Obesity an Escalating Problem Essay -- Health, Diseases

Recent research done by The National Center for Health Statistics (NCHS) showed that more than 64% of the US adult population is overweight (BMI >25 and 30) (Obesity, 2008). This result has got people afraid of what would happen in the future if people don’t change their habits. An excessive storage of fat due to lack of physical activity and high calorie intake that often leads to other diseases is known as obesity. Obesity is an escalating problem, because people consume more calories and aren’t physically active; this could result in health problems, yet people can resolve this issue by consuming a healthy diet and regularly exercising, or consulting a doctor for medication or surgery if other solutions weren’t effective. People are consuming too many calories and aren’t physically active, because of changed lifestyles and technology advancement, in the last 20 years. Both adults and children pass a lot of time watching the television, playing video games and researching or chatting on the computer. These lifestyle changes encourage sedentary behavior. The U.S. Center for Disease Control and Prevention stated that in their study to calculate obesity across the nation. In 2010 â€Å"no state had a prevalence of obesity less than 20%† compared to a maximum of 15% to 19% in 15 to 20 states in 1994 (CDC, 2011). Physical inactivity and high calorie intake from processed food has made big changes to our health. Now days people don’t even cycle or walk to the grocery store, or at least walk to the nearest bus station to go to school or work. Every year people’s physical inactivity increases while our eating habit become worse. Children are also becoming obese at a very... ...artiatric surgery and gastric bypass are most effective. People can’t lose enough weight by just doing the surgery; they need to continue exercising a dieting to reach a healthy weight in one to two years. Side effects include nausea and nutrient deficiencies. Women can also have problems during pregnancy due to lack to nutrients, hence need to be more careful. Obesity can become easy to overcome if people are patient have the will power to keep going and lose weight to become healthy for themselves and the next generation. Overcoming obesity can take a lot of time depending upon a person ideal weight compared to their current weight. If people use these small tips and work their way to healthy lifestyle, the next generation will be healthier and less likely to become obese. All people need to do is work hard and give it time to reach their preferred body weight.

A Brief Sapir-Whorf Hypothesis

A BRIEF SAPIR-WHORF HYPOTHESIS  SUMMARY†¦ October 16, 2010 A reasonable summary of the Sapir-Whorf hypothesis in its tractable form is that different cultures interpret the same world differently and this has an impact on how they both think and construct meaning in language; in fact, language shapes or influences thought to some degree. The Sapir-Whorf Hypothesis combines  linguistic relativity  and  linguistic determinism. Adherents of the hypothesis follow these two principles to varying degrees producing gradient interpretations from weak to strong versions of the Sapir-Whorf Hypothesis.Cognitive linguists are among the only linguists to take this â€Å"mentalist† position seriously, and most linguists of any orientation reject a strong version of the hypothesis. The linguistic determinism portion of the original hypothesis stated that language  determined  thought, and this is the rejected strong version. The linguistic relativity portion asserts that bec ause language determines thought and there are different languages then the ways that those languages think will be different to some degree.Part of the controversy surrounding the hypothesis is the lack of empirical data, or at least appropriate empirical data. This has caused a number of researchers to begin considering how the ideas of linguistic determinism may affect judgment. For instance, in 2008 Daniel Casasanto performed a series of experiments with time, quantity and distance to determine whether or not speakers of Greek and speakers of English would have their judgments affected by the type of metaphors preferred by the language.The language did affect judgment to some degree, but it is not a causal claim about the Whorf-Sapir Hypothesis. Other empirical research has looked at linguistic relativity as a shaper of thought as opposed to a determiner of thought. This hypothesis is important to linguistics because it acknowledges the relationship between thought and language, which may partially give stability to the cognitive claim that language use reflects conceptualization and that different conceptualizations are reflected in different linguistic organizations.This reminds me of a situation I once participated in where a rhetorical question was being translated from one language to another but the source language structure of the rhetorical question would have implied the exact opposite meaning in the target language had it been translated literally rather than in a manner that acknowledged the target language’s normal pattern of organization for rhetorical questions. Although this may be a simplified understanding of the importance of Sapir-Whorf, it at least seems to have vital implications in translation theory. The Sapir-Whorf HypothesisDaniel Chandler Greek Translation now available Within linguistic theory, two extreme positions concerning the relationship between language and thought are commonly referred to as ‘mould theoriesâ €™ and ‘cloak