The equilibrium of venture capital incentive contract: Optimization and Q-learning approaches

. The current study presents an incentive contract model to allocate income to venture projects. In this respect, Venture Capital (VC), as one of the main sources of (cid:12)nancing innovative projects, faces several challenges such as moral hazards, information asymmetry, and interest con(cid:13)icts, often referred to as three agency problems. In addition to the identi(cid:12)cation of the factors that may a(cid:11)ect the income of venture projects and elaboration of cost functions, this study presented an optimal incentive contract model from the perspectives of venture capitalists and entrepreneurs. In this model, a venture capitalist, as an active investor, provides entrepreneurs with managerial and training assistance. The results revealed that the higher the initial ability of the entrepreneur was, the less money the venture capitalist would pay for training. Of note, in case the venture contract was not accepted, the wealth that the contract parties would obtain would become an in(cid:13)uential factor in the contract payment function. This model was studied considering the bounded rationality hypothesis and implemented using the Q-learning algorithm. In addition, the results obtained from the Q-learning approach were found to be reasonably convergent with the Nash equilibrium.

Introduction
Entrepreneurial equity investments by venture capitalists, corporate venture capitalists, angel investors, and more recently, crowd funders and accelerators are the key sources of capital that contribute to fuelling innovation and development [1]. A risk nance and Venture Capital (VC) environment are essential to wealth creation revolution and rapid economic growth that can leverage innovation, promote technology, and harness knowledge-based ideas [2]. In other words, VC is an important intermediary that enables transforming the capital into innovations in a highly productive manner [3]. Among all kinds of entrepreneurial nance, VC mostly address the growth in the revenue of companies, business models, and current investors [4]. In the nancial area, VC is regarded as the frontier of innovation, liberated from the regulations and precedents of traditional markets [5]. The VC market involves earlystage and, consequently, high-risk investments, whose main members are active investors [6].
In VC investment, capital and competence are transferred from the venture capitalist to the entrepreneurial rm at the value-adding phase, and the capital transformation is considered as the nal ending of the investment decision phase [7]. Venture capitalists are mainly nancial intermediaries who not only provide nancing but also o er advice and monitoring, and they play a very active role in negotiating with suppliers, helping recruit and compensate key individuals, and providing strategic advice and access to consultants and lawyers [8]. Therefore, the management team is an important factor for VCs [9].
VCs face four generic concerns in the investment process: 1. The entrepreneur will not work hard to maximize the value after the investment is made; 2. The entrepreneur is more aware of his ability than the VC; 3. After the investment, there will be circumstances when the VC disagrees with the entrepreneur and VC will claim the right to make decisions; 4. The entrepreneur can \hold up" the VC by threatening to leave the venture when the entrepreneur's human capital is particularly valuable to the company [10].
In uncertain environments, VCs fail to exploit even available information due to two obstacles, namely information asymmetry and agency problems [5]. The agency problem arises when the principal (venture capitalist) and agent (entrepreneur) pursue two di erent objectives and when the principal faces di culty understanding what the agent is actually doing [11]. Asymmetric information refers to a situation where one party to a contract has access to the relevant information that is not available to the other party. A venture capitalist needs to trade o the bene ts and costs when attempting to attenuate the agency problems in the investment process [12]. Kaplan and Stromberg reviewed the con icts of interest between an entrepreneur and a venture capitalist and identi ed a number of possible ways that the investor could mitigate these con icts. They found that the investor could rst engage in information collection before deciding whether to make investment in order to screen out the ex-ante unpro table projects and bad entrepreneurs. Second, the investor could engage in information collection and monitoring once the project is under way. Third, nancial contracts, i.e., allocation of the cash ow and control rights between the entrepreneur and investor, would be designed to provide incentives for the entrepreneur to behave optimally [13]. VC investments are made under conditions with potentially extreme information asymmetry and agency cost that can motivate the parties to design nancial contracts [14]. The contract tends to create a mutual understanding between the parties, hence forming a basis for successful cooperation. This justi es the time spent on negotiation in the VC investment process [15].
Contracting and monitoring e orts can improve the portfolio performance by reducing the possible losses caused by moral hazard and adverse selection [16]. Optimal contract arrangement can overcome the revenue con ict between the venture capitalists and entrepreneurs and help them reach mutually motivating conditions [17].
Bitler et al. developed a principal-agent model in an entrepreneurial setting and tested the predictions made by the model based on unique data derived from entrepreneurial e ort and wealth in privately held rms. They found that entrepreneurial ownership shares would decrease upon increase in the rm risk; the entrepreneurial e ort increased with ownership and it increased rm performance [18]. Fluck et al. proposed a model to study the design of nancial contracts between entrepreneurs and venture capitalists. They demonstrated that staged nancing would alleviate the e ort provision problem [19]. Considering the fact that both capitalists and entrepreneurs aim to maximize their own revenue, Zou and Zhou presented an optimal contract model. They also designed an optimal contract for short-and long-term VCs from the venture capitalists and entrepreneurs' perspectives [17].
The advisory, networking, and experience services provided by VCs are complemented by the entrepreneurs' technological and innovative skills. These complementary skills will in turn create a partnership that has a signi cant impact on the company value [20]. Gonzalez-Uribe, referring to direct evidence based on patent citations, reported that venture capitalists di used knowledge about their existing patented innovations among the portfolio companies [21]. Helmers et al. highlighted the signi cant positive e ect of information transmission through the interlocking boards of directors on innovation [22]. A venture capitalist can transfer valuable knowledge to an entrepreneur and facilitate innovation. He/she can also communicate the entrepreneur's innovative knowledge to other portfolio companies. In fact, venture capitalists take on a new role as knowledge intermediaries [23].
Common sense says that to grasp investment opportunities, people do not consider only economic factors; instead, they take into account psychological factors. In fact, individuals face bounded rationality in their decisions. Bounded rationality models aim to answer the question \How do people with bounded time and knowledge make decisions?" In this regard, Rodriguez-Fernandez et al. pointed out that adequate analysis of the past actions of market players improved the decision-making process of negotiators and allowed them to maximize their outcomes. They proposed a new model based on an adaptation of the Qlearning reinforcement learning algorithm to estimate the expected prices given in contracts in a speci c context. They also found that the learning method could identify the best scenario for each situation since the behavior of negotiators would change depending on the negotiation environment [24].
According to the study conducted by Vergara et al. [20], the model proposed in this study took into account several issues such as the e ect of the potential market value on the revenue function, constraints of e ort and capability levels, and exponential growth of these features. This model was designed to allocate the proceeds from the innovative projects with the objective of reducing the con icts of interest between the contract parties by providing su cient incentive for them and leading them to an optimal contract. Of note, the venture capitalists, as active investors, played a key role in the success and accumulation of the project revenue by transferring experiences to entrepreneurs and training them. This model was designed from the venture capitalists and entrepreneurs' perspectives from which the parameters of optimal contract arrangement were derived separately for each case. Further, equilibrium solutions were extracted from optimal solutions to achieve a uni ed strategy.
Followed by this introductory section, Section 2 describes the proposed mathematical investment model from the venture capitalists and entrepreneurs' points of view and o ers optimal solutions for each party. The rest of this section calculates the equilibrium answers so that the parties can reach agreement. Finally, it examines the problem from the perspective of bounded rationality using the Q-learning method and reports the obtained results. Section 3 presents the most relevant conclusions of this research.

