In economics, decision analysis under uncertainty has almost exclusively focused on the two extreme cases of purely subjective probabilities derived from a decision maker’s pref- erences (Savage, 1954) or perfect information about probabilities (objective lotteries) analyzed by von Neumann and Morgenstern (1944). With few exceptions 1 , the more recent literature on decisionmaking under ambiguity takes a purely subjective perspec- tive. Many economic decision problems are, however, characterized by knowledge about the frequencies or probabilities of some events, yet not about all. Giraud and Tallon (2011) raise this issue and point to belief functions as a formal concept allowing one to combine objective, that is inter-subjectively verifiable, information about events, with purely subjective beliefs implicit in an individual’s preferences.
We introduce three different approaches for decisionmaking under uncertainty if (I) there is only partial (both cardinally and ordinally scaled) information on an agent’s preferences and (II) the uncertainty about the states of nature is described by a credal set (or some other imprecise prob- abilistic model). Particularly, situation (I) is modeled by a pair of binary relations, one specifying the partial rank order of the alternatives and the other modeling partialinformation on the strength of preference. Our first approach relies on decision criteria constructing complete rankings of the available acts that are based on generalized expectation intervals. Subsequently, we introduce dif- ferent concepts of global admissibility that construct partial orders between the available acts by comparing them all simultaneously. Finally, we define criteria induced by suitable binary relations on the set of acts and, therefore, can be understood as concepts of local admissibility. For certain criteria, we provide linear programming based algorithms for checking optimality/admissibility of acts. Additionally, the paper includes a discussion of a prototypical situation by means of a toy example.
Abstract: Information has become an essential resource for managing modern organizations. This is so because today’s business environment is volatile, dynamic, turbulent and necessitates the burgeoning demand for accurate, relevant, complete, timely and economical information needed to drive the decision-making process in order to accentuate organizational abilities to manage opportunities and threat. MIS work on online mode with an average processing speed. Generally, it is used by low level management. Decision support system are powerful tool that assist corporate executives, administrators and other senior officials in makingdecision regarding the problem. Management Information Systems is a useful tool that provided organized and summarized information in a proper time to decision makers and enable making accurate decision for managers in organizations. This paper will discuss the concept, characteristics, types of MIS, the MIS model, and in particular it will highlight the impact and role of MIS on decisionmaking.
That‟s why the industry‟s export potential is enormous. As for the directions of its development, they may be selected based on conditions available at the moment of decision-making: the availability of a strategic investor, the demand-and-supply situation in the global marketplace, the efficiency of a business plan, the level of personnel‟s proficiency in accordance with the requirements of export-oriented production etc.
Miller’s contract. They were then informed that additional information about the decision case was available. This information was written by Mr. Miller’s colleagues and included 12 one-page comments. Participants received a list that included the main thesis of each one-page comment summarized in 2-3 sentences. These summaries made it clear whether or not the colleague supported extending Mr. Miller’s contract. An example of a favorable comment was: “Mr. Miller shows intuition and sensitivity for new trends and developments in the fashion industry. His creative ideas might facilitate entering new sales markets. Therefore, his contract should be extended.” An example of a critical comment was: “Mr. Miller has just copied competitors’ business ideas. Thus, his business strategy has doubtful prospects of success. Therefore, his contract should not be extended.” There were six comments favoring the extension and six comments opposing it, ensuring that, regardless of each participant’s preliminary decision, half of the comments were consistent with it and half inconsistent with it. Participants evaluated the expected quality of all of the comments according to their credibility (“How credible do you expect this information to be?”; 0 = not at all, 10 = extremely) and importance (“How important will this information be for making a good decision?”; 0 = not at all, 10 = extremely). They also indicated whether they would like to read the corresponding comments in detail later on. Participants could select freely from the available comments. We computed difference values for information credibility, information importance, and information search by subtracting the corresponding values for decision-inconsistent information from the values for decision- consistent information. For the following analyses, the three difference scores were transformed into z-values and collapsed into an overall index of confirmatory information processing (α = .90).
