Management of Design and Innovation Essay Example | Topics and Well Written Essays - 6000 words

Management of Design and Innovation - Essay Example The scope of design management extends to the internal and external interfaces of the firms. Design management usually is a long and continuous activity which influence the performance of the firms at all levels. Design management processes are practiced by people having different levels of authorities and trainings. Such people function with multiple orientations covering various kinds of organisations of different sizes and nature. Koppelmann and Spies (1993) opine that design management is having multifarious facets and there are different opinions about design management. This paper details the design management process and the application of design management in the case of Zara clothing Spain as a case study. Technology is said to be at the heart of any manufacturing company. The technology helps in designing the products or the processes required to manufacture the products. It is critically important for the companies, that the existing technologies are assimilated into the business, and also anticipate the impact of the emerging technologies on the designs, products, and processes. This is considered critical as the companies are always under constant pressure to bring new products to the market as quickly as possible. Thus the purpose of any organization is to provide the customers products or services with the best possible quality and at affordable price which is greatly facilitated by the design management. An organization is sure to gain a competitive advantage through designs that bring new ideas to the market quickly, do a better job of satisfying the customer needs, or are easier to manufacture, use, and repair than existing products and services. Design can be understood as a critical process for a firm. Strategically design defines a firm's customers as well as its competitors. It capitalizes on the core competencies of a

Mafia Assignment Example | Topics and Well Written Essays - 500 words

Mafia - Assignment Example The mafia is involved in various criminal activities. These include prostitution rings in various cities and towns around the world. These organizations have brothels and operate night clubs to cover up their criminal activities. Most of the women in the prostitution rings are not willing participants but rather victims of human trafficking sold into the sex trade (Artemis 2004). The mafia is also involved in drug trade supplying large quantities of cocaine, amphetamines and other addictive drugs, besides selling to their customers, they also keep their sex workers hooked on these drugs so they have more control over them. Other mafia activities include loan sharking. They use their economic resources to give loans to needy people, most likely gamblers who have been caught up in gambling debts in the illegal gambling dens owned by the mafia. From such people, the mafia will extort large amounts of money to be used in other activities. The mafia is also involved in labor racketeering, smuggling of goods and at times bank robberies. All these activities are used to grow the organizations’ economic resources putting them in a better position to expand the scope of their illegal activities. Where the mafia is involved, slavery is still an issue. The most serious issue is when it comes to the prostitution rings. The mafia will sell young girls and women into sex slavery since most are unwilling to participate knowingly. In rare cases, the mafia will sell people into forced labor.

Artificial Bee Colony Algorithms And Software Testing Computer Science Essay

Artificial Bee Colony Algorithms And Software Testing Computer Science Essay The emerging area in the field of optimization is swarm intelligence. Various meta-heuristics algorithms based on swarm intelligence have been developed by many researchers. These algorithms have been developed by modeling the behaviors of swarm of animals and insects such as birds, bees, ants, fishes etc. The main focus of these algorithms is on the collective behavior which results from the local interactions between the individuals and with their environment. The Artificial Bee Colony Algorithm which has been recently introduced [1] is also a swarm based meta-heuristic algorithm. The algorithm models the intelligent foraging behavior of honey bees and has been introduced for optimizing various numerical problems. This paper gives a brief introduction about Artificial Bee Colony algorithm and also presents a review of applications of Artificial Bee Colony Algorithm in the field of software testing. Keywords Swarm Intelligence, Artificial Bee Colony Algorithm, Software Testing. 1. INTRODUCTION Swarm intelligence as a discipline deals with the artificial and natural systems which are composed of many individuals and they coordinate using the decentralized control and self organization [2]. The main focus of the discipline is on the collective behavior. Local interaction amongst the individuals and with their environment results into collective behavior. Some of the swarm based meta-heuristics algorithms are Particle Swarm Optimization, Ant Colony Optimization, and Artificial Bee Colony Optimization. Dervis Karaboga [1] in 2005 defined the artificial bee colony algorithm, which is the most recently introduced swarm based meta-heuristics algorithm. Since its inception, artificial bee colony algorithm has been applied in various fields. It also finds application in the field of software testing, which is one of the most indispensible phase of the software development lifecycle. This paper is divided into five sections. The next section gives a brief introduction about the nature of bees. Section 3 describes the artificial bee colony algorithm. Section 4 presents a review of application of artificial bee colony algorithm in the field of software testing and the last section give the comparative analysis of the applications on different parameters. 