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Trapezoid Fuzzy Linguistic WGA Operator

Trapezoid Fuzzy Linguistic WGA Operator
Trapezoid Fuzzy Linguistic WGA Operator

Trapezoid Fuzzy Linguistic WGA Operator

Guiwu Wei

School of Economics and Management, Southwest Jiaotong University, Chengdu, Sichuan

610031, P.R. China

Department of Mathematics, North Sichuan Medical College,

Nanchong, Sichuan 637007, P.R. China

E-mail :weiguiwu@https://www.doczj.com/doc/9b6673417.html,

Abstract

With respect to multiple attribute decision making (MADM) problems, in which attribute values take the form of trapezoid fuzzy linguistic variables, by utilizing some operational laws of trapezoid fuzzy linguistic variables, a new aggregation operator called trapezoid fuzzy linguistic weighted geometric averaging (TFLWGA) operator is proposed. The TFLWGA operator is a more general type of aggregation operator, which is based on the trapezoid fuzzy linguistic weighted geometric averaging (TFLWGA). Moreover, based on TFLWGA operator, a practical method for MAGDM is proposed under fuzzy linguistic environment. The method is straightforward and has no loss of information. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Keywords: Multiple attribute decision making; trapezoid fuzzy linguistic variables; some operational laws; trapezoid fuzzy linguistic weighted geometric averaging (TFLWGA) operator; similarity measure

1 Introduction

In the real world, human beings are constantly making decisions under linguistic environment [1-41]. For example, when evaluating the “comfort” or “design” of a car, linguistic terms like “good”, “fair”, “poor” are usually be used [12]. Up to now, several methods have been proposed for dealing with lin-guistic information. These methods are mainly as follows:

(1) The method based on extension principle, which makes operations on the fuzzy numbers that sup-port the semantics of the linguistic terms [19].

(2) The method based on symbols, which makes computations on the indexes of the linguistic terms [20].

(3) The method based on fuzzy linguistic representation model, which represents the linguistic infor-mation with a pair of values called 2-tuple, composed by a linguistic term and a number [21-23]. (4) The method, which computes with words directly [24-41].

In this paper, we shall investigate the multiple attribute decision making (MADM) problems under fuzzy linguistic environment, in which the decision makers are willing or able to provide only their preferences (attribute values) in the form of trapezoid fuzzy linguistic variables because of time pres-sure, lack of knowledge, or data, and their limited expertise related to the problem domain. Up to now, there is no approach developed for dealing with decision making with trapezoid fuzzy linguistic vari-ables based on the weighted geometric averaging (WGA) operator [42]. Therefore, it is necessary to pay attention to this issue. The aim of this paper is to develop an approach to decision making with trapezoid fuzzy linguistic variables. In order to do so, this paper is set out as follows. Section 2 we introduce the concept of trapezoid fuzzy linguistic variable and some operational laws of trapezoid fuzzy linguistic variables, and then develop a similarity measure between two trapezoid fuzzy linguis-tic variables. Section 3 develops a new aggregation operator such as trapezoid fuzzy linguistic weighted geometric averaging (TFLWGA) operator. Section 4 we develop an approach to ranking the decision alternatives based on the similarity measure and the ideal point of attribute values, which is straightforward and has no loss of information. Section 5 we give an illustrative example to verify the developed approach and to demonstrate its feasibility and practicality. Section 6 concludes the paper.

2 Trapezoid Fuzzy Linguistic Variables

Let {}

1,2,,i S s i t ==L be a linguistic term set with odd cardinality. Any label, represents a possible value for a linguistic variable, and it should satisfy the following characteristics: ①The set is ordered:,if ; ② There is the reciprocal operator: i s i j s s >i j >()i rec s s j = such that ;

1i t j =+?

③Max operator:()

max ,i j i s s s =, if ; ④ Min operator: i s s ≥j ()

min ,i j i s s s =, if . For example, S can be defined as [39]

i s s ≤j ,

1234567{,,,,,}

S s extremely poor s very poor s poor s medium s good s very good s extremely good ========

To preserve all the given information, we extend the discrete term set S to a continuous term set

{}

1,[1,]a a q S s s s s a q =≤≤∈, where q is a sufficiently large positive integer. If , then we

call the original linguistic term, otherwise, we call the virtual linguistic term. In general, the deci-sion maker uses the original linguistic term to evaluate attributes and alternatives, and the virtual lin-guistic terms can only appear in calculation [39].

a s S ∈a s a s Since the decision maker is characterized by his own personal background and experience, in some situations, the decision maker may provide fuzzy linguistic information because of time pressure, lack of knowledge, and their limited expertise related with the problem domain. In the following we intro-duce the concept of trapezoid fuzzy linguistic variable.

