Preference information

It is appropriate to recognize that some medications are more susceptible to abuse than others. If two medications are equally effective for a given indication, the one with lower abuse liability would obviously be preferred. Information on abuse liability is necessary for the appropriate regulation of medications and provides a basis for education of physicians, patients, and the public. In this chapter we describe the control of marketed medications, abuse-liability assessment procedures for premarketing testing in laboratory animals and humans, considerations of the formulation properties, and postmarketing surveillance of abuse. Finally, we provide three case studies of marketed medications that have been abused. 

Hwang and Yoon (1981, pp. 8-9) developed the classification of MADA methods shown in Figure 24. It is based on the type and quality of information available on decision makers preferences. The tools most suitable for site assessments belong to the subset of tools that work with cardinal preference information. Major methods from within this subset will be introduced below while the reader is referred to Hwang and Yoon (1981) for the other types of MADA tools. 

Modem Dialing Information If a modem has been detected, you will be asked for country, area code, and dialing preference information. If you do not have a modem, this screen will be skipped. 

As mentioned in the introduction, we here assume that a DM is able to participate in the solution process. (S)he is expected to know the problem domain and be able to specify preference information related to the objectives and/or different solutions. We assume that less is preferred to more in each objective for him/her. (In other words, all the objective functions are to be minimized.) If the problem is correctly formulated, the final solution of a rational DM is always Pareto optimal. Thus, we can restrict our consideration to Pareto optimal solutions. For this reason, it is important that the multi-objective optimization method used is able to find any Pareto op>-timal solution and produce only Pareto optimal solutions. However, weakly Pareto optimal solutions are sometimes used because they may be easier to generate than Pareto optimal ones. A decision vector x G S (and the corresponding objective vector) is weakly Pareto optimal if there does not exist another x G S such that /i(x) < /i(x ) for alH = 1,..., A . Note that Pareto optimality implies weak Pareto optimality but not vice versa. 

Finding a final solution to problem (6.1) is called a solution process. It usually involves the DM and an analyst. An analyst can be a human being or a computer program. The analyst s role is to support the DM and generate information for the DM. Let us emphasize that the DM is not assumed to know multi-objective optimization theory or methods but (s)he is supposed to be an expert in the problem domain, that is, understand the application considered and have insight into the problem. Based on that, (s)he is supposed to be able to specify preference information related to the objectives considered and different solutions. The DM can be, e.g., a designer. The task of a multi-objective optimization method is to help the DM in finding the most preferred solution as the final one. The most preferred solution is a Pareto optimal solution which is satisfactory for the DM. 

The DM can specify preference information in many ways and the task is to find a format that the DM finds most natural and intuitive. One possibility is that the DM specifies aspiration levels Zi i = 1,..., k) that are desirable or acceptable objective function values. The vector z G R consisting of aspiration levels is called a reference point. 

Overall, we can say that it is not necessarily easy for the DM (or the analyst) to control a solution process with weights because weights behave in an indirect way. It makes no sense to end up in a situation where one tries to guess such weights that would produce a desirable solution. Because the DM can not be properly supported in this, (s)he is likely to get frustrated. Instead, it is then better to use real interactive methods (see Section 6.3) where more intuitive preference information can be used. 

As said in the introduction, in interactive multi-objective optimization methods, a solution pattern is formed and repeated and the DM specifies preference information progressively during the solution process. In other words, the solution process is iterative and the phases of preference elicitation and solution generation alternate. In brief, the main steps of a... 

Many interactive methods exist and none of them is superior to all the others but some methods may suit different DMs and problems better than the others. Methods differ from each other by both the style of interaction and technical realization e.g., what kind of information is given to the DM, the form of preference information specified by the DM and what kind of a scalarizing function is used or, more generally, which inner process is used to generate Pareto optimal solutions (Miettinen, 1999). It is always... 