2. BACKGROUND 2.1 Components of Bee Colony The bee (Apis Mellifera) native to Europe and Africa is a social and domestic animal. Bees feed on nectar and pollens, where nectar is the prime source of energy and pollens act as supplements of proteins and other nutrients. Pollen is mostly used as food for larvae [3]. Generally the bee colony consists of a single queen bee that is responsible for laying eggs, thousand of male bees called drones and thousands of worker bees, which are the sterile bees and the young bee larvae called broods. 2.2 Bees Dance Bees randomly searches for food source positions with good supply of nectar. Once a bee finds such a position, it goes back to the hive and communicates about the food source position by dancing in the comb. If the foraging bee finds the food source position close to hive, it performs a simple round dance and if the food source position is far from the hive, then it performs waggle dance. Waggle dance basically forms an eight like figure and the distance and the direction of the food source is indicated by this dance. The speed of the dance conveys the distance. The inclination of the dance (angle between the sun, relative to hive and the food source) indicates the direction of the food source [4]. 3. Artificial Bee Colony Algorithm The artificial bee colony algorithm consists of 3 types of bees- the employed bee, onlooker bee and the scout bee. Scout bee is responsible for carrying out random searches in the environment. A bee who visits the food source visited by it previously is called an employed bee and the bee that waits in the beehive for decision making is called the onlooker bee. Both exploration and exploitation processes are carried out by all the three bees. In the ABC algorithm it is assumed that the colony consists of equal number of employed bees and onlooker bees and for every food source there is an employed bee in the hive. The bee whose food source has been exhausted by other bees becomes a scout bee [5]. The bee has the capability of memorizing the location of the food source once it has been discovered and then immediately starts exploiting it. The foraging bee returns to the hive with load of nectar from the source and then unloads the nectar to a food store. The bee has the following three options after unloading the nectar [1]: It becomes an uncommitted follower once the food source has been abandoned. It dances and recruits other nest mates while returning to the same food source. Without recruiting other bees, it continues to forage at the food source. The search consist a cycle of three steps [1]. In the beginning, some food sources are randomly selected by the bees and the amount of nectar is also determined. Then these bees return to the hive and share this information by performing the waggle dance. In second stage, each employed bee goes to the food source visited by her in previous cycle and then by means of visual information, chooses a new food source in the neighborhood. In the third stage, an onlooker bee visits the food source position depending on the nectar information shared by the employed bees. The food source with maximum nectar quantity is selected by the onlooker bee. After arriving at the selected food source, the onlooker bee according to visual information chooses a new food source in the neighborhood of the selected food source. Once the food source is abandoned by the bee, a new food source is randomly selected by a scout and then the abandoned source is replaced by this new food source. Depending upon the probabilistic value onlooker bee selects a food source. This value pi is calculated as [5]: where SN is the number of food sources which is also equal to employed bees number (BN) and fiti is the fitness value of the solution i evaluated by its employed bee. The fitness value is proportional to the nectar amount of the food source. To produce a candidate food position from the old one, the algorithm uses the following expression [5]: Where the random chosen indexes are k â‚ ¬ {1, 2. . . BN} and j â‚ ¬ {1, 2. . . D} and k is different from i. φij is a random number between [−1, 1]. It controls the production of a neighbor food source position around xij and the modification represents the comparison of neighbor food positions visually by the bee [5]. 4. Application of Artificial Bee Colony Algorithm to Software Testing Software testing is a type of multi variable optimization problem where generation and selection of efficient test cases cannot be achieved within permissible time bounds. Hence for solving these types of problems, meta-heuristics search algorithms have been proposed [6]. These algorithms help in finding the near optimal solution in reasonable running time. The artificial bee colony algorithm, which is also a meta-heuristics search algorithm, is capable of locating efficient solutions. The algorithm models the food foraging behavior of honey bees. The main focus of software testing is on uncovering as many errors as possible in the given time, as this would help in conforming the product to the requirement specifications and also to validate the quality of the software produced [7]. The following paragraphs give the review of application of artificial bee colony algorithm in the field of software testing. Mala et al [6] applied artificial bee colony algorithm in the field of software test suite optimization. The approach is based on population based algorithm where every test case represents a possible solution for the optimization problem. A happiness value has been introduced for each test case corresponding to the fitness or quality of the associated solution. Here the three bees are replaced by search agent, selector agent and optimizer agent. Various properties of agents are autonomy, inter-operability and social ability [11]. These agents help in selecting efficient test cases from infinite number of test cases. The parallel behavior of the agents helps in generating the solution faster. Path coverage has been described as the test adequacy criteria. Initially random test cases are generated for all test paths or sequences. Along the path, as the search agent goes to an executable state, it monitors each test case and also determines a neighbor