Soul Of Descartes Essays - Philosophy Of Mind, Cognition

Soul Of Descartes Out of all the philosophers we have examined in this unit, Rene Descartes (On Thinking and the Soul) presents the best argument about what a soul and body are. In contrast, I believe that Locke`s interpretation of the body, mind, soul and self was my least favorite interpretation. Rene Descartes believed the soul is a pure, unitary thinking thing that has no weight and occupies no space. The soul, according to Descartes, has clear and distinct ideas of matters that can be conceived of in mathematical terms. The body, according to Descartes, is a material thing that operates mechanically, in accordance of cause and effect. The body moves mechanically through muscles and nerves and generates its own heat. Identity, Descartes believed, comes from the soul. The body acts as a container for the soul and is completely separate from a person`s identity. Descartes also believed that thoughts in the soul depend only on the soul and not on the body. Therefore, since the only thing that the soul can do is think than he must be a soul. Locke believed that Descartes equation of the soul is completely false. Locke noted that if the soul left the body during sleep (Descartes) than it could body hop into other individual. This outcome of Descartes theory is completely absurd to Locke. Locke believed that the identity of a person comes from his/her body. According to Locke, the same soul criterion used by Descartes won`t do as an explanation. He believed that same matter could not be used as a criterion for human identity because matter in ones body turns over through the death of old cells and the birth of new ones. I agree with Descartes notion of self-identity because the soul is separate from the body. I believe that ones body is plagued with several particulars such as hair, arms, legs etc. The soul is a universal entity, meaning everyone has one. I disagree with Locke`s account of the body as a person`s identity because he believed that people could lose consciousness or memory over parts of their lives. This lost part of someone`s life would cause that person to change his/her identity. I believe that if the soul is the thinking agent and if it is universal, than it could not and would not lose consciousness or memory, causing a person to keep his/her original identity. Rather, when consciousness or memory is lost, it is lost due to the imperfections found in a person`s material shell, the body I believe, thanks to Descartes, that a person has two separate entities. The first is the body. The body is made up of particulars and other matter that allows it to function like a container. The second entity is the soul. Here, all thinking processes are done, the function of the brain is to separate the sensations throughout the body.

Bacteriophage Life Cycle Animation

Bacteriophage Life Cycle Animation Bacteriophages are viruses that infect​ bacteria. A bacteriophage can have a protein tail attached to the capsid (protein coat that envelopes the genetic material), which is used to infect the host bacteria. All About Viruses Scientists have long sought to uncover the structure and function of viruses. Viruses are unique they have been classified as both living and nonliving at various points in the history of biology. A virus particle, also known as a virion, is essentially a nucleic acid (DNA or RNA) enclosed in a protein shell or coat. Viruses are extremely small, approximately 15 - 25 nanometers in diameter. Virus Replication Viruses are intracellular obligate parasites, which means that they cannot reproduce or express their genes without the help of a living cell. Once a virus has infected a cell, it will use the cells ribosomes, enzymes, and much of the cellular machinery to reproduce. Viral replication produces many progeny that leave the host cell to infect other cells. Bacteriophage Life Cycle A bacteriophage reproduces by one of two types of life cycles. These cycles are the lysogenic life cycle and the lytic life cycle. In the lysogenic cycle, bacteriophages reproduce without killing the host. Genetic recombination occurs between the viral DNA and the bacterial genome as the viral DNA is inserted into the bacterial chromosome. In the lytic life cycle, the virus breaks open or lyses the host cell. This results in the death of the host. Bacteriophage Life Cycle Animation Below are animations of the lytic life cycle of a bacteriophage.Animation AThe bacteriophage attaches to the cell wall of a bacterium.Animation BThe bacteriophage injects its genome into the bacterium.Animation CThis animation shows the replication of the viral genome.Animation DBacteriophages are released by lysis.Animation ESummary of the entire lytic life cycle of a bacteriophage.

