Emergent structures in nature

Emergent structures are patterns not created by a single event or rule. Nothing commands the system to form a pattern. Instead, the interaction of each part with its immediate surroundings causes a complex chain of processes leading to some order. One might conclude that emergent structures are more than the sum of their parts because the emergent order will not arise if the various parts are simply coexisting; the interaction of these parts is central. Emergent structures can be found in many natural phenomena, from the physical to the biological domain. For example, the shape of weather phenomena such as hurricanes are emergent structures.
It is useful to distinguish three forms of emergent structures. A first-order emergent structure occurs as a result of shape interactions (for example, hydrogen bonds in water molecules lead to surface tension). A Second-order emergent structure involves shape interactions played out sequentially over time (for example, changing atmospheric conditions as a snowflake falls to the ground build upon and alter its form). Finally, a third-order emergent structure is a consequence of shape, time, and heritable instructions. For example, an organism's genetic code sets boundary conditions on the interaction of biological systems in space and time.

[edit] Non-living, physical systems
In physics, emergence is used to describe a property, law, or phenomenon which occurs at macroscopic scales (in space or time) but not at microscopic scales, despite the fact that a macroscopic system can be viewed as a very large ensemble of microscopic systems.
An emergent property need not be more complicated than the underlying non-emergent properties which generate it. For instance, the laws of thermodynamics are remarkably simple, even if the laws which govern the interactions between component particles are complex. The term emergence in physics is thus used not to signify complexity, but rather to distinguish which laws and concepts apply to macroscopic scales, and which ones apply to microscopic scales.
Some examples include:
Colour: Elementary particles have no colour; it is only when they are arranged in atoms that they absorb or emit specific wavelengths of light and can thus be said to have a colour.
Friction: Forces between elementary particles are conservative. However, friction emerges when considering more complex structures of matter, whose surfaces can convert mechanical energy into heat energy when rubbed against each other. Similar considerations apply to other emergent concepts in continuum mechanics such as viscosity, elasticity, tensile strength, etc.
Classical mechanics: The laws of classical mechanics can be said to emerge as a limiting case from the rules of quantum mechanics applied to large enough masses. This may be puzzling, because quantum mechanics is generally thought of as more complicated than classical mechanics.
Statistical mechanics was initially derived using the concept of a large enough ensemble that fluctuations about the most likely distribution can be all but ignored. However, small clusters do not exhibit sharp first order phase transitions such as melting, and at the boundary it is not possible to completely categorize the cluster as a liquid or solid, since these concepts are (without extra definitions) only applicable to macroscopic systems. Describing a system using statistical mechanics methods is much simpler than using a low-level atomistic approach.
Patterned ground: the distinct, and often symmetrical geometric shapes formed by ground material in periglacial regions.
Temperature is sometimes used as an example of an emergent macroscopic behaviour. In classical dynamics, a snapshot of the instantaneous momenta of a large number of particles at equilibrium is sufficient to find the average kinetic energy per degree of freedom which is proportional to the temperature. For a small number of particles the instantaneous momenta at a given time are not statistically sufficient to determine the temperature of the system. However, using the ergodic hypothesis, the temperature can still be obtained to arbitrary precision by further averaging the momenta over a long enough time.
Convection in a fluid or gas is another example of emergent macroscopic behaviour that makes sense only when considering differentials of temperature. Convection cells, particularly Bénard cells, are an example of a self-organizing system (more specifically, a dissipative system) whose structure is determined both by the constraints of the system and by random perturbations: the possible realizations of the shape and size of the cells depends on the temperature gradient as well as the nature of the fluid and shape of the container, but which configurations are actually realized is due to random perturbations (thus these systems exhibit a form of symmetry breaking).
In some theories of particle physics, even such basic structures as mass, space, and time are viewed as emergent phenomena, arising from more fundamental concepts such as the Higgs boson or strings. In some interpretations of quantum mechanics, the perception of a deterministic reality, in which all objects have a definite position, momentum, and so forth, is actually an emergent phenomenon, with the true state of matter being described instead by a wavefunction which need not have a single position or momentum. Most of the laws of physics themselves as we experience them today appear to have emerged during the course of time making emergence the most fundamental principle in the universe and raising the question of what might be the most fundamental law of physics from which all others emerged. Chemistry can in turn be viewed as an emergent property of the laws of physics. Biology (including biological evolution) can be viewed as an emergent property of the laws of chemistry. Finally, psychology could at least theoretically be understood as an emergent property of neurobiological laws