state. The happiness value is upda ted for every test case, along every test path. If a particular node is not covered by a particular test case, then that node gets eliminated. The selector agent starts a new search for locating the node with highest feasibility in that path. Only the test case with maximum happiness value is remembered and rests are removed from the memory. Based on the happiness value, the optimizer agent chooses one of the test cases and also selects a neighbor around that for evaluating its happiness value. Abandoned test cases are replaced with the new test cases as discovered by the selector agent and this is repeated until all the nodes have been visited once. This algorithm was implemented on few sample problems and the results were compared to the results of application of Ant colony Optimization for test suite optimization. It was observed that ABC algorithm provided consistent results and the problems faced in ACO such as pheromone updation, memory and time overheads were not faced by ABC algorithm. McCaffrey [8] applied simulated bee colony algorithm for generation of pair wise test sets. A pair wise test set is generally a collection of test vectors where all possible combination of pairs of values has been captured from different parameters. Here a bee has been modeled as a bee object with four types of data fields and the entire colony is represented as a hive object consisting of an array of bee objects [8]. An enumeration type which specifies the current role (active, scout and inactive) is the first field. The second field defines a 2-D array representing a bees memory, which in turn would represent a partial solution. An integer value representing the quality of food source associated with data in bees memory is the third field type and the fourth field is represented by the number of times a particular food source has been visited by the bee object without locating a neighbor source with higher quality value. The algorithm requires an initial seed value which can be any value equal or greater to number of test vectors which are representing a minimal solution. In the proposed algorithm, when the scout bee is in active role, then it leaves the hive and selects a random food location and compares the quality of food. If the quality of the food location is greater than the one in its memory, the memory gets updated with new food location. The simulated scout bee performs the waggle dance in hive and these dances are visible to all the inactive bees in the hive. In the algorithm, the number of times an active foraging bee visits a source has been fixed. Global counter variable helps in tracking the number of simulated bees in each role. Therefore when an active bee becomes inactive, then randomly an inactive bee is selected and gets transitioned to an active state. The algorithm was run against seven pairwise tests set generation benchmark problem. The approach yielded good result in terms of test set size but the performance of the algorithm was slower as compared to other deterministic algorithms. Mala et al [9] again proposed the use parallel behavior of the three bees for automated software test optimization. The main focus was to generate efficient test suite which can cover the software under test within time and less cost. The artificial bee colony algorithm system combines the local search conducted by the employed and onlooker bees with the global search carried out by scout bee. Hence it helps in attaining global or near-global optima. The test adequacy criteria applied here is ensuring the state coverage, path coverage and branch coverage. The cyclomatic complexity of the given program is known and it also indicates the number of independent paths. Three bees functionality gets extended to three agents- search agent, selector agent and replace agent. The algorithm in [6] was implemented on 10 benchmark problems and the results were compared to sequential ABC, random testing and Genetic Algorithm. It was found that the performance of parallel ABC algorithm is better th an the other approaches. In some cases random testing also produced better results as compared to genetics algorithm. For 100% coverage, the number of test cases needed was very less in parallel ABC as compared to other approaches. GA gave only 50% coverage due to strike up local optimal solution. Dahiya et al [10] presented an ABC algorithm based approach for automatic generation of structural software tests. The working of the honey bee is reported as robust and adaptive by [12].The paper applied artificial bee colony based search algorithm for generating test data using symbolic execution technique of static structural testing and therefore corresponding to every path a compound predicate was constructed by anding all the branch predicates of a path. The compound predicate should be evaluated to true by a candidate solution to become a valid test case. For test data generation, random population of candidate solution is generated. Solutions are represented by position of flower patches. The profitability related to each flower is also measured. This profitability is replaced by the fitness of the positions in computer modeling. It includes various parameters such as nectar content in the flower, distance of flower from the beehive and sugar content in nectar etc. In the first phase of the algorithm; the employed bees modify elite flower patches position w.r.t. neighborhood. In the second phase, the onlooker bees modify their patches position w.r.t. elite patches position. A greedy selection process is repeated after every phase where solution or flower patches compete among themselves for retention in the selected or elite flower patches, based on their fitness. Hence some solutions or flowers may migrate from one patch to another patch and some may get abandoned. These search phases of the bees are repeated until some termination criteria are met. The algorithm was implemented on ten real world problems. The output suggested that the proposed algorithm had performed satisfactorily for most of the programs except for the programs having large input domains and various equality based path constraints. 