(the public problem that you choose) Assignment

(the public problem that you choose) - Assignment Example Rate of Obesity in Adults Approximately 68 per cent of grownups are having obesity and in America only 75 million grownups are obese, according to National Health and Nutrition Examination Surveys 2007-2008. Rate of Obesity in Children The rate of obesity is on the rise in children as well. The rate of obesity among the children between the ages of 2 to 5 years has more than doubled in the last 30 years, while the rate of obesity among the children between the ages of 6 to 11 years has tripled in the last 30 years and the rate of obesity in youngsters between the ages of 12 to 19 years has increased to more than triple in the last 30 years. Furthermore, according to statistics I child in every 6 children is obese and almost 17 per cent of American children between the ages of 2 to 19 years have obesity. Why Obesity needs to be addressed? Obesity does not only cause the increase in the weight and makes one lazy but it is also gives rise to many other health problems which remain conne cted with the obese person for the lifetime. The person having obesity has greater chances of having diseases such as heart disease, high blood pressure, type-2 diabetes, some kind of cancers, gout, arthritis, coronary thrombosis and  gall bladder, liver disorders and certain long lasting illnesses. It has been shown by research that a child who has obese becomes overweight and takes obesity in his adulthood. In the United States only among adults of age 20 and older 13 per cent have diabetes and among these 13 per cent 40 per cent have not been diagnosed earlier with obesity, the statistics have been showed by 2005-2006 NHANES survey. 95 per cent of all diabetics have type 2 diabetes and almost all of the undiagnosed patients having diabetes have type 2 diabetes. Pre-diabetes does not have any symptoms and the increased risks of having type 2 diabetes and heart diseases which majorly include heart attacks or strokes are caused by pre-diabetes. Causes and alternatives of Obesity O besity is usually caused as a result of eating more food as compared to the physiological requirement of the body. People who do not take part in the activities of life actively, prefers sedentary routine and are habitual of taking in food more than required by their daily life style are prone to become obese. People do not succeed in adjusting their desire of eating food according to their requirement gain weight and as a result become obese. The possibility of occurrence of obese is equal in both sexes i.e. male and female and can arise at any age. Normally women become obese after their pregnancy or in menopause. During pregnancy women usually gains a lot of weight which they fail to shed after the birth of their new born. This extra fat stored in their body makes them obese. The problem of obesity is a serious public issue and should be dealt seriously as this excessive storage of fat is the cause of having stress not only on heart but also on kidneys and liver as well. This ext ra weight caused by the excessive storage of fats puts strain on the joints like knees, ankles and hips that causes shortening of duration of life. Though recently much have been done to increase the awareness about obesity and the dangers it causes to the human life but a lot is still left to be done. Isolated, bored, unloved, hopeless, sad, unsatisfied and displeased with their family members, financial dissatisfied are the people who become involved in the habit of

Air Transat Internal and External Analysis (Aviation Industry) Research Paper

Air Transat Internal and External Analysis (Aviation Industry) - Research Paper Example The airline industry has a number of dominant economic features that determine the success of a company. These include:Service life cycle: the airline industry has reached the maturity stage of service lifecycle meaning no growth or decline. The industry consists of many small and large airline companies with the service being provided to local, regional, and global levels according to the size of the company. The number of buyers: buyers consist of groups, individuals and families in the airline industry with bulk buyers having more bargaining power compared to individuals. Buyers who have loyalty cards access the most bargaining power owing to discounts. Differentiation: differentiating on price, service, and quality ensures companies success in the market. Since there are many buyers in the market, low price, quality, and customer reviews are the main focus in the airline industry. Suppliers: two main suppliers, Airbus and Boeing supply the whole airline industry with aircrafts co nsisting of thousands of aircraft companies. Technological advancement aids in product improvement and development. Experience: having experience in the airline industry is the main advantage for success resulting in the inability of instant success for new entrants. Experience allows for airline companies to develop economies of scale allowing for cheaper cost and pricing strategies. Experience also allows companies to have a better understanding of costs and profitability strategies.

