CN109003036A - A kind of anti-act-short-view innovation talent Salary Structure setting method - Google Patents
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Abstract
A kind of anti-act-short-view innovation talent Salary Structure setting method is claimed in the present invention; it solves for improvement type, expansion type and changes the corresponding talent needed for formula innovates (successively abbreviation white swan, grey swan, black Swan), calculate corresponding emolument negotiation reference point problem；The invention introduces the constraint condition for making great efforts cost and advantage variation is employed in limit, realizes the computability conversion of Salary Structure model；Unknown parameter is calculated referring initially to point, progress Parameter Sensitivity Analysis using the model -1 for minimizing recruiting costs；The Salary Structure formed using the fixed emolument of -2 optimal setting of model for employing benefit, pay down bonus, delay in payment bonus is maximized, to reach excitation effort, maximum talent's effectiveness, with anti-act-short-view purpose, that is, the decision and behavior eager for instant success and quick profits, that detrimental effect is generated to long-term goal for preventing short-term interests from driving；Its core application value is to support the matching and calculating of Innovation Demand talent classification and Salary Structure.
Description
Technical field
The invention belongs to human resource management, innovation talent's management, Innovation-based Economy, enterprise and institution managements, emolument pipe
The application fields such as reason, DSS；Different Innovation Demand classification (improvement type, expansion type, changes are directed to more particularly to one kind
The innovation of leather formula) corresponding talent's Salary Structure optimal setting analysis calculation method.
Background technique
Oxford University John doctor Thanassoulis has delivered in Management Science about pipe within 2013
Manage theoretical research (Thanassoulis J.Industry structure, the executive pay, and of talent's Salary Structure
Short-termism [J] .Management Science, 2013,59 (2): 402-419), to enterprise's senior bar group
Salary Structure has done propositional logic with the influence factor for making great efforts cost, external selection etc., has derived anti-act-short-view and tolerance is short
Depending on the Salary Structure under the conditions of behavior, and the external upper limit value condition selected and other related propositions is determined.
Wherein, cost factor Λ is made great efforts in Thanassoulis (2013) setting should have the upper limit, be exerted with ensuring to employ the talent
Power executes, and sets it and meet Λ u < (χ+α+η) ρ S；Present invention discover that this constraint condition excessively relaxes, do not have real
Border meaning, reason are that limit employs benefit and is typically much deeper than 1, i.e.,And 0 < χ, α, η < 1, so that the effort cost
The upper limit of constraint condition is typically much deeper than 1, more relaxes than 0 < Λ < 1 instead；Therefore, it is necessary to further derive about exerting
The constraint condition of power cost；
In addition, Thanassoulis (2013) has derived anti-act-short-view Core constraint (χ-η) v/ (1+r) >=α b；
The relationship of the model variables such as optimal Salary Structure { f, b, v } and { μ, χ, α, η, Λ, r }:
And the corporate profit function z under the Salary Structure:
The model basis for having stood quantitative analysis is established in the above theoretical Salary Structure research for anti-act-short-view, but is not completed
Computability development to model；Therefore further transformation model is needed, integrates variable, introducing helps to calculate the pact realized
Beam condition, design parameter determine method, the computable feature of implementation model.
Urgency and popularity of the country's Innovation-based Economy driving economic development at present to innovation talent's demand, each city
Incentive Compensation measure is neatly used with the enterprises and institutions in area, talent competition is unfolded, but innovation talent's Incentive Compensation at present
The act-short-view of long-range innovative competitiveness is driven, damaged in mechanism in the prevalence of short-term interests；The diversity of classification is innovated,
Also diversity requirement correspondingly is produced for Salary Structure.Therefore, for the Salary Structure of the talent needed for a variety of innovation classifications
It correspondingly optimizes, is a problem for needing to solve.