5. Analysis and Discussion Four different applications of Artificial Bee Colony algorithm in the field of software testing has been reviewed and based on the analysis a table has been formulated which compares all the applications on different parameters. Parameters Application by D Jeya Mala (2009)[6] Application by James D McCaffrey (2009)[8] Application by D Jeya Mala (2010)[9] Application by S S Dahiya (2010)[10] Main Objective Test suite optimization Generation of pairwise test sets Automated software test optimization Automatic generation of structural software tests. Output obtained Generated optimal results and it converges within less number of test runs. Good results in terms of test set size and suggests the use where test sets are intended to be reused. Generated global or near global optimal results and it converges within less number of test runs. Generated test cases for all paths. Tool used for implementation Java PICT in C++ QICT in C# Java MATLAB Output Comparison With Ant Colony Optimization Algorithm With published results of 7 benchmark problems. With Sequential ABC, Random Testing and Genetics Algorithm No Comparison made Behavior of Bees Parallel behavior of bees Sequential behavior Parallel behavior Sequential behavior Cyclomatic complexity YES NO YES YES Type of bees Search Agent, Selector Agent, Optimizer Agent Employed bee, Onlooker bee, Scout bee Search agent, Selector agent, Replace Agent Employed bee, Onlooker bee, Scout bee Test Adequacy Criteria Path Coverage Not Mentioned Path coverage, Branch Coverage, State coverage Path coverage Drawbacks Not Mentioned Longer generation time Not mentioned Did not perform well on programs having large input domain and many equality based path constraints. Benchmark problem used 6 problems 7 benchmark problems Many 10 real world problems Table1. Comparison of various applications of ABC algorithm Mala et al [6] applied artificial bee colony algorithm for test suite optimization and the results obtained were better than the use of Ant Colony Optimization. McCaffrey [8] applied ABC algorithm for generation of pairwise test sets and suggested the use where test sets are intended to be reused. Mala et al [9] again applied ABC algorithm for automated software test optimization and compared the output with that of sequential ABC algorithm, Random Testing and Genetics Algorithm. For 100% coverage the number of test cases needed was very less in parallel ABC algorithm. Dahiya et al [10] used ABC algorithm for automatic generation of structural software tests. The algorithm performed satisfactorily except for programs with large input domains. 6. Conclusion In this research the artificial bee colony algorithm has been studied and a review based on application of the artificial bee colony algorithm in the field of software testing has been performed. Based on review a table has been formulated which compares all the applications on different parameters. It was also observed that the current application of artificial bee colony algorithm is in the field of structural testing and for test suite optimization only.

Tennysons In Memoriam Essay -- Tennyson Elogy Memoriam Essays

Tennyson's In Memoriam In Memoriam is an elegy to Tennyson's friend Arthur Hallam, but bears the hallmark of its mid nineteenth century context, 'the locus classicus of the science-and-religion debate.'Upon reflection, Hallam's tragic death has proved to be an event that provoked Tennyson's embarkation upon a much more ambitious poetic project than conventional Miltonian elegy, involving meditation upon the profoundest questions faced by mankind. Scientific advancements, most notably in the fields of geology and biology, challenged the beliefs that form the foundation of Christianity: the belief in a beneficent God responsible for creation and ensuing superintendence and the belief in man's immortal soul. By the mid nineteenth century apologist arguments such as those of William Paley could no longer convincingly reconcile science and faith. In Memoriam stands as a work that truly represents the anxieties within the Victorian mind. Queen Victoria once remarked that In Memoriam was her closest con solation, after the bible, following her husband's death. This essay charts the consoling properties of In Memoriam and interrogates the notion of Tennyson as a reinventor of faith for the troubling scientific age. There is a consensus among critics, such as Matthes and Willey, that Lyell?s Principles of Geology provoked much of Tennyson?s troubling religious doubts that were to be compounded when his dearest friend was robbed from him. Lyell made no explicit challenge to Christian scripture (and indeed made attempts in his work to ensure readers did not interpret his work as such), but his assertion that the Earth?s landscape was shaped by an extremely long and gradual process of weathering presupposed a much greater age for the Earth than was allowed for in biblical chronology. Essentially Lyell?s theories questioned the Christian belief in Divine creation of the Earth over a period of seven days. Lyell?s discussion of the discovery of fossilised remains of extinct animals was perhaps even more troubling because it questioned the existence of a beneficent providential power and the notion of divine superintendence. Principles of Geology was so earth-shattering because essentially it questione d the very validity of euthesitic belief, whether God really does have his eye cast on every sparrow that falls to earth. Brooke asserts that In Memoriam i... ...ress to God seems to a critical reader too much like a denial of deep seated doubts through religious immersion. In Memoriam demonstrates Tennyson?s masterful handle of language to create a fitting tribute to his deceased friend, but his genius lies in transcending this initial subject matter to embrace one at the heart of the Victorian psyche- the challenge of scientific discoveries to deeply held Christian belief. He reinvents faith in the sense that he encourages a different angle to view it from, and encourage a holistic approach to the study of nature in which scientific and religious approaches are not mutually exclusive. Bibliography Baldick, Chris: The Concise Oxford Dictionary of Literary Terms (Oxford: Oxford University Press, 2001) Brooke, Stopford A: Tennyson: His Art and Relation to Modern Life (London: Ibister and Company Limited, 1894) Hunt, John (ed.) Tennyson: In Memoriam: A casebook (London: Macmillan, 1970) Mattes, Eleanor Bustin: In Memoriam: The Way of a Soul (New York: Exposition Press, 1951) Moi, Torl: Sexual Textual Politics (London: Routledge, 1995) Willey, Basil: More Nineteenth Century Studies (London: Chatto and Windus, 1956)