theories'. Mould theories  represent language as ‘a mould in terms of which thought categories are cast' (Bruner et al. 1956, p. 11). Cloak theories  represent the view that ‘language is a cloak conforming to the customary categories of thought of its speakers' (ibid. ). The doctrine that language is the ‘dress of thought' was fundamental in Neo-Classical literary theory (Abrams 1953, p. 90), but was rejected by the Romantics (ibid. ; Stone 1967, Ch. 5). There is also a related view (held by behaviourists, for instance) that language and thought are  identical. According to this stance thinking is entirely linguistic: there is no ‘non-verbal thought', no ‘translation' at all from thought to language. In this sense, thought is seen as completely determined by language. The Sapir-Whorf theory, named after the American linguists Edward Sapir and Benjamin Lee Whorf, is a  mould  theory of language.Writing in 1929, Sapir arg ued in a classic passage that: Human beings do not live in the objective world alone, nor alone in the world of social activity as ordinarily understood, but are very much at the mercy of the particular language which has become the medium of expression for their society. It is quite an illusion to imagine that one adjusts to reality essentially without the use of language and that language is merely an incidental means of solving specific problems of communication or reflection. The fact of the matter is that the ‘real world' is to a large extent unconsciously built upon the language habits of the group.No two languages are ever sufficiently similar to be considered as representing the same social reality. The worlds in which different societies live are distinct worlds, not merely the same world with different labels attached†¦ We see and hear and otherwise experience very largely as we do because the language habits of our community predispose certain choices of interp retation. (Sapir 1958 [1929], p. 69) This position was extended in the 1930s by his student Whorf, who, in another widely cited passage, declared that: We dissect nature along lines laid down by our native languages.The categories and types that we isolate from the world of phenomena we do not find there because they stare every observer in the face; on the contrary, the world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds – and this means largely by the linguistic systems in our minds. We cut nature up, organize it into concepts, and ascribe significances as we do, largely because we are parties to an agreement to organize it in this way – an agreement that holds throughout our speech community and is codified in the patterns of our language.The agreement is, of course, an implicit and unstated one,  but its terms are absolutely obligatory; we cannot talk at all except by subscribing to the organization and classification of data which the agreement decrees. (Whorf 1940, pp. 213-14; his emphasis) I will not attempt to untangle the details of the personal standpoints of Sapir and Whorf on the degree of determinism which they felt was involved, although I think that the above extracts give a fair idea of what these were. I should note that Whorf distanced himself from the behaviourist stance that thinking is entirely linguistic (Whorf 1956, p. 6). In its most extreme version ‘the Sapir-Whorf hypothesis' can be described as consisting of two associated principles. According to the first,  linguistic determinism, our thinking is determined by language. According to the second,  linguistic relativity, people who speak different languages perceive and think about the world quite differently. On this basis, the Whorfian perspective is that translation between one language and another is at the very least, problematic, and sometimes impossible. Some commentators also apply this to the ‘transl ation' of unverbalized thought into language.Others suggest that even within a single language  any  reformulation of words has implications for meaning, however subtle. George Steiner (1975) has argued that  any  act of human communication can be seen as involving a kind of translation, so the potential scope of Whorfianism is very broad indeed. Indeed, seeing reading as a kind of translation is a useful reminder of the reductionism of representing textual reformulation simply as a determinate ‘change of meaning', since meaning does not reside  in  the text, but is generated by  interpretation.According to the Whorfian stance, ‘content' is bound up with linguistic ‘form', and the use of the medium contributes to shaping the meaning. In common usage, we often talk of different verbal formulations ‘meaning the same thing'. But for those of a Whorfian persuasion, such as the literary theorist Stanley Fish, ‘it is impossible to mean the same thing in two (or more) different ways' (Fish 1980, p. 32). Reformulating something transforms  the ways in which meanings may be made with it, and in this sense, form and content are inseparable. From this stance words are not merely the ‘dress' of thought.The importance of what is ‘lost in translation' varies, of course. The issue is usually considered most important in literary writing. It is illuminating to note how one poet felt about the translation of his poems from the original Spanish into other European languages (Whorf himself did not in fact regard European languages as significantly different from each other). Pablo Neruda noted that the best translations of his own poems were Italian (because of its similarities to Spanish), but that English and French ‘do not correspond to Spanish – neither in vocalization, or in the