Actuarial science applied to other forms of insurance

Actuarial science is also applied to short-term forms of insurance, referred to as Property & Casualty or Liability insurance, or General insurance. In these forms of insurance, coverage is generally provided on a renewable annual period, (such as a yearly contract to provide homeowners insurance policy covering damage to a house and its contents for one year). Coverage can be cancelled at the end of the period by either party.
In the property & casualty insurance fields, companies tend to specialize because of the complexity and diversity of risks. A convenient division is to organize around personal and commercial lines of insurance. Personal lines of insurance include the familiar fire, auto, homeowners, theft and umbrella coverages. Commercial lines would include business continuation, product liability, fleet insurance, workers compensation, fidelity & surety, D&O insurance and a great variety of coverages required for businesses. Beyond these, the industry needs to provide catastrophe insurance for weather-related risks, earthquakes, patent infringement and other forms of corporate espionage, terrorism and all its implications, and finally coverage for the most unusual risks sometimes associated with Lloyds of London. In all of these ventures, actuarial science has to bring data collection, measurement, estimating, forecasting, and valuation tools to provide financial and underwriting data for management to assess marketing opportunities and the degree of risk taking that is required. Actuarial science needs to operate at two levels: (i) at the product level to facilitate politically correct equitable pricing and reserving; and (ii) at the corporate level to assess the overall risk to the enterprise from catastrophic events in relation to its underwriting capacity or surplus. Actuaries, usually working in a multidisciplinary team must help answer management issues: (i) is the risk insurable; (ii) does the company have effective claims administration to determine damages; (iii) does the company have sufficient claims handling to cover catastrophic events; (iv) and the vulnerability of the enterprise to uncontrollable risks such as inflation, adverse political outcomes; unfavorable legal outcomes such as excess punitive damage awards, and international turmoil.
In the reinsurance fields, actuarial science is used to design and price reinsurance and retro-reinsurance schemes, and to establish reserve funds for known claims and future claims and catastrophes. Retro-reinsurance, also known as retrocession occurs when a reinsurance company reinsures risks with yet another reinsurance company. Reinsurance can be used to spread the risk, to smooth earnings and cash flow, to reduce reserve requirements and improve the quality of surplus, Reinsurance creates arbitrage situations, and retro-reinsurance arbitrage can create Spirals which can lead to financial instability and bankruptcies. A spiral occurs (as an example) when a reinsurer accepts a retrocession which unknowingly contains risks that were previously reinsured. Some reported cases of arbitrage and spirals have been found to be illegal. The Equity Funding scam was built on the abusive use of financial reinsurance to transfer capital funds from the reinsurance carrier to Equity Funding. In the broadest sense of the word, reinsurance takes many forms: (i) declining a risk; (ii) requiring the insured to self insure part of the contingent or investment risk; (iii) limiting the coverage through deductibles, coinsurance or exclusionary policy language; (iv) placing a policy in a risk pool with a cohort of competitors to achieve a social objective; (v) ceding or transferring a percentage of each policy to another insurance company (i.e. the reinsurer); (vi) ceding or transferring excess amounts or excess coverages to the reinsurer; (vii) ceding or transferring asset based policies to the reinsurer in exchange for capital; (viii) purchasing stop loss insurance; (ix) purchasing umbrella coverages for a basket of risks; (x) purchasing catastrophe insurance for specific contingent events. Reinsurance is complex. Company management and their actuaries need to deal with all the known insurable contingent events, as well as underwrite the quality of their cedant companies, and maintain the information tools and auditing practices to identify arbitrage and spirals.