Discipline and Improve Students Behaviour in Classroom Education Essay

The problem of how best to discipline and improve students’ behaviour in classroom is of permanent interest. This review is oriented to searching different methodologies concerning students’ behaviour in classrooms, teachers’ discipline strategies and behavioural management. Different points of view and different examples for appropriate behaviour have been discussed referring to the topic. The sources reviewed present different solutions. This paper examines also the classroom environment and its relation to successful behaviour implementation. The first paragraphs give different definitions conversant with behaviour and discipline according to the authors’ view. The continuation of the literature review is presented by different approaches and strategies concerning a good behavioural management. This elaboration sets out some of the arguments and recommendations which are discussed in more detail. Charles C. M. submits several definitions corresponding to behaviour: Behaviour refers to everything that people do. Misbehaviour is behaviour that is not appropriate to the setting or situation in which it occurs. Discipline†¦ are strategies, procedures, and structures that teachers use to support a positive learning environment. Behaviour management is a science that puts an accent on what teachers have to do to prevent misbehaviour (Charles 1). Students’ behaviour depends on several factors such as traditions, demographic settings, economic resources, family, experiences, and more. Some authors have made important contributions in managing classroom discipline related the twentieth century. Jacob Kounin (1971), one of them, reports that appropriate student behaviour can be maintained through classroom organization, lesson management, and approach to individual students. Rudolf Dreikurs (1972) on the other hand emphasizes the desire to belong as a primary need of students in school. He identifies types of misbehaviour and gives ideas about how to make students feel a part of the class or group (p. 63). William Glasser (1986) shows another view, making a case that the behaviour of someone else cannot be controlled. He reckons that everybody can only control his own behaviour. Personally I support this idea that we must control ourselves. According to the opinion of the other authors, Linda Albert’s, Barbara Coloroso’s, Nelson and Lott’s a good discipline in the classroom can be achieved through Belonging, Cooperation, and Self-Control. A similar idea of classroom management is also presented by Rackel C. F who declares that the teachers, considered it was necessary, â€Å"to develop students’ sense of belonging to the school† (p. 1071) The author supports the opinion of the significance of a good school climate and tells that it might be precondition for facilitating positive youth development (Rackel C. F 1071). In order to attain to a good classroom atmosphere there is a need of growing positive relationship between students and teachers, motivation the students’ participation and clear rules to control classroom discipline (Rackel C. F 1072). In addition these above-mentioned views can be defined as a positive outlook as regards to improving the classroom management. Another point of view inside the subject of managing discipline is through active student involvement and through pragmatic Classroom management (Charles, C. M. 2007, p. 7). Discipline through raising student responsibility is also positively oriented approach for classroom management. The three principles that improve behaviour presented in the article â€Å"Self-assessment of understanding† are positivity, choice, and reflection (Charles, C. M. 12). There the author explains the principles meaning. He states that being positive means being a motivator. When students have opportunity to share their choices they can present themselves with a good behaviour. â€Å"Asking students questions that encourage them to reflect on their behaviour can help them to change behaviour† (Charles 14). Rebecca Giallo and Emma Little (2003, p. 22) from RMIT University Australia give their comments also on classroom behaviour management. They claim that confidence is one of the most important characteristic that influence teachers’ effectiveness in classroom management. Giallo and Little (2003, 22) based on the previous statement of Evans & Tribble accept that less confident teachers seem more vulnerable to stressful classrooms. They maintain the theory that the classroom stress is a reason for giving up a teacher’s career. In school the stress can be overcome through involving of drastic measures concerning managing a good discipline. One of the most popular strategy for solving behaviour problems is punishment. By reason of the popularity of the subject in the field of education, many experts have written articles and books as well as given lectures on discipline and punishment. Anne Catey based on Dreikur’s words considers that there is no need of using punishment in class. Based on Catey’s words kids need to have a chance they can share their ideas in the class (1). This is the best way to â€Å"smooth, productive functioning in schools† (Charles, C. M, 1999). Anne Catey from Cumberland High School gets an interview from several teachers in Illinois district about their discipline practices. She accepts the suggestion given by Lawrence as mentioning that, â€Å"very effective technique is a brief conference, either