placement, or the colour, or the weight of words. He continued: ‘It is not a question of interpretative equivalence: no, the sense can be right, but this correctness of translation, of meaning, can be the destruction of a poem. In many of the translations into French – I don't say in all of them – my poetry escapes, nothing remains; one cannot protest because it says the same thing that one has written. But it is obvious that if I had been a French poet, I would not have said what I did in that poem, because the value of the words is so different. I would have written something else' (Plimpton 1981, p. 3). With more ‘pragmatic' or less ‘expressive' writing, meanings are typically regarded as less dependent on the particular form of words used. In most pragmatic contexts, paraphrases or translations tend to be treated as less fundamentally problematic. However, even in such contexts, particular words or phrases which have an important function in the original language may be acknowledged to present special problems in translation. Even outside the humanities, academic texts co ncerned with the social sciences are a case in point.The Whorfian perspective is in strong contrast to the extreme  universalism  of those who adopt the  cloak  theory. The Neo-Classical idea of language as simply the dress of thought is based on the assumption that the same thought can be expressed in a variety of ways. Universalists argue that we can say whatever we want to say in any language, and that whatever we say in one language can always be translated into another. This is the basis for the most common refutation of Whorfianism. The fact is,' insists the philosopher Karl Popper, ‘that even totally different languages are not untranslatable' (Popper 1970, p. 56). The evasive use here of ‘not untranslatable' is ironic. Most universalists do acknowledge that translation may on occasions involve a certain amount of circumlocution. Individuals who regard writing as fundamental to their sense of personal and professional identity may experience their written style as inseparable from this identity, and insofar as writers are ‘attached to their words', they may favour a Whorfian perspective.And it would be hardly surprising if individual stances towards Whorfianism were not influenced by allegiances to Romanticism or Classicism, or towards either the arts or the sciences. As I have pointed out, in the context of the written word, the ‘untranslatability' claim is generally regarded as strongest in the arts and weakest in the case of formal scientific papers (although rhetorical studies have increasingly blurred any clear distinctions).And within the literary domain, ‘untranslatability' was favoured by Romantic literary theorists, for whom the connotative, emotional or personal meanings of words were crucial (see Stone 1967, pp. 126-7, 132, 145). Whilst few linguists would accept the Sapir-Whorf hypothesis in its ‘strong', extreme or deterministic form, many now accept a ‘weak', more moderate, or limited Whorf ianism, namely that the ways in which we see the world may be  influenced  by the kind of language we use.Moderate Whorfianism  differs from extreme Whorfianism in these ways: * the emphasis is on the potential for thinking to be ‘influenced' rather than unavoidably ‘determined' by language; * it is a two-way process, so that ‘the kind of language we use' is also influenced by ‘the way we see the world'; * any influence is ascribed not to ‘Language' as such or to one language compared with another, but to the use  within a language  of one variety rather than another (typically a  sociolect  Ã¢â‚¬â€œ the language used primarily by members of a particular social group); * emphasis is given to the social context of language use rather than to purely linguistic considerations, such as the social pressure in particular contexts to use language in one way rather than another. Of course, some polemicists still avour the notion of language as a  strait-jacket  or  prison, but there is a broad academic consensus favouring moderate Whorfianism. Any linguistic influence is now generally considered to be related not primarily to the formal systemic structures of a language (langue  to use de Saussure's term) but to cultural conventions and individual styles of use (or  parole). Meaning does not reside  in  a text but arises in its interpretation, and interpretation is shaped by sociocultural contexts. Conventions regarding what are considered appropriate uses of language in particular social contexts exist both in ‘everyday' uses of language and in specialist usage. In academia, there are general conventions as well as particular ones in each disciplinary and methodological context.In every subculture, the dominant conventions regarding appropriate usage tend to exert a conservative influence on the framing of phenomena. From the media theory perspective, the  sociolects  of sub-cultures and the  idiol ects  of individuals represent a subtly selective view of the world: tending to  support  certain kinds of observations and interpretations and to  restrictothers. And this transformative power goes largely unnoticed, retreating to transparency. ————————————————- The Relationship between Language and Culture Jan 4th, 2010 | By  Emma  | Category:  Topic It is generally agreed that language and culture are closely related. Language can be viewed as a verbal expression of culture. It is used to maintain and convey culture and cultural ties.Language provides us with many of the categories we use for expression of our thoughts, so it is therefore natural to assume that our