Research novelty: the research links up probability, risk theories with their practical application at immigration service work;
Aim of the research: to find out risk diminishing factors taking a decision at immigration service work;
Object of the research: immigration service;
Subject of the research: risk diminishing factors;
Base of the research: immigration service officials;
Tasks of the research:
1) to find out risk factors;
2) to analyse risk impact on decision making;
3) to define risk diminishing factors;
4) to put into practice risk diminishing factors.
Research methods:
The theoretical ones: to analyse references and service documentation;
The practical ones: observation, surveys, experiments, analysis.
Questions of the research:
1) What is decision?
2) What can influence decision taking?
3) Which is the best decision?
4) Which are the reasons and consequences of incorrect decision?
Theoretical model and references used:
Juridical, mathematical, technical references were used including: books on probability, risk, decision taking theories, systemization and logic.
Hypothesis of the research:
Systematization of actions may reduce the probability to take incorrect decisions at immigration service work.
CONTENT
Annotation
Preface
1. Decision’s essence
1.1. Decision under uncertainty
1.2. Analysis of a particular decision
1.3. Correct question
2. How reading skills influence comprehension of text
3. Possible solutions of remote identification
3.1. Identification systems
3.1.1. Comparison of BIS and FRS identification systems
4. Morphological analysis
4.1. The author’s research-morphological analysis and decision taking
5. Results of the author’s research
6. Conclusions
7. Suggestions
References
PREFACE
Research novelty: the research links up probability, risk theories with their practical application at immigration service work;
Aim of the research: to find out risk diminishing factors taking a decision at immigration service work;
Object of the research: immigration service;
Subject of the research: risk diminishing factors;
Base of the research: immigration service officials;
Tasks of the research:
1) to find out risk factors;
2) to analyse risk impact on decision making;
3) to define risk diminishing factors;
4) to put into practice risk diminishing factors.
Research methods:
The theoretical ones: to analyse references and service documentation;
The practical ones: observation, surveys, experiments, analysis.
Questions of the research:
1) What is decision?
2) What can influence decision taking?
3) Which is the best decision?
4) Which are the reasons and consequences of incorrect decision?
Theoretical model and references used:
Juridical, mathematical, technical references were used including: books on probability, risk, decision taking theories, systemization and logic.
Hypothesis of the research:
Systematization of actions may reduce the probability to take incorrect decisions at immigration service work.
DECISION’S ESSENCE
Each decision deliberately taken is always a choice from variety of possible decisions. Each decision, in its turn, brings consequences: positive or negative. If these consequences are known in advance, the tactic is very simple —to choosethe decision which will lead to the targeted results or consequences. Unfortunately, it is not always possible to predict consequences of the decision taken. According to the decision theory, various options we choose are called alternatives. So, if the decision “A is better thanB,andB isbetter than C, then logically that “A is better than C” (33, 13). Thesealternativesmay also be expressed by numbers or by words,such as if we „ assign toA thevalue 15, toB thevalue 13 and to C the value 7” and since “A has a higher number than eitherB orC,Ashould be chosen." (33, 13) Even if the best decision has been taken, it can be hard to prove or express in numbers its quality or value. For example the expression “A is better thatB”is “binary relation” (33, 14) because it is impossible to define how much A is better then B? Another good example could be “a cup of coffee and sugar”.For example there are 1000 cups of coffee where “C0 –cup has no sugar, C1 – one grain of sugar, up to C999!” (33, 19) A man drinking coffee from both cups C0,C1 „can not taste the difference” (33,19) in one granule and the coffee in both cups will taste for him as without sugar. The opposite situation will occur when comparing coffee from cups C0, C1 with the cup C999. In the latter case Hoffman will be able to feel the sugars and “clearly taste the difference” (33, 19). Clearly there exists some sensitivity threshold -Cxwhich depends on the number of sugar