Asymmetric Information Game Research Articles

The influence of effort level on profit distribution strategies in IPD projects

PurposeConstruction projects have become increasingly long, complex and costly with waste and inefficiencies and often fail to achieve the desired results. Integrated project delivery (IPD) is believed to change these problems. A reasonable and fair profit distribution mechanism is a critical factor for ensuring the success of the IPD projects. This study aims to investigate the strategies of all participants in the profit distribution of an IPD project with respect to the factor of the effort level.Design/methodology/approachThis study describes the influence of owners and participants on profit distribution due to their respective efforts in the IPD project alliance. The influence of effort level on profit distribution is discussed based on the Holmstrom-Milgrom model of asymmetric information game theory and principal-agent theory, combined with incentive compatibility (IC) constraints and individual rationality (IR) constraints.FindingsThe results show that the optimal level of effort by each participant optimizes the profit distribution of an IPD project. At the same time, in the revenue incentive contract, the effort level of the participants is positively correlated with the profit distribution, proportional to their contribution coefficient and inversely proportional to the square of the cost of their creative activities in terms of effort. Each party of an IPD project can adopt a series of measures to improve their own effort level and choose the optimal level of effort based on the profit distribution, while satisfying their own utility maximization.Originality/valueThis study introduces the Holmstrom-Milgrom model in the principal-agent theory to explore the influence of the effort level on profit distribution in IPD projects. The quantitative model can contribute to establish a fair and efficient profit distribution scheme for the IPD projects.

Dynamic Information Sharing and Punishment Strategies

In this article, we study the problem of information sharing among rational self-interested agents as a dynamic game of asymmetric information. We assume that the agents imperfectly observe a Markov chain, and they are called to decide whether they will share their noisy observations or not at each time instant. We utilize the notion of <i>conditional mutual information</i> to evaluate the information being shared among the agents. The challenges that arise due to the interdependence of agents&#x2019; information structure and decision making are exhibited. For the finite horizon game, we prove that agents do not have incentive to share information. In contrast, we show that cooperation can be sustained in the infinite-horizon case by devising appropriate punishment strategies, which are defined over the agents&#x2019; <i>beliefs</i> on the system state. We show that these strategies are closed under the best-response mapping and that cooperation can be the optimal choice in some subsets of the state belief simplex. We characterize these <i>equilibrium regions</i>, prove uniqueness of a <i>maximal</i> equilibrium region, and devise an algorithm for its approximate computation.

Information Validates the Prior: A Theorem on Bayesian Updating and Applications

We develop a result on expected posteriors for Bayesians with heterogenous priors, dubbed information validates the prior (IVP). Under familiar ordering requirements, Anne expects a (Blackwell) more informative experiment to bring Bob’s posterior mean closer to Anne’s prior mean. We apply the result in two contexts of games of asymmetric information: voluntary testing or certification, and costly signaling or falsification. IVP can be used to determine how an agent’s behavior responds to additional exogenous or endogenous information. We discuss economic implications. (JEL C11, D82, D84)

Aspiration and poverty in an asymmetric information game: a case study of Southern Xinjiang

PurposeThe purpose of this paper is to reveal the internal mechanism of the deviation of targeted poverty alleviation under the condition of asymmetric information.Design/methodology/approachIntroducing a traditional signaling game theory model with dynamic asymmetric information, this study uses a dataset covering 813 poor households from Southern Xinjiang, China, to theoretically and empirically study the effect of aspiration of the poor households on poverty alleviation.FindingsFirst, there exists asymmetric information between the poor households and village leaders. Second, the “short, arbitrary and fast” poverty alleviation approach may quickly pull people out of poverty, but it may also nurture a dependency culture where poor households lose their aspiration and ability to escape poverty through their own efforts. Third, due to long lasting universal state support, poor households in the national designated poor counties are less able and ambitious to escape poverty by themselves than their counterparts living in the national nondesignated areas.Originality/valueThe research results show that pro-poor development policies should be upheld to improve the residual utility that can benefit all households as a result of the poverty alleviation campaign. Relevant policy recommendations are made for China's continuous effort to fight relative poverty beyond 2020.