The proposed model
In the VC sphere, the entrepreneur's e orts and abilities are the determinants of income function [17]. In addition, the potential market value is believed to be a ecting the income functions of the VC as well as the concerning e ort and ability; however, it is not possible to increase the level of e ort and ability inde nitely. At early stages, increase in the e ort and ability is observed at a higher rate; however, from the level one onwards, it takes much capital to make a small change in the level of ability or e ort. Therefore, some nonlinear functions in exponential forms were taken into account, indicating the entrepreneur's e ort and ability. The ability function is developed based on the desired properties in Eq. (3). Accordingly, the VC income function (venture capitalist's observable income) can be de ned as: where f s (x) and f (T ) represent the entrepreneur's e ort and ability functions, respectively. In addition, V stands for the potential market value of the company that depends on the environment and market conditions of the innovative project. Finally, ", as the random error of VC income function, follows the normal distribution: " N(0; 2 ).
Realization of the entrepreneurs' innovative ideas requires their e ort. In the e ort function, their e ort level is denoted by x which takes a value in the of range of [0; 1). The e ort function is regarded as a positive but descending function, i.e., f s (0) = 0, f 0 s > 0, and f 00 s < 0. The e ort function is de ned as: where ; is a constant coe cient that indicates the degree of convexity of f s (x). In the discussed function, the greater the entrepreneur's e ort level, the closer the e ort function to 1. Of note, in the case of in nite e ort level, it takes the value of (0 f s (x) 1) [19].
It should be noted that the venture capitalists actively invest their funds in the existing plans to obtain the ability function, meaning that in addition to investment, they increase the knowledge of entrepreneurs and improve their ability level by holding training courses and providing management assistance. One of the advantages of VC companies over other methods of nancing is the management and training assistance provided by venture capitalists. Hence, entrepreneur's ability to function varies with respect to the level of cost (T ) paid by the venture capitalist to enhance the former ability. Therefore, it can be stated that: where 0 is the ability level of the entrepreneur.
As obviously observed in the de ned function, if the investor (venture capitalist) does not invest in entrepreneur training, the initial ability level of the entrepreneur will not change. On the contrary, if the venture capitalist invests in entrepreneur training (T ), the ability level will be greater than 1. However, learning can compensate for lack of e ort; in other words, the ultimate value of the learning function can be greater than 1: 0 f (T ).
Knowing the e ort and ability functions, we can de ne the revenue function of the plan as follows: This function acts such that if an in nite level is attributed to the entrepreneur's e ort and no money is allocated by the venture capitalist, both parties will reach the maximum value of the company. However, it is obviously costly and sometimes impossible to make in nite e orts. In this situation, the investor attempts to improve the entrepreneur's ability level by spending money on training in order to achieve the maximum predicted value for the company. To ensure the e ort level of the entrepreneur and maximize the venture project, both parties should work out an incentive contract where the incentive payment function to the entrepreneur is de ned as follows: S(; ) = () + (); (5) where () stands for the xed component and () the variable component of the payment [17].
Followed by the contract approval, the VC parties incur costs. The cost incurred by the entrepreneur depends on his e ort and ability. The venture capitalist pays for managing, monitoring, and training. Accordingly, the cost functions of venture capitalist and entrepreneur are de ned as follows: Entrepreneur: Venture capitalist: where e x is the e ort cost with x representing the effort level of entrepreneur, and the constant coe cient certi ed based on each person's characteristics. Subsequently, the monetary income of the contract parties is described as: Monetary income of the venture capitalist: (9) In this case, the certainty equivalent wealth for the venture capitalist and entrepreneur, respectively, are given as follows: Venture capitalist: Entrepreneur: where v is the risk aversion coe cient of the venture capitalist, 2 v (1 ()) 2 where m is the wealth that the entrepreneur can obtain if the venture contract is not accepted.

Entrepreneur
The optimum investment model from the entrepreneur's perspective is: s.t.: where q is the wealth that the investor can obtain if the venture contract is not accepted.

Optimal solution to the model
Based on the model presented in Section 2.1, this issue falls into the category of constrained multi-objective optimization problems. To solve this model, the Karush, Kuhn, Tucker (KKT) method was employed.
In addition, to implement the conditions of the KKT, the model must be written as a maximization problem and the constraints must be ( 0). The conditions of the KKT are: In the following, we will solve the model through these conditions.
Upon solving IC and obtaining the optimal value of x, the IC constraint is removed from the model and then, According to the rst condition of KKT: @f @ ; @f @ = 1 @g 1 @ ; @g 1 @ ; 2 4 @f @ @f @  x   Finally, we came up with the following optimal solutions for venture capitalist that are listed in Table 1.

Entrepreneur
As described in the previous section, the model proposed in Subsection 2.1.2 is solved from the entrepreneur's point of view and the obtained optimal values are listed in Table 2.

Equilibrium in the optimal solutions of VC parties
A comparison of the optimal results obtained for the contract parties of the VC reveals that the optimum values of the e ort level (x) and constant component of the contract ( ) are di erent for the parties. In this situation, the parties will agree if their optimum answers are close.
The equilibrium values of the parties are obtained in this section and listed in Tables 1 and 2. An important question may arise here: how much does the venture capitalist pay (T ) for training the entrepreneur and increasing his/her ability to equalize the e ort level of the contracting parties? In addition, in the case of non-participation in the plan (m + q), how much should the total amount of income of the parties be for the constant component of the contract to make an equilibrium between the parties? 2.3.1. Parametric equilibrium values In order to obtain the parametric equilibrium solution, the optimal values of the e ort level, from both parties' perspective, as well as the optimal values of the constant component of the contract must be equalized. To equalize the optimal e ort level of the parties, rst, it should be determined that how much the capitalist should pay to train the entrepreneur in order to balance the optimal e orts level of the parties. Consequently:

Stating numeric values
Once tending to fund through the VC, entrepreneurs must present their innovative plan(s) to the venture capitalist for assessment and examination. The capitalist's assessment illustrates that the entrepreneur's innovative plan, if successful, could bring a value of 15 units (V = 15) for the venture company. Moreover, the entrepreneur's proposed plan was estimated to be 0.4 at the total investment required in the plan (r = 0:4). The entrepreneur's initial ability level was placed at the closed interval of [0; 1] (in this study, it was ( 0 = 1); however, it can vary while the capitalist holds training courses). The standard deviation, as a measurement of risk aversion, equals 1 ( = 1). In all studies, the entrepreneur was considered to be more risk averse than the venture capitalist. Therefore, in this study, it is assumed that entrepreneur's and venture capitalist's risk aversion coe cients are 3 and 1, respectively ( u = 3; v = 1). There are also constant coe cients in the functions allocated to e ort and cost which are used to determine the degree of convexity and gradient of the functions. In this numerical study, according to the personal characteristics of the entrepreneur and capitalist, the following values are estimated for these coe cients: Finally, once the optimal value for T is determined, the optimal and equilibrium values of the e ort level, from the perspective of the contract parties, are as follows: x v = x u = 4:216: By determining the equilibrium values of and x as well as the desired cost of holding the training courses (T ), we can create equilibrium in the values of the constant component of the contract ( ). By substituting the numbers into Eq. (28), the following result will be obtained: m + q = 1:5313: Therefore, if the total saved capital of the capitalist and entrepreneur (if not participating in the plan) is 1.5313 units, equilibrium in the constant component of the contract will be reached.
Ultimately, with regard to the assumptions of the problem and values de ned for the variables, the optimal values from the parties' perspectives are summarized in Table 3. Of note, the optimal values given in Table 3 were calculated, assuming that T = 0:6018.
As shown, , income, and pro t are sensitive to m and q. The e ects of m and q values on and pro t are illustrated in Figures 1 and 2, respectively.

Bounded rationality vs. game theory
Here, the venture capitalist and entrepreneur, as the two parties in the contract, are consequently considered as parties to the game, facing numerous strategic decisions to cooperate with each other. Given the nature of the model and variables used in it, the values of Table 3. Optimal values of numerical example from the perspective of the parties.  this is that if the investor proposes a low , a higher cost will be allocated to training the entrepreneur since the proposed is proportional to the ability of the entrepreneur, and choosing a low indicates that the investor believes in the entrepreneur's low ability, hence forced to provide more educational services. On the contrary, there are two views concerning the entrepreneur: 1. Entrepreneurs increase their e ort with the increase in the proposed (Table 4), 2. Entrepreneurs decrease their e ort with the increase in the proposed (Table 5). In this matrix, the entrepreneur's and venture capitalist's strategies are shown in the rst row and column, respectively. The values written in the matrix represent the players' income where the upper value is related to the venture capitalist and the lower value belongs to the entrepreneur. In each of the matrix cells, the numbers on the left correspond to the individuals' pro t, while those on the right (the larger number) correspond to their income regardless of the costs.   Individuals' incomes and bene ts are calculated based on the items such as the cost of risk-taking in the cost section as well as the non-real and tangible costs in order to clarify the results.
To meet the conditions of bounded rationality in the model, assume that in the initial stage, the contract parties with bounded knowledge and incomplete recognition of each other will make one of the strategic decisions. With additional information and a greater understanding of the environment over time, the parties will update their strategies. As a result, learning will happen during the implementation of the contract and will a ect the decisions of individuals. Therefore, learning algorithms are used in the model. This study also employed Q-learning algorithm as one of the reinforcement learning algorithms.
In this learning algorithm, the player number i performs the activity a i t at moment t and receives the reward R i t (a i t ; a i t ) based on the activity a i t , which is played by the rival. Then, function Q is updated based on the following recursive formula: where is the reducing factor and t = C i t 1 (a i ) the learning rate variable. Then, the next activity, a i t+1 , is randomly chosen from the probabilities determined through the following exponential decision-making formula: The results of this algorithm depend on many factors including the parameters used in the model such as , , and that have a great impact on the results. Other factors are the frequency of learning the model and its run time. In this regard, numerous studies have been carried out to nd proper values for these parameters. The values = 0:96, = 0:63, and = 6 were taken into account in this study [25]. Among the most important and in uential factors are the condition of stopping the algorithm (the degree of probability convergence of the chosen strategy) and random choices in the algorithm. This study took into account the probability of higher than 0.999 for the players simultaneously. On the other hand, random choices of the algorithm made the software output results not the same. To solve this problem, the algorithm should be repeated until learning takes place. In doing so, the probability of choosing each matrix cell and each strategy by the parties can be determined. The results of the two previously presented games are given in Tables 6 and 7. In the given matrix, the upper and lower numbers in each cell represent the number of repetitions and the corresponding probability, respectively.
The created matrices can be examined based on game theory. This game has two players who decide at the same time. Here, each player must maximize his/her expected income based on his/her perceptions of the other player. Based on interpretations, Nash  equilibrium in the generated game matrices can be calculated. The most important feature of the Nash equilibrium is that the outcome is not the greatest for the players. In this balance, the assumption of the rationality of the players is a necessary condition, and the correctness of each player's belief in choosing the opponent is a su cient condition. The distinct cell in Tables 6 and 7 indicates the Nash equilibrium.  Tables 8 and 9 show the probability of choosing each strategy for the investment parties based on the rst and second views of the entrepreneurs. The basis of the calculations is the pro t earned so that the results can be compared with those of the mathematical solution.

Conclusion
This paper attempted to design a model for venture capital contracts to prove that despite di erent expectations of the contract parties, equilibrium points exist that could convince the parties to sign the contract. As mentioned earlier, the optimal values of the models from the perspectives of each party varied, thus making it impossible to achieve a single strategy  and sign a contract in the real world. To nd the equilibrium values and sign the contract, the optimal values of the e ort level (x) and xed component of the incentive payment function ( ) from the perspective of each party should be equalized. Then, the values of T (the cost of entrepreneur training) and m+q (the total wealth that both parties to the contract could obtain if the contract was not accepted) were determined. According to our expectations, the higher the initial ability of the entrepreneur is, the less money the venture capitalists will pay for their training. In addition, the higher the value of q is, the lower the value of ( xed component of the entrepreneur's payment function) will be suggested by the venture capitalist; in addition, the higher the value of m is, the greater the value the entrepreneur will call for ; the results con rm these expectations.
As is clear, both entrepreneurs and venture capitalists seek to optimize the model from their own perspectives. This study shows that if the values of T (the cost of entrepreneur training) and m + q (total wealth that the parties to the contract can obtain if the contract is not accepted) are close to the equilibrium points obtained in the models, the optimal values of decision variables in the models of each party will be equal. In addition, as the distance from the equilibrium values increases, the probability of concluding a contract is reduced because of the con ict of interest.
In the learning algorithms section, the computational results of the Q-learning algorithm indicate that learning has been completely done for the investors because they only choose the strategies that generate the highest income in the rst and second rounds, but learning has not been well done for the entrepreneurs; thus, they select all of their strategies with di erent possibilities (higher than 10%). In the second scenario, the investors also learn very well and are more likely to choose pro table strategies. Finally, learning happens for the entrepreneurs and they select the pro table strategies with higher probabilities.
According to Table 6, in the rst scenario, the rst matrix cell is introduced as the Nash equilibrium. Based on Table 6, which shows the results of 100 iterations of the Q-learning algorithm, we nd that the parties most likely choose the Nash equilibrium point. That is, Q-learning outcomes converge with Nash equilibrium results. Also, according to Table 7, in the second scenario, the cell 1 5 is selected as the Nash equilibrium. This cell would be most likely selected by the parties compared to other cells. Therefore, the results of the Q-learning are reasonably convergent with Nash equilibrium.