Summary of the invention
Present invention seek to address that the above problem of the prior art.Propose a kind of anti-act-short-view innovation talent emolument knot
Structure setting method.Technical scheme is as follows:
A kind of anti-act-short-view innovation talent Salary Structure setting method comprising following steps:
Firstly, enterprises and institutions' innovation talent's demand of employing is divided into three classes, i.e., improvement type innovation, expansion type innovation and
The innovation of change formula, successively referred to as " white swan ", " grey swan " and " black Swan "；With fixed emolument coefficient fg, pay down prize
Golden coefficient bg, delay in payment bonus coefficient vgSalary Structure setting is formed, whereinF is fixed
Emolument, b are pay down bonuses, and v is delay in payment bonus, and u is external selection, i.e. the market competition price for employing demand；
Salary Structure coefficient function { fg,bg,vgExpression formula are as follows:
Secondly, with minimize recruiting costs M1 model calculating parameter { μ, χ, α, η, Λ } Parameter Sensitivity Analysis at the beginning of
Beginning reference point, wherein μ: innovation talent has the probability of high fulfillment capability；χ: the probability of success of event；α: high fulfillment capability people
The successful increment of；η: the successful probability of later event；Λ: make great efforts cost needed for innovation talent's successful execution event；R: innovation
The discount rate that the talent uses delay in payment bonus
Analyze sensitivity of the Salary Structure to each parameter；It introduces the constraint condition for making great efforts cost and benefit change is employed in limit
Amount realizes the computability conversion of Salary Structure model, i.e., original incalculable Salary Structure is passed through tightening constraint condition
And the limit of introducing innovation talent employs benefit to be converted into computable Salary Structure；
Again, benefit ρ is employed according to event probability of success χ, the high fulfillment capability probability μ of innovation talent's tool and limit
The judgement of S/u is made great efforts with excitation, prevents the eager for instant success and quick profits act-short-view for damaging long term object, is maximized innovation talent and is employed function
Effect is target, and the Salary Structure of three classes innovation talent is calculated using M2 model, negotiates reference or Salary Structure optimization as emolument
Adjust foundation.
Further, the white swan ", " grey swan " and " black Swan " are defined as follows: white swan={ whole realization energy
Power, the event probability of success is higher, and cost is relatively low for effort }, black Swan={ high fulfillment capability, the event probability of success is lower, effort
Higher cost }, grey swan falls between.
Further, described to calculate unknown parameter referring initially to point, analysis emolument with the M1 model for minimizing recruiting costs
Sensitivity of the structure to each parameter, specifically includes:
Step 1: the part rational conditions minimum by emolument cost, calculate the sensitivity point of parameters such as { μ, χ, α, η, Λ }
Analysis is referring initially to point；
Step 2: from sensitivity analysis is successively carried out to { μ, χ, α, η, Λ } parameter set referring initially to point above；
It is obtained by the parameters analysis method of two above step: fixed emolument coefficient fgSensitive parameter be only effort
Cost Λ, and to other parameters μ, χ, α, η is insensitive；Pay down bonus coefficient bgSensitive parameter collection be { χ, α, η, Λ },
And it is insensitive to μ；Delay in payment bonus coefficient vbSensitive parameter collection be { χ, α, η, Λ, r }, and it is insensitive to μ.
Further, it is described expectation limit employ benefit introducing beShow each stage effort to user is engaged
Reach the benefit successfully generated and outside selects or the desired value of the post market price ratio, typically much deeper than 1；Wherein ρ is
The successful average resource return rate of event, S are that the innovation event can use average abundance, and u is external selection, i.e., this, which is employed, needs
The market competition price asked；
Further, described to introduce the constraint condition for making great efforts costχ: the probability of success of event；α: high
The successful increment of the fulfillment capability talent；R: the discount rate that innovation talent uses delay in payment bonus, μ: innovation talent has height
The probability of fulfillment capability, i.e., when making great efforts cost more than this upper limit value, innovation talent would not select effort, and innovation event is then
It will not succeed.
Further, the M1 model for minimizing recruiting costs are as follows:
Anti- act-short-view constraint condition
Further, described to be made great efforts with excitation, prevent the eager for instant success and quick profits act-short-view for damaging long term object, maximize wound
New recruitment effect is target, and the Salary Structure of three classes innovation talent is calculated using M2 model specifically:
WhereinBenefit and external selection price ratio are employed in expression, that is, user is engaged to abandon unit external selection price
And what is generated employs benefit, z indicates to prevent that enterprise in short-sighted situation goes out the profit outside emolument cost, and u is faced by innovation talent
Outside selection.