in the hallway or after class, with the misbehaving student† (Punishment, 1). Anne Catey has her own techniques for classroom management. She disagrees with Lawrence viewing about humour as one of the bad strategies for effective discipline and believes that using of humour can be effective if done without abasing the students (Punishment, 1). In this way she gives each one a bit of individual attention. When some of her students are a bit distracted on one task, talking to friends instead of reading Catey says, â€Å"Since I always assume the best of my students, I assume the noise I hear is students reading aloud or discussing their novels. However, it’s time to read silently now instead of reading aloud† (Punishment, 1). This sounds as a good strategy but personally I disclaim this thesis. This doesn’t work all the time. I am trying to be strict with my students and according to this the pupils have to observe the rules in my classes. That doesn’t mean that I admit the severe punishment but rarely the stern warnings. I agree with the following techniques used by Anne Catey (2001) to modify behaviour including giving â€Å"zeroes for incomplete, inappropriate, and/or missing work and taking points off at the end of a quarter for lack of participation and/or poor listening†. As expected, these methods are effective for some of the pupils but not for the others. Related to the above-mentioned topic it could be noticed some of the classroom discipline strategies utilized in Australia, China and Israel. On the basis of elaborated research in these countries some psychologists and school principals (Xing Qui, Shlomo Romi, 2005) conclude that Chinese teachers appear less punitive and aggressive than do those in Israel or Australia. Australian classrooms are presented as having least discussion and recognition and most punishment. In Australia (Lewis, 2005) as concerned to the study the teachers are characterized by two distinct discipline styles. The first of these is called â€Å"Coercive† discipline and comprises punishment and aggression (yelling in anger, sarcasm group punishments, tc). The second style, comprising discussion, hints, recognition, involvement and Punishment, is called â€Å"Relationship based discipline† (Lewis 7). Coercive discipline according to the above-mentioned authors means the teacher’s behaviour is such as â€Å"shouting all the time, unfairly blaming students, picking on kids, and being rude, to stimulate student resistance and subsequent misbehaviour† (Lewis, Ramon 2). The importance of classroom discipline arises not only from students’ behaviour and learning as outlined above. It depends also on the role of the teacher. Sometimes it is obvious that teachers are not be able to manage students’ classroom discipline and it can result in stress. So,â€Å"classroom discipline is a cohesion of teacher stress† (Lewis 3). Chan (1998), reports on the stressors of over 400 teachers in Hong Kong, claims that student behaviour management rates as the second most significant factor stressing teachers. In the article Teachers’ Classroom discipline several strategies have been presented for improving classroom management. They are Punishing (move students’ seats, detention), Rewarding (rewards, praises), Involvement in decision-making (decides with the class what should happen to students who misbehave), Hinting, Discussion and Aggression. Another strategy for improving discipline in class is conducting questionnaires between the students. It is an appropriate approach for defining students’ opinion about behaviour problems. In each Chinese and Israeli school a random sample of classes at all year levels have been selected. As a research assistant administered questionnaires to these classes their teachers completed their questionnaires (Yakov J. Katz 7). In comparison to all of the mentioned countries the model in China is a little different in that students support use of all strategies except Aggression and Punishment. Based on the conducted research the only strategy to range within a country by more than 2 ranks is Punishment, which ranks as the most common strategy in Australia, and the fourth and fifth most commonly used strategy in Israel and China. The author, Xing Qui generalises that, â€Å"there is not more Punishment at the level 7-12. Classroom discipline techniques showed that students in China, compared to those in Australia or Israel, report less usage of Punishment and Aggression and greater use of Discussion and the other positive strategies. At the end of their article â€Å"Teachers’ classroom discipline and Student Misbehaviour in Australia, China and Israel â€Å"(p. 14) the authors recommend that teachers need to work harder to gain quality relationships with difficult students. What I have drawn from reviewing literature so far is that teachers are able to use different techniques for enhancing classroom management in their profession. After making a thorough survey on the above-mentioned issue I would like calmly to express my position. It is harder for the teacher to keep the student focused on any frontal instruction. That’s why as with all classroom management practices, the teachers should adapt what they like to their classroom, taking into consideration the age, ethnicity, and personality of the class as a group, and of them as teachers. Much of the disruptive behaviour in the classroom can be alleviated before they become serious discipline problems. Such behaviours can be reduced by the teacher’s ability to employ effective organizational practices. These skills are individual for each teacher. The lecturer should become familiar with school policies concerning acceptable student behaviour and disciplinary procedures. Establishing rules to guide the behaviour of students is also important. Once these standards are set up the teachers have to stick to them. I agree with the authors who prefer involving the positive approach in behaviour management. But I also accept that some situations are more complicated than the others and in this case the teachers must take drastic measures against inappropriate students’ behaviour.