Models of Multiple Events in the Analysis of Subsequent Registrations in the Labour Office

In many fields of science, it is necessary to analyse recurrent events. In medical science, the problem is to assess the risk of chronic disease recurrence. In economic and social sciences, it is pos‐ sible to analyse the time of entering and leaving the sphere of poverty, the time of subsequent guar‐ antee or insurance claims, as well as the time of subsequent periods of unemployment. In these stud‐ ies, there are different ways of defining risk intervals, i.e. the time frame over which an event is at risk (or likely to occur) for an entity. Research on registered unemployment in Poland shows a high percentage of people returning to the labour office and registering again. The aim of the article is assessment of the risk of subsequent reg‐ istrations in the labour office depending on selected characteristics of the unemployed: gender, age, education, and seniority. In the study, methods of survival analysis were used. The results obtained for four models being an extension of the Cox proportional hazard model were compared. The Anderson‐Gil model does not distinguish between first and next events. The number of events that occurred is important. Two Prentince‐Williams‐Peterson conditional models and the Wei, Lin and Weissfeld models are based on the Cox stratified model. The strata are consecutive events. They differ in the way risk intervals are determined. In the analysed period, only age and education influenced the risk of multiple registrations at the Po‐ viat Labour Office in Szczecin. Gender and seniority did not have a significant impact on this risk. The analysis performed for subsequent registrations confirmed the impact of the same features on the first subsequent registration. In general, it can be stated that the analysed characteristics of the unem‐ ployed did not have a significant impact on the second and subsequent returns to the labour office.

Introduction
Since 2013 the economic situation in the Polish labour market has been improving. This is reflected in the decreasing unemployment rate, the number of persons registered as unemployed and the increased outflow of persons registered in labour offices. From the point of view of social policy, it is important to activate unemployed persons to take up employment. The best solution would be to take up a permanent job or start a long-term business activity. Unfortunately, statistical data show a high percentage of people returning to the labour office and registering again. In 2004In , 2090.4 thousand people were registered, of whom 1417.7 thousand were re-registered. It constituted 68% of registered persons and it was a minimum value in the period 2004-2018. Since then, this percentage has increased and remains relatively stable. In 2016, it was 83% (the maximum value in the presented period), and in 2017-2018 -82% ( Figure 1).  In the presented study, methods derived from the survival analysis were used to analyse subsequent registrations in the labour office. The duration of the unit in a specific state is analysed until the moment of occurrence of the specific event ending the observation. They are derived from demography and technical sciences. In the past, they were used to study the duration of human life and failure-free operation of devices. They are now also used in the analysis of the duration of economic phenomena. The investigated phenomenon may result in the occurrence of a specific event (death, illness, failure, company failure, entry into In the presented study, methods derived from the survival analysis were used to analyse subsequent registrations in the labour office. The duration of the unit in a specific state is analysed until the moment of occurrence of the specific event ending the observation. They are derived from demography and technical sciences. In the past, they were used to study the duration of human life and failure-free operation of devices. They are now also used in the analysis of the duration of economic phenomena. The investigated phenomenon may result in the occurrence of a specific event (death, illness, failure, company failure, entry into unemploy-ment, entry into or exit from the poverty sphere). The event may not occur before the end of the observation period. Such observations in the analysis of duration shall be taken as censored. In addition to observations not yet completed before the end of the observation period, censorship includes those where the observed entity disappears from sight or there is an observation ending event that excludes the occurrence of a relevant event (Pepe, 1991), i.e. a competing event (Bieszk-Stolorz, 2018c).
A very important issue is the analysis of processes during which the unit can be in the state defined by the study several times. These processes can be analysed using selected methods from the area of survival analysis. These methods in the technical sciences are used for testing downtime on assembly lines or for analysing software fault detection and troubleshooting processes. In medical science, the time until the relapse of disease symptoms is examined (Sagara et al., 2014). In economic and social sciences, it is possible to analyse the time of entering and leaving the sphere of poverty (Sączewska-Piotrowska, 2015), the time of subsequent guarantee or insurance claims or subsequent periods of unemployment (Gałecka-Burdziak, 2016). The analysed random variable T in such studies is the time to occurrence of the event.
The aim of the article is assessment of the risk of subsequent registrations in the labour office depending on selected characteristics of the unemployed: gender, age, education, and seniority.

Risk intervals in the analysis of recurrent events
Multiple events (recurrent events) are defined as processes that repeatedly generate specific events (Klein, Goel, 1992;Hosmer, Lemeshow, 1999;Therneau, Grambsch, 2000;Machin, Cheung, Parmar, 2006;Cook, Lawless, 2007;Aalen, Borgan, Gjessing, 2008). Figure 2 shows examples of objects with recurrent events. Objects 1, 2 and 3 have experienced one, two and three full events respectively. Subject 4 has suffered two complete incidents and subject 3 is being censored. This censorship is related to the end of the observation period. Object 5 has suffered an event that initiated the process, but has not suffered a final event. In this case, the observation is also censored because of the loss of the unit from the observation field. Intervals between successive events are often referred to as episodes in the literature.
In the case of recurrent events, the time is indexed on two scales: calendar and between consecutive events (Sączewska-Piotrowska, 2015). In this case, risk ranges (episodes) shall be defined, i.e. the ranges within which the unit is exposed to an event along a given time scale. There are three types of such intervals: the time gap, the total time and the counting process (Prentice, Williams, Peterson, 1981). Intercurrence time (gap time) is time between events. In order to determine it, the clock is restarted after each subsequent event ( Figure 3). In the case of total time, the clock is not restarted, i.e. the time is counted from the selected point on the time scale. Most often it is the beginning of the observation of the unit (Figure 4). The counting process is a combination of calendar time and time gaps. In this case, the time is calculated on the same scale as the total time, but the fact that the beginning of each subsequent period coincides with the end of the previous period is taken into account ( Figure 5). The common feature of all presented methods of determining the time is the same risk range for the first event.

