Negotiation for Cooperation in Logistic Networks - An ... - CiteSeerX

Negotiation for Cooperation in Logistic Networks - An Experimental Study Daniel Rief and Clemens van Dinther FZI - Research Center for Information Technologies at Universit¨ at Karlsruhe (TH) Haid-und-Neu-Str. 10-14 D-76131 Karlsruhe, Germany {rief|vanDinther}@fzi.de

Summary. This paper investigates the negotiation problem in a supply chain in which a supplier can realize cost savings if she reaches an agreement with a retailer to use the retailers accurate market data for production planning. We study the participants behavior in an asymmetric and a symmetric information scenario and model the experimental study as a reverse ultimatum game. The main finding is that the revelation of information in the game leads to higher payoffs for both parties and to a higher welfare. Keywords. Information sharing, cooperation, supply chain, negotiation, ultimatum game.

1 Introduction The division of labor has led to highly integrated economies in which suppliers have formed networks of business connections to serve customers on various markets. Information plays an important role for the frictionless operation of economic networks. The asymmetric distribution of information amongst the participants in a supply network can have a negative impact on the efficiency. The “Bullwhip effect” is a prominent example for the negative impact of information asymmetry in supply networks [10]. Consider a series of companies in a supply chain in which each company orders goods from its upstream partner. In such a chain, the incoming orders from the downstream partner serve as information for the production and inventory management. As long as future demand is estimated on basis of the incoming orders the demand estimation is subject to uncertainty, i.e. the order information can be misinterpreted. These demand uncertainties and variabilities tend to be magnified in upstream direction. This phenomenon is called Bullwhip effect since the amplitude of a whip increases down its length.

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Daniel Rief and Clemens van Dinther

It can be shown that the Bullwhip effect can be diminished by reducing the demand uncertainty through additional (more accurate) information [4]. If e.g. a retailer estimates a high demand within the next period, she might order only a small amount of a product since she still has products on stock. If the upstream partners estimate on basis of this small order they will be misled. If the upstream partners were able to plan on basis of the retailer’s demand estimates directly, they could optimize their production and warehouse planning and thus save costs [11]. This example illustrates that the exchange of information can improve efficiency in a supply chain. It implicitly describes the negotiation problem. The partners up the stream in the chain are interested to obtain information of their downstream partners (such as point-of-sale (POS) data, products on stock, demand estimations, etc.)1 in order to reduce costs [14]. Since the information provision is not possible at zero costs, incentives are necessary to achieve a cooperation. In reality, many negotiations fail although both participants could benefit from an agreement. In order to design appropriate incentives for cooperation we are interested in the participants negotiation strategies given certain information they possess about the opponents situation. The outlined decision problem can be analyzed as a two player ultimatum game. We are interested in how the participants in such a game behave dependent on the information they have regarding potential cost savings and information provision costs. We consider two scenarios. In the first scenario, participants solely have information about their own cost savings (the supplier) or information provision costs (retailer) respectively. In the second scenario both participants possess total information about costs and savings (of both parties). The supplier has to decide on how to fairly divide the costs savings in order to get the information from the retailer. In other words, the supplier has to make a decision on how to let the retailer take a share on the cost savings in order to compensate the arising provision costs and to provide an incentive for cooperation. These two scenarios have been studied in a field experiment with 493 participants. The design of the experiment and the hypotheses are described in Section 2. The results of the experiment are discussed in Section 3. The paper closes with a brief summary of the results and an outlook for future work.

2 Design of the experimental study The negotiation scenario as introduced in Section 1 can be summarized as follows: There are two parties involved, a retailer and a supplier. The supplier can realize cost savings of s on basis of information provided by the retailer. In 1

Let us assume for simplicity reasons that the information flow takes place unidirectional in upstream direction.