thinking is influenced by the language which we use. The values and customs in the country we grow up in shape the way in which we think to a certain extent. Cultures hiding in languages, examin es the link between Japanese language and culture. An Insight into Korean Culture through the Korean Language discusses how Korean culture influences the language. Languages spoken in Ireland, focuses on the status of the Irish language nowadays and how it has changed over time. In our big world every minute is a lesson looks at intercultural communication and examines how it can affect interactions between people from countries and backgrounds. ———————————————— Language, culture and thoughts: do languages shape the way we think? Apr 27th, 2011 | By  Teresa  | Category:  English Members of different cultures speak different languages. Does it mean that people who speak, let us say, English, see things differently than people who speak Chinese or Spanish? In other words, does language lead our way of thinking or is it the other way around? According to  Benjamin Lee Whorf  and his theory of linguistic relativity, language shapes the way we think, and determines what we think about. He believed that depending on the language we speak we see the world differently.His best example was the comparison between the idea of snow of an English person and an Eskimo person. The Eskimo has many words to describe snow, while the English only has one. An Eskimo has a specific word to describe the wet snow, the snow currently falling and so on. Therefore an Eskimo perceives the snow in a different way than an English person. Another example is the  Dani  people, a farming group from New Guinea. They only have two words to describe the two basic colors: dark and bright. Hence a Dani person cannot differentiate colors as well as an English person is able to. Although Benjamin's theory is not yet completely clarified, it is correct to say that a language could facilitate some ways of thinking.True or not, this topic is an interesting one to reflect upon. Linguists and people who speak many languages have come up with the same idea. Holy Roman EmperorCharles V  spoke 6 languages fluently and said the following: I speak Italian to ambassadors, French to women, German to soldiers, English to my horse and Spanish to God. What is the relationship between language and culture? Answer Language is the verbal expression of culture. Culture is the idea,custom and beliefs of a community with a distinct language containing semantics – everything a speakers can think about and every way they have of thinking about things as medium of communication.For example, the Latin language has no word for the female friend of a man (the feminine form ofamicus  is  amica, which means mistress, not friend) because the Roman culture could not imagine a male and a female being equals, which they considered necessary for friendship. Another example is that Eskimos have many different terms for snow†¦ there are nuances that make each one differ ent. Answer Language and culture are NOT fundamentally inseparable. At the most basic level, language is a method of expressing ideas. That is, language is communication; while usually verbal, language can also be visual (via signs and symbols), or semiotics (via hand or body gestures). Culture, on the other hand, is a specific set of ideas, practices, customs and beliefs which make up a functioning society as distinct.A culture must have at least one language, which it uses as a distinct medium of communication to conveys its defining ideas, customs, beliefs, et al. , from one member of the culture to another member. Cultures can develop multiple languages, or â€Å"borrow† languages from other cultures to use; not all such languages are co-equal in the culture. One of the major defining characteristics of a culture is which language(s) are the primary means of communication in that culture; sociologists and anthropologists draw lines between similar cultures heavily based o n the prevalent language usage. Languages, on the other hand, can be developed (or evolve) apart from its originating culture.Certain language have scope for cross-cultural adaptations and communication, and may not actually be part of any culture. Additionally, many languages are used by different cultures (that is, the same language can be used in several cultures). Language is heavily influenced by culture – as cultures come up with new ideas, they develop language components to express those ideas. The reverse is also true: the limits of a language can define what is expressible in a culture (that is, the limits of a language can prevent certain concepts from being part of a culture). Finally, languages are not solely defined by their developing culture(s) – most modern languages are amalgamations of other prior and current languages.That is, most languages borrow words and phrases (â€Å"loan words†) from other existing languages to describe new ideas and c oncept. In fact, in the modern very-connected world, once one language manufactures a new word to describe something, there is a very strong tendency for other languages to â€Å"steal† that word directly, rather than manufacture a unique one itself. The English language is a stellar example of a â€Å"thief† language – by some accounts, over 60% of the English language is of foreign origin (i. e. those words were originally imported from another language). Conversely, English is currently the world's largest â€Å"donor† language, with vast quantities of English words being imported directly into virtually all other languages.