Further, the matching primitives of event and the talent set expected limit and employ benefit according to innovation requirements, eventAnd required talent's classification, and the judgement and evaluation to the above innovation category feature variable respectively；It applies on this basis
M2 model calculates the decision variable { α, η } for employing the talent for matching candidate and selects from the candidate talent quasi- on this basis
Employ the talent；
Determine that event employs the parameter after the talent matches and variate-value with quasi- using above step, using Salary Structure system
Number function, calculates the optimal Salary Structure { f of all types of talentsg,bg,vg}。
It advantages of the present invention and has the beneficial effect that:
1) present invention is innovation talent's human resource management, and proposing anti-act-short-view, (as short-term interests drives and damages
The eager for instant success and quick profits behavior of evil innovation event long-term goal) emolument mechanism setting calculation method；This method is to improve innovative competition
Power provides specific decision support method；
2) the computability conversion of Salary Structure theoretical model is realized, including derives convergent effort cost constraint life
Topic, limit employ the operations such as benefit, the conversion of Salary Structure coefficient function；
3) it is white, grey, black Swan (i.e. improvement type, expansion type, the innovation of change formula) classification by innovation category division, and answers
High fulfillment capability probability required for being innovated with such, makes great efforts cost at event probability of success, carries out quantificational description；
4) the local rationality for minimizing recruiting costs, the initial ginseng of calculating parameter sensitivity analysis are more dexterously applied
According to point, the sensitivity analysis of further progress parameter；
5) it calculates using the estimation for employing benefit He the above parameter is maximized again and needs matched decision variable, go forward side by side
One step selects the candidate talent to calculate corresponding Salary Structure according to the estimated value of complete parameter.
It is solved originally by relevant technical literature retrieval as exhausted as possible, the innovative point of above technical scheme with caution
Computational problem is arranged in anti-act-short-view innovation talent Salary Structure, is the effective ways for innovating human resource management, for enterprise
Industry innovative competitiveness, which is promoted, has significant application value.
Detailed description of the invention
Fig. 1 is that the present invention provides the anti-act-short-view innovation talent Salary Structure setting method schematic diagram of preferred embodiment.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, detailed
Carefully describe.Described embodiment is only a part of the embodiments of the present invention.
The technical solution that the present invention solves above-mentioned technical problem is:
Present invention seek to address that for improvement type innovation, expansion type innovation, changing the different classes of innovation talents such as formula innovation
The anti-act-short-view Salary Structure optimization problem of demand, convert the Salary Structure optimal setting of innovation talent to can quantify,
The Analysis of Policy Making problem of calculating；In order to solve this computational problem, on the basis of Thanassoulis (2013) theoretical model
On, propose the technical solution of the module of method containing following three:
Module one: the computability conversion of Salary Structure model；
1) present invention introducesBenefit is employed as desired limit, shows to reach into the effort of each stage for being engaged user
The desired value of benefit and external selection or the post market price ratio that function generates, typically much deeper than 1；Wherein ρ be event at
The average resource return rate of function, S are that the innovation event can use average abundance, and u is external selection, the i.e. city for employing demand
Field competitive price；
2) Thanassoulis (2013) Salary Structure theoretical model is directed to about effort cost constraint because excessively loose
The problem of relaxing and cannot playing effect of contraction, the invention proposes the effort cost constraints more tightened:I.e. when making great efforts cost more than this upper limit value, innovation talent would not select effort, and innovation event then will not
Success.
3) the fixed emolument coefficient f of applicationg, pay down bonus coefficient bg, delay in payment bonus coefficient vgForm emolument knot
{ f is arranged in structureg, bg, vg, whereinF is fixed emolument, and u is external selection, i.e. the market competition valence for employing demand
Lattice, bg, vgAnalogize.