The relationship between female leisure participation and Research Paper

The relationship between female leisure participation and psychological well being - Research Paper Example 126) Leisure activities can be divided into two main categories: solo and group activities. Both categories serve unique purposes and each has its characteristic benefits. Solo activities are based on an individual’s personal interests; it can also be defined as a hobby like: gardening, reading, writing, watching television/cinema, going for a walk or skating or swimming - such individual-based leisure activities help women de-stress and unwind from their day to day activities. Women have to go through a lot of biological and social changes throughout their lives. As they enter puberty, life suddenly starts moving in all directions. As they struggle through their education, careers, relationships, parenting, and family life - they find it highly difficult to find out time for leisure activities. As a result, they start developing various physical or psychological disorders, which are manifested in their performance at work or at home. Psychological well being is important beca use it helps a person to balance work and relationships properly. An overburdened female with tremendous responsibilities is not able to feel contentment or being rewarded for whatever she does for herself or for her family. She cannot derive pleasure or satisfaction from the work she does at home or office. It is for this reason that participation in leisure activities should be encouraged to boost the psychological well being of women. Solo leisure activities are a great way to relax and to get entertained. It satisfies the inner yearning of doing something for self. As one chooses leisure activities according to one’s free will, therefore, a person feels a sense of freedom while participating in leisure activities. There are no deadlines to fear about; nor does one have to worry about following rules and regulations. Spa, travelling, hiking, and joining leisure clubs are great ways to improve psychological well being. Likewise, joining gym, music and dance classes, or yoga can boost their self confidence as their body image improves. Low levels of self confidence hinder personal development in a lot of ways. However, leisure activities carried out in groups have far greater effects on the psychological well being of a woman because she can derive pleasure by socializing with people belonging to a cross-section of society. It could mean meeting with new and interesting people and sharing innovative ideas. It could be for a social cause; thus magnifying the benefits of such activities. It has been found out that social networking or socializing has positive effects on the psyche and mind of a person. As women get older, they find themselves lonely or isolated for various reasons: being single, or widowed, or retired, or living independently. Isolation from the society is detrimental to the physical and emotional health of a person; therefore, it is beneficial for such women to participate in leisure activities- particularly, group activities, so that t hey can give something back to society or Mother Nature. Working for a social cause is highly rewarding because it gives a