Using Decision Trees In Financial Management - Free Essay Example

Sample details Pages: 11 Words: 3428 Downloads: 2 Date added: 2017/06/26 Category Finance Essay Type Research paper Did you like this example? Decision trees are diagrams that show the sequence of interrelated decisions and the expected results of choosing one alternative over the other. Typically, more than one choice or option is available when youre faced with a decision or, in this case, potential outcomes from a risk event. The available choices are depicted in tree form starting at the left with the risk decision branching out to the right with possible outcomes. Decision trees are usually used for risk events associated with time or cost. Steps in decision tree analysis Main steps in decision tree analysis are as follows: 1. Identifying the problem and alternatives To understand the problem and develop alternatives, it is necessary to acquire information from different sources like marketing research, economic forecasting, financial analysis, etc. As the decision situation unfolds, various alternatives may arise which are to be identified. There would also be kinds of uncertainties in terms of market size, market share, prices, cost structure, availability of raw material and power, governmental regulation. Technological change, competition, etc. Recognising that risk and uncertainty are inherent characteristics of investment projects, persons involved in analyzing the situation must be encouraged to express freely their doubts, uncertainties, and reservation and motivated to suggest contingency plans and identify promising opportunities in the emerging environment. 2. Delineating the decision tree The decision tree represents the anat omy of decision situation. It illustrates decision points along with the alternative options available for experimentation and action at these decision points chance points where outcomes are dependent on a chance process and the likely outcomes at these points This decision tree diagrammatically reflects the nature of decision situation in terms of alternative courses of action and chance outcomes which have been identified in the first step of the analysis. If myriad possible future events and decisions are considered, it can become very complex and cumbersome. As a result, it would not be a useful tool of analysis. If many elaborate events are taken into account then it may obfuscate the critical issues. Hence it is necessary to simplify the decision tree so that focus can be given on major future alternatives. 3. Specifying probabilities and monetary outcomes After delineating the decision tree, probabilities corresponding with each of the possible outcomes at various chance points and monetary value of each combination of decision alternative and chance outcome have to be gathered. The probabilities of various outcomes can be defined objectively. For instance, based on objective historical data the probability of good monsoon can be defined. On the other hand, probabilities for real life outcomes are somewhat difficult and cannot be obtained. For example, one cannot determine the probabilities for success of a new automobile launch. These have to be defined subjectively and based on experience, judgment, understanding of informed executives and their intuition. Also, it is difficult to assess cash flows corresponding to these outcomes. So again judgment of experts helps in defining these cash flows. 4. Evaluating various decision alternatives The final step in decision tree analysis includes evaluation of various alternatives. This can be done as follows: starting with the right- hand end of the tree and then we calculate the expected monetary value at various chance points that come first as we proceed leftward. Given the expected monetary values of chance points in step 1, evaluate the alternatives at the final stage decision points in terms of their expected monetary values. At each of the final stage decision points, select the alternative which has the highest expected monetary value and truncate the other alternatives. Each decision point is assigned a value equal to the expected monetary value of the alternative selected at that decision point. Proceed backward (leftward) in the same manner, calculating the expected monetary value at chance points, selecting the decision alternative which has the highest expected monetary value at various decision points, truncating inferior decision alternatives, and assigning values to decision points, till the first decision point is reached. Wikipedia A decision tree is a decision support tool that uses a tree-like graph or model of decisions a nd their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. Another use of decision trees is as a descriptive means for calculating conditional probabilities. When the decisions or consequences are modelled by computational verb, then we call the decision tree a computational verb decision tree[1]. In decision analysis, a decision tree and the closely-related influence diagram is used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated. A decision Tree consists of 3 types of nodes:- 1. Decision nodes commonly represented by squares 2. Chance nodes represented by circles 3. End nodes represented by triangles Drawn from left to right, a decision tree has only burst nodes (splitting paths) but no sink nodes (converging paths). Therefore, used manually, they can grow very big and are then often hard to draw fully