Discussion on the Marketing Impacts of the Global Recession Essay

Discussion on the Marketing Impacts of the Global Recession - Essay Example The Global Recession By the end of 2007, what started as an apparently isolated turbulence in the sub-prime segment of the US housing market turned into a full-blown recession by the end of 2007 (Verick & Islam, 2010). The US housing sector, as stated by Verick and Islam (2010), was unaware of the true extent of the complexities and liabilities which, in turn, caused liquidity to dry up, bringing down the global financial system. The economic drawback was spread rapidly and simultaneously through the global financial system to all corners of the world (Jannson, Hilmersson, & Sandberg, 2010). The result is the global economic recession that peaked in 2008. There had been massive lay-offs, unemployment, unpaid mortgages, bigger debts, enormous financial problems and fraud, financial deregulation of credit, automotive sales loss and manufacturing decline. Marketing Impacts of the Global Recession Up to now, recession is still evident in the global scenario. The increasing globalization of economic activity – the interconnectedness of economic activity across national frontiers calls companies and countries to adapt to the negative effects of the recession (Kitching, Blackburn, Smallbone, & Dixon, 2009). Kotler and Keller in 2009 stated that speed of global business is accelerating diversity but that has slowed down, yet, as they say, its business is as usual. The companies must change their marketing strategies and management capabilities to keep up in the market, at the same time giving customer satisfaction. Several methods and strategies were devised or emerged as the reaction of the global market to the recession. According to Kitching et al. (2009), recessions are regarded as periods of â€Å"creative destruction†, during which some businesses and industries decline while new ideas, technologies, products and industries emerge and become the driving forces of subsequent economic activity and growth. Business strategy and performance vary with ow ner perceptions, resources, and opportunities available of the threats faced (Kitching et al., 2009). According to Orr (2010), findings suggest that two principal factors are dominant in determining international strategy during times of financial crisis – home country market conditions, and the level of domestic industry protection introduced by the foreign country government in response to the economic downturn. Other factors including the variability in relative exchange rates also influenced international strategy during financial crises. Marketing Impacts in Certain Countries of the Global Recession Several developed countries had certain impacts in both marketing and management aspects due to the recession. These countries mentioned in the discussion include Japan, the United States, European Union in general, Australia, Singapore and China. In Japan, Toyota Motor Corporation's profitability was badly affected by the recession, resulting in a fall from record profits to record losses (Greimel, 2009 as cited by Orr, 2010). In response, Toyota is planning to limit expenditure and return to its original focus on quality. In reaction to global currency fluctuations, the lower value of currencies in countries such as the US, and

The White Hotel :: essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The White Hotel Donald Michael Thomas began his writing career as a poet, and his early work was notable for the way it ranged across the heights of the fantasy worlds of science fiction and of sensuality. Thomas was a superb writer, meticulous researcher, and a genius in deceiving the reader. He skillfully wrote The White Hotel, combining prose, poem, and science fiction, to make it a believable, conceivable, and a touching piece of literature. In his novel, Thomas makes realistic and believable references to Sigmund Freud and his psychoanalytic theories. Furthermore, he was able to capture the real Freud so well that many Freudian scholars believed this â€Å"case study† of Frau Anna G. to be a lost work of Sigmund Freud. This leads us to conclude that Thomas did not only possess a great imagination for fiction, but was also well studied in his accounts of Freud and the Holocaust. Composed of a prologue and six sections, The White Hotel utilizes a variety of literary forms. The main characters of this novel are the celebrated psychoanalyst and theorist Sigmund Freud and Lisa Erdman, a twenty-nine-year-old, half-Jewish Viennese opera singer who comes to Freud for treatment of hysteria in 1919. This novel is by far one of the greatest works of English literature, exploring such concepts as, premonition, inhumanity, sexuality, and briefly, the concept of life after death. It is fashioned with many images of love, death, life, and desire, taking the audience on a horrifying and historical depiction of the Holocaust. Thomas’ novel is written using the third and first person narrator, which seems to have more knowledge than the reader or the character. I have to admit that I was distracted and even caught off guard by Thomas’ disorganization of chronological events. For example, the novel begins with presumably the middle of the story, after which the novel continues with the beginning and then ends the novel with a metaphorical new beginning for Lisa Erdman. Furthermore, many parallels and symbols can be seen in each section, which brilliantly connects them into a cohesive story filled with meaning and dire premonitions of an inevitable future.   Ã‚  Ã‚  Ã‚  Ã‚  Throughout this course, we have discussed various novels, from a psychoanalytic point of view, and we have been able to deconstruct many of the characters according to Freud’s psychoanalytic theories. Ironically, in The White Hotel, it is those theories that allow the reader to be misguided, and not realize the important symbolism of Lisa’s symptoms.