Herd Behavior in Financial Market Essay

Definition of herding On Friday 14 September 2007, when Northern Rock in the UK opened it branches, many customers wanted to withdraw their savings and à ¯Ã‚ ¿Ã‚ ½1 billion, about 5% of the total bank deposits were withdrawn that day. And on Monday 17 September, a similar situation happened in front of Northern Rock branches in the UK. Even though every customer does not have the same amount of information, they all decided to behave in the same way and some were following the others on the following days without any clear plan. People thought that they were going to lose their bank deposits and that type of bank customers’ behavior caused liquidity problem and made the situation even worse. However, none of the clients who kept their deposits lost due to the fact the British Government and the Bank of England would guarantee the safety of the deposits. How can we explain that kind of behavior? Originally Herding is a term meaning animal flocking behavior. And according to the definition of Wikipedia Herding is the act of bringing individual animals together into a group (herd), maintaining the group and moving the group from place to place-or any combination of those. Apart from this bank run case, Herd behavior describes how individuals in a group can act together without planned direction. POSSIBLE EXPLICATION AND MECHANISM OF HERD BEHAVIOR Animals’ Herd Behavior According to evolutionary biologist W. D. Hamilton’s theory animals are forming a group to reduce the danger of being hunted by predictors. As a unit, they are moving together to the same direction. Animals are behaving in the same way to minimize the risk on the behalf of self-protection. Maybe this kind of behavior sounds rational if the result is always optimistic but copying your neighbor can be the worst decision sometimes. When something goes wrong and someone leads the group to the wrong direction, the whole group is going to be in danger. Human Herd Behavior However, human herd behavior is much more complicated than animals’ one and several scholars tried to explain it. Friedrich Nietzsche referred it as â€Å"herd morality† and the â€Å"herd instinct† which explain the phenomena when a lot of people are behaving in the same way at the same time. And according to Thorstein Veblen’s theory, some people imitate the other people with higher status. Human beings are continuously competing with others in order to survive or surpass others, and they try to move faster in order to take advantage of the others. As the proverbs says the early bird catches the worm, they think the faster they make the decision or do whatever they can, the better it is. However, this does not always lead to success. Those decisions are based on the sources they have and the sources are Sanctions upon deviants – dictators put their rivals in the prison (opposition is not allowed) Preference interactions – some people are wearing Burberry coats just because the majority is wearing it while others prefer to wear coats with the colors they like Direct communication – someone from your reference group or someone with credibility says that s/he likes certain products Observational influence – you observe the consequences of others’ actions Based on such sources, people make decision whether to herd or disperse, but people are herding for different reasons and their behavior is classified into several models. Herding Models Payoff Externalities Models (also called Network Externalities) – If more people are using facebook, it will attract more people to use facebook. In this case, people feel like they have to participate in the same situation so that they can have the same benefits. Information Cascade Models – When you have a flood of information coming in, it is much more difficult to make a rational decision. Nowadays there are too many sources to consider and you can barely judge if information is true or false. In this kind of situation, people are getting irrational and they tend to make decision based on the decision of the majorities, and this situation is called information cascade which occurs when people observe the actions of others and then make the same choice that the others have made, independently of their own private information signals. They are seen in groups under immediate stress from external forces, such as herd behaviour. A cascade arises naturally when people usually see what others do but not what they know. Because it is usually sensible to do what other people are doing, even this can be against what the individual believes to be true. This behavior is independent from their own private information or opinion. Concept of information cascade is based on observational and social learning. People learn from their environment. Generally, people are oriented to avoid negative consequences of their decisions or behaviors. They wish to have positive results or effects. That’s why their behavior is related to social and observational learning. People subconsciously have the idea of ‘It is more likely that I am wrong than that all those other people are wrong. Therefore, I will do as they do’. Examples of Herding Behavior Bank runs: depositors running on banks when they observe other depositors doing so. More specifically, First; investors can observe in long run when others are running on banks. Second, forcing long-term projects to liquidate early possibly leads to shortfall of funds. From the payoff externalities model’s view, people are withdrawing their deposits because they feel like they are losing their money if they keep their money on the bank account. And from informational cascade model’s view, some people may think they are not going to lose their money on their bank account but they are following the others because they think they are not wise enough and others are withdrawing their money. In real case, Argentina experienced such a run in the last two days of November 2001, with total deposits in the banking system falling by more than 2 billion (US) dollars, or nearly 3 percent, on the second day of the run alone.1 Such runs were a common occurrence in the United States in the late nineteenth and early twentieth centuries and have also occurred in recent times in several developing countries, including Brazil