Definition 1. Let [,,,]s

s s s s S αβγη=∈%%, where ,,,s s s s S αβγη∈, s β and s γ indicate the inter-

val in which the membership value is 1, with s α and s η indicating the lower and upper values of s %, respectively, then s

% is called a trapezoid fuzzy linguistic variable, which is characterized by the fol-lowing member function (see Fig. 1) [43]

()()

()()

()10,,,,1,

,,,0,s q s s s d s s s s s d s s s s s d s s s s s d s s s s s θαθααθββαβθγθηγθηγηηθμθ?≤≤?

?≤≤?

??

=≤≤??

?≤≤???≤≤?

%

Where S

% is the set of all trapezoid fuzzy linguistic variables, Especially, if any two of ,,,αβγη are equal, then s

% is reduced to a triangular fuzzy linguistic variable; if any three of ,,,αβγη are equal, then s % is reduced to an uncertain linguistic variable [43].

αβγη Fig1 A trapezoid fuzzy linguistic variables s %

Consider any three trapezoid fuzzy linguistic variables [,,,]s s s s s αβγη=%, 11111[,,,]s s s s s αβγη=%,

22222[,,,]s s s s s αβγη=S ∈%%, and suppose that []0,1λ∈, then we define their operational laws as

follows:

(1) ;

111122221212121212[,,,][,,,][,,,s s s s s s s s s s s s s s αβγηαβγηααββγγηη?=?=%%](2) . [,,,][,,,]s

s s s s s s s s λλλλλλ

αβγηαβγη==% In order to measure the similarity degree between any two trapezoid fuzzy linguistic variables

11111[,,,]s s s s s αβγη=% and 22222[,,,]s s s s s S αβγη=∈%%, we introduce a similarity measure as follows

()21212121

12,14s s s q

ααββγγηη?+?+?+?=?

%% (1)

where is called the similarity degree between (12,s s s %%)1s % and 2s %.Obviously, the greater the value of , the closer (12,s s s %%)1s % to 2s %.

The properties of the similarity degree ()12,s s s %% are shown as follows: (1) ;

()120,s s s ≤≤%%1 (2) ()12,1s s s =%% iff , that is 1s s =%%221212121,,,ααββγγηη====; (3) .

()(1221,,s s s s s s =%%%%)3 The Trapezoid Fuzzy Linguistic WGA Operator

Some operators are introduced or proposed as follows:

Definition 2. Let :n

TFLWGA S S →%%, if

()()()(121212TFLWGA ,,,n

n n

s s s s s s )ωωω

ω=???%%%%%%L L (2)

where (12,,,n )ω

ωωω=L is the exponential weighting vector of trapezoid fuzzy linguistic vari-

ables with ()

,1,2,,i i s s S i n ∈=%%%L []0,1i ω∈

,1

1n

i i ω==∑, then function TFLWGA is called the

trapezoid fuzzy linguistic weighted geometric averaging (TFLWGA) operator of dimension n. Espe-

cially, if (1,1,,1n n n ω

=L ), then TFLWGA operator is reduced to a trapezoid fuzzy linguistic

geometric averaging (TFLGA) operator. Example 1. Assume

()

0.2,0.3,0.4,0.1ω=,

[]

11235,,,s s s s s =%,

[]23456,,,s s s s s =%,[]32345,,,s s s s s =%, and []41357,,,s s s s s =%, then by the operational laws of

trapezoid fuzzy linguistic variables, we have

()[]

[][]

[][]

0.2

0.30.4

12341235345623450.1

1357 1.835 3.016 4.129 5.462TFLW GA ,,,,,,,,,,,,,,,,,,s s s s s s s s s s s s s s s s s s s s s s s s ω=???=%%%%

4 MADM Problems Based on the TFLWGA Operator

For the MADM problems, in which attribute values take the form of trapezoid fuzzy linguistic vari-ables and attribute weights take the form of real numbers, we shall develop an approach based on the

TFLWGA operator to MADM under fuzzy linguistic environment as follows: Let {}12,,,m A A A A =L be the set of alternatives, and {}12,,,n G G G G =L be the set of attrib-

utes. Let (12,,,n )ω

ωωω=L be the exponential weighting vector of trapezoid fuzzy linguistic

variables, where []0,1j ω∈

,

,1,2,,j n =L 1

1n

j j ω==∑. Suppose that ()

ij

m n

R r ×=%% is the fuzzy

linguistic decision matrix, where ()()()(),,,ij ij ij ij ij

r r r r r αβγμ?=?S ?∈?