Research methodology
The basic concept of the duration analysis is the duration function (survival function) which determines the probability that an event will not occur at least up to time t. It is defined as follows: where: T -duration of the phenomenon, F(T) -cumulative density distribution of the random variable T. The most commonly used estimator of the survival function is the Kaplan-Meier estimator (Kaplan, Meier, 1958): where: d j -the number of events at the moment t j , n j -the number of threatened entities until the moment of t j .
The second function used in the survival analysis is the hazard function. It describes the intensity of the event, i.e. the probability of the event occurring at moment t provided that it survives till time t, and is defined as follows (Kleinbaum, Klein, 2012): In this case, the semi-parametric model of Cox proportional hazard, defined by the formula (Cox, 1972;1975), is popular: If we do not have a parametrically specified function for baseline hazard, the traditional method of maximum likelihood cannot be used. The basic method for estimating the parameters of the Cox model is the partial likelihood method (Cox, 1972;1975). Let Y t (t) represent the set of objects at risk at time t. For the model (4), this set is defined as follows: The partial likelihood function proposed by Cox is defined as follows (Ozga, Kieser, Rauch, 2018): where δ i takes the value 1 if the observation is full and 0 if the observation is censored. The partial likelihood function (or any of its approximation) is then maximised iteratively.
In the case of recurrent events in the study, models that are an extension of the Cox regression model may be used. Replacement of recurrent events models has been applied: Prentice, Williams and Peterson (1981) (counting process model -PWP-CP, time gap model -PWP-GT), Andersen and Gill (1982, AG), Wei, Lin and Weissfeld (1989, WLW). Each unit (i) present in the study is assigned successive events that form strata. The model (4) therefore takes one of two forms (Sousa-Ferreira, Abreu, 2019): where: t -time, β = [β 1 , β 2 , …, β m ] -vector of model's parameters, X = [X 1 , X 2 , …, X m ] -vector of explanatory variables, h 0 (t) -basic hazard for all events, h 0k (t) -basic hazard for all events from the episode (stratum) k, i -the number of the object, k -the number of the subsequent episode (stratum).
Let s be the number of strata. Then the partial likelihood function can be written as the product of the partial likelihood function specific to subsequent layers (Ozga, Kieser, Rauch, 2018): where δ ik takes the value 1 if the observation is full and 0 if the observation is censored. The risk sets Y ik (t) are defined separately for each stratum.
To assess the relative intensity of the event, the hazard ratio (HR) determined by the following formula is used: In addition to basic hazard, these models differ in the time formula used, which is linked to different risk ranges.
In the Prentice, Williams and Peterson (PWP) models, the hazard function has the form (8). It is assumed that for each episode (stratum) k the basic hazard is different. Thus, the intensity of the event is considered in strata, i.e. the events that occur in an orderly manner are analysed. The risk of another event occurring is affected by the previous event. The PWP model allows us to save risk intervals on two possible time scales: the counting process or the time gap. In the model, the larger the sequence of events, the smaller the size of the subsequent strata, which may lead to unreliable estimates (Cai, Schaubel, 2003). Therefore, in order to avoid such a situation, the number of strata should be carefully selected.
In the Prentice, Williams and Peterson models, for the Counting Process (PWP-CP model), intervals are counted. The counting process is the determination of a time from the start of a study where the initial time of each risk interval coincides with the end of the previous event. In this case, the risk set indicator is defined as: The Prentice, Williams and Peterson gap time models (Gap Time, PWP-GT) are based on the time interval between two events. The clock restarts when each event occurs. The indicator of the risk set is defined as: where g ik = t ik -t i(k -1) represents the observed time of interruption between two consecutive events. Instead of basic hazard h 0k (t), basic hazard h 0k (t ik -t i(k -1) ) is therefore being considered.
In the next model -Andersen and Gill (AG model) -the hazard function has a form (7). In this case, strata are not considered and the assumption is that the basic hazard is the same. The unit is still assigned several events, but they are not ordered. Events have the same risk of occurrence. Similarly to the PWP-CP model, the periods are counted. The risk set indicator is defined as: This model was developed for situations in which events do not depend on the observed time from the last event or on the number of events that occurred before. Although counting the formulation of the process requires a conditional structure of dependencies, among the events it is assumed that the times between them are independent. Some authors believe that the AG model is the simplest model, but the one with the strongest assumptions (Therneau, Grambsch, 2000;Cai, Schaubel, 2003).
The last of the presented models -the Wei, Lin and Weissfeld model (WLW model) has the hazard function calculated by means of equation (8). It is therefore a stratified model with a differentiated function of basic hazard. The risk intervals are based on the total time, i.e. the time from the start of the observation. The indicator of the risk set is defined as: There are similarities and differences between the presented models. The PWP and WLW models are the stratified ones and have the same hazard function, but differ in the definition of risk intervals. The AG and PWP-CP models have the same defined risk interval, but differ in their basic hazard function. The main limitation of PWP models is that they can give unbelievable results for higher-order events. As the sequence of events increases, the number of objects in the risk range decreases. The construction of the data set in the WLW model allows us to avoid this problem.
The analyses of the labour market can examine subsequent periods of unemployment or employment. The studies on the duration of unemployment in the labour market in Szczecin show that for the first episodes gender was a strong determinant of the intensity of de-registrations for any reason, taking up employment and removal. In the case of the second episodes, gender determined the intensity of de-registration for any reason and removal from the labour market. For the third episodes, gender was the only determinant of de-registration. It did not determine the intensity of the fourth and subsequent de-registrations and de-registrations for other reasons for each episode. In these studies, a stratified model of proportional Cox hazard in baseline and alternative versions were used to assess the intensity of the registrations (Bieszk-Stolorz, 2018a; 2018b).

Data used in the study
In the study, individual data on the unemployed persons registered in the Poviat Labour Office in Szczecin, generated from the Syriusz system, were used. The cohort consisted of persons registered for the first time in 2016 and were observed until the end of 2017. The entire history of their registrations was analysed. Their subsequent registration in the office was accepted as an event. As a starting point for the observation of each person (t = 0), his/her first registration was accepted. Each history consists of events, i.e. successive registrations in the office. These are recurrent events. Among the observed persons, the highest number of them consisted of persons that did not register again in the analysed period (2808 persons) and there were 836 returning persons. A total of 3644 histories were analysed. After a preliminary analysis of the number of events in the history of registrations, it was decided to divide them into four groups: without subsequent return, with one, two and three or more events. The separation of the latter group resulted from the small number of people registered with at least three events. The number of separated groups is presented in Table 1. Due to such defined events, censored observations appeared. These are observations in which a given unit suffered the k-th event subsequent registration at the office for k = 1, 2, 3), and did not experience, by the end of 2017, an event with the number k + 1. In the case of the study, all observations are of the same nature as object 4 in Figure 2. The observation period ended at the end of 2017, but the observed persons may register again with the labour office in the future. The calculations were performed in the R environment by means of the survival package. The study took into account four characteristics of the unemployed: gender -a dichotomous variable: gender: women (1), men (0, reference group); education -five levels: at most lower secondary (S 1 , reference group), basic vocational (S 2 ), general secondary (S 3 ), secondary vocational (S 4 ) and higher (S 5 ); age -six age groups: 18-24 (W 1 , reference group), 25-34 (W 2 ), 35-44 (W 3 ), 45-54 (W 4 ), 55-59 (W 5 ) and 60+ (W 6 ); seniority (D) -two groups: people with no professional experience (0, reference group) and people with professional experience (1).