What is Bayesian Thinking?

It is common knowledge that human beings commit errors in judgment all the time. In areas of uncertainty, most of us go with our gut intuition, and in most cases this intuition turns out to be wrong. Much of this is derived from the fact that humans are poor statistical thinkers, and thus poor Bayesian thinkers. What is Bayesian thinking? Let us start with an illustrative example, called the Monty Hall problem — famously depicted in the Kevin Spacey movie â€Å"21.† There are three doors, and behind each door is either a goat or a car. There will always be two doors with goats and one door with a car. The player first chooses a door without opening, and the game show host whose interests are opposed to the player, proceeds to open a different door. Since the host knows what is behind each door, he always opens a door with a goat. Now that the player is left with the initially chosen door and another closed door, the host offers an opportunity to switch to the other unopened door. Should the player switch? The answer for an intuitive Bayesian, a purely statistical thinker, should be easy. Unfortunately, human beings are not intuitive Bayesians. In fact, most people answer that it doesn't matter if the player switches or not, since the probability of winning a car is 50% between the two doors anyways. They would be wrong. Now, before we examine the correct way to think about this problem, one might ask, so what? Why does it matter if humans are not intuitive Bayesians, or even more broadly, bad statistical thinkers? Simply, Bayesian reasoning corrects some of the issues with bad statistical thinking. Bad statistical thinking leads to bad judgments and decisions, which have a wide variety of consequences in everyday life as well as in arenas such as politics and science. Thus, everyone should become better Bayesian thinkers, because under uncertainty, accurate probabilistic judgments are useful and important.To give a accurate depiction of how Bayesian reasoning works, let us return to the Monty Hall problem, and examine why not only switching doors matters, but that it is beneficial to switch. When the host first opened the door with the goat, something happened: opening the door gave the player extra information, and thus changed the probability of outcomes. By utilizing this extra information, it is no longer a 50% chance for the player to win the car after switching doors, but a ~67% (2/3) chance. Let us suppose that the player picks the door which contains the car. The host opens either the first goat door or the second (it does not matter), and the player switches to the other goat door and loses. Now, suppose the player picks the first goat door instead, which means the host is forced to open the second goat door. Since the only other door contains the car, the player switches and wins. Lastly, suppose the player picks the second goat door. The host is forced to open the first goat door, which again, means the player will win the car after a switch. These are the only three possible scenarios, and so we see that the probability of winning is two out of three if the player switches. Conversely, what if the player doesn't switch? In the first scenario, the player wins the car, but in scenarios two and three, the player obviously loses. Thus, to not switch is to have only a 33% (1/3) chance to win the car.The Monty Hall problem is a rather simple illustration of how Bayesian reasoning works, so in order to gain a more complete understanding, we must explore its principles. In 1763, a paper by Reverend Thomas Bayes was published posthumously called â€Å"An Essay towards solving a Problem in the Doctrine of Chances,† and brought about a paradigmatic shift in statistics: by using ever-increasing information and experience, one can gradually approach the unknown or understand the unknown (of course, his main motive was to prove the existence of God). Fundamentally, Bayesian reasoning believes in the correction of probabilities over time, and that all probabilities are merely estimates of the likelihood of events to occur. Through the further efforts of mathematicians like Lagrange in perfecting the Bayesian framework, we now have a modern and complete theory of probability. First, there are what we call priors, which is the strength of our beliefs, or put it another way, the likelihood that we are to change our beliefs. Then, we have our posteriors, which is the empirical aspect, or the influx of new information. The Bayesian framework then takes these two components and mathematically analyzes how posteriors affect priors. If we know nothing about an event, then all we can do is estimate a probability. However, if there is new information, then the probability must be corrected based on this new information. Over time, as experiences grow through more information, these estimates of probabilities will eventually fit â€Å"reality.† In the Monty Hall case, the moment the the host opened the goat door, that influx of new information, or change in posteriors, immediately influences the player's priors. If the host doesn't open a door, the player merely has a 33% chance to win the car between the three doors, and switching makes no difference. However, since the host removes a door, and specifically the door that contains a goat, these two new posteriors directly influence the original prior from 33% to 66%. One might think that this method of thinking is mysteriously similar to the scientific method, which is certainly true. However, To put it another way, Bayesian thinking is how to use some known information or experience to judge or predict the unknown. For example, event A is â€Å"rainy tomorrow† and event B is â€Å"cloudy tonight†. If you see cloudy tonight, what is the probability of raining tomorrow? If you use the Bayes theorem directly, you only need to know the probability of raining every day, the probability of cloudy nightly, and if one day it rains, then the probability of the cloudy night of the previous night will be substituted into the formula and done. The question is, where do these probabilities come from, and how do we infer the possibility based on the information we have . In fact, most of the valuable problems are backward problems, for example: the stock market, through those few signs can be judged to be a more or less opportunity; the hospital, through which symptoms can determine what is the disease; science Research, through several experimental data, you can construct what theory to explain the model and so on. In general, mathematicians, physicists, etc. are all about backward problems, or they can not predict or judge the outcome with few signs or phenomena, and there is no value (by the way, do not know the reverse Problem-thinking people can not fight in the financial market or the stock market. At present, the most advanced research in the speculative market is almost a process of backward stochastic process and martingale theory. It is known that the incidence of a disease is 0.001, that is, 1 in 1,000 people is sick. There is a reagent that can test whether a patient is sick or not, and its accuracy is 0.99, which means that 99% of the patients may be positive when the patient really gets sick. Its false positive rate is 5%, which means that 5% of the patients may get positive if they do not get sick. There is a positive test result of a patient, what is the probability that he does get sick?We got a staggering result of about 0.019. In other words, even if the test is positive, the probability of getting sick is only increased from 0.1% to 2%. This is the so-called â€Å"false positive†, that is, the positive result is not enough to show that the patient is sick.Why is this? Why is the accuracy of this test up to 99%, but the credibility is less than 2%? The answer is related to its false positive rate. Here we see the power of the Bayesian theorem, that it allows us to deduce the unknown probability from the known probability and the information at hand.The human brain and quantification vs heuristic thinking. The advantage of Bayesian analysis is that it does not require any objective estimation, just guess a priori casually. This is the key, because most of the events that occur in the real world have no objective probability. This is actually very similar to the scientific method: we did not know anything from the beginning, but we are willing to experiment and gradually find out the laws of nature. Bayesian reasoning operates in the same way, through continually the posterior probability in accordance with existing experimental data. Biggest problem with Bayesian reasoning is that human brains cannot quantify information easily. The most commonly raised example is Malcolm Gladwell's â€Å"Outliers†, where many people who are trained enough in certain low-chaotic environments make correct decisions and judgments without using the Bayesian framework at all. Firefighters, for example, do not undergo a Bayesian calculus before deciding whether or not it's safe to pull a child out of a burning building. They just do it because they've done it many times before, and have a rough heuristic estimate on the safety of such an action. Similarly, chess players do not use Bayesian analysis to think many turns ahead; what research has found is that through thousands of hours of practice and becoming familiar and experienced with similar setpieces in the past, gives them an ability to predict moves assuming that the opposing player is also rational. Conversely, high chaotic environments, such as the political sphere, is where Bayesian reasoning thrives due to the high amount of uncertainty.The other criticism are from the frequentists. In general, the probability of teaching in school can be called frequencyism. An event, if performed repeatedly multiple times independently, dividing the number of occurrences by the number of executions yields a frequency. For example, throwing coins, throwing 10000 times, 4976 times positive, the frequency is 0.4976. Then if the implementation of many many, the frequency will tend to a fixed value, is the probability of this time. In fact, to prove it involves the central limit theorem, but it does not start.