Let us point out that expressing preference information as a reference point (Miettinen and Makela, 2002 Miettinen et ai, 2006) is closely related to classification. However, when classification assumes that some objective function must be allowed to get worse, a reference point can be set without considering the current solution. Even though it is not possible to improve all objective function values of a Pareto optimal solution simultaneously, the DM can still express preferences without pa3ung attention to this fact and then see what kind of solutions are feasible. On the other hand, when using classification, the DM is more in control and selects functions to be improved and specifies amounts of impairment for the others. 

There exist several variants of NIMBUS (Miettinen, 1999 Miettinen and Makela, 1995, 1999, 2000, 2006). Here we concentrate on the synchronous version (Miettinen and Makela, 2006), where several scalarizing functions can be used based on a classification once expressed. Because they take the preference information into account in slightly different ways (Miettinen and Makela, 2002), the DM can learn more about different solutions satisfying his/her hopes and choose the one that best obeys his/her preferences. An example of the scalarized problems used is... 

If the problem considered has only two objective functions, methods generating a representation of the Pareto optimal set, like EMO approaches can be applied because it is simple to visualize the solutions on a plane. However, when the problem has more than two objectives, the visualization is no longer trivial and interactive approaches offer a viable alternative to solve the problem without artificial simplifications. Because interactive methods rely heavily on the preference information specified by the DM, it is important to select such a user-friendly method where the style of specif3ung preferences is convenient for the DM. In addition, the specific features of the problem to be solved must be taken into consideration. 

Note that each of the above statements involves a comparison or relation between a pair of outcomes. Let Y denote the set of all possible outcomes. Any revealed preference information, accumulated or not, can be represented by a subset of the Cartesian product Y X T, or by a so-crilled binary relation. We have the following definition. 

The preference information captured by an acyclic CP-net N can be viewed as a set of logical assertions about a user s preference ordering over complete assignments to variables in the network. These statements are generally not... 

As an example case, let us consider three players and one mediator for three issues. In this framework, the players and mediator can be run on different computers. When starting the negotiation process, all players report their overall preference information about negotiation issues to the mediator. Then, the mediator creates induced preference graphs for all players based on their CP-nets. 

In practice, if there exists information on fhe weighfs, eifher having an expected weight or having a preference order of benefifs and risks, the rank acceptability index can be used to compare the alternatives. Otherwise, if no preference information is available, fhe cenfral weighf vectors and confidence factors can be used to explore comparisons of fhe alternatives to assist the BRA. 

In the case study, we considered the weight vector with a uniform distribution, that is, no prior preference information available, and with a Dirichlet distribution with parameter vector Wg = (0.48,0.29,0.04,0.19) so that the average weights are the same as their relative importance. Evaluation was based... 

Softer techniques as outranking aggregation methods (such as ELECTRE and derived methods (Roy 1991)) demand lesser effort, but also require some preference information not always available, such as the coherence of the ID family involved and the indifference, preference and veto thresholds of each ID. 

Methods can be classified as either analytical or affective. Analytical methods are either discriminative or descriptive they provide product analytical information. Affective tests are hedonic or paired preference they provide product liking or preference information. These methods provide different kinds of information and should not be combined, a topic discussed later in this section. These methods are the foundation on which none evaluation has developed. Each provides different kinds of information and none of them is superior to another in terms of sensitivity. 

Number of prototypes If they are cheaper, it may make sense to develop more prototypes. How many and how to best make use of the increased flow of customer preference information are researchable questions (see Dahan and Mendelson, 2001, for an analysis of how the optimal number of prototypes and the expected profit are affected by the cost of building and testing product concepts). 

Method of global criterion (Hwang and Masud, 1979) and compromise programming (Zeleny, 1982) fall under the class of MCMP methods that do not require any preference information from the DM. 

As with any piping layout, information for an underground gravity flow drain system is often less than what is required at the outset of a project. A list of the most preferred information includes ... 

Maximize the amount of information gained from a sensory test. Traditional sensory testing has strict rules about getting a limited amount of information from a given test. Yet it is desirable to use a difference test and then ask for preference information (does the panelist prefer a given sample He or she may like the objectionable sample) and how confident the panelist was in his or her choice. 

High risk admit to psychiatric hospital, preferably informally. Arrange a MHA assessment if he refuses or lacks capacity to consent to admission. 

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