Empirical test of employee incentive in supply chain network based on asymmetric information game analysis and fuzzy model

The construction of the evaluation index system of employee satisfaction level is the basis for an enterprise to measure the level of employee satisfaction. In this paper, the author analyzes the employee incentive in supply chain network based on asymmetric information game analysis and fuzzy model. Through the correction of network weight and threshold value, the error function decreases along the gradient direction. The construction and training of the network can be realized by MATLAB. The newff function in the software is used to construct the network, and the train function is used to train. This evaluation index system is a multi-level target evaluation system based on asymmetric information game, and its basic principle is to simplify complex problems. Long term effort is bigger when the salesperson’s risk averse parameter is more than a certain value or risk averse parameter is less but discount factor is more than a threshold; short term effort is bigger when risk averse parameter and discount factor is both less. For both information scenarios, the compensation contracts are designed and comparison analyses are conducted.

Existence of Structured Perfect Bayesian Equilibrium in Dynamic Games of Asymmetric Information

In~[1],authors considered a general finite horizon model of dynamic game of asymmetric information, where N players have types evolving as independent Markovian process, where each player observes its own type perfectly and actions of all players. The authors present a sequential decomposition algorithm to find all structured perfect Bayesian equilibria of the game. The algorithm consists of solving a class of fixed-point of equations for each time $t,\pi_t$, whose existence was left as an open question. In this paper, we prove existence of these fixed-point equations for compact metric spaces.

Dynamic Information Design

We consider the problem of dynamic information design with one sender and one receiver where the sender observers a private state of the system and takes an action to send a signal based on its observation to a receiver. Based on this signal, the receiver takes an action that determines rewards for both the sender and the receiver and controls the state of the system. In this technical note, we show that this problem can be considered as a problem of dynamic game of asymmetric information and its perfect Bayesian equilibrium (PBE) and Stackelberg equilibrium (SE) can be analyzed using the algorithms presented in [1], [2] by the same author (among others). We then extend this model when there is one sender and multiple receivers and provide algorithms to compute a class of equilibria of this game.

On the Sustainability of Technology Licensing Under Asymmetric Information Game

Past research indicates that a licensor tends to adopt the fixed fee, in order to obtain higher profit rather than secure royalty when he participates in zero production in the market. This study instead finds that the patentee’s optimum strategy may vary. In addition to the fixed-fee strategy, royalty or mixed licensing, or fixed fee plus royalty may be potential choices for the patentee as well which is depend on the market scale, incidence of market scale, and magnitude of cost-saving. The patentee may choose to only authorize a type of high market size based on self-interested motives. The technology licensing market is not sustainable.

Screening Contract Excitation Models Involving Closed-Loop Supply Chains Under Asymmetric Information Games: A Case Study with New Energy Vehicle Power Battery

In closed-loop supply chain systems for power battery remanufacturing, recycling and dismantling tasks will be relegated to third-party recyclers. This has significant disadvantages, inasmuch as the asymmetric exchange of information regarding the level of recycling capacity and effort after signing a contract fiscal risks to the manufacturers. The purpose of this paper is to study the “adverse selection” of recyclers and “moral hazards” hidden in their purported effort levels, based on Information Screening Models in the principal-agent theory. Our information screening model for revenue sharing will be presented, and subsequently verified using numerical simulation to demonstrate the impact of the screening contract on the expected returns of both parties. Our results show that the sharing coefficient of the remanufacturing revenue for low-capability recyclers is distorted downwards, and only truthful reporting can retain profits. High-capacity recyclers will obtain additional information while retaining profit. At the same time, as the proportion of high-capacity recyclers in the market increases, the expected return of the entrusting party increases. One critical area where this will impact the Chinese economy is in the area of new energy vehicles. We investigate a case study of our approach in new energy vehicles, which are being used to reduced CO 2 emissions, but have environmentally hazardous batteries that must be recycled safely and economically.