Actuaries outside insurance

There is an increasing trend to recognise that actuarial skills can be applied to a range of applications outside the insurance industry. One notable example is the use in some US states of actuarial models to set criminal sentencing guidelines. These models attempt to predict the chance of re-offending according to rating factors which include the type of crime, age, educational background and ethnicity of the offender (Silver and Chow-Martin 2002). However, these models have been open to criticism as providing justification by law enforcement personnel on specific ethnic groups. Whether or not this is statistically correct or a self-fulfilling correlation remains under debate (Harcourt 2003).
Another example is the use of actuarial models to assess the risk of sex offense recidivism. Actuarial models and associated tables, such as the MnSOST-R, Static-99, and SORAG, have been used since the late 1990s to determine the likelihood that a sex offender will recidivate and thus whether he or she should be institutionalized free (Nieto and Jung 2006 pp. 28–33).Effects of technology
In the 18th century and nineteenth centuries, computational complexity was limited to manual calculations. The actual calculations required to compute fair insurance premiums are rather complex. The actuaries of that time developed methods to construct easily-used tables, using sophisticated approximations called commutation functions, to facilitate timely, accurate, manual calculations of premiums (Slud 2006). Over time, actuarial organizations were founded to support and further both actuaries and actuarial science, and to protect the public interest by ensuring competency and ethical standards (Hickman 2004 p. 4). However, calculations remained cumbersome, and actuarial shortcuts were commonplace. Non-life actuaries followed in the footsteps of their life compatriots in the early twentieth century. The 1920 revision to workers compensation rates took over two months of around-the-clock work by day and night teams of actuaries (Michelbacher 1920 p. 224, 230). In the 1930s and 1940s, however, the rigorous mathematical foundations for stochastic processes were developed (Bühlmann 1997 p. 168). Actuaries could now begin to forecast losses using models of random events, instead of the deterministic methods they had been constrained to in the past. The introduction and development of the computer industry further revolutionized the actuarial profession. From pencil-and-paper to punchcards to current high-speed devices, the modeling and forecasting ability of the actuary has grown exponentially, and actuaries needed to adjust to this new world

Life insurance, pensions and healthcare

Actuarial science became a formal mathematical discipline in the late 17th century with the increased demand for long-term insurance coverages such as Burial, Life insurance, and Annuities. These long term coverages required that money be set aside to pay future benefits, such as annuity and death benefits many years into the future. This requires estimating future contingent events, such as the rates of mortality by age, as well as the development of mathematical techniques for discounting the value of funds set aside and invested. This led to the development of an important actuarial concept, referred to as the Present value of a future sum. Pensions and healthcare emerged in the early 20th century as a result of collective bargaining. Certain aspects of the actuarial methods for discounting pension funds have come under criticism from modern financial economics.
In traditional life insurance, actuarial science focuses on the analysis of mortality, the production of life tables, and the application of compound interest to produce life insurance, annuities and endowment policies. Contemporary life insurance programs have been extended to include credit and mortgage insurance, key man insurance for small businesses, long term care insurance and health savings accounts (Hsiao 2001).
In health insurance, including insurance provided directly by employers, and social insurance, actuarial science focuses on the analyses of rates of disability, morbidity, mortality, fertility and other contingencies. The effects of consumer choice and the geographical distribution of the utilization of medical services and procedures, and the utilization of drugs and therapies, is also of great importance. These factors underlay the development of the Resource-Base Relative Value Scale (RBRVS) at Harvard in a multi-disciplined study. (Hsiao 1988) Actuarial science also aids in the design of benefit structures, reimbursement standards, and the effects of proposed government standards on the cost of healthcare (cf. CHBRP 2004).
In the pension industry, actuarial methods are used to measure the costs of alternative strategies with regard to the design, maintenance or redesign of pension plans. The strategies are greatly influenced by collective bargaining; the employer's old, new and foreign competitors; the changing demographics of the workforce; changes in the internal revenue code; changes in the attitude of the internal revenue service regarding the calculation of surpluses; and equally importantly, both the short and long term financial and economic trends. It is common with mergers and acquisitions that several pension plans have to be combined or at least administered on an equitable basis. When benefit changes occur, old and new benefit plans have to be blended, satisfying new social demands and various government discrimination test calculations, and providing employees and retirees with understandable choices and transition paths. Benefit plans liabilities have to be properly valued, reflecting both earned benefits for past service, and the benefits for future service. Finally, funding schemes have to be developed that are manageable and satisfy the Financial Accounting Standards Board (FASB).
In social welfare programs, the Office of the Chief Actuary (OCACT), Social Security Administration plans and directs a program of actuarial estimates and analyses relating to SSA-administered retirement, survivors and disability insurance programs and to proposed changes in those programs. It evaluates operations of the Federal Old-Age and Survivors Insurance Trust Fund and the Federal Disability Insurance Trust Fund, conducts studies of program financing, performs actuarial and demographic research on social insurance and related program issues involving mortality, morbidity, utilization, retirement, disability, survivorship, marriage, unemployment, poverty, old age, families with children, etc., and projects future workloads. In addition, the Office is charged with conducting cost analyses relating to the Supplemental Security Income (SSI) program, a general-revenue financed, means-tested program for low-income aged, blind and disabled people. The Office provides technical and consultative services to the Commissioner, to the Board of Trustees of the Social Security Trust Funds, and its staff appears before Congressional Committees to provide expert testimony on the actuarial aspects of Social Security issues

