2) = 0.52), with increased benefits at moderate to higher doses. CONCLUSIONS: There is preliminary evidence that 70 min per week of moderate intensity supervised walking was safe and feasible for people with severe OA of the knee; for higher doses there was a risk of exacerbating knee pain levels.

An optimal strategy for extracting probabilistic rules by combining rough sets and genetic algorithm

This paper proposes an optimal strategy for extracting probabilistic rules from databases. Two inductive learning-based statistic measures and their rough set-based definitions: accuracy and coverage are introduced. The simplicity of a rule emphasized in this paper has previously been ignored in the discovery of probabilistic rules. To avoid the high computational complexity of rough-set approach, some rough-set terminologies rather than the approach itself are applied to represent the probabilistic rules. The genetic algorithm is exploited to find the optimal probabilistic rules that have the highest accuracy and coverage, and shortest length. Some heuristic genetic operators are also utilized in order to make the global searching and evolution of rules more efficiently. Experimental results have revealed that it run more efficiently and generate probabilistic classification rules of the same integrity when compared with traditional classification methods.

Optimal H∞ insulin injection control for blood glucose regulation in diabetic patients

The theory of H/sup /spl infin// optimal control has the feature of minimizing the worst-case gain of an unknown disturbance input. When appropriately modified, the theory can be used to design a "switching" controller that can be applied to insulin injection for blood glucose (BG) regulation. The "switching" controller is defined by a collection of basic insulin rates and a rule that switches the insulin rates from one value to another. The rule employed an estimation of BG from noisy measurements, and the subsequent optimization of a performance index that involves the solution of a "jump" Riccati differential equation and a discrete-time dynamic programming equation. With an appropriate patient model, simulation studies have shown that the controller could correct BG deviation using clinically acceptable insulin delivery rates.

A new framework with similarity reasoning and monotone fuzzy rule relabeling for fuzzy inference systems

A complete and monotonically-ordered fuzzy rule base is necessary to maintain the monotonicity property of a Fuzzy Inference System (FIS). In this paper, a new monotone fuzzy rule relabeling technique to relabel a non-monotone fuzzy rule base provided by domain experts is proposed. Even though the Genetic Algorithm (GA)-based monotone fuzzy rule relabeling technique has been investigated in our previous work [7], the optimality of the approach could not be guaranteed. The new fuzzy rule relabeling technique adopts a simple brute force search, and it can produce an optimal result. We also formulate a new two-stage framework that encompasses a GA-based rule selection scheme, the optimization based-Similarity Reasoning (SR) scheme, and the proposed monotone fuzzy rule relabeling technique for preserving the monotonicity property of the FIS model. Applicability of the two-stage framework to a real world problem, i.e., failure mode and effect analysis, is further demonstrated. The results clearly demonstrate the usefulness of the proposed framework.

Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction

A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.

Building fuzzy inference systems with similarity reasoning : NSGAII-based fuzzy rule selection and evidential functions

In our previous investigations, two Similarity Reasoning (SR)-based frameworks for tackling real-world problems have been proposed. In both frameworks, SR is used to deduce unknown fuzzy rules based on similarity of the given and unknown fuzzy rules for building a Fuzzy Inference System (FIS). In this paper, we further extend our previous findings by developing (1) a multi-objective evolutionary model for fuzzy rule selection; and (2) an evidential function to facilitate the use of both frameworks. The Non-Dominated Sorting Genetic Algorithms-p (NSGA-p) is adopted for fuzzy rule selection, in accordance with the Pareto optimal criterion. Besides that, two new evidential functions are developed, whereby given fuzzy rules are considered as evidence. Simulated and benchmark examples are included to demonstrate the applicability of these suggestions. Positive results were obtained.

Role of index bonds in an optimal dynamic asset allocation model with real subsistence consumption

We investigate the role of index bonds in a dynamic consumption and asset allocation model where the rate of real consumption at any given time cannot fall below a fixed level. An explicit form of the optimal consumption and portfolio rule for a class of Constant Relative Risk Aversion (CRRA) utility functions is derived. Consumption increases above the subsistence level only when wealth exceeds a threshold value. Risky investments in equity and nominal bonds are initially proportional to the excess of wealth over a lower bound, and then increase nonlinearly with wealth. The desirability of investing in the risky assets are related to the agent’s risk preference, the equity premium, and the inflation risk premium. The demand for index bonds is also obtained. The results should be useful for the management of defined benefit pension funds, university endowments, and other portfolios which have a withdrawal pre-commitment in real terms.