Analysis of Attribution Selection Techniques

Analysis of Attribution Selection Techniques ABSTRACT: From a large amount of data, the significant knowledge is discovered by means of applying the techniques and those techniques in the knowledge management process is known as Data mining techniques. For a specific domain, a form of knowledge discovery called data mining is necessary for solving the problems. The classes of unknown data are detected by the technique called classification. Neural networks, rule based, decision trees, Bayesian are the some of the existing methods used for the classification. It is necessary to filter the irrelevant attributes before applying any mining techniques. Embedded, Wrapper and filter techniques are various feature selection techniques used for the filtering. In this paper, we have discussed the attribute selection techniques like Fuzzy Rough SubSets Evaluation and Information Gain Subset Evaluation for selecting the attributes from the large number of attributes and for search methods like BestFirst Search is used for fuzzy rough subset evaluati on and Ranker method is applied for the Information gain evaluation. The decision tree classification techniques like ID3 and J48 algorithm are used for the classification. From this paper, the above techniques are analysed by the Heart Disease Dataset and generate the result and from the result we can conclude which technique will be best for the attribute selection. 1. INTRODUCTION: As the world grows in complexity, overwhelming us with the data it generates, data mining becomes the only hope for elucidating the patterns that underlie it. The manual process of data analysis becomes tedious as size of data grows and the number of dimensions increases, so the process of data analysis needs to be computerised. The term Knowledge Discovery from data (KDD) refers to the automated process of knowledge discovery from databases. The process of KDD is comprised of many steps namely data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge representation. Data mining is a step in the whole process of knowledge discovery which can be explained as a process of extracting or mining knowledge from large amounts of data. Data mining is a form of knowledge discovery essential for solving problems in a specific domain. Data mining can also be explained as the non trivial process that automatically collects the useful hidd en information from the data and is taken on as forms of rule, concept, pattern and so on. The knowledge extracted from data mining, allows the user to find interesting patterns and regularities deeply buried in the data to help in the process of decision making. The data mining tasks can be broadly classified in two categories: descriptive and predictive. Descriptive mining tasks characterize the general properties of the data in the database. Predictive mining tasks perform inference on the current data in order to make predictions. According to different goals, the mining task can be mainly divided into four types: class/concept description, association analysis, classification or prediction and clustering analysis. 2. LITERATURE SURVEY: Data available for mining is raw data. Data may be in different formats as it comes from different sources, it may consist of noisy data, irrelevant attributes, missing data etc. Data needs to be pre processed before applying any kind of data mining algorithm which is done using following steps: Data Integration – If the data to be mined comes from several different sources data needs to be integrated which involves removing inconsistencies in names of attributes or attribute value names between data sets of different sources . Data Cleaning –This step may involve detecting and correcting errors in the data, filling in missing values, etc. Discretization –When the data mining algorithm cannot cope with continuous attributes, discretization needs to be applied. This step consists of transforming a continuous attribute into a categorical attribute, taking only a few discrete values. Discretization often improves the comprehensibility of the discovered knowledge. Attribute Selection – not all attributes are relevant so for selecting a subset of attributes relevant for mining, among all original attributes, attribute selection is required. A Decision Tree Classifier consists of a decision tree generated on the basis of instances. The decision tree has two types of nodes: a) the root and the internal nodes, b) the leaf nodes. The root and the internal nodes are associated with attributes, leaf nodes are associated with classes. Basically, each non-leaf node has an outgoing branch for each possible value of the attribute associated with the node. To determine the class for a new instance using a decision tree, beginning with the root, successive internal nodes are visited until a leaf node is reached. At the root node and at each internal node, a test is applied. The outcome of the test determines the branch traversed, and the next node visited. The class for the instance is the class of the final leaf node. 3. FEATURE SELECTION: Many irrelevant attributes may be present in data to be mined. So they need to be removed. Also many mining algorithms don’t perform well with large amounts of features or attributes. Therefore feature selection techniques needs to be applied before any kind of mining algorithm is applied. The main objectives of feature selection are to avoid overfitting and improve model performance and to provide faster and more cost-effective models. The selection of optimal features adds an extra layer of complexity in the modelling as instead of just finding optimal parameters for full set of features, first optimal feature subset is to be found and the model parameters are to be optimised. Attribute selection methods can be broadly divided into filter and wrapper approaches. In the filter approach the attribute selection method is independent of the data mining algorithm to be applied to the selected attributes and assess the relevance of features by looking only at the intrinsic propert ies of the data. In most cases a feature relevance score is calculated, and lowscoring features are removed. The subset of features left after feature removal is presented as input to the classification algorithm. Advantages of filter techniques are that they easily scale to highdimensional datasets are computationally simple and fast, and as the filter approach is independent of the mining algorithm so feature selection needs to be performed only once, and then different classifiers can be evaluated. 4. ROUGH SETS Any set of all indiscernible (similar) objects is called an elementary set. Any union of some elementary sets is referred to as a crisp or precise set otherwise the set is rough (imprecise, vague). Each rough set has boundary-line cases, i.e., objects which cannot be with certainty classified, by employing the available knowledge, as members of the set or its complement. Obviously rough sets, in contrast to precise sets, cannot be characterized in terms of information about their elements. With any rough set a pair of precise sets called the lower and the upper approximation of the rough set is associated. The lower approximation consists of all objects which surely belong to the set and the upper approximation contains all objects which possible belong to the set. The difference between the upper and the lower approximation constitutes the boundary region of the rough set. Rough set approach to data analysis has many important advantages like provides efficient algorithms for find ing hidden patterns in data, identifies relationships that would not be found using statistical methods, allows both qualitative and quantitative data, finds minimal sets of data (data reduction), evaluates significance of data, easy to understand. 