Voluntary information acquisition in an asymmetric-Information game:comparing learning theories in the laboratory

This paper uses an experimental design of voluntary information acquisition to assess the information assumptions of different learning models. The design is implemented in two-stage asymmetric-information games. Subjects’ information-seeking behavior reveals that they tend to choose certain information sets that are consistent with belief-based learning theories rather than reinforcement theories. A hybrid-learning model with information acquisition that is a variant of the Generalized Experience-Weighted-Attraction (GEWA) model (Shafran, 2012) is also proposed. It successfully captures the different learning speeds of two groups of subjects (i.e., informed and uninformed subjects), and shows that once information acquisition data is added into a structural model that focuses on action data alone, the performance is enhanced. Additional individual analysis indicates that the information acquisition behavior assumed by learning models appears to suggest the learning rule subjects follow. The results suggest that tracking subjects’ voluntary information choices is a useful tool for analyzing their learning behaviors.

Legal Advertising and Frivolous Lawsuits

Following a recent wave of deregulation, lawyers now use a large variety of media to advertise their services. A common argument against this increasing reliance on advertising is that it might stir up frivolous lawsuits. In this article, we investigate the relevance of this argument by developing an asymmetric information game of litigation. The main results show that this stirring-up effect does not necessarily occur in equilibrium and advertising may improve welfare by decreasing the social cost of accidents. Therefore, the liberalization of legal advertising should not necessarily be considered as a threat to the efficiency of the tort system.

Jammer-Type Estimation in LTE With a Smart Jammer Repeated Game

Long-term evolution (LTE)/LTE-Advanced (LTE-A) networks are known to be vulnerable to denial-of-service (DOS) and loss-of-service attacks from smart jammers. The interaction between the network and the smart jammer has been modeled as an infinite-horizon general-sum (nonzero-sum) Bayesian game with asymmetric information, with the network being the uninformed player. Although significant work has been done on optimal strategy computation and control of information revelation of the informed player in repeated asymmetric information games, it has been limited to zero-sum games with perfect monitoring. Recent progress on the strategy computation of the uninformed player is also limited to zero-sum games with perfect monitoring and is focused on expected payoff formulations. Since the proposed formulation is a general-sum game with imperfect monitoring, existing formulations cannot be leveraged for estimating true state of nature (the jammer type). Hence, a threat-based mechanism is proposed for the uninformed player (the network) to estimate the informed player's type (jammer type). The proposed mechanism helps the network resolve uncertainty about the state of nature (jammer type) so that it can compute a repeated-game strategy conditioned on its estimate. The proposed algorithm does not rely on the commonly assumed “full monitoring” premise and uses a combination of threat-based mechanism and nonparametric estimation to estimate the jammer type. In addition, it requires no explicit feedback from the network users, nor does it rely on a specific distribution (e.g., Gaussian) of test statistic. It is shown that the proposed algorithm's estimation performance is quite robust under realistic modeling and observational constraints, despite all the aforementioned challenges.

Mean-payoff games with partial observation

Mean-payoff games are important quantitative models for open reactive systems. They have been widely studied as games of full observation. In this paper we investigate the algorithmic properties of several sub-classes of mean-payoff games where the players have asymmetric information about the state of the game. These games are in general undecidable and not determined according to the classical definition. We show that such games are determined under a more general notion of winning strategy. We also consider mean-payoff games where the winner can be determined by the winner of a finite cycle-forming game. This yields several decidable classes of mean-payoff games of asymmetric information that require only finite-memory strategies, including a generalization of full-observation games where positional strategies are sufficient. We give an exponential time algorithm for determining the winner of the latter.

Dynamic Games With Asymmetric Information and Resource Constrained Players With Applications to Security of Cyberphysical Systems

We model attacks on a cyberphysical system as a game between two players—the attacker and the system. The players may not acquire the complete information about each other, and that leads to an asymmetric information game. Furthermore, the players may have a certain fixed amount of resources, which constrains their strategies across time. Accordingly, we consider a dynamic multiplayer nonzero sum game with asymmetric information in which controllers have total resource constraints. Under certain assumptions on the information structure of the game, we devise an algorithm that computes a subclass of Nash equilibria of the game. We also study a denial-of-service attack on a cyberphysical system, model it as two-player zero-sum games, and apply our algorithm to compute the saddle-point equilibrium strategies of the attacker and the controller.