Negotiation for Cooperation in Logistic Networks

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order to provide the information the retailer faces costs c. Thus, the supplier proposes a distribution of s between the retailer and herself. A similar decision problem has been studied in economics and became known as the “Ultimatum Game” [9, 12]. In the Ultimatum Game there are two players, a proposer and a responder. They have to divide a known amount of money q, and therefore, follow a primitive negotiation protocol. The proposer offers a distribution of the money (p, q − p) between proposer (p) and responder (q − p), and the responder can either accept this distribution or reject it. If she accepts, both get the amount agreed upon. Otherwise, in case of rejection, none of the participants receives a reward. Since it is unrealistic for the scenario described above that the proposer (i.e. supplier) only makes one proposition, we decided to modify the ultimatum game. The next sections give a review of related work and introduce our experimental design. 2.1 Related work The first experiments using the design of the ultimatum game were conducted by [9]. According to [12], two results should be observed2 . First, proposers are expected to make offers close to zero. Second, responders should accept all positive offers. In the subgame-perfect equilibrium (SPE) of this game responders would accept any offer and proposers would make the smallest possible offer. Experimentally, however, it can be observed that actions taken by proposers and responders are not equivalent to theory. The actions of the proposers could be interpreted by one of two incitements or a combination of both. Proposers could be determined to play fairly (i.e. make significantly positive offers), and/or might be worried that seemingly unfair offer will be rejected. The ladder is known in literature as direct reciprocity which “focus on the selfish incentives for cooperation in bilateral long-term interactions” [6]3 . The responder’s actions are easier to explain. By rejecting a positive offer, the responder demonstrates that her utility function is also determined by non-monetary attributes (e.g. fairness). Rejecting the proposed offer could also be understood as altruistic punishment which means that individuals punish although the punishment is costly for them [6]. It is observed that even selfish subject are willing to cooperate if enough altruistic punishers participate. In order to clarify the question whether “a proposer is intrinsically interested in the responder’s well-being or only fearing a rejection in the face of unfair 2

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[12] has remarked that these predictions were not derived from game theory only. As additional assumptions, such as that the players being expected utility maximizers with path independent utility functions, were necessary as well [13]. Direct reciprocity describes the mechanism for the evolution of cooperation. It assumes repeated encounters of players which lead to cooperative behavior in the long-term even if defecting is beneficial in the short-term (e.g. see [2, 1]). [5] have studied direct reciprocity in games.

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offers” [8, p. 593], they study ultimatum bargaining with incomplete information4 and observe that “those who can hide their greed want to appear to be fair, whereas those who cannot hide their greed seem to balance their desire to exploit their fear of rejection”[8, p. 593]. The ultimatum game has implications for the study of economics that go way beyond bargaining theory. Any time a monopolist (or monopsonist) sets a price (or wage), it has the quality of an ultimatum. Just as the recipient in an ultimatum game may reject a small but positive offer, a buyer may refrain from purchasing at a price that leaves a small bit of consumer surplus but is viewed as dividing the surplus in an unfair manner [13]. Any trading mechanism can be thought of as being a bargaining game over the surplus generated from the exchange [3]. Thus, negotiating the disclosure of additional information between supply chain partners can be modeled as an ultimatum game between a retailer and a supplier. A modification of the classic ultimatum game is discussed by [3]. In order to show varying behavior of the players, the experiment contains four treatments. The treatments differ in the way offers are made (dollar amounts or percentages) and the responder’s knowledge about the size of the pie (informed or uninformed). In the latter variation of the ultimatum game the responder does not know the size of the pie q and might therefore provide a better description of a posted-price exchange than the classic ultimatum game [3]. In general, the buyer (responder) of a product does not know the amount of surplus that will be shared amongst seller and buyer. This study models two treatments in order to determine factors involved in the behavior of the participants. As depicted earlier in this section, negotiating over additional information is an iterative process. Therefore, proposers should have multiple chances to propose a division of some fixed pie. This variation was introduced as the “reverse ultimatum game” (RUG)5 by [7]. To follow the design of the experiment, the game ends if the responder accepts an offer, or if the proposer decides not to make a better offer after a previous rejection of the responder. Additionally to the iterative negotiation process, deadlines were studied experimentally. For our purposes time as a strategic factor in negotiations is out of scope6 . For our experimental design, we use a combination of the two illustrated studies as described in the following section. 4 5

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Incomplete information in this context means that only the proposer knows the size of the cake. The experiment is called “reverse ultimatum game” because it is a kind of “reverse” ultimatum. The rejection of an offer by the responder can be interpreted as “give me more, or we will each get nothing”. In addition, SPE is the reverse of that in the classic ultimatum game. Adding a deadline reverses the SPE one more time, i.e. the proposer gets the entire surplus.