5. ID3 DECISION TREE ALGORITHM: From the available data, using the different attribute values gives the dependent variable (target value) of a new sample by the predictive machine-learning called a decision tree. The attributes are denoted by the internal nodes of a decision tree; in the observed samples, the possible values of these attributes is shown by the branches between the nodes, the classification value (final) of the dependent variable is given by the terminal nodes. Here we are using this type of decision tree for large dataset of telecommunication industry. In the data set, the dependent variable is the attribute that have to be predicted, the values of all other attributes decides the dependent variable value and it is depends on it. The independent variable is the attribute, which predicts the values of the dependent variables. The simple algorithm is followed by this J48 Decision tree classifier. In the available data set using the attribute value, the decision tree is constructed for assort a new item. It describes the attribute that separates the various instances most clearly, whenever it finds a set of items (training set). The highest information gain is given by classifying the instances and the information about the data instances are represent by this feature. We can allot or predict the target value of the new instance by assuring all the respective attributes and their values. 6. J48 DECISION TREE TECHNIQUE: J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. C4.5 is a program that creates a decision tree based on a set of labeled input data. This algorithm was developed by Ross Quinlan. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier (†C4.5 (J48)†. 7. IMPLEMENTATION MODEL: WEKA is a collection of machine learning algorithms for Data Mining tasks. It contains tools for data preprocessing, classification, regression, clustering, association rules, and visualization. For our purpose the classification tools were used. There was no preprocessing of the data. WEKA has four different modes to work in. Simple CLI; provides a simple command-line interface that allows direct execution of WEKA commands. Explorer; an environment for exploring data with WEKA. Experimenter; an environment for performing experiments and conduction of statistical tests between learning schemes. Knowledge Flow; presents a â€Å"data-flow† inspired interface to WEKA. The user can select WEKA components from a tool bar, place them on a layout canvas and connect them together in order to form a â€Å"knowledge flow† for processing and analyzing data. For most of the tests, which will be explained in more detail later, the explorer mode of WEKA is used. But because of the size of some data sets, there was not enough memory to run all the tests this way. Therefore the tests for the larger data sets were executed in the simple CLI mode to save working memory. 8. IMPLEMENTATION RESULT: The attributes that are selected by the Fuzzy Rough Subset Evaluation using Best First Search method and Information Gain Subset Evaluation using Ranker Method is as follows: 8.1 Fuzzy Rough Subset Using Best First Search Method === Attribute Selection on all input data === Search Method: Best first. Start set: no attributes Search direction: forward Stale search after 5 node expansions Total number of subsets evaluated: 90 Merit of best subset found: 1 Attribute Subset Evaluator (supervised, Class (nominal): 14 class): Fuzzy rough feature selection Method: Weak gamma Similarity measure: max(min( (a(y)-(a(x)-sigma_a)) / (a(x)-(a(x)-sigma_a)),((a(x)+sigma_a)-a(y)) / ((a(x)+sigma_a)-a(x)) , 0). Decision similarity: Equivalence Implicator: Lukasiewicz T-Norm: Lukasiewicz Relation composition: Lukasiewicz (S-Norm: Lukasiewicz) Dataset consistency: 1.0 Selected attributes: 1,3,4,5,8,10,12 : 7 0 2 3 4 7 9 11 8.2 Info Gain Subset Evaluation Using Ranker Search Method: === Attribute Selection on all input data === Search Method: Attribute ranking. Attribute Evaluator (supervised, Class (nominal): 14 class): Information Gain Ranking Filter Ranked attributes: 0.208556 13 12 0.192202 3 2 0.175278 12 11 0.129915 9 8 0.12028 8 7 0.119648 10 9 0.111153 11 10 0.066896 2 1 0.056726 1 0 0.024152 7 6 0.000193 6 5 0 4 3 0 5 4 Selected attributes: 13,3,12,9,8,10,11,2,1,7,6,4,5 : 13 8.2 ID3 Classification Result for 14 Attributes: Correctly Classified Instances 266 98.5185 % Incorrectly Classified Instances 4 1.4815 % Kappa statistic 0.9699 Mean absolute error 0.0183 Root mean squared error 0.0956 Relative absolute error 3.6997 % Root relative squared error 19.2354 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 52.2222 % Total Number of Instances 270 8.3 J48 Classification Result for 14 Attributes: Correctly Classified Instances 239 88.5185 % Incorrectly Classified Instances 31 11.4815 % Kappa statistic 0.7653 Mean absolute error 0.1908 Root mean squared error 0.3088 Relative absolute error 38.6242 % Root relative squared error 62.1512 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 92.2222 % Total Number of Instances 270 8.4 ID3 Classification Result for selected Attributes using Fuzzy Rough Subset Evaluation: Correctly Classified Instances 270 100 % Incorrectly Classified Instances 0 0 % Kappa statistic 1 Mean absolute error 0 Root mean squared error 0 Relative absolute error 0 % Root relative squared error 0 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 25 % Total Number of Instances 270 8.5 J48 Classification Result for selected Attributes using Fuzzy Rough Subset Evaluation: Correctly Classified Instances 160 59.2593 % Incorrectly Classified Instances 110 40.7407 % Kappa statistic 0 Mean absolute error 0.2914 Root mean squared error 0.3817 Relative absolute error 99.5829 % Root relative squared error 99.9969 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 100 % Total Number of Instances 270 8.6 ID3 Classification Result for Information Gain Subset Evaluation Using Ranker Method: Correctly Classified Instances 270 100 % Incorrectly Classified Instances 0 0 % Kappa statistic 1 Mean absolute error 0 Root mean squared error 0 Relative absolute error 0 % Root relative squared error 0 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 33.3333 % Total Number of Instances 270 8.7 J48 Classification Result for Information Gain Subset Evaluation Using Ranker Method: Correctly Classified Instances 165 61.1111 % Incorrectly Classified Instances 105 38.8889 % Kappa statistic 0.3025 Mean absolute error 0.31 Root mean squared error 0.3937 Relative absolute error 87.1586 % Root relative squared error 93.4871 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 89.2593 % Total Number of Instances 270 CONCLUSION: In this paper, from the above implementation result the Fuzzy Rough Subsets Evaluation is gives the selected attributes in less amount than the Info Gain Subset Evaluation and J48 decision tree classification techniques gives the approximate error rate using Fuzzy Rough Subsets Evaluation for the given data set than the ID3 decision tree techniques for both evaluation techniques. So finally for selecting the attributes fuzzy techniques gives the better result using Best First Search method and J48 classification method.