A case involving Bath and North East Somerset (BaNES) (BANES v Information Commissioner, October 2010) is useful in our developing a framework for understanding the disclosure of financial information. In this £500 million brownfield redevelopment scenario, there are a number of parallels to the Lakota case, for example, the local authority had a land owning interest; the proposed development was in the city-wide UNESCO World Heritage Site; and there were heritage questions associated with the scheme. In this case it was decided by the ICO that the financial information should not be released as the public interest favoured maintaining confidentiality. The key difference was that the viability report related to a planning obligation, a legal agreement relating to securing planning gain, rather than the financial costs relating to the purchase and redevelopment of a listed building and, significantly, here the local authority was granted ‘open-book’ (‘live’ and specific) access to information in relation to the question of viability. The Lakota case was referenced in this case with the hypothetical nature of the viability calculations being highlighted. The Tribunal found the financial information relating to the planning application should not be released to the public in this case because the public interest favoured maintaining confidentiality given the sensitivity of the information. It was found in this case that releasing the information could compromise the willingness of developers to engage in open discussion and negotiation in the future. Therefore, a partial release of financial
Uncertainty Attitude and Variable Information Structures
Seminal models of decisionmaking under uncertainty often consider information
implicit and fixed (e.g., Anscombe and Aumann (1963); Schmeidler (1989); Gilboa and Schmeidler (1989); Klibanoff et al. (2005)). On this account, the information a decision maker (DM) perceived, as well as the imprecision or ambiguity inherent in the decision problem, are often reflected by parts (conditions) of the representations. 1 Meanwhile, attitude towards uncertainty is a primary feature to which the revealed choice behavior is often attributed. However, when we apply the seminal models to the environments of uncertainty that involve fixed contingencies with associated consequences yet allow ex- ogenous information about the likelihoods of contingencies to vary, we might experience difficulties in isolating the influence of information on the uncertainty attitude which the DM’s choice exhibits. Namely, seminal models often become uninformative about whether and how the revealed uncertainty attitude translates among the choices made under different information. Explicating the translatability of uncertainty attitude requires a model that accommodates information-dependency of choice under uncer- tainty, thereby necessitating a formalism that takes variable information as primitive. The objective of this chapter is to develop such a model by explicitly incorporating vari- able information into the benchmark subjective expected utility (SEU, Anscombe and Aumann (1963)) framework and connect the representation of preference to behavioral definitions of uncertainty attitude.
Accounting generally involves the process of identifying, measuring, and communicating economic information to permit informed judgments and decisions of users of the information.
In other words, accounting is concerned with providing information, which will help decision makers to make decisions. To enhance creditability and utility of the information, the decisionmaking process, established concepts, principles, standard and legal requirements are strictly followed in order to translate physical facts into money values and ensures that all types of report are integrated and prepared on consistent basis.
Ratios of financial statement analysis and the business decision
Ratio is rational or relative number which means that one economic value is put into relation (it is being divided) with other economic value. Since there is no sense in connecting any two economi- cal values, we can speak about prerequisites of ratio’s accuracy. Considering the time dimension, financial ratios can be basically divided into two groups. One group of financial ratios includes company’s business within the particular time period (usually a year). This group is based on the data from profit and loss account and cash flow statement. The other group of financial ratios re- fers to the exactly defined moment which corresponds with the balance sheet date and talks about company’s financial position in that moment. Ratios contain concentrated information that is needed for business quality measurement and decisionmaking process as well.