%%%%%% is attribute value, which takes the form of trapezoid fuzzy linguistic variables, given by the decision maker, for the alternative

with respect to the attribute . Let i A A ∈j G G ∈[]12,,,i i i in r r r r =

%%%%L

be the vector of the attribute values corresponding to the alternative ,.

i A 1,2,,i m =L Definition 3. Let that

()

ij m n

R r ×=%% be the fuzzy linguistic decision matrix, then we call

12,,,n L L L L ?=?%%%%L ??

the positive ideal point of attribute values, where

()()()(),,,j j j j j L L L L L αβγη??=??

%%%%%,()(){}

max j i i

j L r αα=%%,()

(){}

max j i i

L r ββ

=%%j

,()

(){}

max j i i

j

L r γγ

=%%,

()(){}

max j ij

i

L r ηη=%%1,2,,j n =L . 43 ,Definition 4. Let that

()

ij m n

R r ×=%% be the fuzzy linguistic decision matrix, then we call

12,,,n I I I I ?=?%%%%L ?? the negative ideal point of attribute values, where ()()()(),,,j j j j j I I I I I αβγη??=??%%%%%,()(){}min j i i I r αα=%%j ,()(){}min j i i j I r ββ=%%,()(){}

min j i i

j I r γγ=%%,()(){}

min j

ij

i

I r

ηη=%%1,2,,j n =L ,. Step 1. Utilize the fuzzy linguistic decision information given in matrix R , and the TFLWGA opera-tor

()()()()1212

12,,,,1,2,,.n

i i i in i i in r TFLWGA r r r r r r i m ωωω

ω==???%%%%%%%L L =L (3) to derive the overall preference value ()1,2,,i r i m =%L

of the alternative ()1,2,,i A i m =L ,

where (12,,,n )ω

ωωω=L is the exponential weighting vector of attributes.

.

Step 2. Utilize the TFLWGA operator

()()()()

121212,,,,1,2,,.n n n r TFLWGA L L L L L L i m ωωωω

+==???%%%%%%%L L =L (4) to derive the overall preference value r +% of the positive ideal point 12,,,n L L L L ??=??

%%%%L ,where (12,,,n )ωωωω=L is the exponential weighting vector of attributes.

Step 3. Utilize the TFLWGA operator

()()()()

1212

12,,,,1,2,,.n n n r TFLWGA I I I I I I i m ωωωω?==???%%%%%%%L L =L (5) to derive the overall preference value r ?% of the negative ideal point 12,,,n I I I I ??=??

%%%%L ,where (12,,,n )ωωωω=L is the exponential weighting vector of attributes.

Step 4. By (1), we get the similarity degree (),i s r r +%% between i r % and r +%, (),i s r r ?%% between i r % and r ?%()1,2,,i m =L .

Step 5. Calculate the relative similarity degree by the formula as follows:

()()()

,,,i i i i s r r D s r r s r r ++?=

+%%%%%% (6)

Obviously, the greater the value of , the better the alternative of .

i D i A Step 6. Rank all the alternatives (1,2,,i )A i =L m and select the best one(s) in accordance with the relative similarity degree ()1,2,,i D i m =L

.

Step 7. End.

5 Illustrative Example

Let us suppose there is an investment company, which wants to invest a sum of money in the best op-tion (adapted from Ref. [11.). There is a panel with five possible alternatives to invest the money:①

A 1 is a car company;②A 2 is a food company;③A 3 is a computer company;④A 4 is a arms company;⑤A 5 is a TV company. The investment company must take a decision according to the following four attributes:①G 1 is the risk analysis;②G 2 is the growth analysis;③G 3 is the social-political impact analysis;④G 4 is the environmental impact analysis. The five possible alternatives are to be evaluated using the linguistic scale

(1,2,,5i A i =L

)

1234567{,,,,,}

S s extremely poor s very poor s poor s medium s good s very good s extremely good ========,

by the decision maker under the above four attributes(whose exponential weighting vector

{}0.3,0.1,0.2,0.4ω=), and construct the decision matrix ()()

54

k ij

R r ×=%% as follows:

[]

[]

[][[][][][[]

[][][[][][][1

234

1124524561345234621367245723461245345673456134512674134613473567346751,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,G G G G A s s s s s s s s s s s s s s s s A s s s s s s s s s s s s s s s s R A s s s s s s s s s s s s s s s s A s s s s s s s s s s s s s s s s A s =%[]

[][]

[234156724562345,,,,,,,,,,,s s s s s s s s s s s s s s s ???