granules. If thereareless than x sugar granules in thecupthe person will not distinguish its presence and will consider such coffee without any sugar and vice versa. The threshold may vary for different people and even sometimes does not exist at all when, for example, one is ill with agnosia an illness characterized with the loss of taste functions of the tongue, or “inability to taste." (44, 368) When for example, we say to our child “the weather today was better than yesterday and you may leave umbrella at home” it is not necessary to compare physical qualities of cyclone like wind speed, pressure, moisture. In our daily life we can evaluate many things approximately and it is enough to understand each other without any numbers or formulas, but there are cases when precise data is essential. No one would argue that precise data is the basis of such sciences as mathematics, physics, mechanics, but what about jurisdiction? How to define in which indices it is possible to measure the good or bad decision? Each decision depends on some conditions, for example there are two alternatives for a man: to take or not take an umbrella before leaving home? Here the main condition on which the decision taking will depend is if it “rains or it does not rain." (33, 25) Rain is here risk factor. After having taken the decision one may predict and expect possible outcomes or consequences of this decision, for example, if an official takes the decision he too must predict possible risks, choose an appropriate decision and after all taking responsibility for it. Let us try to measure decisions and their outcomes on the example of “umbrella and rain." (33, 25) So, if someone has taken an umbrella with him, but the rain will not pour that day, in turns out that there is no rain but his suitcase is heavier. Andopposite, if someone has not taken an umbrella but there is a heavy downpour – his suitcase is lighter but he will be totally wet. One can conclude that the ideal opportunity for such person would be an easy suitcase and no rain! Let us describe all possible decisions and their outcomes in the following table which in scientific slang is called “decision matrix." (33, 25)
Table Nr.1.1. Possible outcomes in situation “to take or not to take an umbrella” (33, 25)
illustration not visible in this excerpt
Then let us “value” (33, 26) these outcomes in the scale from 1 to 10, where 10 is the best and most suitable, desirable outcome. As it has been written before the best opportunity for a person, especially a woman, is to go out in a sunny morning without an umbrella and therefore let us mark this opportunity with 10 and, on the contrary, no one would like to be under a heavy downpour without an umbrella. (See the following table)
Table Nr.1.2. possible outcomes in situation “to take or not to take an umbrella” expressed with numbers in 10 point value scale. (33, 26)
illustration not visible in this excerpt
* the values in the table are introduced by the author
In the example of rain and umbrella it was possible, at least approximately, to define outcomes and even to determine their probability, but there exist decisions where one will not be able to predict their outcomes and therefore they are called decisions under “non-certainty." (33, 26) Each event, according to the probability theory may occur with probability from zero to one or in other words from 0% to 100%. In the example of “rain and umbrella” it is acceptable to define possible outcomes and their probability. In our case the probability of being wet or dry will greatly depend on wind, moisture in the atmosphere and if one wishes one can find out quite accurate weather forecast in his region for a particular time. The fact is thatthere are decisions which may cause unpredictable outcomes like:will my deposit bring profit or loss in two years; will the sportsman win or lose competition, etc. An official must always try to foresee possible outcomes of his decision because on this will depend someone's health and sometimes even life. If all possible outcomes of a decision were evident the problem couldbesolved easily-choosethe decision which will be correct, suitable, profitable, but unfortunately or fortunately there are also decisions which will be taken under non-certainty, then the lawmay sound as follows: the more one knows or the more information one possess on the object, the better one can predict its future behavior. In our daily language we usually describe the probability of some event with ordinary words and phrases like:certainly, unlikely, sure. (See the next table)
Table Nr.1.3. Scale of knowledge situations and decision problems (33, 28).