Analysis of subsequent registrations in the labour office
The study was carried out in two stages. The first one consisted in a joint analysis of events, and the second one was an analysis for subsequent events. Both stages were preceded by an initial analysis of the duration of individual episodes in order to justify the choice of the methods used. First, the median and the maximum duration of subsequent event-end episodes were determined (without taking into account censored observations). Both the median and the maximum time in the analysed period were shortened. It follows that if a person re-registered in the office, they did so more and more quickly (Table 2). In the next step, the Kaplan-Meier estimators were calculated ( Figure 6).  The analysis of Kaplan-Meier's estimators indicates that the probability of survival decreases with the observation of recurrent events. Subsequent events carry an increasing risk of occurrence. Differences between the survival curves (confirmed by a log-rank test for each pair of duration curves) therefore justify the use of methods for analysing multiple events.
In the first stage of the study, a total analysis of the events was carried out using all four models: PWP-CP, PWP-GT, AG and WLW. It was examined whether there were significant differences between the successive events and whether the characteristics of the unemployed influenced the risk of subsequent registrations in the office. The results are presented in Table 3. All the models gave similar results. Gender and seniority did not determine the risk of subsequent registration in the office. People with at most lower secondary education or up to 24 years of age were most at risk of being subsequently registered in the office. This risk decreased as education levels increased. However, for people with higher education, it increased again. In the case of age groups, the lowest risk was observed in the 55-59 age group. In both models, the risk was 38% lower than for people aged 18-24. For people aged 60+, the parameters of the model were statistically insignificant.
In the second stage of the study, the relative intensity of subsequent events was analysed. It was examined whether the characteristics of unemployed people influenced the risk of k-th registration in the office. In this case, models based on Cox's layered regression model, i.e. models, were used: PWP-CP, PWP-GT and WLW. Table 4 shows the hazard quotients for subsequent episodes determined using these models. According to the assumptions, the estimators for the first event are the same. Women did not differ significantly from men in terms of the risk of subsequent registration for each episode. Education and age (except for the oldest persons) differentiated between the unemployed only in the case of the first event. Professional experience was not a determinant of the intensity of unemployment exit. The analysed features were not determinants (with small exceptions) of the intensity of second, third and subsequent registrations in the labour office.

Conclusions
The paper presents a review of four methods useful in the analysis of data of subsequent events and applies them to model the event consisting in subsequent registrations in the labour office. The declining median and the maximum value of time until the next registration indicate that people who repeatedly register at the office have problems with finding a permanent job or are not interested in it. In the analysed period, only age and education influenced the risk of multiple registrations at the Poviat Labour Office in Szczecin. Gender and seniority did not have a significant impact. The analysis performed in each stratum, i.e. for subsequent registrations, confirmed the impact of the same features in the first stratum, i.e. on the first subsequent registration. In general, it can be stated that the analysed characteristics of the unemployed did not have a significant impact on the second and subsequent returns to the labour office. The risk of subsequent registrations was the highest in the case of people with low education (and then with higher education) and aged up to 24 (and then at the age of 25-34). The result could be influenced by the number of strata. The number of observations decreased with the next event. This may have affected the reliability of estimates in the last strata, especially in the PWP models. However, similar results were also obtained by means of the WLW model, where the construction of the data set eliminated the problem of decreasing number of events in particular strata.
Some researchers assume that, if there are significant differences in the course of the duration curves for subsequent episodes, it is appropriate to use the PWP or WLW model. If, in addition, it is known that there is a relationship between subsequent episodes, the PWP (CP or GT) model (Sousa-Ferreira, Abreu, 2019) gives better results. In the presented study, the first assumption is satisfied. Nothing is known about the satisfaction of the second one. The conducted study has not provided a clear answer to the question which model is better. This is probably due to the fact that the analysed features did not significantly affect the subsequent episodes. Perhaps the differences in the course of the duration curves are caused by other features of the examined units.


Food supply pressure in France and Germany during COVID-19: Causes from manufacturing

Liu examines the food supply pressure in France and Germany during the COVID-19 pandemic with regard to the causes from manufacturing The food supply has been disrupted by COVID-19 The pandemic's impact on the food supply has attracted attention from scholars and practitioners alike, and there have been many studies based on evidence from developing countries However, there is still a lack of research based on the experiences of more developed and industrialized economies such as France and Germany This is an important knowledge gap to be bridged, as people in developed countries tend to consume more food than those in developing countries Developed countries are also usually in the center of global food supply chains due to their stronger influence in trade

and normal business operation. For example, it was difficult for households to buy sufficient preserved food and long-life milk from the supermarkets and grocery stores they usually visit, and shops had to set quotas on customers' purchasing of many products. The situation seemed to improve after strong measures were taken to restore the food supply in France and Germany. For example, food delivery has been an approved exception to the governmental travel restrictions during the pandemic. Nevertheless, varieties of food and drinks were still in short supply or out of stock during the pandemic, especially at the early stage, as shown in Photos 1 and 2. Therefore, the reasons for the food supply crisis in France and Germany are worth exploring.
Although the panic from the COVID-19 pandemic is perceived as a catalyst for the pressure on the food supply, there could be other interpretations. As noted by the Food and Agriculture Organization of the United Nations (FAO, 2020), COVID-19's disruption to agriculture is minimal, and the food market is largely stable. Furthermore, in France and Germany, technology is widely applied in the agriculture sector, and therefore COVID-19 is unlikely to have significant impact on the countries' agricultural activities, which are not highly labor-intensive. In addition, media have reported that excessive amounts of milk have been produced and producers have to reduce the milk supply in France and Germany (Stöcker, 2020), but in the supermarket milk is often sold out. Therefore, in France and Germany, if the sudden increase in demand is not the only reason for the strain on food supply, and COVID-19's impact on agricultural activities is minimal, what could be the other reasons? A less mentioned yet important reason is the shrinking of manufacturing, and particularly of a few sub industries closely related to food production and delivery.
Data from the United Nations Industrial Development Organization (UNIDO) provides evidence to support this argument. As shown in Table 1, the manufacturing of food products in both France and Germany dropped in March 2020. Specifically, in France, manufacturing of food products fell by 2.3%, compared to February 2020. Similarly, Germany observed a reduction (-4.1%) in manufacturing of food products in March 2020 compared to March 2019. Since the sub-industry 'manufacturing of food products' mainly includes the 'processing and preservation' of food and dairy products (United Nations Statistics Division, 2008), it is not difficult to explain why long-life milk and preserved foods were quickly sold out in supermarkets in France and Germany. The shrinking of beverage manufacturing was much more notable in France than in Germany. In short, the reduction in manufacturing of food and beverages (industries with low technology intensity that were therefore more significantly affected by COVID-19) (UNIDO, 2019) is fueling the pressure on the food supply in France and Germany.