Development of information and communication technologies as change structures of societies, it also affected task of manager’s makingdecision. Many organizations prepare them self for effective and efficient use of new information and communications technologies. Information and communication technology has two benefits for organization. First, it enables organizations and managers to easily acquire data. This will cause further support the decisionmaking process. Second, the use of information and communication technology enables organizations to have better operate in a global competitive environment and make effective decisionmaking. Information and communication technology improve the quality of decisionmaking that is crucial factor for organization. Cause dramatic changes in levels of the organization, including organizational leadership and strategy, and even members behavior. The information and communication technology has become an essential component in the process of decisionmaking in organization and managers at all levels increasingly get help from information and communication technologies (Feizi&Moghadassi, 2012). No doubt modern information and communication technologies provide the field of information management system. Information and communication technology enables collect, analyze and evaluate data and transferring them from one point to another and cause instant access to information, Reduce costs, Produce better, Carefully, Coordination, Leading time, improved control and will lead to better services. No doubt, management has been a necessity for human since past, If you consider different management activities can be clearly seen that the essence of all the management activities is makingdecision. Decisionmaking is an integral component of management In each task, the management is so smart. In determine organization's policies, development objectives. Organization design, Choice, Assessment and management practices in all forms, Decision-making are one of the main fundamental pillars. In a simple definition, decisionmaking is choose a way between different paths (Alvani,2012).
DSS can support decision makers in a number of different ways. They can store data and provide means to search for relevant data items. More advanced techniques include query languages and data warehouses. Data can be viewed and analysed using pivot tables and other methods of on-line analytical processing (OLAP). DSS can provide computational and statistical models, for instance for trend analysis. With data mining algorithms, the decision maker can find interesting patterns in data. The results can be presented in reports and tables, as well as graphically using advanced visualisation techniques. DSS can incorporate all types of decision analysis and operational research models presented above. Consequently, using these models, DSS can evaluate and assess decision alternatives or find optimal solutions of mathematically formulated problems. DSS can integrate data from different sources and of different types (relational data, documents, video, etc.). Also, DSS can contain rules that guide specific decision processes. Last but not least, DSS can provide communication and other means to support the collaboration of decision makers.
However, in many cases each DM is an expert in an area (the main reason of group decisionmaking) and most likely be- cause of that his/her opinion in that area (attribute) is much more important whether or not it is different from others. And this relative importance of DM as an expert also can be measured. For example, a member of the Chinese Academy of Engi- neering/Sciences (at a national level), a famous expert in a province (at a provincial level), and a famous expert in a local city (at a local level), in Chain, can be scored as 1, 0.8, 0.6, respectively. The importance of an expert in his/her area is called the individual importance, denoted by I k .
tion from the doctor and taking part in decisions as their first priority for a change and improvement (Table 2). Unlike studies that addressed preferences of special patient populations and subgroups such as cancer patients or patients at the end of life, most patients reported here had no major decisions ahead. Neverthe- less, many wished to become partners to the doctor's infor- mation and decision-making relevant to their care. In the case of some particular patient's subgroups however, alter- native preferences might be found. For example, a promi- nent physician who became ill with cancer gave a moving account of being flooded with information yet he had a strong wish for a physician with authority who would make the 'best' decision on his behalf . Such possible dichotomy between wanting greater information but not necessarily greater decisional control was also identified in several studies of cancer patients [16-18].
The increased importance of social networking may translate into the increased importance of the impact those communities have on the purchasing process. The most pronounced economic implication of participation in online communities is their impact on the process of finding information in the early phase of the decision-making process. In order to obtain the information they seek, consumers may contact like-minded people or consumers who have already had experience with a given product. Recommendations from friends, family members and like-minded consumers have a significant impact on the decision to make a purchase, both in offline and online conditions. They are particularly important in the case of services, when the level of uncertainty and the risk associated with the purchase are particularly high. The increased importance of social networking causes a significant increase in opinions and recommendations available to the customer (Cheung, Lee &Rabjohn, 2008, pp. 231-234).