by hand. Analysis can take into account the decision makers (e.g., the companys) preference or utility function, for example: The basic interpretation in this situation is that the company prefers Bs risk and payoffs under realistic risk preference coefficients (greater than $400K in that range of risk aversion, the company would need to model a third strategy, Neither A nor B). Don’t waste time! Our writers will create an original "Using Decision Trees In Financial Management" essay for you Create order Uses in teaching This section requires expansion. Decision trees, influence diagrams, utility functions, and other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or management science methods. [edit] Advantages Amongst decision support tools, decision trees (and influence diagrams) have several advantages: Decision trees: Are simple to understand and interpret. People are able to understand decision tree models after a brief explanation. Have value even with little hard data. Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and their preferences for outcomes. Use a white box model. If a given result is provided by a model, the explanation for the result is easily replicated by simple math. Can be combined with other decision techniques. The following example uses Net Present Value calculations, PERT 3-point estimations (decision #1) and a linear distribution of expected outcomes (decision #2): [edit] Example Decision trees can be used to optimize an investment portfolio. The following example shows a portfolio of 7 investment options (projects). The organization has $10,000,000 available for the total investment. Bold lines mark the best selection 1, 3, 5, 6, and 7, which will cost $9,750,000 and create a payoff of 16,175,000. All other combinations would either exceed the budget or yield a lower payoff.[2] Decision Making Tools: Decision Tree Analysis and EMV Decision Makers Toolkit Decision-making is the cognitive process of selecting a course of action from among multiple alternatives. Every decision-making process produces a final choice. Thats what Wikipedia says anyway. What it doesnt say is that some decisions must be made for outcomes that will occur in the future. However, there are a couple of tools that can be put to use in helping make complex decisions, namely, Expected Monetary Value and Decision Tree Analysis. Expected Monetary Value (EMV) EMV is a balance of probability and its impact over the range of possible scenarios. If you have to make a decision between two scenarios, which one will provide the greater potential payoff? Scenario 1 Best case provides a 20% probability of making $180,000 BC = 20% X $180,000= $36,000 Worst case provides a 15% probability of loosing [-$20,000] WC = 15% X(-$20,000) =(-$3,000) Most likely case provides a 65% probability of making $ 75,000 MLC = 65% X $75,000 = $48,750 Total Expected Monetary Value 100% $81,750 Scenario 2 Best case provides a 15% probability of making $200,000 BC=15% X $200,000 =$30,000 Worst case provides a 25% probability of making $15,000 WC= 25% X $ 15,000 = $ 3,750 Most likely case provides a 60% probability of making $45,000 MLC=60% X $45,000 = $27,000 Total Expected Monetary Value 100% $60,750 Which scenario do you choose? Number one, because it has the highest EMV, or $81,750 Decision Tree Analysis In decision tree analysis, a problem is depicted as a diagram which displays all possible acts, events, and payoffs (outcomes) needed to make choices at different points over a period of time. Example of Decision Tree Analysis: A Manufacturing Proposal Your corporation has been presented with a new product development proposal. The cost of the development project is $500,000. The probability of successful development is projected to be 70%. If the development is unsuccessful, the project will be terminated. If it is successful, the manufacturer must then decide whether to begin manufacturing the product on a new production line or a modified production line. If the demand for the new product is high, the incremental revenue for a new production line is $1,200,000, and the incremental revenue for the modified production line is $850,000. If the demand is low, the incremental revenue for the new production line is $700,000, and the incremental revenue for the modified productio n line is $150,000. All of these incremental revenue values are gross figures, i.e., before subtracting the $500,000 development cost, $300,000 for the new production line and $100,000 for the modified production line. The probability of high demand is estimated as 40%, and of low demand as 60%. The development of a decision tree is a multi step process. The first step is to structure the problem using a method called decomposition, similar to the method used in the development of a work breakdown structure. This step enables the decision-maker to break a complex problem down into a series of simpler, more individually manageable problems, graphically displayed in a type of flow diagram called a decision tree. These are the symbols commonly used: The second step requires the payoff values to be developed for each end-position on the decision tree. These values will be in terms of the net gain or loss for each unique branch of the diagram. The net gain/loss will be revenue less expenditure. If the decision to not develop is made, the payoff is $0. If the product development is unsuccessful, the payoff is $500,000. If the development is successful, the decision is to