?????

?

?

??????

]]]]] Then, we utilize the approach developed to get the most desirable alternative(s). The following steps

are involved.

Step 1. Utilize the decision information given in matrix R %, we get the vector of the attribute values

corresponding to the alternative , positive ideal point and negative ideal point as

follows:

(1,2,,5i A i =L ) (1) []111121314,,,r r r r r =%%%%%, where []111245,,,r s s s s =%, []122456,,,r s s s s =%,

[]131345,,,r s s s s =%,[]142346,,,r s s s s =%. (2) []221222324,,,r r r r r =%%%%%, where []211367,,,r s s s s =%, []222457,,,r s s s s =%, []232346,,,r s s s s =%,[]241245,,,r s s s s =%. (3) []331323334,,,r r r r r =%%%%%, where []314567,,,r s s s s =%, []323456,,,r s s s s =%, []331345,,,r s s s s =%,[]341267,,,r s s s s =%. (4) []441424344,,,r r r r r =%%%%%, where []411346,,,r s s s s =%, []421347,,,r s s s s =%, []433567,,,r s s s s =%,[]443467,,,r s s s s =%. (5) []551525354,,,r r r r r =%%%%%, where []511234,,,r s s s s =%, []521567,,,r s s s s =%, []532456,,,r s s s s =%,[]542345,,,r s s s s =%.

(6) 1234,,,L L L L L ??=??

%%%%%, where []14567,,,L s s s s =%, []23567,,,L s s s s =%, []33567,,,L s s s s =%,[]43467

,,,L s s s s =%. (7) 1234,,,I I I I I ??=??

%%%%%, where []11234,,,I s s s s =%, []21346,,,I s s s s =%, []31345,,,I s s s s =%,[]41245

,,,I s s s s =%. Step 2. Utilize (3) to derive the overall preference value ()1,2,,i r i m =%L

of the alternatives ,

()1,2,3,4,5i A i =[]1 1.414 2.734 4.090 5.477,,,r s s s s =%, []2 1.231 2.625 4.619 5.933,,,r s s s s =%, []3 1.692 3.060 5.433 6.444,,,r s s s s =%, []4 1.933 3.728 5.102 6.684,,,r s s s s =% []5 1.516 2.961 3.995 5.016,,,r s s s s =%.

Step 3. Utilize (4) to derive the overall preference value ()1,2,3,4,5r i +=% of the positive ideal point L

%

[]3.270 4.573 6.0007.000,,,r s s s s +=%

Step 4. Utilize (5) to derive the overall preference value ()1,2,3,4,5r i ?=% of the positive ideal point I

% []1.000 2.259 3.669 4.762,,,r s s s s ?=%

Step 5. By (1), (suppose that q=7), we get the similarity degree (),i s r r +%% between i r % and r +%,

(),i s r r ?%% between i r % and r ?%()1,2,,i m =L :

()1,0.806s r r +=%%(,)2,0.7s r r +=%%61,()3,0.746s r r +=%%s r r +=%%()5,0.838s r r +=%% ()4,0.749,.

()1,0.653s r r ?=%%,,()2,0.628s r r ?=%%()3,0.549s r r ?=%% ()4,0.520s r r ?=%%,.

()5,0.661s r r ?=%%Step 6. Utilize (6) to calculate the relative similarity degree:

123450.553,0.548,0.576,0.590,0.559D D D D D =====

Step 7. Rank all the alternatives ()1,2,,5i A i

=L in accordance with relative similarity degree

()1,2,3,4,5i D i =:, and thus the most desirable alternative is . 4351A A A A A f f f f 24A 6 Conclusion

In this paper, we have introduced the concept and some operational laws of trapezoid fuzzy linguistic variables. A new aggregation operator called trapezoid fuzzy linguistic weighted geometric averaging (TFLWGA) operator for the trapezoid fuzzy linguistic variables has been proposed. Based on the rela-tive similarity degree, positive ideal point and negative ideal point of attribute values, we have devel-oped an approach to MADM with trapezoid fuzzy linguistic variables, which utilizes the TFLWGA operator to fuse all the given decision information corresponding to each alternative, and uses the rela-tive similarity degree to rank the decision alternatives and then select the most desirable one. Finally an illustrative example has been given to show the developed method. The approach is straightforward can avoid the loss of information. In the future, we shall continue working in the application and exten-sion of the developed method.