Scalable Games: Modelling Games of Incomplete Information

We provide conditions to allow modelling situations of asymmetric information in a tractable manner. In addition, we show a novel relationship between certain games of asymmetric information and corresponding games of symmetric information. This framework establishes links between certain games separately studied in the literature. The class of games considered is defined by scalable preference relations and a scalable information structure. We show that this framework can be used to solve asymmetric contests and auctions with loss aversion.

A game of asymmetric information between a therapist and a mentally ill patient

A game of asymmetric information between a therapist and a mentally ill patient

Study on the project supervision system based on the principal-agent theory

Purpose: In order to solve problems in the current project management system, the paper presents the asymmetric information games existing in construction projects through information economics viewpoints. Design/methodology/approach: The owner has private information about the project profitability and he exerts an unobservable level of effort in order to increase the feasibility of successfully completing the project in terms of meeting product specifications. The paper analyzes the principal-agent relationship between the owner and supervisor with “principal-agent theory” of the game theory. In addition, the paper validates the model through two project cases. Findings: We can conclude that the incentive contract plays an important role in reducing the moral hazard. The main contribution of this studyis to examine the influence of both pre-contractual private information and the sensitivities between the interrelated performance measures on the design of an optimal incentive contract. Social implications: At last, some advices are put forward to advance the project management system in China, and some external mechanism can effectively inhibit themoral hazard and adverse selection to occur. Originality/value: A model of principal-agent relationship between the owner and supervisor is formulated. This model takes consideration of the moral hazard, which isdifferent from most existing researches in this field.

Common Information Based Markov Perfect Equilibria for Linear-Gaussian Games with Asymmetric Information

We consider a class of two-player dynamic stochastic nonzero-sum games where the state transition and observation equations are linear and the primitive random variables are Gaussian. Each of the two players/controllers of the system acquires possibly different dynamic information about the state process and the other controller's past actions and observations. This leads to a dynamic game of asymmetric information among the controllers. Building on our earlier work on finite games with asymmetric information, we devise an algorithm to compute a Nash equilibrium by using the common information among the controllers. We call such equilibria common information based Markov perfect equilibria of the game, which can be viewed as a refinement of Nash equilibrium in games with asymmetric information. If the players' cost functions are quadratic, then we show that under certain conditions a unique common information based Markov perfect equilibrium exists. Furthermore, this equilibrium can be computed by solving a sequence of linear equations. We also show through an example that there could be other Nash equilibria in a game of asymmetric information that are not common information based Markov perfect equilibria.

Learning to Use Private Information: Re-examining the Asymmetric-Information Game in the Laboratory

This paper proposes an experimental design of voluntary information acquisition to discriminate among different learning models and implements this design in two-stage asymmetric-information games. Subjects information-seeking behavior reveals that, in relation to belief-updating, they tend to choose certain information set that is consistent with belief-based learning theories rather than reinforcement theories. A hybrid learning model with information acquisition- a variant of the Generalized Experience-Weighted-Attraction model (Shafran, 2012) - is also proposed. It successfully captures the different learning speeds of players with asymmetric roles at the aggregate level, and shows that players best respond to information that they choose to examine. This study suggests that tracking subjects voluntary information choice is a complementary tool for analyzing players learning behaviors.

Asymmetric Information Supply Chain Models with Credit Option

Credit option is a policy that has been studied by many researchers in the area of supply chain management. This policy has been applied in practice to improve the profits of supply chain members. Usually, a credit option policy is proposed by the seller, and often under a symmetric information environment where members have complete information on each others' operations. In this paper, we investigate two scenarios: firstly, the seller offers a credit option to the buyer, and secondly, the buyer attempts to stretch the length of the credit period offered by the seller. The proposed model in both scenarios will be investigated under an asymmetric information structure where some information are private and are only known to the individual who has knowledge of this information. The interactions between buyer and seller will be modeled by non-cooperative Stackelberg games where the buyer and seller take turn as leader and follower. Among some of the numerical results obtained, the seller and buyer's profits obtained from symmetric information games are larger than those obtained from an asymmetric information game in both scenarios. Furthermore, both buyer and seller's profit in the second scenario are better than in the first scenario.