build a new production line (NPL) or modify an existing production line (MPL). The payoff for the NPL high demand is ($ 1,200,000 $500,000 development cost -$300,000 build cost) or $400,000. For a low demand, the payoff is ($700,000 $500,000 development cost -$300,000 build cost) or -$100,000. The payoff for the MPL high demand is ($850,000 -$500,000 development cost $100,000 build cost) or $250,000. For a low demand, the payoff is ($720,000- $500,000 development cost $100,000 build cost) or $120,000. The third step is to assess the probability of occurrence for each outcome: Development Successful = 70% NPL High Demand = 40% MPL High Demand = 40% Development Unsuccessful = 30% NPL Low Demand = 60% MPL Low Demand = 60% Probability Totals* 100% 100% 100% *Probabilities must always equal 100%, of course. The fourth step is referred to as the roll-back and it involves calculating expected monetary values (EMV) for each alternative course of action payoff. The calculation is (probability X payoff) = EMV This is accomplished by working from the end points (right hand side) of the decision tree and folding it back towards the start (left hand side) choosing at each decision point the course of action with the highest expected monetary value (EMV). Decision D2: New Production Line vs. Modified Production Line high demand + low demand = EMV high demand + low demand = EMV (4 0% X $400,000) + (60%X -$100,000) (40% X $250,000)+(60% X $120,000) $100,000 $172,000 Decision Point 2 Decision: Modified Production Line with an EMV of $172,000 Decision 1: Develop or Do Not Develop Development Successful + Development Unsuccessful (70% X $172,000) (30% x (- $500,000)) $120,400 + (-$150,000) Decision Point 1 EMV=(-$29,600) Decision: DO NOT DEVELOP the product because the expected value is a negative number. When doing a decision tree analysis, any amount greater than zero signifies a positive decision. This tool is also very useful when there are multiple cases that need to be compared. The one with the highest payoff should be picked. Real options analysis: tools and techniques for valuing strategic ÂÂ  By Johnathan Mun https://books.google.co.in/books?id=X47bm9Etd7ICpg=PA649lpg=PA649dq=decision+tree+applications+oil+and+gassource=blots=W47wkDY2Xtsig=YwtNvZ8KEDJ-60CEK87Xhodouishl=enei=Bnz3TP7kMI-srAec-63vDwsa=Xoi=book_resultct=resultresnum=2ved=0CB0Q6AEwATge#v=onepageq=decision%20tree%20applications%20oil%20and%20gasf=false pgs.258,474 Remington: the science and practice of pharmacy https://books.google.co.in/books?id=NFGSSSbaWjwCpg=PA743lpg=PA743dq=decision+tree+applications+pharmaceuticalssource=blots=V64QMimyuosig=cdQYBEgEJSf_lON-Alkhv4E6B4Ehl=enei=8oT3TLOOLcS3rAfw1azvDwsa=Xoi=book_resultct=resultresnum=6ved=0CDYQ6AEwBTg8#v=onepageqf=false pg.740 decision tree analysis the project manager can use decision tree analysis when a decision involves a series of several interrelated decisions. The project manager computes the Expected Monetary value (EMV) of all strategies and chooses the strategy with the highest EMV. Assume that the project manager has four alternative strategies, S1, S2, S3, S4. The resulatant values for each strategy at different probability levels are R1, R2, and R3. Assume that the probability of occurrence of these results is 0.5, 0.2 and 0.3. the payoff matrix for this problem is given in table 18.4. Table 18.4. Payoff Matrix R1 R2 R3 S1 13 10 9 S2 11 10 8 S3 10 12 11 S4 8 11 10 P=0.5The project manager can also represent this problem as a decision tree. Figure 18.3. depicts the decision tree for the given problem. The project manager finally selects strategy S1 as it has the highest value. EMV (A) = 0.5 ( 13)+0.2(10) +0.3(9) = EMV (B) = 0.5(11) +0.2(10) + 0.3(8) = EMV (C) = 0.5(10) +0.2(12) + 0.3(11) = EMV (D) = 0.5(8) +0.2(11) + 0.3(10) = Review of literature 1. Introduction R and D management, by its very nature, is characterized by uncertainty since effective R and D requires a complex interaction of variables. It is important to balance strategic management (allocate resources and do the right R and D) with operational management (execution of projects) and at the same time take into account issues of people management (leadership, motivation, organisation and teamwork) (Menke, 1994). The strategic aspect of R and D management alone requires the resolution of some very important questions, namely Do we have the right total R and D budget? Are we allocating it to the right business and technology areas? Do we have the right balance of risk and return; of long- and short-term projects; of research vs development; of incremental vs innovation? Are we working on the right projects and programmes with the right effort? It is clear that for success in R and D it is critical to determine what is right for the particular company. The normal p rocess for doing this is through the development of a technology strategy. In practice, the approach used will be that which best fits the operating method of the company but, as Braunstein (1994) has pointed out, the approach is less important than the output, which has to link the corporate goals and strategy to the companys major functional units. Having defined what the business objectives should be for the R and D programme and the overall strategic framework that will define the technology plan, it is then possible to move on to what is probably one of the most problematic parts of technology management, the selection of individual R and D programmes. There is a