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2.2 Experimental design Basically, the design of our experiment is according to the introduced RUG. Thus, we use a two player bargaining game, the supplier in the role of the proposer and the retailer as the responder. First of all, two assumptions have to be made. First, the supplier as the upstream partner in the supply chain can realize cost savings from obtaining additional information like e.g. POS data. Second, the retailer as the downstream partner has information provision costs. In our case we assume cost savings to be 30 currency units, while the information provision costs come at a price of 20 units. The amount of cost savings and information provision costs are chosen in reference to related work of the classic ultimatum game. Most experiments in the past conducting the classic ultimatum game use 10 currency units as the size of the pie [9, 3]. In order to draw a comparison to further work, this study uses a total surplus of 10 units to divide amongst supply chain partners as well. In order to reduce the demand uncertainty through additional information, the negotiation object are POS data from the retailer. In each negotiation one supplier negotiates with one retailer. These roles stay fixed for the duration of the experiment. The supplier is requested to make integer offers to the retailer that have to be at a price between 0 and 30 currency units. Offers must be strictly increasing at a minimum increment of one unit. The retailer then has to decide whether to accept or reject the offer. Accepting the offer results in an agreement and ends the game. If the offer gets rejected, the supplier is faced with the decision of either making a strictly higher offer or ending the game. If the supplier ends the game, both participants receive zero payment. The supplier exiting the negotiation in the RUG resembles the responder’s (retailer) rejection in the classic ultimatum game. Using the “reverse ultimatum game” the supplier has to handle a series of small ultimata as it is common in a negotiation process. The supplier has to decide on how to fairly divide the cost savings in order to get the POS data from the retailer. Actually, the total surplus to be divided amongst supplier and retailer amounts to 10 currency units. 2.3 Treatments The present experimental study consists of two treatments: (1) the Asymmetric information treatment and (2) the Symmetric information treatment. In both treatments suppliers are asked to divide a pie of 30 currency units which states the amount of cost savings. As mentioned above we focus on the behavior of the participants dependent on the information they have on the cost savings and the information provision costs. The treatments and their information allocations are depicted in Table 1. In the Asymmetric information treatment both players have information about their own cost savings (supplier) or information provision costs (retailer). In fact, they do have some information about the opponent. Suppliers

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Daniel Rief and Clemens van Dinther Table 1. Information allocation. Treatment

Supplier

Retailer

Asymmetric information Cost savings informed uninformed Provision costs uninformed informed Symmetric information Cost savings Provision costs

informed informed

informed informed

do know about the occurrence of information provision costs. However, they do not know the amount of costs. Equally, retailers do know about the possibility of realizing cost savings but do not know the amount of savings. In the Symmetric information treatment players possess total information, i.e. the amount of cost savings and provision costs were provided to both players. Additionally, all participants in both treatments were informed about a payoff of zero if they do not achieve an agreement during the negotiation. 2.4 Hypotheses For the analysis of the results we examine four hypotheses in this experiment. 1. We expect suppliers not to bid more than 29 currency units. A bid of 30 units implies a payoff of zero. Since this condition will be the same in both treatments we predict identical maximum bids in the Asymmetric information treatment and the Symmetric information treatment. This hypothesis will be referred to as the maximum bid hypothesis. 2. Retailers should accept any offer greater than 20 units and the SPE predicts no differences among the two treatments. Thus the retailer’s minimal acceptable price should be identical in both the Asymmetric information treatment and the Symmetric information treatment. This hypothesis will be referred to as the minimal price hypothesis. 3. As a result of the maximum bid hypothesis and the minimal price hypothesis we predict an equal rate of agreement in both treatments. This hypothesis will be referred to as the rate of agreement hypothesis. 4. Conforming to the rate of agreement hypothesis we expect no difference in the average agreement price in both treatments. This hypothesis will be referred to as the average agreement price hypothesis. 2.5 Conducting the experiment The study was conducted as a lecture hall experiment. All participants are university students of different levels. The largest group of a size of 451 are first