The Age of Innocence Essay -- Literary Analysis, Edith Wharton

The book The Age of Innocence by Edith Wharton presents a glance into the society of old New York, as seen through the eyes of the main character, Newland Archer. Newland Archer’s character is an interesting one, and it seems to change throughout the story, representing the idea that the rules set by society aren’t always perfect. In the beginning it is said that he does what is expected, is fashionable, and follows the rules set by New York society in which he grew up. However, toward the end of the book, we see changes in his character, reflected in his suggestions or thoughts about doing things that people from the elite New York society wouldn’t consider. Newland Archers follows the rules that have been set to him by the elite New York Society. There are many references to the way that things are and aren’t done, and the importance he places on them. It is stated that â€Å"what was or was not ‘the thing’ played a part as important in Newland Archer’s New York as the inscrutable totem terrors that had ruled the destinies of his forefathers thousands of years ago† (2). This belief in following the rules is also reflected in what Archer thinks of himself, his future wife, and the way he reacts to Countess Olenska’s presence. Archer is someone who is vain, has high self-esteem, a big ego, and believes he is superior. He states that he â€Å"felt himself distinctly superior of these chosen specimens of old New York gentility; he had probably read more, thought more, and even seen a good deal more of the world, than any other man of the number† (4).Archer believed that his wife should  "develop a social tact and readiness of wit enabling her to hold her own with the most popular married women of the ‘younger set,’ in which it was the recog... ...away, and he considers divorcing May so that he could marry Madame Olenska. Newland Archer is a very complex character. Although at first he seems to be the typical male in New York society, we soon see that through Madame Olenska’s influence, he changes the way that he sees the world that he grew up in. He begins to question the rules, routines, and patterns, and begins to understand topics that were once considered taboo and not talked about. Newland Archer seems to have many layers to his personality, and in a way May represents the proper, formal, and routine part of society that he knows so well, and Ellen seems to represent the part of his personality that wishes to be free of all rules and explore the world before him. Ultimately, fate seems to force him back into the rules of society in which he grew up in, showing a pattern that one can’t seem to escape. The Age of Innocence Essay -- Literary Analysis, Edith Wharton The book The Age of Innocence by Edith Wharton presents a glance into the society of old New York, as seen through the eyes of the main character, Newland Archer. Newland Archer’s character is an interesting one, and it seems to change throughout the story, representing the idea that the rules set by society aren’t always perfect. In the beginning it is said that he does what is expected, is fashionable, and follows the rules set by New York society in which he grew up. However, toward the end of the book, we see changes in his character, reflected in his suggestions or thoughts about doing things that people from the elite New York society wouldn’t consider. Newland Archers follows the rules that have been set to him by the elite New York Society. There are many references to the way that things are and aren’t done, and the importance he places on them. It is stated that â€Å"what was or was not ‘the thing’ played a part as important in Newland Archer’s New York as the inscrutable totem terrors that had ruled the destinies of his forefathers thousands of years ago† (2). This belief in following the rules is also reflected in what Archer thinks of himself, his future wife, and the way he reacts to Countess Olenska’s presence. Archer is someone who is vain, has high self-esteem, a big ego, and believes he is superior. He states that he â€Å"felt himself distinctly superior of these chosen specimens of old New York gentility; he had probably read more, thought more, and even seen a good deal more of the world, than any other man of the number† (4).Archer believed that his wife should  "develop a social tact and readiness of wit enabling her to hold her own with the most popular married women of the ‘younger set,’ in which it was the recog... ...away, and he considers divorcing May so that he could marry Madame Olenska. Newland Archer is a very complex character. Although at first he seems to be the typical male in New York society, we soon see that through Madame Olenska’s influence, he changes the way that he sees the world that he grew up in. He begins to question the rules, routines, and patterns, and begins to understand topics that were once considered taboo and not talked about. Newland Archer seems to have many layers to his personality, and in a way May represents the proper, formal, and routine part of society that he knows so well, and Ellen seems to represent the part of his personality that wishes to be free of all rules and explore the world before him. Ultimately, fate seems to force him back into the rules of society in which he grew up in, showing a pattern that one can’t seem to escape.