comprehensive literature of potential methods which can be used (Baker and Pound, 1964; Gear et al., 1971; Souder, 1978). Many of these compare projects with different distributions of possible outcomes and risk, often using relatively complex quantitative methods. There are a number of interdependencies that have t o come good before the project finally produces value for the company and it has been argued (Morris et al., 1991) that because many of the major decisions (and many sub-decisions at intermediate milestones) can be taken singly, the overall process is less risky that might initially be thought. Not surprisingly, therefore, Morris goes on to propose that, when choosing R and D projects, there is merit in going for long shots since this is effectively the purchase of options which can be dropped later if the project does not look like bearing fruit. Moreover, the higher risk projects (almost by definition) tend to be the ones that have the highest payback if they are successful (see also Kester, 1984). 2. Decision making under uncertainty Uncertainty in a business situation is often expressed verbally in terms such as it is likely, it is probable, the chances are, possibly, etc. This is not always very helpful because the words themselves are only useful when they convey the same meaning to all parties. It is clear that different people have different perceptions of the everyday expressions which are often used to describe uncertainty. Uncertainty exists if an action can lead to several possible outcomes and an essential, but, challenging aspect of R and D management is to identify the likelihood or probability that these outcomes or events will occur. There are two main interpretations of probability. The first is grounded in the estimation of the probability of an event in terms of relative frequency with which the event has occurred in the past and is usually referred to as objective probability. The second views probability as being the extent of an individuals or groups belief in the occurrence of an event a nd is usually termed subjective probability. Subjective probability estimates are often included in the models suggested as useful for project selection in R and D planning. Such probabilities might be derived from past experience with similar research projects plus any special features that make the current effort unique or different and alter the past up or down from this base line. A number of tools have been proposed to help in the process of generating probabilities, though they are by no means perfect. Schroder (1975) draws attention to some of the problems that occur in deriving probabilities of technical success and concludes that subjective probabilities are a rather unreliable predictor of the actual outcome of individual success. He proposes a number of reasons for this which he categorises as either intentional or unintentional (conscious biasing). To decrease the unintentional errors he suggests the following actions: O ensure that risk assessors have sufficient e xpertise in their field and a comprehension of subjective probabilities. O improve the availability of information and particularly documentation. O fully exploit information systems and attempt to utilize incentive systems which reward accuracy and reliability. O analyse past performance in assessing probabilities to provide valuable insight into potential improvements. O utilise well-tried approaches to help in the subjective probability assessment. It is evident, however, that some confidence levels need to be established and perhaps the most obvious way of achieving this is by the collation over a period of time, of how prior assessments have compared with reality. For this to have genuine value will require a comparison of the assumptions that have been made at each assessment. 3. The use of financial methods for risk analysis Benefit/cost ratios have been popular for some time, since they are simple and are an attempt to understand the potential gain for the effort required. In performing even a simple benefit/cost analysis, it is necessary for the decision-maker to provide quantitative information in order to ascribe a value to a project. When this has been done, the project can be viewed as a relatively simple financial investment and therefore subject to more standard financial investment tools. The danger of this is that it gives no consideration to the fact that technical programmes are often aimed at a wide range of strategic objectives, a point made by Mitchell and Hamilton (1988) who made a separation into: O exploratory/fundamental type work which is aimed primarily towards the concept of knowledge building. For this type of work, the business impact of which is often poorly defined and wide ranging and here R and D is often best considered as a necessary cost of business. O well under stood technical programmes usually associated with incremental improvements of existing products which can be clearly defined. Here the R and D can be seen as an investment and treated accordingly. As usual with two extremes, the difficult part is the mid-ground where neither approach is particularly suitable. Authors have attempted to use techniques borrowed from the financial community which often has to deal with uncertainty. Risk analysis is a key area in financial markets and several of the approaches used in financial analysis are also found in the R and D management area; for example, decision trees and Monte Carlo analysis.