Wulff Hous Historic

Historic District
Because of its status as a historic urban center, the architecture and layout of San Antonio are more traditionally urban than other cities in Texas, such as Dallas and Austin, which have developed in the last half century.
Downtown is encircled by three numerical freeways, I-35, I-37, and I-10. Together the three highways create a rectangular route around the downtown area of San Antonio: I-35 to the north and west, I-37 to the east, and I-10 to the south.
Downtown is home to many districts including the Alamo District, Alamodome District, Central Business District, Convention Center District, Historic Civic District, Houston Street District, King William Historic District, La Villita District, Market Square District, North Downtown, North River District, River Bend District, SoSo (South of Southtown), Southtown, and the University District.
The Central Business District is home to Rivercenter, anchored by Dillard's and Macy's. The five-level Art Deco Dillard's, at the corner of Alamo and Commerce streets, opened in 1887 as Joske's. Joske's flagship store was 551,000 square feet (51,200 m²) in floor space until Dillard's bought the Joske's chain in 1987. Today, Dillard's only occupies a fraction of the original building.
Housing the famous Alamo many people can be seen traveling to visit the historic district. Attractions such as the river walk are home to many of the festivities throughout the year including NIOSA (Night In Old San Antonio) which celebrates Fiesta, Cinco de Mayo, and numerous parades such as celebrations for their home NBA team the Spurs, Christmas parades and much more.