Handled by above 3 points, propose model -1, model -2 be respectively applied to Parameter analysis step, talent's matching and
Salary Structure optimization calculates step.Wherein,
Model -1 (M1)
Solve Parameter Sensitivity Analysis referring initially to point, to meet minimum recruiting costs
Anti- act-short-view constraint condition
Model -2 (M2)
It solves Salary Structure and employs benefit to maximize company
Anti- act-short-view constraint condition:
According to parameter judgement, talent's match decision variable as a result, calculating corresponding Salary Structure:
Module two: Parameter analysis module
The present invention calculates necessary Parameter Estimation Problem for model, has carried out Parameter analysis using following steps:
Step 1: application model -1, i.e. Salary Structure optimize the part rationality minimized about recruiting costs or piece foliation
Property, calculating parameter collection [μ, χ, α, η, Λ] sensitivity analysis referring initially to point；
Step 2: being respectively independent variable, { f with μ, χ, α, η, Λ on the basis of reference pointg,bg,vgIt is dependent variable, into
Line sensitivity analysis；
It obtains through the above steps:
Fixed emolument coefficient fgSensitive parameter be only to make great efforts cost Λ, and to other parameters μ, χ, α, η is insensitive；I.e.
Shi Zhifu bonus coefficient bgSensitive parameter collection be { χ, α, η, Λ }, and it is insensitive to μ；Delay in payment bonus coefficient vbIt is sensitive
Parameter set is { χ, α, η, Λ, r }, and insensitive to μ.Wherein,
μ: innovation talent has the probability of high fulfillment capability；
χ: the probability of success of event；
α: the successful increment of the high fulfillment capability talent；
η: the successful probability of later event；
Λ: make great efforts cost needed for innovation talent's successful execution event
R: the discount rate that innovation talent uses delay in payment bonus
Module three: innovation talent's Salary Structure computing module
On the basis of the first two module analysis, the present invention further will innovation classification be divided into three classes improvement type innovation
--- i.e. white swan={ μ is lower, χ higher, and Λ is lower }, innovative form innovation --- i.e. black Swan=μ higher, χ are lower, and Λ compared with
It is high }, expansion type innovation --- i.e. grey swan falls between；
Step 1: the matching primitives of event and the talent.According to innovation requirements, event, sets expected limit and employ benefitAnd required talent's classification, and the judgement and evaluation to the above innovation category feature variable respectively；It applies on this basis
Model -2 calculate for matching candidate employ the talent decision variable { α, η } (ability increment, the act-short-view later period success it is general
Rate), on this basis, is selected from the candidate talent and intend employing the talent；
Step 2: determine that event employs the parameter after the talent matches and variate-value with quasi- using above step, using emolument
Structural coefficient function calculates the optimal Salary Structure { f of all types of talentsg,bg,vg}。
One company engages an innovation talent with regard to an Innovation Demand is quasi-, now needs to carry out Salary Structure optimization, be exerted with excitation
Power prevents short-term interests from driving, the successful execution innovation event.