The gap between conceptual feasibility and practical implementation is immense. A major problem lies in finding new modes of penetrating water management (Jacobs and Pulwarty, 2003). One of Gilbert White's (1966) most important contributions to understanding decision-making about environmental risks was in developing a framework for structuring the analysis of adjustment decisions. He distinguished between the theoretical and practical ranges of choices. The physical environment at a given stage of technology sets the theoretical range of choice open to any resource manager. The practical range of choice is set by culture and institutions, which permit, prohibit, or discourage a given choice. As argued in this paper, an avenue for integration between these two frames may lie in collaborative explorations of information communication and use. While there has been increasing focus on the processes by which knowledge has been produced, less time has been spent examining the capacity of audiences to critically assess knowledge claims made by others for their reliability and relevance to those communities (Fischoff, 1996). The ability of practitioners themselves to manipulate data and to reconcile scientific claims with their own knowledge plays important roles in their choices. There is a strong need for the inquiry into and development of interactive approaches between decisive (policy and operations) and non-decisive (research) participants to take advantage of new opportunities as systems evolve. However, to avoid appearance of advocacy researchers interested in effective use of information should focus on system management needs, as opposed to single stakeholder consultancies. Addressing future climate change will only be
(1) Asset value V m . Here, assets belong to organizations, which are valuable information assets for attackers. If the assets are worthless, even they suffer from security threat; there is not any loss, so no risk. In information security risk assessment, there are two ways to represent the asset value, namely the absolute value and the relative value. The former refers to the actual value of assets, denoted by currency. And the latter is a range of asset value given by subjects of assessment according to the value of each asset in information systems and their importance. The absolute value of assets is adopted in this paper to make for representing risks intuitively and security risk decision-making analysis. The assessment of asset values usually employs expert evaluation.
of cognitive bias is larger when the evidence is more difficult to assess . As mentioned earlier, where raw data is close to a cut-off value, cases may be deemed ‘inconclusive’ or ‘borderline’. Although the mathematically correct answer is clear, immunoassay is known to produce both false positives and false negatives [27, 39]. Given this, a valid scientific strategy could be to repeat or confirm all borderline cases (e.g., those within a certain specified ± of the cut-off value) but only if this is the pre-documented procedure in the laboratory, not post-hoc and dependent on whether or not the ‘expected’ or ‘wanted’ results were achieved. This strategy would acknowledge the inherent measurement uncertainty associated with using a single cut-off number , particularly in a non-quantitative test such as an immunoassay, but would not depend on the context of any particular case to make a decision.
• Direct investigations are those where credit information is collected by the creditor either through direct contact with the customer or through direct contact with noncommercial sources of information such as individuals, banks or other trade references that may have useful information.
value judgment. Chen Dongfeng et al.  make an uni- formization of four common preference information given by decision makers, which are real value, interval values, language phrases and intuitionistic fuzzy value, based on conversion of formula between deferent fuzzy preference information. Li Bingjun and Liu Sifeng  construct a defining-number judgment matrix of certain credibility which is equivalent to group information of interval number reciprocal judgment matrix based on- set-valued statistics principle. Wu Jiang and Huang Dengshi  construct the relation functions based on four interval number preference information, namely interval number preference orderings, interval number utility values, interval number complementary judgment matrices and interval number reciprocal judgment matri- ces, which uniforms different uncertain preference in- formation. Zhou Shizhong et al.  analysis characteris- tics of four kinds uncertain preference and propose a goal programming model to aggregate the group preference. Zhu Jianjun  studies the group aggregation approach of interval number reciprocal comparison matrix and interval number complementary comparison matrix by using UOWA method. Feng Xiangqian et al.  con- struct an aggregating of interval number judgment ma- trixes with maximum satisfaction. Guiwu Wei  pro- poses intui- tionistic fuzzy ordered weighted geometric operator and interval-valued intuitionistic fuzzy ordered weighted geometric operator to study multiple attribute group decisionmaking issue. H. J. Zimmermann and P. Zysno  representing the criteria (subjective categories)