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English Linguistic

Chapter 1 Introduction 1. Linguistic and English Linguistic Linguistic 的目的 aims at developing a theory general linguistic 一般语言学 descriptive linguistic 描述性语言学 general linguistic (一般语言学 ) 为 descriptive linguistic (描述性语言学) 提供了 framework(框架), 这也就是为什么 general linguistic 能够被分析和被描述. General linguistic and descriptive linguistic are complementary to each other (相互补充). English linguistic is a kind of descriptive linguistics. 2.The nature of languages (语言的本质) 1. language is a system 2. language is symbolic 3. language is a system of vocal symbols The system of language is called langue 1. language is a system the speaker ’s speech is called parole Competence is the speaker-hearer’s knowledge of his language Performance is the actual use of language in concrete situation 2. language is symbolic ( 语言是象征意义的) 3. language is a system of vocal symbols ( 语言是一种声音符号) 语言学侧重研究的是 speech 不是 written form Reason 1. Biologically (生物上来讲) 儿童学习说比学习读写早得多. 2. Functionally (功能上来讲) 日常生活中口语使用比书面语频繁的多. 3. Historically ( 历史而言) 口头语使用在书面语之前,当今世界有许多语言并没有 留 下 文 字 记录 Language is arbitrary Language is creative Language is double-structured Language is changeable de Saussure 的理论 Chomsky 的理论