Photos 1 and 2. Empty Supermarket Food Stock in France and Germany
Source: Xinhua News (2020aNews ( , 2020b. Note: Picture 2 has been cross-posted via WeChat. Journal of Agriculture, Food Systems, and Community Development ISSN: 2152-0801 online https://www.foodsystemsjournal.org Another reason for the strain on the food supply is the sharp decrease in the manufacturing of motor vehicles, trailers, and semi-trailers (MTS). MTS manufacturing is noteworthy because these vehicles are the most widely used tools to deliver food products from producers to customers, including intermediary customers such as supermarkets and grocery stores. Although it is sometimes perceived that the existing fleets of MTS may be sufficient to satisfy the needs under normal circumstances, this is not the case during the pandemic. The demand for delivery services using MTS increased sharply amid COVID-19 health concerns (Kendall, 2020), which may lead to longer-running shifts and higher frequency of MTS use. Hence there will be more depreciation, scrapping (writing-off), and repairing of MTS, which reduces the availability of MTS to meet the soaring demand of food delivery services. As the pandemic effectively shuttered the manufacturing of MTS, Europe's freight market is predicted to shrink significantly (van Marle, 2020), especially in the delivery of food products. This has been exacerbated by the 'just in time' manufacturing model that has emerged in recent years and the fact that many companies do not keep extra resources for unexpected disturbances (Bloom, 2020). That is why refrigerated vehicles used for storing dead bodies during the pandemic have been reused for food delivery (Crump, 2020). Fleets of supply trucks owned by some business closed in the pandemic are also to be used for food delivery (Bloom, 2020). Table 1 shows that in France, the manufacturing of MTS dropped by over 50% in March 2020 compared with the same period in 2019. The decrease of MTS manufacturing in Germany is also substantial (-37.7%). A sudden and significant reduction of the MTS manufacturing would strain to food supply chains. This explains an aforementioned imbalance, where the production of food and beverages has been excessive, but such a huge supply cannot be transported to the market due to the lack of MTS. Therefore food and beverages are easily sold out in the supermarkets and grocery stores. This lack of MTS is also an important factor in the long wait times for online food delivery in France and Germany (Reimann, 2020).
This brief commentary demonstrates that the reduction in manufacturing production, especially that of food, beverages, and MTS, is an important reason for the pressure on the food supply chain in France and Germany during the pandemic. This commentary also has a few implications. The examination of the food supply should not be restricted to agriculture and service sectors; and it should also be extended to manufacturing, which plays a critical role in the food supply chain. This commentary therefore calls for more interdisciplinary inquiries. In addition, more research on the food supply in developed countries during the pandemic could enrich the knowledge of how COVID-19 affects the food system.


Empowerment of Gunung Sari Village Community Groups, to Optimize the Potential of the Village Towards the Tourism Independent Village

The tourism sector in Gunung Sari Village, Pamijahan District, Bogor Regency, is not the main sector in its community activities. The existence of natural tourism in the form of waterfalls, camping ground and hot springs is a potential for the village to realize the Bogor Regency Mission. Existing tour operators are not residents or village communities. The active participation of village communities has not been seen in the management, operational and maintenance of tourist attractions. The tour paramilitary group 20 can be an agent for villages to empower communities in the tourism sector. Srikandi Gunung Sari group is a community group that involves mothers and produces local food products in the form of banana chips and cassava. In an effort to increase the active participation of village communities in tourism activities in Gunung Sari Village, the empowerment of the Laskar Tourism Group 20 and Srikandi Gunung Sari was carried out through education, workshops, mentoring and facilitation activities. The main target of this activity is the village community who can independently manage, maintain and improve the tourism sector in Gunung Sari village towards a sustainable tourism independent village.

Introduction
Gunung Sari Village, Pamijahan District, Bogor Regency is one of the agricultural villages in the southeast, 25 km from the center of Bogor. The condition of the geographical area is almost 60% in the form of agricultural land so that most of their income and employment are as farmers. The population of Gunung Sari Village reaches around 11,000. Productive age of approximately 4000 people. The age of those who are still active in school is around 2000 people (Bogor Regency, 2012). Gunung Sari Village is one of the villages in the Pamijahan sub-district. Pamijahan District is one of the areas in Bogor Regency which is a major tourist destination. There are 15 villages, most of which are villages with a natural tourism priority. In Gunung Sari Village there are several tourist destinations that the village has to offer including 7 waterfall tours, camping ground, hot springs and water tracking. There are supporting facilities that are commonly used by local and foreign tourists including lodging, e-ISSN 2721-477X p-ISSN 2722-5046 temporary resting and eating places. There are 18 large and small scale lodgings which most (94%) are owned by residents outside Gunung Sari Village. Some communities and village governments are aware of the tourism potential of their villages (Fannel, 2015). This is based on the enthusiasm for the service activities that have been carried out previously, namely in 2015 and in 2016 through the Community Development activities conducted by the Computer Science Student Association (HIMAKOM) and the Computer Science Study Program. Several activities and efforts have been carried out to support Gunung Sari village to become an ecotourism area through community participation. These activities include the design of the Gunung Sari ecotourism web site (www.ekowisatagunungsari.com) and the formation of the Gunung Sari tourism driving community called LASKAR WISATA 20. The resulting output is the result of the Village Development Grant program conducted by HIMAKOM through funding by the Directorate of Learning and Ristekdikti Student Affairs in 2016. LASKAR WISATA 20 (LW-20) is a community group containing a joint group of students, youth groups and village leaders. In the service activities previously carried out LW-20 has been equipped with the ability of basic computer skills and management of information media. The role of the LW-20 in the Gunung Sari tourism sector is as a driver of the community to be aware of tourism and the potential of the region it has. LW-20 is still carrying out routine activities in community empowerment especially youth groups by providing computer access and assistance especially in information technology. Gunung sari ecotourism web that has been provided by the University of Pakuan is still managed simply by LW-20. The existing web is still static, there is no twoway optimization between web visitors and the village, especially with regard to the Gunung Sari ecotourism (Tosida et al., 2019.a;2019.b;2019.c).
Gunung Sari Village already has several community groups that carry out activities related to family empowerment, including: PKK and Posyandu Groups, Gunung Sari Srikandi Business Group, and SPP groups. One of the active groups in implementing the family empowerment program is the Gunung Sari Srikandi Business Group. This group is a group that brings together a home-based business in processing banana chips. The srikandi group is chaired by the Lurah with representatives from several RTs in Gunung Sari Village, Pamijahan District. Raw materials obtained from banana plantations are found on residents' lands. Banana production in Gunung Sari Village is not yet a large scale production because most of the land in Gunung Sari village is paddy fields. The village's local products are produced and then sold in markets, stalls and restaurants around the village. There are opportunities for processing products that are more creative, attractive, healthy and can be produced on a large scale to later become the mainstay products of the Gunung Sari village for visitors (Tosida et al., 2018;2020).
The people of Gunung Sari Village in managing their resources, especially natural resources, technology, and in managing their economy, still need support through empowering community elements. The beauty of nature and occasional tours, this of course becomes a problem that must be addressed. One way to overcome this problem is to provide supplies and knowledge about good and correct economic management to the people of Gunung Sari Village. So that their knowledge about the use of resources and technology, as well as economic management will be maximized, as well as generating creative ideas by using technology media in order to increase village income and progress.
Gunung Sari Village, Pamijahan District is still one of the nature-based tourist destinations. There are tourist areas including waterfalls, hot springs and camping ground which are targeted by visitors especially those from the capital. Tourism management is still done voluntarily by the village. Gunung Sari village tourism has the opportunity to become an independent tourism village that collaborates with community groups, business people, village government. This can at the same time create tourism with the Sapta Pesona. Salah satu unsur sapta pesona adalah lingkungan. Dalam pengembangan desa wisata lingkungan menjadi salau satu unsur yang harus dijaga (Regina, 2013).
Activities undertaken in the realization of a tourist village are community empowerment through mastery of technology and economic improvement opportunities. Mastery of technology in the community can open up opportunities for villages to become open, known to the general public and increase cooperation with outside parties ( Zainal, 2017;Tosida et al., 2017)). Economic activities that arise due to the tourism village have a good effect on the financial condition of the family and ultimately the welfare of the village.

