Perfect information

Managing chemical risk must proceed in the absence of perfect information on risks and how to avoid them. The lack of critical information or good alternatives is no excuse for inactiom... 

Conversely, when RP < EFP, the marginal cost for the patient is likewise zero, so the totality of the saving (the distance between RP and EFP) becomes less cost for the insurer, and therefore demand will be more inelastic after the introduction of RP. In this case, the patient s and the doctor s demand is indifferent to a price rise, as long as it does not exceed RP. D2 represents demand after the introduction of RP under the assumption that doctors have perfect information on EFP and RP prices, and shows a kink at RP. Note that in a pure kinked demand model it will never be optimal to fix a price below RP. Thus, those companies that market products whose EFP was lower than RP prior to the introduction of RP may now have an incentive to raise EFP to the level of RP. 

Danzon and Liu22 show that the short-term effect of RP is to produce a kink in the demand curve at the point corresponding to the RP, assuming that all doctors have perfect information on prices. The kinked demand model put forward by these authors to explain the behaviour of prices subject to RP predicts that it will never be optimal to fix a price below RP, the optimal pricing response being EFP = RP (see box above). 

In the above sections we have supposed that pharmaceuticals are conventional goods and that their market shows no essential differences from any other normal market. The effects of co-payment that we have studied will occur if a number of conditions are fulfilled, including the condition that the rational and perfectly informed consumer is an independent generator of demand. The phenomenon of induced demand and incomplete agency relationships, together with uncertainty as to the effectiveness of alternative treatments and doses, can cloud these theoretical results. In this section we analyse to what extent the pharmaceutical market fulfils or fails to fulfil these conditions. 

This chapter addresses the planning, design and optimization of a network of petrochemical processes under uncertainty and robust considerations. Similar to the previous chapter, robustness is analyzed based on both model robustness and solution robustness. Parameter uncertainty includes process yield, raw material and product prices, and lower product market demand. The expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) are also investigated to illustrate numerically the value of including the randomness of the different model parameters. 

Since stochastic programming adds computational burden to practical problems, it is desirable to quantify the benefits of considering uncertainty. In order to address this point, there are generally two values of interest. One is the expected value of perfect information (EVPI) which measures the maximum amount the decision maker is willing to pay in order to get accurate information on the future. The second is the value of stochastic solution (VSS) which is the difference in the objective function between the solutions of the mean value problem (replacing random events with their means) and the stochastic solution (SS) (Birge, 1982). 

A solution based on perfect information would yield optimal first stage decisions for each realization of the random parameter Then the expected value of these decisions, known as wait-and-see (WS) can be written as (Madansky, 1960) ... 

This implies that if it were possible to know the future realization of the demand, prices and yield perfectly, the profit would have been 2 724 040 instead of 2 698 552, yielding savings of 25 488. However, since acquiring perfect information is not viable, we will merely consider the value of the stochastic solution as the best result. These results show that the stochastic model provided an excellent solution as the objective function value was not too far from the result obtained by the WS solution. 

If sponsors had perfect information, it would be easy to set rewards. McGuire has shown that the optimum reward is, in principle, solvable as a utility maximization problem (McGuire 2003 Institute of Medicine 2004). However, McGuire s proof assumes that the R D production function is known to second order. In fact, the values (and even the sign) of these coefficients are very uncertain and depend on information that is not public knowledge (Sloan and Easley, Chapter 6). ... 

In the United Kingdom, the Department of Health asks NICE to evaluate health technologies that have a major impact on the National Health Service. Although not formally based on efficiency considerations, this approach is more consistent with obtaining the best value for money from the use of resources on economic evaluation. More recently, methods involving the estimation of the expected value of perfect information have been used in a pilot study to inform research priorities in the United Kingdom (Claxton et al. 2004). 

The decision rule assumes that perfect information has been obtained from an unlimited number of samples and that the sample mean concentration (x) is equal to the true mean concentration (p). (The definitions of sample mean and true mean concentrations can be found in Appendix 1). The reality is that we never have perfect information and unlimited data, and that is why this decision rule is only a theoretical one. In fact, environmental project decisions are made on data that are obtained from... 

Equivocality reduction It is generally accepted that high-quality information may reduce the imperfection or equivocality that might otherwise be present. This equivocality generally takes the form of uncertainty, imprecision, inconsistency, or incompleteness. It is very important to note that it is neither necessary nor desirable to obtain decision information that is unequivocal or totally perfect. Information need only be sufficiently unequivocal or unambiguous for the task at hand. To make it better may well be a waste of resources ... 

In economic theory, price influences buyer choice because price serves as an indicator of product or service cost. Assuming the buyer has perfect information concerning prices and wants satisfaction of comparable product alternatives, he or she can determine a product/service mix that maximizes satisfaction within a given budget constraint. However, lacking complete tmd accurate infoimation about the satisfaction associated with the alternative choices, the buyer assesses them on the basis of known information. Generally, one piece of information aveiilable to the buyer is a product s price. Other pieces of information about anticipated purchases are not always known, and buyers cannot be sure how reliable and complete this other information is. And because this other infoimation is not always available, buyers may be uncertain about their ability to predict how much they will be satisfied if they purchase the product. For example, if you buy a new car, you do not know what the relative incidence of car repairs will be for the new car until after some months or years of use. Ai a result of this imperfect information, buyers may use price both as an indicator of product cost as well as an indicator of quality (want satisfaction attributes). 

Recent behavioral research has provided additional explanations of how people form value judgments and make decisions when they do not have perfect information about alternatives. Moreover, these explanations further our understanding of why buyers are more sensitive to price increases than to price decreases and how they respond to comparative price advertisements, coupons, rebates, and other special price promotions. The common element in these explanations is that buyers judge prices comparatively, that is, a reference price serves to anchor their judgments (Monroe 1993). 

So is there any room for opinion in CRM Ultimately operating in CRM requires us to make judgements. In the ideal world we would make those judgements entirely on the basis of hard, indisputable facts. But the real world of safety, just like the law, is generally less black and white. There comes a point where we are forced to draw conclusions from less than perfect information. We can manage this by ensuring... 

Now suppose it was possible for the company to obtain a perfect forecast of demand and then choose capacity. In such a case, it is optimal to choose a capacity level that matches demand. Such a context is called the perfect-information expected profit. The expected capacity would be... 

For this example, the expected profit under perfect information would be... 

The profit impact of demand uncertainty is thus 15,000 — 11,700 = 3300, or 28.2% of expected profit. This example shows how demand uncertainty interacts with capacity to affect profit. It also shows the potential value of perfect information, obtained through sources such as market surveys, expert forecasts, and test markets. 

Accepting that developed, liquid markets, such as that for Treasuries, are efficient, with near-perfect information available to most if not all participants, then the inflation expectation is built into the conventional Treasury yield. If the inflation premium understates what certain market participants expect, investors will start buying more of the index-linked bond in preference to the conventional bond. This activity will force the indexed yield down (or the conventional yield up). If, on the other hand, investors think that the implied inflation rate overstates expectations, they will buy more of the conventional bond. 

In other contexts we encounter the distinction between aleatory uncertainty (due to the stochastic nature of a process or system) and epistemic imcer-tainty (due to our lack of knowledge) - see e.g. (Stamatelatos et al. 2002). This distinction can be useful to recognize the fact that even with perfect information available, there will still be imcertainty related to our decisions - i.e. that the decision making process will not converge into a deterministic analysis no matter the extent of our knowledge. 

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