电大公共行政学小抄名词解释

1.公共行政学:公共行政学是研究公共组织依法处理政务的有效性、公平性、民主性的规律的交叉性与综合性学科。(在这里公共组织主要是指政府,公共行政就是政府行政。) 2.公共行政环境:公共行政环境是指直接或间接地作用或影响公共组织、行政心理、行政行为和管理方法与技术的行政系统内部和外部的各种要素的总和。 3.组织文化:组织文化是指组织在一定的环境中,逐步形成的全体公共组织成员所共同信奉和遵守的价值观,并支配他们的思维方式和行为准则。(组织文化在政府也可以称之为公共行政组织文化,在企业则称之为企业文化。组织文化包括组织观念、法律意识、道德感情和价值观等。) 4.政府职能:政府职能是指政府在国家和社会中所扮演的角色以及所应起的作用。(换句话说,就是指政府在国家和社会中行使行政权力的范围、程度和方式。) 5.市场失效:市场失效是指因为市场局限性和缺陷所导致资源配置的低效率或无效率,并且不能解决外部经济与外部不经济的问题以及社会公平问题。 6.行政体制:行政体制指政府系统内部行政权力的划分、政府机构的设置以及运行等各种关系和制度的总和。 7.地方政府体制:地方政府体制是指地方政府按照一定的法律或标准划分的政府组织形式. 8.行政区划体制:行政区划体制是指根据一定的原则将全国领土划分为若干部分和若干层次的管理区域,并设置相应的行政机关的组织体制。 9.完整制:完整制又叫一元统属制,是指公共组织的同一层级或同一组织内部的各个部门,完全接受一个公共组织或同一位行政首长的领导、指挥和监督的组织类型。 10.分离制:分离制又称多元领导制,是指一个公共组织的同一层级的各个组织部门或同一组织部门,隶属于两个或两个以上公共组织或行政首长领导、指挥和监督的组织类型。 11.首长制:首长制又称独立制、一长制或首长负责制。它是指行政首长独自掌握决策权和指挥权,对其管辖的公共事务进行统一领导、统一指挥并完全负责的公共组织类型。 12.层级制:层级制又分级制,是指公共组织在纵向上按照等级划分为不同的上下节制的层级组织结构,不同等级的职能目标和工作性质相同,但管理范围和管理权限却随着等级降低而逐渐变小的组织类型。 13.机能制:机能制又称职能制,是指公共组织在横向上按照不同职能目标划分为不同职能部门的组织类型。14.行政领导者:行政领导者是指在行政系统中有正式权威和正式职位的集体或个人。 15.委任制:亦称任命制,是指由立法机关或其他任免机关经过考察而直接任命产生行政领导者的制度。 16.考任制:考任制是指由专门的机构根据统一的、客观的标准,按照公开考试、择优录取的程序产生行政领导者的制度。 17.行政领导权力:行政领导权力是指行政领导者在行政管理活动中,利用其合法地位以不同的激励方式和制约方式,引导下属同心协力达成行政目标的影响力。18..行政领导责任:行政领导责任是指行政领导者违反其法定的义务所引起的必须承担的法律后果。 19.人事行政:人事行政是指国家的人事机构为实现行政目标和社会目标,通过各种人事管理手段对公共行政人员所进行的制度化和法治化管理。20.人力资源:人力资源是指在一定范围内能够作为生产性要素投入社会经济活动的全部劳动人口的总和。它可分为现实的人力资源和潜在的人力资源两部分。 21.程序性决策:也叫常规性决策,是指决策者对所要决策的问题有法可依,有章可循,有先例可参考的结构性较强,重复性的日常事务所进行的决策。 22.非程序性决策:也叫非常规性决策,是指决策者对所要决策的问题无法可依,无章可循,无先例可供参考的决策,是非重复性的、非结构性的决策。 23.危机决策:是指领导者在自然或人为的突发性事件发生后,迅速启动各种突发事件应急机制,大胆预测,做出决定的过程。 24.行政决策参与:是指行政领导者个人或集体在行政决策时,专家学者、社会团体、公民等对决策提出意见或建议的活动。 25.行政执行:行政执行是行政机关及行政人员依法实施行政决策,以实现预期行政目标和社会目标的活动的总和。 26.行政控制:行政控制指行政领导者运用一定的控制手段,按照目标规范衡量行政决策的执行情况,及时纠正和调节执行中的偏差,以确保实现行政目标的活动。27.行政协调:行政协调是指调整行政系统内各机构之间、人员之间、行政运行各环节之间的关系,以及行政系统与行政环境之间的关系,以提高行政效能,实现行政目标的行为。 28.法制监督:法制监督,又称对行政的监督,是指有权国家机关对行政机关及其工作人员是否合法正确地行使职权所进行的监督与控制。 29.舆论监督:舆论监督是指通过在公共论坛的言论空 间中所抒发的舆论力量对政府机构和政府官员滥用权力等不当行为的监督与制约。 30.行政立法:行政立法一般是指立法机关通过法定形 式将某些立法权授予行政机关,行政机关得依据授权法(含宪法)创制行政法规和规章的行为。 31.行政法规:行政法规是指国务院根据宪法和法律,按照法定程序制定的有关行使行政权力,履行行政职责的规范性文件的总称。 32.标杆管理: 标杆管理是指公共组织通过瞄准竞争的 高目标,不断超越自己,超越标杆,追求卓越,成为强中之 强组织创新和流程再造的过程. 33.政府全面质量管理:政府全面质量管理是一种全员 参与的、以各种科学方法改进公共组织的管理与服务的,对公共组织提供的公共物品和公共服务进行全面管理,以获得顾客满意为目标的管理方法、管理理念和制度。 34.行政效率:行政效率是指公共组织和行政工作人员 从事公共行政管理工作所投入的各种资源与所取得的成果和效益之间的比例关系。 35.行政改革:行政改革是指政府为了适应社会环境,或者高效公平地处理社会公共事务,调整内部体制和组织结构,重新进行权力配置,并调整政府与社会之间关系的过程。 36.政府再造:政府再造是指对公共体制和公共组织绩 效根本性的转型,大幅度提高组织效能、效率、适应性以及创新的能力,并通过改革组织目标、组织激励、责任机制、权力结构以及组织文化等来完成这种转型过程。

电大专科《公共行政学》名词解释简答题题库及答案(试卷号:2202)

