scholarly journals Spillovers and R&D Incentive under Incomplete Information

2018 ◽  
Vol 6 (1-2) ◽  
pp. 50-65 ◽  
Author(s):  
Rittwik Chatterjee ◽  
Srobonti Chattopadhyay ◽  
Tarun Kabiraj
Keyword(s):  
Distribution Function ◽  
Incomplete Information ◽  
Private Information ◽  
Complete Information ◽  
Jel Classification ◽  
General Distribution ◽  
Two Stage ◽  
Stage Game ◽  
Alternative Scenarios

Spillovers of R&D outcome affect the R&D decision of a firm. The present paper discusses the R&D incentives of a firm when the extent of R&D spillover is private information to each firm. We construct a two-stage game involving two firms when the firms first decide simultaneously whether to invest in R&D or not, then they compete in quantity. Assuming general distribution function of firm types we compare R&D incentives of firms under alternative scenarios based on different informational structures. The paper shows that while R&D spillovers reduce R&D incentives under complete information unambiguously, however, it can be larger under incomplete information. JEL Classification: D43, D82, L13, O31

scholarly journals Outsourcing under incomplete information

2017 ◽  
Vol 10 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Tarun Kabiraj ◽  
Uday Bhanu Sinha
Keyword(s):  
Asymmetric Information ◽  
Incomplete Information ◽  
Private Information ◽  
Low Cost ◽  
Complete Information ◽  
Fixed Cost ◽  
Perfect Bayesian Equilibrium ◽  
Content Type ◽  
Bayesian Equilibrium ◽  
Plant Level

Purpose The purpose of this paper is to show that outsourcing can occur as outcome of a separating or pooling perfect Bayesian equilibrium although it is not profitable under complete information. Therefore, asymmetric information can itself be a reason for outsourcing. Design/methodology/approach The present paper constructs a model of two firms interacting in the product market under asymmetric information where one firm has private information about its technological capability, and it has the option to produce inputs in-house or buy inputs from an input market. However, using outsourced inputs involves a fixed cost at the plant level. The model solves for perfect Bayesian equilibrium. Findings There are situations when under complete information, outsourcing of the input will not occur, but, under incomplete information, either only the low-cost type or both high and low-cost types will go for outsourcing, and there always exist reasonable beliefs supporting these equilibria. In particular, when the fixed cost is neither too small not too large, a separating equilibrium occurs in which the low-cost type outsources inputs from the input market but the high-cost type produces in-house; hence, outsourcing signals the firm’s type. Outsourcing by only the high-cost type firm will never occur in equilibrium. Originality/value That incomplete or asymmetric information can itself be a reason for strategic outsourcing is never identified in the literature. The present paper is an attempt to fill this gap and raise the issue of outsourcing in an incomplete information environment.

scholarly journals Endogenizing the Cost Parameter in Cournot Oligopoly

2020 ◽  
Vol 22 (02) ◽  
pp. 2040004
Author(s):  
Stefanos Leonardos ◽  
Costis Melolidakis
Keyword(s):  
Incomplete Information ◽  
Demand Uncertainty ◽  
Wholesale Price ◽  
Consumer Surplus ◽  
Comparative Statics ◽  
High Price ◽  
Cournot Oligopoly ◽  
Market Demand ◽  
Two Stage ◽  
Stage Game

We study the effects of endogenous cost formation in the classic Cournot oligopoly through an extended two-stage game. The competing Cournot firms produce low-cost but limited quantities of a single homogeneous product. For additional procurements, they may refer to a revenue-maximizing supplier who sets a wholesale price prior to their orders. We express this chain as a two-stage game and study its equilibrium under two different information levels: complete and incomplete information on the side of the supplier about the actual market demand. In the deterministic case, we derive the unique subgame-perfect Nash equilibrium for different values of the retailers’ capacity levels, supplier’s cost and market demand. To study the incomplete information case, we model demand uncertainty via a continuous probability distribution. Under mild assumptions, we characterize the supplier’s optimal pricing policy as a fixed point of a proper translation of his expectation about the orders that he will receive from the retailers. If this expectation is decreasing in his price, then such an optimal policy always exists and is unique. Based on this characterization, we are able to proceed with comparative statics and sensitivity analysis, both analytically and numerically. Incomplete information gives rise to market inefficiencies because the supplier may ask for a too high price. Increasing supplier’s cost results in increasing wholesale prices, decreasing orders from the retailers and hence decreasing consumer surplus. Increasing retailers’ production capacities results in decreasing wholesale prices and increasing consumer surplus. Finally, as the number of second-stage retailers increases, the supplier’s profit may initially rise but eventually drops.

scholarly journals A Two-Stage Auction Mechanism for 3PL Supplier Selection under Risk Aversion

2021 ◽  
Vol 13 (17) ◽  
pp. 9745
Author(s):  
Fuqiang Lu ◽  
Hualing Bi ◽  
Yanli Hu ◽  
Wenjing Feng ◽  
Suxin Wang ◽  
...  
Keyword(s):  
Incomplete Information ◽  
Scoring Function ◽  
Complete Information ◽  
Third Party ◽  
Two Stage ◽  
Auction Mechanism ◽  
The Third Party Logistics ◽  
Attribute Weights ◽  
Sustainable Operation ◽  
Fourth Party Logistics

The third party logistics (3PL) suppliers selection is a key issue in sustainable operation of fourth party logistics (4PL). A two-stage auction mechanism is designed for the selection of 3PL suppliers. Different from previous studies, the paper considers risk preference of 4PL integrators during the auction and uses the prospect theory to establish the auction scoring function of 4PL integrators. First, a first score sealed auction (FSSA) mechanism is used to solve the selection problem. However, the results show that FSSA is not an ideal method. Hence, the English auction (EA) mechanism is combined with the FSSA mechanism to form a two-stage auction. The FSSA is taken as the first stage auction, and the EA is taken as the second stage auction, and the two-stage auction mechanism is constructed. The two-stage auction can improve the utility of the 4PL integrator and the auction efficiency. In addition, for the degree of disclosure of attribute weights in the scoring function, two states, complete information and incomplete information is designed. In case analysis, the validity of the designed two-stage auction mechanism is verified. The 4PL integrator can obtain higher utility under the risk-neutral auction than the risk-averse auction. The complete information auction does not make the 4PL integrator obtain higher utility than the incomplete information auction.