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year students in business engineering and management. The second group are 25 students in their third year of information engineering and management. The third group of 18 students are master graduates in different subjects. Overall, the lecture hall experiment was separately conducted by each group7 . Each student was assigned to a functional role, either supplier or retailer. In order to play the two treatments, both groups, suppliers and retailers, were divided. Thus, there were four groups of almost equal size. It is difficult to separate students in a lecture hall in groups of exactly the same size. Consequently, the group size is not equal. Table 2 shows the distribution. Table 2. Number of participants and their roles. Treatment

Supplier Retailer Sum

Asymmetric information

142

104 246

Symmetric information

116

131 247

Sum

258

235 493

In the lecture hall experiment suppliers were asked to determine their strategy for the negotiation in terms of all incremental bids between their minimum and maximum bid. Similarly, retailers were asked to give their minimal acceptable price. In addition to the requested bids (supplier) and minimal acceptable prices (retailer), all participants were asked to provide explanations to their choices. A critical point in conducting experiments is whether the participants were sufficiently motivated to participate and answer the questionnaire truthfully. In order to meet the demands, we randomly selected in each of the three enforcements two suppliers and two retailers. If the supply chain partners achieve an agreement, participants were paid in Euro accordingly the negotiated division of the surplus of 10 currency units. The results of the descriptive and inductive analysis are presented in the next Section.

3 Results Assuming fully rational agents, we expect the supplier not to bid more than 29 currency units. For 30 units the supplier would be indifferent while receiving a payoff of zero. This applies for both treatments since the maximum bid is dependent on the supplier’s private information which is the same in both treatments. 7

Due to the two smaller groups we do not differentiate the three groups when analyzing the results.

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In both treatments, it is not reasonable for the retailer to accept bids below 20 currency units since the costs for information provision is 20 units. The retailer is indifferent for bids of 20 since his payoff is zero in that case. Thus, rationally agents accept bids greater than 20 currency units. Experimental results of the ultimatum game have shown that agents humans are unlikely to accept unfair offers. Two treatments of a reverse ultimatum game have been conducted. The results of this experiment are discussed in this section. At first the results are evaluated descriptively in the next section. The inductive analysis and hypotheses tests are presented in Section 3.2. This section closes with a summary of the main findings in Section 3.3. 3.1 Descriptive Results Descriptive results of both treatments are summarized in Table 3. For the suppliers we examine the average starting offer, the average number of bids and the average maximum bid. For the retailer we show the average minimal acceptable price. The outcome of the negotiations is shown by the rate of agreement reached in the negotiation process and the average agreement price. Table 3. Descriptive Results. Treatment

Asymmetric Symmetric information information

Supplier Average starting offer Average number of bids Average maximum bid

6.24 14.50 23.64

14.17 10.44 25.82

Retailer Average minimal acceptable price

26.17

24.11

Negotiation Rate of agreement Average agreement price

46.5% 22.01

72.3% 24.22

For detailed results, especially the average maximum bid of the supplier and the average minimal acceptable price of the retailer, we refer to the following sections. Treatment 1: Asymmetric Information The average starting offer of the 142 suppliers was 6.24 currency units and the average maximum bid was 23.64. The average number of bids was 14.5. Suppliers used both non-incremental and incremental bids. The boxplot in Figure 1 shows an overview on the submitted maximum bids. The median is


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25.0, i.e. 50% of the suppliers have submitted a maximum bid of at least 25.0 and the other half has offered at maximum 25.0. Since the 0.25-quantile is 20.0 and the 0.75-quantile is 29.0 it can be observed that 50% of the suppliers have offered bids between 20.0 and 29.0. Recall, that the suppliers in treatment 1 have no information about the retailer’s information provision costs of 20.0.