电大专科《公共行政学》名词解释简答题题库及答案(试卷号:2202) 盗传必究 一、名词解释 1.政府职能:是指政府在国家和社会中所扮演的角色以及所应起的作用。 2.行政区划体制:是指根据一定的原则将全国领土划分为若干部分和若干层次的管理区域,并设置相应的行政机关的组织体制。 3.完整制:又叫一元统属制,是指公共组织的同一层级或同一组织内部的各个部门,完全接受一个公共组织或同一位行政首长的领导、指挥和监督的组织类型。 4.行政效率:是指公共组织和行政工作人员从事公共行政管理工作所投入的各种资源与所取得的成果和效益之间的比例关系。 5.市场失效:是指因为市场局限性和缺陷所导致资源配置的低效率或无效率,并且不能解决外部性问题以及社会公平问题。 6.行政体制:指政府系统内部行政权力的划分、政府机构的设置以及运行等各种关系和制度的总和。 7.程序性决策:也叫常规性决策,是指决策者对所要决策的问题有法可依,有章可循,有先例可参考的结构性较强,重复性的日常事务所进行的决策。 8.行政法规:是指国务院根据宪法和法律,按照法定程序制定的有关行使行政权力,履行行政职责的规范性文件的总称。 9. 管理幅度:是指领导机关或领导者直接领导下属的部门或人员的数额。 10.行政决策参与:是指行政领导者个人或集体在行政决策时,专家学者、社会团体、公民等对决策提出意见或建议的活动。 11.电子政府:是指在政府内部采用电子化和自动化技术的基础上,利用现代信息技术和网络技术,建立起网络化的政府信息系统,并利用这个系统为政府机构、社会组织和公民提供方便、高效的政府服务和政务信息。 12. 公共行政学:是研究公共组织依法处理政务的有效性、公平性、民主性的规律的交叉性与综合性学科。 13. 行政领导责任:是指行政领导者违反其法定的义务所引起的必须承担的法律后果。 14. 风险型决策:是指决策者对决策对象的自然状态和客观条件比较清楚,也有比较明确的决策目标,但是实现决策目标结果必须冒一定风险。 15. 事前监督:是指在某种公共行政管理活动开展之前,监督部门围绕公共行政管理主体的行政行为进行的监督检查。 16. 政府再造:是指对公共体制和公共组织绩效根本性的转型,大幅度提高组织效能、效率、适应性以及创新的能力,并通过改革组织目标、组织激励、责任机制、权力结构以及组织文化等来完成这种转型

What is linguistic

What is linguistics? I think that what should we realize is the fundamental things of linguistics about is that language is more than just a big bound of words. It’s easy to think that what are we doing, what are we speaking, is producing words that we learn when we were kids and now we know them, and now we string them together and found some sort of ways. Maybe there were some wrong ways to string them together, you learn at school. But basically what we doing is using big bound of words. Now if you really think about it. Words alone can’t be even most of what using a language in terms of what we are interested about using languages. And that’s because you think about it. Imagine if you in heeled a Russian dictionary, that’s say that you for all the attendance purpose knew every single word in the language. If you think about it really, even then you would be unable to have any kind of meaning for conversation of language. And 3 and 4 years olds would like be the years hard of you and that’s because obviously, there is also an issue how you put the words together in order to convey meaning. There’s also a said say, if a language is more than just a big bucket of words, then as Saussure’s grammar’s concern, it’s the matter of setting whether the words fit into one of a part of speech. So the idea is that when you got these parts of speech, then

公共行政学名词解释

1.委员会制 委员会制是指在公共组织中,由两个人以上掌握决策权和指挥权,按照多数原则进行决策的公共组织类型。 2.层级制 层级制又分级制,是指公共组织在纵向上按照等级划分为不同的上下节制的层级组织结构,不同等级的职能目标和工作性质相同,但管理范围和管理权限却随着等级降低而逐渐变小的组织类型。 3.机能制 机能制又称职能制,是指公共组织在横向上按照不同职能目标划分为不同职能部门的组织类型。 4.战略管理 公共组织的战略管理是指对公共组织在一定时期的全局的、长远的发展方向、目标、任务和政策,以及资源调配做出的决策和管理艺术。 5.政府再造的含义 政府再造是指对公共体制和公共组织绩效根本性的转型,大幅度提高组织效能、效率、适应性以及创新的能力,并通过改革组织目标、组织激励、责任机制、权力结构以及组织文化等来完成这种转型过程。 政府再造就是用企业化体制取代官僚体制,即创造具有创新习惯和持续改进质量能力的公共组织和公共体制,而不必靠外力驱使。 企业家政府是政府再造的重要内容。企业家政府是指具有企业家精神的行政管理者,用企业的管理方式,以低成本高产出为目标,敢于冒风险、敢于创新、敢于打破僵化官僚体制,取得高绩效的政府。

企业家政府重视政府的成本效益,重视创新与改革,强调利用市场机制和竞争,强调对执行者授权,主张顾客导向,主张放松规制。 6.目标管理的涵义和特点 目标管理是以目标为导向,以人为中心,以成果为标准,而使组织和个人取得最佳业绩的现代管理方法。目标管理的特点是以人、工作和成果为中心的现代管理方法。 7.行政规章的含义 行政规章是指特定的行政机关根据法律和法规,按照法定程序制定的具有普遍约束力的规范性文件的总称。行政规章简称规章。 8.事前监督; 事前监督是指在某种公共行政管理活动开展之前,监督部门围绕公共行政管理主体的行政行为进行的监督检查。 9.行政评估的含义 行政评估是指对行政执行活动的进展情况和效果进行评价和总结,包括行政执行过程评估和行政执行效果评估两个方面。一般意义上所说的行政评估主要是指行政执行效果评估。 10.行政领导权力的概念 行政领导权力是指行政领导者在行政管理活动中,利用其合法地位以不同的激励方式和制约方式,引导下属同心协力达成行政目标的影响力。 11.完整制 完整制又叫一元统属制,是指公共组织的同一层级或同一组织内部的各个部门,完全接受一个公共组织或同一位行政首长的领导、指挥和监督的组织类型。

