An important goal of risk-adjusted capitation payments (RACPs) to competitive community-rated health plans-that may differ in coverage and/or the organisation of delivering care-is to reduce incentives for risk selection while maintaining incentives for efficiency. In most schemes, RACPs are simply based on the average observed costs in risk groups (in a prior year). We show that under this procedure, incentives for efficiency will not always be maintained: when identical risk types are concentrated in the same health plans-due to selection, specialisation or just coincidence-cost savings can be captured by the RACPs and leak away from these plans.
In goal-directed decision-making, animals choose between actions that are associated with different reward outcomes (e.g., foods) and with different costs (e.g., effort). Rapid advances have been made over the past few years in our understanding of the computations associated with goal-directed choices, and of how those computations are implemented in the brain. We review some important findings, with an emphasis on computational models, human fMRI, and monkey neurophysiology studies.
Little is known about the neural networks supporting value computation during complex social decisions. We investigated this question using functional magnetic resonance imaging while subjects made donations to different charities. We found that the blood oxygenation level-dependent signal in ventral medial prefrontal cortex (VMPFC) correlated with the subjective value of voluntary donations. Furthermore, the region of the VMPFC identified showed considerable overlap with regions that have been shown to encode for the value of basic rewards at the time of choice, suggesting that it might serve as a common valuation system during decision making. In addition, functional connectivity analyses indicated that the value signal in VMPFC might integrate inputs from networks, including the anterior insula and posterior superior temporal cortex, that are thought to be involved in social cognition.
The prefrontal cortex (PFC) is thought to modulate the neural network state in favor of the processing of task-relevant sensory information prior to the presentation of sensory stimuli. However, this proactive control mechanism cannot always optimize the network state because of intrinsic fluctuation of neural activity upon arrival of sensory information. In the present study, we have investigated an additional control mechanism, in which the control process to regulate the behavior is adjusted to the trial-by-trial fluctuation in neural representations of sensory information. We asked normal human subjects to perform a variant of the Stroop task. Using functional magnetic resonance imaging, we isolated cognitive conflict at a sensory processing stage on a single-trial basis by calculating the difference in activation between task-relevant and task-irrelevant sensory areas. Activation in the dorsolateral PFC (DLPFC) covaried with the neural estimate of sensory conflict only on incongruent trials. Also, the coupling between the DLPFC and anterior cingulate cortex (ACC) was tighter on high-sensory conflict trials with fast response. The results suggest that although detection of sensory conflict is achieved by the DLPFC, online behavioral adjustment is achieved by interactive mechanisms between the DLPFC and ACC.
This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of neutral and investmentspecific technology shocks in explaining aggregate fluctuations. To achieve this, we estimate the model’s posterior density using Bayesian
methods. Within this framework we first extend Ireland’s (2001, 2004) hybrid estimation approach to allow for a vector autoregressive moving average (VARMA) process to describe the movements and comovements of the model’s errors not explained by the basic RBC model. Our main findings for the model with neutral technical change are: (i) the VARMA specification of the errors significantly improves the hybrid model’s fit to the historical data relative to the VAR and AR alternatives; and (ii) despite setting the RBC model a more difficult task under the VARMA specification, neutral technology shocks are still capable of explaining a significant share of the observed variation
in output and its components over shorter- and longer-forecast horizons as well as hours at shorter horizons. When the hybrid model is extended to incorporate investment shocks, we find that: (iii) the VAR specification is preferred to the alternatives; and (iv) the model’s ability to explain fluctuations improves considerably.