5

10

20

25

2930

M axim um bid

Fig. 1. Boxplot of the suppliers’ bids in treatment 1

Retailers were asked to determine a minimal acceptable price. The boxplot8 for the retailers is depicted in Figure 2. The average minimal acceptable price is 26.17. The 0.25-quantile is 21.0, the median 25.0 and the 0.75-quantile is 29.5. Again, 50% of the retailers selected minimal prices between 21.0 and 29.5 which is almost the same spread as determined by the suppliers. Recall, that the retailers in treatment 1 had no information on the supplier’s cost savings of 30.0. It is to be noted that 8 retailers selected a minimal acceptable bid below 20.0 that is below their costs. Although the participants were asked to provide explanations to their choices no reasonable argument was given. There were also 17 additional outliers9 with minimal acceptable prices above 30.0.

1

10

21

25

29,5

42

79 M inim alprice

Fig. 2. Boxplot of the retailers’ bids in treatment 1

Comparing each of the 142 supplier offers to each of the 104 retailer bids results in 14.768 possible negotiations with 6.869 successful matches. That is 8 9

There was one outlier at 300.0. For further evaluations we neglect this value that is ten times the theoretical bid a supplier could offer. This results in an average minimal price of 22.57 neglecting the outliers.

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in 46.5% of the negotiations an agreement is reached. The average agreement price is 22.01. Treatment 2: Symmetric Information The average lowest bid of the 116 suppliers was 14.17 and the average maximum bid was 25.82 currency units. Suppliers posed 10.4 bids on average. The boxplot in Figure 3 depicts the statistical spread parameters. The 0.25quantile is 25.0, the median is 27.0 and the 0.75-quantile is 29.0. The standard deviation from the mean is 4.3 currency units. It is interesting to note that 50% of the maximum bids lie between 25.0 and 29.0 which is a smaller interval compared to treatment 1. It is also to be noticed that the 0.25-quantil value is also greater compared to treatment 1.

9 10

14...16 18 19

25 27 29 30

M axim um bid

Fig. 3. Boxplot of the suppliers’ bids in treatment 2

In treatment 2, 131 participants played the role of the retailer. The boxplot in Figure 4 shows the distribution of the bids. The average bid value is 24.11. It is interesting to note that both, the median and the 0.75-quantile, have a value of 25.0 and the 0.25-quantile is at 24.0. This spans just a small interval for the medial 50% of the bids.

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15 17 18 20 ... 24 25 ... 29

M inim alprice

Fig. 4. Boxplot of the retailers’ bids in treatment 2

The combination of all suppliers’ and retailers’ bids result in 15.196 negotiations of which 10.982 (72.3%) are successful. The average negotiated price is 24.22 currency units. Compared to treatment 1, there are much more successful negotiations and the average negotiated price is higher. These are just


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the descriptive results of the study. In order to assess the results we apply inductive statistical analysis that is described in the next section. 3.2 Inductive Analysis For verification of our hypotheses we utilize two methods for testing: a twosample t-test and a χ2 -test. Except for the rate of agreement hypothesis we use the two-sample t-test since we assume our data is from a normal distribution. Verifying the rate of agreement we conduct a χ2 -test. Each hypothesis will be validated by providing the corresponding p-value. Maximum bid hypothesis Since the supplier’s private information is the same in both treatments we predict identical bids. Given the normal distribution assumption of our data we use the two-sample t-test for verifying the hypothesis. The main result from this analysis is that bids in treatment 1 are statistically highly significant different than those in treatment 2 (p < 0.01). This result is not consistent with the maximum bid hypothesis. The average maximum bid in treatment 2 (25.82) is 2.18 units higher than the average maximum bid in treatment 1 (23.64). The difference in the average maximum bid between both treatments suggests different behavior of the suppliers. There are two possible explanations: (a) suppliers consider the information provision costs of the retailer in order to reach a successful agreement in general, or (b) suppliers care more about fairness. Minimal price hypothesis In the minimal price hypothesis we predict identical minimal acceptable prices in both treatments. The average minimal acceptable price in treatment 2 is 24.11 units and 26.17 in treatment 1. Recall, the retailers in treatment 1 had no information on the supplier’s cost savings. Thus, minimal acceptable prices above 30.0 have to be taken into consideration. However, comparing the proportion of the average minimal acceptable price we found weak statistical support for rejecting the minimal price hypothesis (p = 0.0651). We observe no highly significant differences in the average minimal acceptable price distributions of both treatments like depicted in the boxplots of the retailers (Figure 2 and Figure 4). Retailers in treatment 2 seem to care about a fair division of the surplus. Retailers in 1 treatment seem to calculate an additional fee on their information provision costs in order to realize a profitable business. Therefore, we cannot prove the rejection of the minimal price hypothesis.