Innovation talent's Salary Structure optimization system is described as follows first:
1) company employs benefit to innovation talent limit and is desired forWherein, ρ returns for the successful average resource of event
Report rate, S are that the innovation talent can use average abundance, and u is the external selection that the talent faces, the i.e. market for employing demand
Competitive price；
2) innovation talent's Salary Structure is expressed as { fg,bg,vg, wherein fgFor fixed emolument coefficient, bgFor pay down prize
Golden coefficient, vgFor delay in payment bonus coefficient,F is fixed emolument price, bg, vgAnalogize；
3) the decision phase of innovation talent is divided into short-term and long-term two stages, the first stage be it is short-term, indicated with t=1,
Second stage be it is long-term, indicated with t=2.Innovation talent makes a policy in the first stage, in the first stage at the end of obtain it is short
Then phase income obtains long-term gain at the end of second stage；
4) innovation classification be divided into three classes, respectively formula improvement type innovation --- abbreviation white swan=μ is lower, χ higher, and Λ compared with
It is low }, innovative form innovation --- abbreviation black Swan={ μ higher, χ are lower, Λ higher }, expansion type innovation --- referred to as grey swan,
It falls between；Wherein, μ is the probability that innovation talent has high fulfillment capability, and χ is the probability of success of event, and Λ is innovation
Make great efforts cost needed for talent's successful execution event；
It is described based on system above, the anti-act-short-view Salary Structure setting method of innovation talent, technical solution embodiment
By three functional modules, totally five steps are carried out, specific as follows:
Functional module one: the computability conversion of Salary Structure model.Wherein, it proposes as step 1 and more tightens
Effort cost constraint proposition: that is,
There are an innovation talents to make great efforts the cost upper limitWhen making great efforts cost more than this upper limit value, wound
The new talent would not select effort, and innovation event will not then succeed；
The proposition shows whether innovation talent makes great efforts the decision executed and event probability of success χ, with high fulfillment capability
Probability μ, the successful increment α of high fulfillment capability and the discount rate r of delay in payment reward are related, and unrelated with outside selection u.
Specific derivation is as follows:
Assuming that the fulfillment capability for no matter employing the talent is high or low, then there is company when the minimum harmless effort of innovation talent
Benefit:
2 χ ρ S (1) of z <
According to Thanassoulis (2013),
Λ u < (χ+α+η) ρ S (3)
(2) (1) is substituted into obtain:
(4) (3) are substituted into obtain:
It enables
A, B, C is brought into (5) formula to obtain
Above-mentioned inequality is arranged, is obtained:
(2 α μ Λ B-AB) (χ+α Λ μ) < α Λ rCA (6)
Again according to Thanassoulis (2013)
χ > α+η (7)
In view of 0 < χ, α, η < 1, then obtained by (7):
(6) are substituted by (8) formula α < χ to obtain:
(2 α μ Λ B-AB) (1+ Λ μ) < Λ rCA
Obvious Λ < 1, then
B (2 α μ Λ-A) (1+ μ) < rCA
It enables
Obviously
Then
I.e.
It is converted to
(2 α μ Λ-A) (1+ μ) < Cr
∵ C=χ+α μ and α < χ
∴ α (1+ μ) < C < χ (1+ μ)
I.e.
2 α μ Λ < r χ+A
2 χ of A=χ+α+η < is obtained by formula (7), therefore
+ 2 χ of 2 α μ Λ < r χ
I.e.
Therefore making great efforts cost constraint proposition must demonstrate,prove.
Module one produces following three parts functional relation as a result, they are respectively:
The first, make great efforts the anti-act-short-view constraint condition of cost tightening:
The second, to minimize model -1 of the recruiting costs part rationality as target, constraint condition is same as above；
Third employs model -2 of the benefit as target using maximization company, and constraint condition is same as above；
Innovation event-is completed by model 2 to intend after employing the matching of innovation talent's parameter, is calculated by Salary Structure function
All kinds of anti-act-short-view Salary Structures of innovation talent, wherein Salary Structure function are as follows:
Module one realizes the computability conversion of Salary Structure model, and next step or functional module are naturally
Parameter analysis；
Functional module two: Parameter analysis module
The implementation steps refinement of Parameter analysis functional module is described as follows by existing example:
Step 1: initialization.Assuming that market discount rate r=0.1, other parameters initial value be set as [μ, χ, α, η, Λ]=
[0,0,0,0,0]；
Step 2:, will by user is engaged for fixed emolument, the weight preference of pay down bonus and delay in payment bonus
Three functional relations of Salary Structure model are integrated into integration objective, such as W=0.6fg+0.1bg+0.3vg, as model -1
Integrated objective function；
Step 3: to minimize above-mentioned emolument payment targets as local rationality, solving parameter vector x1=[μ1,χ1,α1,η1,
Λ1], as Parameter Sensitivity Analysis referring initially to point.This example calculates referring initially to point x1=[μ1,χ1,α1,η1,Λ1]
=[0.0034,0.6461,0,0.0630,0.2455,0.1696].The reference point shows part reasonability, i.e., if company
Using minimize emolument payment as target, while again by anti-act-short-view constraint condition restrict, then company be only ready offer or
Employ: event probability of success χ high, act-short-view later period still are able to that successful probability η is larger, it is not high to make great efforts cost Λ, and right
Successful increment α that fulfillment capability height μ, the high fulfillment capability of the talent generates etc. simultaneously pays no attention to；
Step 4: reference point being obtained as basic point using step 3, respectively with μ, χ, α, η, Λ, u is independent variable, Salary Structure fg,
bg,vgFor dependent variable, Parameter Sensitivity Analysis is carried out one by one；It obtains, fixed emolument coefficient fgSensitive parameter to make great efforts cost
Λ is insensitive to other parameters μ, χ, α, η；Pay down bonus coefficient bgSensitive parameter collection be { χ, α, η, Λ }, not to μ
It is sensitive；Delay in payment bonus coefficient vbSensitive parameter collection be { χ, α, η, Λ, r }, it is insensitive to μ.