Xiaotingia - Facts and Figures

Name: Xiaotingia; pronounced zhow-TIN-gee-ah Habitat: Woodlands of Asia Historical Period: Late Jurassic (155 million years ago) Size and Weight: About two feet long and five pounds Diet: Insects Distinguishing Characteristics: Small size; long tail; primitive feathers About Xiaotingia In order to understand the importance of Xiaotingia, you need a short lesson about a much more famous animal, Archaeopteryx. When the exquisitely preserved fossils of Archaeopteryx were discovered in Germanys Solnhofen fossil beds in the mid-19th century, naturalists identified this flying, feathered creature as the first true bird, the key missing link in avian evolution. Thats the image that has persisted ever since in the popular imagination, even though better-informed paleontologists now know that Archaeopteryx possessed a weird mix of bird-like and dinosaur-like characteristics, and probably should have been classified as a feathered dinosaur (rather than a primitive bird) all along. So what does all of this have to do with Xiaotingia? Well, this very Archaeopteryx-like critter, discovered in Chinas Liaoning fossil beds, predated its more prominent cousin by five million years, living about 155 rather than 150 million years ago. More important, the research team that examined Xiaotingia identified it right off the bat as a small maniraptoran theropod that shared important features in common with raptor dinosaurs like Microraptor and Velociraptor, rather than a prehistoric bird--the implication being that if Xiaotingia wasnt a true bird, then neither was Archaeopteryx, which was only recently descended from it. This has caused a large amount of consternation in the Archaeopteryx was a bird camp, but hasnt impressed those more dubious paleontologists who doubted Archaeopteryxs credentials in the first place!