Cognitive Linguistic

Linguistic Coursework ---Cognitive Linguistics

Linguistic course/work -----Cognitive Linguistics Introduction Cognitive Linguistics is a new approach to linguistics which appeared in the late 1980s and it has grown rapidly at home and abroad, and has gradually turned into the major school of linguistics. To cognitive linguists, language not only enables communication, but also reflects mankind’s conceptual world. In other words, linguistic categories not only enable us to communicate, but also impose a certain way of understanding of the world. It integrates the research ways of language typology and functional linguistics, depicting and elaborating the constitution of human language.Cognitive Linguistics presents a forum for high-quality linguistic research on topics which investigate the interaction between language and cognition. Compared with linguistic structuralism, which sparked off substitution drills, and speech act theory, which initiated a complete reorganization of teaching strategies, the impact of cognitive linguistics is much less revolutionary. Yet the influence of cognitive linguistics may prove very valuable, because it lends theoretical support to a number of accepted teaching approaches in the fields of both vocabulary and grammar. The most influential linguists focusing centrally on cognitive principles and organization were Wallace Chafe, Charles Fillmore, George Lakoff, Ronald Langacker, and Leonard Talmy etc. Masterpieces including: Handbook of Pragmatics、What Categories Reveal about the Mind、Metaphors we Live by、Foundations of Cognitive Grammar、An introduction to Cognitive Linguistics etc. At present, the study of cognitive linguistics is very active in Europe and the United States .Cognitive linguistics in the two research center in the United States has formed two different schools: Berkeley School( Lakoff、Fillmore、Kay、Sweetser) and San Diego School(Langacker、Fauconnier). The Foundations of Cognitive Grammar was written by Langacker, father of Cognitive Linguistics. This book introduces a new and fundamentally different conception of language structure and linguistic investigation.

公共行政学作业3答案

公共行政学作业3答案 一、名词解释 1、行政监察管辖:行政监察管辖,是指对某个监督对象确定由哪一级或者哪一个行政监察机关实施监督和哪一级或者哪一个行政监察机关对哪些特定监督事项有权进行管辖的法律制度。 2、招标性采购:是指通过招标的方式,邀请所有的或一定范围的潜在的供应商参加投标,采购主体通过某种事先确定并公布的标准从所有投标商中评选出中标供应商,并与之签订合同的一种采购方式。 3、标杆管理:是指一个组织瞄准一个比其绩效更高的组织进行比较,以便取得更好的绩效。 4、行政诉讼:所谓行政诉讼,就是公民或法人对行政机关或行政工作人员就违法行政行为向司法机关提起诉讼。俗称“民告官”。 二、单项选择题 1.整个行政执行过程中最具实质意义的、最为关键的阶段是( C )。 A.协调阶段 B.总结阶段 C.实施阶段 D.准备阶段 2.对具有公务员身份的中国共产党党员的案件,需要给予处分的,由( D )给予处分。

A.检察机关 B.行政监察机关 C.党的纪律检查机关 D.党的纪律检查机关和行政监察机关 3.国家预算中占主导地位的是( A )。 A.中央预算 B.县级预算 C.省级预算 D.市级预算 4.根据《立法法》,行政法规和规章应当在公布后的( B )天内报有关部门备案。 A.15 B.30 C.45 D.60 5.批准是一种约束力较强的( A )监督方式。其内容包括:要求监督对象报送审批材料、审查和批准(含不批准)三个基本步骤。 A.事先 B.事中 C.事后 D.全面 6.从20世纪( C )年代开始,西方发达国家相继开始进行行政改革,然后许多发展中国家因为实行市场化也进行不同程度的行政改革。 A.50 B.60 C.70 D.80 7.为了解决在实施决策的过程中出现的而一时又难以查清原因的问题的决策方案,称为( D )。 A.积极方案 B.追踪方案 C.应变方案 D.临时方案 8.行政决策体制的核心( D )。

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