Materials
Materialas icluding: object, location, data & informations, and tolls used in data analysis. Dalam kegiatan ini ma

Methods
Public service activities are carried out from February to April 2019. The place for the implementation of community service activities is Gunung Sari Village, Pamijahan District, Bogor Regency. In its implementation the tools used are personal computers and laptops. The existing computer set is a grant from the implementation of Community Service in 2017. The stages of the activities carried out can be seen in the figure 1.

Dssemination and Identification Stage
In this dissemination phase, LASKAR WISATA 20 and villagers will be educated to develop village tourism and the role of village communities. Sosilisasi collected data on the potential and achievements of the village and Gunung Sari community to support the Tourism Village. In addition, data collection was also carried out on the number of tourist facilities available, whether commercial inns or residential homes that can be guaranteed homestays) (Syafii, 2015). In this activity potential and achievement will be generated, and an action plan for further activities. Potential cluster of tourism villages will be determined. For example, environment-based tourism, culture-based, craft-based and natural nuances. Based on community service activities carried out previously, it has been identified that the main cluster in Gunung Sari Village is nature-based tourism. To support and develop the tour, it is necessary to diversify the efforts of local residents to increase community participation in tourism in Gunung Sari and directly increase the economy and income of the village community (Dean, 2009)

Learning and Provisioning Stage of Promotion Material
The debriefing phase is intended for Gunung Sari villagers to be able to get to know how to market a product through a technological approach. Learning is carried out by providing training to youth groups and LW 20 to prepare interactive, interesting and able media to provide complete information on tourism in Gunung Sari Village. The LW 20 group, assisted by the PKM implementation team, provided training and mentoring to the Gunung Sari tourism mover group. Promotion of local products and culture in Gunung Sari village to support tourism development must involve businesses (Tosida et al., 2016.a;2016.b).

Evaluasi and Monitoring Stage
Monitoring is carried out by the PKM Team together with LW20. This activity is carried out in the context of monitoring the consistency of community partnership programs so that the vision will be achieved, namely the realization of the Tourism Independent Village by fulfilling all aspects of Sapta Pesona.

Results and Discussion
Activities that have been carried out are as follows: a. Socialization activities In the socialization activities carried out in conjunction with teaching activities. In this socialization explained more about the ecotourism in Gunung Sari Village, Pamijahan District to local youth. At the same time socializing the Gunung Sari ecotourism web. The parties involved were the Community Service Implementation Team (3 lecturers and 7 students), LW20 (20 people), the Gunung Sari Srikandi Business Group and the Village Government (including village secretaries, RT heads, hamlet heads. Socialization activities carried out can be seen in the figure 2.

Figure 2. Socialization activities
Based on the results of the socialization and interview, the following are obtained: 1. Utilization of the existing promotional media is still not running optimally, this is because a set of PCs is the only mainstay of LW 20 to improve content, tourism-related administration is not functioning. Another necessity is a village profile video to illustrate the village's potential. 2. Tourist attractions that have been fully managed by local residents have been disregarded to the Province, so we need another mechanism for the recommendation of problems by the village government.Terdapat tempat wisata baru yang berpotensi untuk dikembangkan 3. Extracting information related to community activities that uphold local wisdom can be one of the village's flagship programs so that it can become part of the tour packages offered.
b. Educational activities This youth activity was held on January 25 and February 1, 2019. This activity aims to strengthen the talisilaturahmi and strengthen faith and religion. In this youth activity there are usually events such as recitations, prayers, and lectures from religious teachers or local community leaders. This teaching activity was delivered to local youth and carried out for 3 nights. Material delivered and taught to teenage friends about technology between Microsoft Word, Microsoft Power Point, and photo editing. Local teenagers are very enthusiastic about this teaching activity because they are rarely on school benches getting material about computers. This teaching activity was carried out on January 26,27,28 and 2,3 February 2019. This activity aims to provide insights about computers. Because seeing the lack of learning about technology obtained by local teenagers in their school. At the same time providing provisions and understanding for friends when entering the workforce. Educational activities carried out can be seen in the figure 3. Level of acceptance of participant material can be seen in table 1.  c. Information media production activities. Information media will be made in the form of a website and village profile video. The web that will be developed is a web of design results that have been carried out in previous service activities, on the site www.ekowisatagunungsari.com. In this stage, updating the web is still static, news is not updated and the subscription period is extended for promotional needs. Web appearance can be seen in the figure 4.

Conclussion
Ecotourism development activities in Gunung Sari involve two partners who have been formed through community service activities. The existence of partners plays an important role in the sustainability of the program. Partner members who always turn out to be a challenge for the servants to develop this program into a sustainable program. The program to enhance the ability of tourism management through information media management activities has given rise to a new program that can be an opportunity for village development, namely: 1). Establishment of an independent business data collection on water use by utilizing a computer program developed by the servants in collaboration with one of the members of Laskar Wisata 20, 2). Srikandi Gunung Sari Business Group Empowerment through diversification of marawis culture by utilizing creative industries