Functional module three: innovation talent's Salary Structure computing module
Module three carries out innovation talent's Salary Structure calculating according to the following steps:
Step 1: expection or judgment value according to Types of Innovation to following parameter, as benefit expectation is employed in limitIt is set as
100, χ, for μ depending on Types of Innovation, the probability of the relatively easy innovation success realized, executed of improvement type innovation is higher, right
It is required generally in fulfillment capability, effort cost is not high, is set as χ=0.8, μ=0.5, Λ=0.4；Change formula innovation difficulty is big,
The probability of success is low, requires height for fulfillment capability, is set as χ=0.2, μ=0.9, Λ=0.8；And expansion type innovation difficulty is suitable
In, improvement type innovate and change formula innovation between, successful probability also accordingly these two types innovation the probability of succesies it
Between.
Step 2: the parameter set that applying step 1 determines executes model -2, calculates α, η, carries out parameter with candidate is employed
Matching is selected and intends employing the talent；
Step 3: quasi- after application matching employs talent's complete parameter collection and Salary Structure coefficient function, and calculating is prevented short-sighted
Innovation talent's Salary Structure { f of behaviorg、bg、vg}。
The above embodiment is interpreted as being merely to illustrate the present invention rather than limit the scope of the invention.
After the content for having read record of the invention, technical staff can be made various changes or modifications the present invention, these are equivalent
Variation and modification equally fall into the scope of the claims in the present invention.
Claims (8)
1. a kind of anti-act-short-view innovation talent Salary Structure setting method, which comprises the following steps:
Firstly, enterprises and institutions' innovation talent's demand of employing is divided into three classes, i.e. improvement type innovation, expansion type innovation and change
Formula innovation, successively referred to as " white swan ", " grey swan " and " black Swan "；With fixed emolument coefficient fg, pay down bonus coefficient
bg, delay in payment bonus coefficient vgSalary Structure setting is formed, whereinF is fixed emolument, b
It is pay down bonus, v is delay in payment bonus, and u is external selection, i.e. the market competition price for employing demand；
Secondly, calculating { μ, χ, α, η, Λ } referring initially to point with the M1 model for minimizing recruiting costs, wherein μ: innovation talent's tool
There is the probability of high fulfillment capability；χ: the probability of success of event；α: the successful increment of the high fulfillment capability talent；η: later event success
Probability；Λ: make great efforts cost needed for innovation talent's successful execution event；Analyze sensitivity of the Salary Structure to each parameter；It introduces
The constraint condition and limit for making great efforts cost employ advantage variation, realize the computability conversion of Salary Structure model, i.e., not by original
Computable Thanassoulis, J.2013, Management Science Salary Structure theoretical model pass through tightening constraint item
Part and the limit for introducing innovation talent employ benefit to be converted into computable Salary Structure；
Again, has high fulfillment capability probability μ according to event probability of success χ, innovation talent, and benefit ρ S/u's employed in limit
Judgement is made great efforts with excitation, prevents the eager for instant success and quick profits act-short-view for damaging long term object, and maximizing innovation talent and employing effect is mesh
Mark, using M2 model calculate three classes innovation talent Salary Structure, as emolument negotiate referring to or Salary Structure optimize and revise according to
According to.