Classification of life

The hierarchy of biological classification's major eight taxonomic ranks. Life is divided into domains, which are subdivided into further groups. Intermediate minor rankings are not shown.Traditionally, people have divided organisms into the classes of plants and animals, based mainly on their ability of movement. The first known attempt to classify organisms, as per personal observations, was conducted by the Greek philosopher Aristotle.
He classified all living organisms known at that time as either a plant or an animal. Aristotle distinguished animals with blood from animals without blood (or at least without red blood), which can be compared with the concepts of vertebrates and invertebrates respectively. He divided the blooded animals into five groups: viviparous quadrupeds (mammals), birds, oviparous quadrupeds (reptiles and amphibians), fishes and whales. The bloodless animals were also divided into five groups: cephalopods, crustaceans, insects (which also included the spiders, scorpions, and centipedes, in addition to what we now define as insects), shelled animals (such as most molluscs and echinoderms) and "zoophytes". Though Aristotle's work in zoology was not without errors, it was the grandest biological synthesis of the time, and remained the ultimate authority for many centuries after his death. His observations on the anatomy of octopus, cuttlefish, crustaceans, and many other marine invertebrates are remarkably accurate, and could only have been made from first-hand experience with dissection.[11]
The exploration of parts of the New World produced large numbers of new plants and animals that needed descriptions and classification. The old systems made it difficult to study and locate all these new specimens within a collection and often the same plants or animals were given different names because the number of specimens were too large to memorize. A system was needed that could group these specimens together so they could be found, the binomial system was developed based on morphology with groups having similar appearances. In the latter part of the 16th century and the beginning of the 17th, careful study of animals commenced, which, directed first to familiar kinds, was gradually extended until it formed a sufficient body of knowledge to serve as an anatomical basis for classification.
Carolus Linnaeus is best known for his introduction of the method still used to formulate the scientific name of every species. Before Linnaeus, long many-worded names (composed of a generic name and a differentia specifica) had been used, but as these names gave a description of the species, they were not fixed. In his Philosophia Botanica (1751) Linnaeus took every effort to improve the composition and reduce the length of the many-worded names by abolishing unnecessary rhetorics, introducing new descriptive terms and defining their meaning with an unprecedented precision. In the late 1740s Linnaeus began to use a parallel system of naming species with nomina trivialia. Nomen triviale, a trivial name, was a single- or two-word epithet placed on the margin of the page next to the many-worded "scientific" name. The only rules Linnaeus applied to them was that the trivial names should be short, unique within a given genus, and that they should not be changed. Linnaeus consistently applied nomina trivialia to the species of plants in Species Plantarum (1st edn. 1753) and to the species of animals in the 10th edition of Systema Naturae (1758). By consistently using these specific epithets, Linnaeus separated nomenclature from taxonomy. Even though the parallel use of nomina trivialia and many-worded descriptive names continued until late in the eighteenth century, it was gradually replaced by the practice of using shorter proper names combined of the generic name and the trivial name of the species. In the nineteenth century, this new practice was codified in the first Rules and Laws of Nomenclature, and the 1st edn. of Species Plantarum and the 10th edn. of Systema Naturae were chosen as starting points for the Botanical and Zoological Nomenclature respectively. This convention for naming species is referred to as binomial nomenclature. Today, nomenclature is regulated by Nomenclature Codes, which allows names divided into ranks; separately for botany and for zoology. Whereas Linnaeus classified for ease of identification, it is now generally accepted that classification should reflect the Darwinian principle of common descent.
The Fungi have long been a problematic group in the biological classification: Originally, they were treated as plants. For a short period Linnaeus had placed them in the taxon Vermes in Animalia because he was misinformed: the hyphae were said to have been worms. He later placed them back in Plantae. Copeland classified the Fungi in his Protoctista, thus partially avoiding the problem but acknowledging their special status. The problem was eventually solved by Whittaker, when he gave them their own kingdom in his five-kingdom system. As it turned out, the fungi are more closely related to animals than to plants.
As new discoveries enabled us to study cells and microorganisms, new groups of life where revealed, and the fields of cell biology and microbiology were created. These new organisms were originally described separately in Protozoa as animals and Protophyta/Thallophyta as plants, but were united by Haeckel in his kingdom Protista, later the group of prokaryotes were split of in the kingdom Monera, eventually this kingdom would be divided in two separate groups, the Bacteria and the Archaea, leading to the six-kingdom system and eventually to the three-domain system. The 'remaining' protists would later be divided into smaller groups in clades in relation to more complex organisms. Thomas Cavalier-Smith, who has published extensively on the classification of protists, has recently proposed that the Neomura, the clade which groups together the Archaea and Eukarya, would have evolved from Bacteria, more precisely from Actinobacteria.
As microbiology, molecular biology and virology developed, non-cellular reproducing agents were discovered, sometimes these are considered to be alive and are treated in the domain of non-cellular life named Acytota or Aphanobionta, which are virus.
And thus all the primary taxonomical ranks were established: Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species
Since the 1960s a trend called cladistics has emerged, arranging taxa in an evolutionary or phylogenetic tree. If a taxon includes all the descendants of some ancestral form, it is called monophyletic, as opposed to paraphyletic, groups based on traits which have evolved separately and where the most recent common ancestor is not included are called polyphyletic.
A new formal code of nomenclature, the PhyloCode, to be renamed "International Code of Phylogenetic Nomenclature" (ICPN), is currently under development, intended to deal with clades, which do not have set ranks, unlike conventional Linnaean taxonomy. It is unclear, should this be implemented, how the different codes will coexist