Sequential Preference Revelation in Incomplete Information Settings

2021 ◽  
Vol 13 (1) ◽  
pp. 116-147
Author(s):  
James Schummer ◽  
Rodrigo A. Velez
Keyword(s):  
Incomplete Information ◽  
Real World ◽  
Private Information ◽  
Allocation Rules ◽  
Is Strategy ◽  
Strategy Proof ◽  
Simultaneous Move

Strategy-proof allocation rules incentivize truthfulness in simultaneous move games, but real world mechanisms sometimes elicit preferences sequentially. Surprisingly, even when the underlying rule is strategy-proof and nonbossy, sequential elicitation can yield equilibria where agents have a strict incentive to be untruthful. This occurs only under incomplete information, when an agent anticipates that truthful reporting would signal false private information about others’ preferences. We provide conditions ruling out this phenomenon, guaranteeing all equilibrium outcomes to be welfare-equivalent to truthful ones. (JEL C73, D45, D82, D83)

Local Graph Edge Partitioning

2021 ◽  
Vol 12 (5) ◽  
pp. 1-25
Author(s):  
Shengwei Ji ◽  
Chenyang Bu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Real World ◽  
Graph Partitioning ◽  
Large Scale ◽  
Complete Information ◽  
Local Information ◽  
Experimental Results ◽  
Two Stage ◽  
Graph Computation ◽  
Local Graph ◽  
Edge Partitioning

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.

Selective crowdsourcing with various type task: models and analysis

2018 ◽  
Vol 13 (4) ◽  
pp. 815-839 ◽  
Author(s):  
Qinglong Gou ◽  
Fangdi Deng ◽  
Yanyan He
Keyword(s):  
Incomplete Information ◽  
Private Information ◽  
Marginal Cost ◽  
Design Methodology ◽  
Learning Effect ◽  
Task Type ◽  
Expected Payoff ◽  
Content Type ◽  
The Past ◽  
Winner Takes All

Purpose Selective crowdsourcing is an important type of crowdsourcing which has been popularly used in practice. However, because selective crowdsourcing uses a winner-takes-all mechanism, implying that the efforts of most participants except the final winner will be just in vain. The purpose of this paper is to explore why this costly mechanism can become a popularity during the past decade and which type of tasks can fit this mechanism well. Design/methodology/approach The authors propose a game model between a sponsor and N participants. The sponsor is to determine its reward and the participants are to optimize their effort-spending strategy. In this model, each participant's ability is the private information, and thus, all roles in the system face incomplete information. Findings The results of this paper demonstrate the following: whether the sponsor can obtain a positive expected payoff are determined by the type of tasks, while the complex tasks with a strong learning effect is more suitable to selective crowdsourcing, as for the other two types of task, the sponsor cannot obtain a positive payoff, or can just gain a rather low payoff; besides the task type, the sponsor's efficiency in using the solutions and the public's marginal cost also influence the result that whether the sponsor can obtain a positive surplus from the winner-takes-all mechanism. Originality/value The model presented in this paper is innovative by containing the following characteristics. First, each participant's ability is private information, and thus, all roles in the system face incomplete information. Second, the winner-takes-all mechanism is used, implying that the sponsor's reward will be entirely given to the participant with the highest quality solution. Third, the sponsor's utility from the solutions, as well as the public's cost to complete the task, are both assumed as functions just satisfying general properties.

scholarly journals Distance Metrics of D Numbers

2020 ◽  
Author(s):  
Liguo Fei ◽  
Yuqiang Feng
Keyword(s):  
Number Theory ◽  
Incomplete Information ◽  
Distance Function ◽  
Distance Measure ◽  
Complete Information ◽  
Belief Function ◽  
Distance Metrics ◽  
Modeling Methods ◽  
The Difference ◽  
D Numbers

Belief function has always played an indispensable role in modeling cognitive uncertainty. As an inherited version, the theory of D numbers has been proposed and developed in a more efficient and robust way. Within the framework of D number theory, two more generalized properties are extended: (1) the elements in the frame of discernment (FOD) of D numbers do not required to be mutually exclusive strictly; (2) the completeness constraint is released. The investigation shows that the distance function is very significant in measuring the difference between two D numbers, especially in information fusion and decision. Modeling methods of uncertainty that incorporate D numbers have become increasingly popular, however, very few approaches have tackled the challenges of distance metrics. In this study, the distance measure of two D numbers is presented in cases, including complete information, incomplete information, and non-exclusive elements

scholarly journals Two-Stage Pursuit Strategy for Incomplete-Information Impulsive Space Pursuit-Evasion Mission Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 299
Author(s):  
Bin Yang ◽  
Pengxuan Liu ◽  
Jinglang Feng ◽  
Shuang Li
Keyword(s):  
Reinforcement Learning ◽  
Incomplete Information ◽  
Trajectory Optimization ◽  
Game Problem ◽  
Gradient Algorithm ◽  
Two Stage ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Close Distance ◽  
Pursuit Strategy

This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios.

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