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Rate of agreement hypothesis The rate of agreement hypothesis proves the rate of agreement in treatment 1 and treatment 2. It predicts the same number of agreements, which was not the case. For the test of the hypothesis we use the χ2 -test. This test is used to examine the significance of the association between two variables in a 2 × 2 contingency table. To meet the demands of independent variables we had to match one supplier to one retailer in both treatments10 . Hence, in treatment 1 we had to match 104 retailers to randomly selected suppliers out of 142 and in treatment 2 again 116 suppliers to the same number of retailers. Table 4 shows the distribution of the 220 independent negotiations. Table 4. Distribution of negotiations. Distribution of Asymmetric Symmetric Sum negotiations information information Agreement No Agreement Sum

48 56 104

87 135 29 85 116 220

The χ2 -test for independence evaluates statistically highly significant differences between the two treatments. With one degree of freedom we get χ2 = 19.2458. The rate of agreement in treatment 2 is significantly different than in treatment 1 (p < 0.01). Given the descriptive results11 , in 46.5% of the negotiations an agreement is reached in treatment 1 compared to 72.3% in treatment 2. Average agreement price This hypothesis predicts no difference of the average agreement price in both treatments. Statistically comparing the average agreement price in both treatments suggests a higher agreement price in treatment 2 (24.22) than in treatment 1 (22.01). That is the average agreement price in the treatment 2 is significantly higher than in treatment 1 (p < 0.01).12 The distribution of final accepted offers is given in Figure 5 and Figure 6. 10

11 12

Imagine we match one supplier to one outlier with an acceptable minimal price above 30.0 there will be no agreement. Again no agreement will be reached if we match that outlier to another supplier. Effectively, we cannot ensure independence of the variables that is assumed by the χ2 -test. Please note that for the 220 negotiations needed for testing there is an agreement in 46, 2% (treatment 1) compared to 75.0% (treatment 2). Similarly as in the rate of agreement, we tested the average agreement price on the basis of the 135 successful negotiations out of 220 by utilizing the t-test.


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R elative frequency of acceptances

0,25

0,20

0,15

0,10

0,05

0,00 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Last offer

Fig. 5. Asymmetric information treatment: Relative frequency of acceptances. 0,50

R elative frequency of acceptances

0,45 0,40 0,35 0,30 0,25 0,20 0,15 0,10 0,05 0,00 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Last offer

Fig. 6. Symmetric information treatment: Relative frequency of acceptances.

3.3 Summary As result we feature three main findings from this experimental study: 1. Maximum bids in treatment 2 are significantly higher than offers in treatment 1 (p < 0.01). This result is not consistent with the maximum bid hypothesis. 2. The rate of agreement in treatment 2 is significantly higher than that in treatment 1 (p < 0.01). This result is not consistent with the prediction in the rate of agreement hypothesis.