illustration not visible in this excerpt
If someone is certain of something that means that he possesses exhaustive information on something and due to it he is able to predict its future behavior with the highest possible degree. For example, rocket designers and engineers know their constructed objects or systems almost in detail and it is compulsory in order to be convinced that during the real flight the object will function according to the calculated characteristics.Otherwise, the less one knows about an objectthe worsewill be one's prediction degree. The ability to predict is necessary not only for engineers, but also for officials. For a policeman it is important to know if a detainee will assail or not, will he escape or not, will he submit or not, etc. If one could certainly predict the above-mentioned problems they would be solved simply: it is compulsory to put handcuff-because the detainee will assail, it is compulsory to put him behind the bars-because he will escape, but due to the lack of information about the detainee one can never certainly predict his future behavior. Though one hundred percent probability degree is a theoretical value, some events are predictable to the degree close to 100% and due to such a highnumber they may be considered certain in occurrence. From author's point of view, probability degree is conditional value because each seemingly inevitable event may not happen due to the possible but. For example, if one throws a stone-it will definitely land, but only if Earth's gravity does not diminish, if there is no atomic blast at the moment of the up-throw, etc. Evidently these but will never be taken into calculation of many formulas, like path of projectile, but nevertheless their possible appearance and influence will make the formula absurd. Our human language, mathematics is incapable of explaining some natural events. For example, “anevent with probability of 0,000001 (one chance in a million) is a rather an unlikely event” (19, 14), but from psychological point of view, people dealing with the event which appears with probability of 0,000001 will be so accustomed to its absence that will eventually qualify it as impossible. Furthermore, humankind helplessly struggles to describe events in the grand scheme of things, where due to universal domains “distinctions of any kind become impossible." (16, 99) But nevertheless being curious by nature, humans try to use available, restricted methods of their imperfect science to explore a boundless and perfect universe. Let us return to the earth, in order to exclude risks from assault the law allows policemen to use handcuffs and other special means. In case of need a policeman will choose from two possible options: to put or nottoput handcuffs. Here, decision taking process is fast and almost entirely depends on detainee's behavior and health condition. Aprocess of decision taking may extend and become far more complicated in the cases when a number of detaineesincrease, if they are in different places, they have committed various crimes, they are speaking different languages. In such complicated condition sophisticated computer based systems, particularly “decision-making program” (35, 179) may be effectively applied. There exist various systems of that kind, but if an official is a manager he can choose “information reporting system,decisionsupport system and executive information systems” to “facilitaterapidand effective decision making,” (18, 291) The above-mentioned systems differ in amount of data stored, their aim, but the common feature is that they processinformation,thenprovideanswers. Main function of decision systems may be expressed in the chain “if-then”: if you do this, then the consequences will be these." The application of decision-making systems is preferable in the cases when there isa lot of data which must be processed quickly, but accurately. For example, some law enforcement institutions in the USA apply “computer-aided dispatch systems or CAD” (43, 74) to provide effective management of victim-police chain. The role of CAD system is so great that it is even “in the core of police departments` decision support process!" (43, 74)
1.1 DECISION TAKING UNDER UNCERTAINTY
Decision is “usually understoodto besynonymous with choice” (13, 1), but sometimes it may be dangerousto choose. At his work an official also has to choose one alternative among others which in the end will be his decision he will be responsible for. American scientist Bruce F. Baird writes that every decision taken may influence taker's “reputation” far in the future. (12, 5) As it has been written in the previous part, more often one has to take decision under uncertainty where one can not absolutely predict its outcomes, but nevertheless one has to work and take decisions. In order to organize this process, not only computer software, but morphological analysis is used as well. Let us take a look at decision making from the standpoint of combinatorial analysis. For example, a manager has to divide three prizes: award, appreciation, cash bonusamonghis 6 juniors. According to the “permutation” it can be done in 6*5*4=120different combinations! (42, 2) More likely that an ordinary manager due to the overload, lack of time or simply idleness, doesnot considereven the tenth part of these combinations, but ratherkeepsin mind his best subordinates anddistributesthe prizes according to their merits. In the above-mentioned problem a manager may calculate all possible variations or choose several ones, but in the end particular decision must be taken and it will be judged by its consequences or “final result”(12, 14). The above-mentioned example of distributing prize is an illustrative one because in practice it is difficult to imagine a manager considering all 120 variations according to the combinatorial theory.Finallythere may be and there are real situations when manager has to find andanalyseall possible variations seriously.
[...]
- Quote paper
- Master of administration Victor V. (Author), 2011, Decision and risk at immigration service work, Munich, GRIN Verlag, https://www.grin.com/document/270461