2. anti-act-short-view innovation talent Salary Structure setting method according to claim 1, which is characterized in that described white
Swan ", " grey swan " and " black Swan " are defined as follows: white swan={ whole realization ability, the event probability of success is higher, effort
Cost is relatively low }, black Swan={ high fulfillment capability, the event probability of success is lower, make great efforts higher cost }, grey swan between the two it
Between.
3. anti-act-short-view innovation talent Salary Structure setting method according to claim 1, which is characterized in that the use
The M1 model for minimizing recruiting costs calculates sensitivity of the unknown parameter referring initially to point, analysis Salary Structure to each parameter, tool
Body includes:
Step 1: the part rational conditions minimum by emolument cost, the Parameter Sensitivity Analysis of calculating parameter { μ, χ, α, η, Λ }
Referring initially to point；
Step 2: from sensitivity analysis is successively carried out to { μ, χ, α, η, Λ } parameter set referring initially to point above；
It is obtained by the parameters analysis method of two above step: fixed emolument coefficient fgSensitive parameter be only make great efforts cost Λ,
And to other parameters μ, χ, α, η is insensitive；Pay down bonus coefficient bgSensitive parameter collection be { χ, α, η, Λ }, and not to μ
It is sensitive；Delay in payment bonus coefficient vbSensitive parameter collection be { χ, α, η, Λ, r }, and it is insensitive to μ.
4. anti-act-short-view innovation talent Salary Structure setting method according to claim 1, which is characterized in that the phase
Hope limit employ benefit introducing beShow to reach the benefit successfully generated and outside to the effort of each stage for being engaged user
The desired value of selection or the post market price ratio, typically much deeper than 1；Wherein ρ is the successful average resource return rate of event,
S is that the innovation event can use average abundance, and u is external selection, i.e. the market competition price for employing demand.
5. anti-act-short-view innovation talent Salary Structure setting method according to claim 1, which is characterized in that described to draw
Enter to make great efforts the constraint condition of costWherein, r is the discount rate that innovation talent uses delay in payment bonus, i.e.,
When making great efforts cost more than this upper limit value, innovation talent would not select effort, and innovation event will not then succeed.
6. anti-act-short-view innovation talent Salary Structure setting method according to claim 5, which is characterized in that the use
Minimize the M1 model of recruiting costs are as follows:
Anti- act-short-view constraint condition
7. anti-act-short-view innovation talent Salary Structure setting method according to claim 5, which is characterized in that it is described with
Excitation makes great efforts, prevents the eager for instant success and quick profits act-short-view for damaging long term object, and maximizing innovation talent and employing effect is target, uses
The Salary Structure of M2 model calculating three classes innovation talent specifically:
WhereinBenefit and external selection price ratio are employed in expression, that is, user is engaged to abandon unit external selection price and generate
Employ benefit, benefit, the external selection that u is faced for innovation talent are employed in z expression.
8. anti-act-short-view innovation talent Salary Structure setting method according to claim 5, which is characterized in that event with
The matching primitives of the talent set expected limit and employ benefit according to innovation requirements, eventAnd required talent's classification, and point
Other judgement and evaluation to the above innovation category feature variable；It is employed on this basis using the calculating of M2 model for matching candidate
The decision variable { α, η } of the talent is selected from the candidate talent on this basis and intends employing the talent；
Determine that event employs the parameter after the talent matches and variate-value with quasi- using above step, using Salary Structure coefficient letter
Number calculates the optimal Salary Structure { f of all types of talentsg,bg,vg}。
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| WO2021003791A1 (en) * | 2019-07-11 | 2021-01-14 | 清华大学 | Black swan event decision method and apparatus based on subjective and objective joint prediction |
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| WO2021003791A1 (en) * | 2019-07-11 | 2021-01-14 | 清华大学 | Black swan event decision method and apparatus based on subjective and objective joint prediction |
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