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3. The average agreement price in treatment 2 is significantly higher than that in treatment 1 (p < 0.01). This is inconsistent with the average agreement price hypothesis. The most significant finding of past ultimatum games is that participants do not play the predicted SPE strategy. That is in the RUG the responder (retailer) gets all the surplus. However, the division of the surplus is not like the outcomes of many ultimatum game experiments summarized in [13]. We can argue for both treatments: • Treatment 1: Since there are 30.0 units to divide, the equal split in case of the proposer is 15.0. The SPE predicts that the retailer gets all the surplus. We should not be surprised that the split is tilted in favor of the retailer, but rather about the divergence from the equal split. Recall, that the suppliers have no information about the retailer’s information provision costs of 20.0. Justified by the supplier’s lack of information and the retailer’s information provision costs, the retailers receive more than the proposers. • Treatment 2: Because of total information, the equal split for both participants is 25.0. The SPE is the same as in treatment 1. Thus, the average agreement is near an equal split and the retailers receiving slightly more than the suppliers. In fact, the average agreement price of 24.22 comes closer to the equal split than in treatment 1. However, we would expect a division of the surplus in favor of the retailer instead of the supplier. As depicted in the boxplots of treatment 2, suppliers rather bid more than 25.0 (median of 27.0) than retailers demand (median of 25.0). From the collected feedback of the participants, no explainable arguments were given. This study uses two different treatments in an attempt to illuminate the contribution of the factor information for explaining this behavior. Regarding the cost savings and the information provision costs there is to divide a surplus of 10 currency units among both participants. By adding information, retailers alter the surplus distribution between the negotiation parties significantly to their advantage. More important than the average agreement price is the fact of a significant higher rate of agreement. Table 5 shows the average outcome for successful negotiations and all possible negotiations. Neglecting the allocation of the surplus, we observe higher outcomes for both participants in the Symmetric information treatment while comparing the outcome of all negotiations. Thus, we observe that the payoff is higher in case of additional information for both players. In detail suppliers can increase their payoffs from 3.72 to 4.18 (12.5%). Retailers can increase their payoffs even more (226.4% from 0.93 to 3.72). Together they increase their expected payoff from 4.65 to 7.90. Regarding the specific attribute of additional information amongst the supply chain partners, the “welfare” of the total supply chain will be raised by approximately 55% when revealing their information.


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Table 5. Payoffs. Treatments

Supplier Retailer Welfare (sum)

Asymmetric information Successful negotiations All negotiations

7.99 3.72

2.01 0.93

10.00 4.65

Symmetric information Successful negotiations All negotiations

5.78 4.18

4.22 3.72

10.00 7.90

3.4 Limitations The main issue to be mentioned is that the design of the experiment does not allow any interaction between the involved partners of the supply chain. Suppliers were asked to determine their strategy for the negotiation in terms of all incremental bids between their minimum and maximum bid. Retailers were asked to give their minimal acceptable price. Thus, we do not give the supplier the opportunity to react on rejected offers. Accordingly, the retailer does not have the occasion to learn from the given offers of the suppliers (nonincremental or incremental bids). Therefore, we suggest further experimental analyses in this field to support the main findings of our experimental study.

4 Conclusion The objective of this study was to investigate how participants behave dependent on the information they have in the context of the reverse ultimatum game. Ultimatum bargaining can be used as a model of posted-price purchasing. What can be seen in reality is that many negotiations fail although the participants could benefit from an agreement. This experimental study applies to a logistic network consisting of a supplier and a retailer. As described earlier, the exchange of information can improve the efficiency in a supply chain. This experiment is designed to analyze how participants negotiate subject to the information they possess about the opponents situation. Particularly, it is investigated which role information plays for determining maximum bids and minimum acceptable prices in a two player reverse ultimatum game. The experimental design consists of two treatments and tests the predictions of four hypotheses. We find three main results that suggest better negotiation agreements under symmetric information. First, maximum bids in case where total information is available are significantly higher than in case of local information (p < 0.01). Second, the rate of agreement is significantly higher in the Symmetric information treatment compared to the Asymmetric information treatment (p < 0.01). Finally, a

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significantly higher average agreement price is reached in the Symmetric information treatment (p < 0.01). Consequently, we determine two extensions of this study. First of all, additional information moves the average agreement price to a more fair distribution than without information. Strictly speaking, this is just an incentive to cooperate for the retailer in our supply chain. Nevertheless, the expected value of the payoffs will increase for both participants. Additional information gives incentives to all participants in a logistic network in terms of extra gains. Involved partners benefit from successful negotiations in a different manner. However, the welfare of the total supply is increased significantly. A challenging research question is how to design incentives to provide information in a supply network truthfully. Human experiments will contribute to figure out this issue of asymmetric information. It is necessary to identify additional incentives apart from monetary aspects which lead truthful information revelation and to a trustful cooperation in a supply chain while minimizing opportunistic behavior.

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13. Richard H. Thaler. Anomalies: The ultimatum game. The Journal of Economic Perspectives, 2(4):195–206, 1988. 14. Zhenxin Yu, Hong Yan, and T. C. Edwin Cheng. Benefits of information sharing with supply chain partnerships. Industrial Management & Data Systems, 101(3):114–119, 2001.