in 1990 and Ecuador in 1999. Asian crisis of 97-98, herding and speculation infection The Asian crisis of 1997-98 that led to a regional economic fall in East Asia can be traced to overexpansion and under-regulation. The center of the Asian crisis was Thailand’s careless macroeconomic management that featured a fraudulent financial sector. The Asian expansion of the crisis was a due to the existing global financial integration (and similar export dependencies), current account inequities and attached exchange rates all mixed with the damaging effect of speculation and herding spreading all over the region. Resulting structural reforms and adjustments in Thailand and other damaged Asian nations came from the International Monetary Fund. A major result was a balanced exchange rate regime now prevalent in much of East Asia. Facts: During 1995 a number of experts started to wonder if the countries of Southeast Asia might be vulnerable to a macroeconomic crisis do to the poor administration of its financial procedures and to the volatility of their related economies. The main indicator was the rise of very large current account deficits among several Asian countries. Closer examination also revealed that several of the countries had developed some financial weaknesses: heavy investment in highly speculative real estate ventures, financed by borrowing from badly informed foreign sources or by credit from non regulated domestic financial institutions. It’s now known that during 1996 officials from the IMF and World Bank actually began warning the governments of Thailand, Malaysia, and other countries of the existing risks by their financial situation, and asked them to apply corrective policies. However, those governments rejected the warnings. On July 2 1997, after months of declaring that it would not happen, the government of Thailand abandoned its efforts to maintain a fixed exchange rate for its currency, the baht. The currency was quickly depreciated by more than 20 percent so within a few days most neighboring countries fell like Thailand. What forced Thailand to devalue its currency was the massive speculation against the baht, assumptions that over a few months had consumed most of what initially seemed as a large war of foreign exchange. And why were speculators betting against Thailand? Because they expected the baht to be devalued, of course. This kind of circular logic – in which investors escape a currency because they expect it to be devalued, and much of the pressure on the currency comes precisely because of this investor shortage of confidence – is the defining actor of a currency crisis and is known as Bank Run theory. In the context of a currency crisis, such behavior could mean that a wave of selling, whatever its initial cause, could be magnified through complete imitation and turn, into a rush out of the currency. Bank run in Thai currency devaluation can be viewed in two main behaviors. First; investors run when other investors are running the bank; a magnified opinion of a certain group starts to be spread in some others by just herding or imitation. Second, when banks that were investing in long-term projects were forced to liquidate early (because of the invertors running away), there was a potential lost of funds. Consequently, the last depositors to withdraw were left empty-handed (first-come, first-served limitation). BUBBLES Bubbles are sort of mass errors caused by the nature of herd. Even though there is a convincing evidence of bubbles, people are still overly convinced by their belief that market is efficient and rational. Therefore people are optimistic of their investment and they take part in the bubble. Some people may doubt the situation and find some evidence of bubbles but they still invest their capital in the market because others are doing it which is a sort of informational cascade. However, the bubble collapses and that sort of herding behavior makes the impact of the collapse much significant. The Dot-com Bubble The dot-com bubble (also referred to as the Internet bubble) was a speculative which had its climax on March 10, 2000, with the NASDAQ hitting up to 5132.52 but closing at 5048.62 in the same day. During the dot-com bubble period mostly the developed countries experienced the growth in the Internet sector and related fields. Companies such as Cisco Systems, Dell, Intel, and Microsoft were the dominant player of NASDAQ. And related to the Internet business a group of new Internet-based companies commonly referred to as dot-coms were founded. Just because of the fact that Companies had a name with an â€Å"e-† prefix to their name and a â€Å".com† the stock price was going up. Investors were overly confident of their future profits due to the advancement of technology and individual speculation while they overlooked traditional stock market value until the bubble was collapsed. Conclusion As we can see massive herding behavior turned out to be a cause of crisis at the end, and herd behavior is seen as something very negative to the market. As we have seen bank runs, bubbles, and several forms of crises. However, we cannot prevent from herding because it is a sort of instinct and it is closely related to psychological factors. Partially, individuals can make profit of their herding behavior as they are following famous investors such as Warren Buffet but the fact is that no investor can really avoid bubbles and forecast the coming crises. What we have to remember is the financial market is a complex of rational and irrational behavior and we can barely categorize them before the disaster happens. We have to be prepared of the consequence the herd behavior and be rational when the irrationality happens. Works Cited BIKHCHANDANI, S., 1998, Learning from the behavior of others: conformity, fads, and informational cascades BIKHCHANDANI, S., D. HIRSHLEIFER and I. WELCH, 2001. Informational Cascades and Rational Herding: An Annotated Devenow, Andrea and Ivo Welch, 1996, Rational Herding in Financial Economics, European Economic Review 40, 603-615 Ennis, Huberto M. and Todd Keister, 2009, Bank Runs and Institutions: The Perils of Intervention. Hirshleifer, David and Teoh, Siew Hong, 2011, Herd Behavior and Cascading in Capital Markets: A Review and Synthesis, MPRA Paper No. 5186