Dynamic Incomplete Information Research Articles

Game Analysis and Optimization for Evolutionary Dynamic Heterogeneous Redundancy

Dynamic Heterogeneous Redundancy (DHR) has been considered as a proactive defense architecture against unknown vulnerabilities and backdoors. It is a heterogeneous, fault-tolerant, and redundant system, which ensures the security by constantly switching heterogeneous executors. However, a limited number of heterogeneous executors can be merely switched and combined statically. It is difficult to cope with persistent threats. In this paper, we propose an evolutionary DHR system, add evolutionary sub-strategies of executors to solve this problem, optimizing defense mechanisms of DHR. To demonstrate theoretical validity, a game-theoretic analysis of the DHR security mechanism is performed under dynamic incomplete information, and the dilemma of defense is discussed. We construct a DHR game model based on evolutionary, and the results are extended to the general case to analyze the Bayesian equilibrium when each service strategy has a different number of evolved sub-strategies. In addition, the correlation factor is added to the game to investigate the impact of the correlation between different heterogeneous executors on defense results. Finally, Gambit and NS2 simulation experiments for the proposed method are shown. To verify the applicability of the physical system, a prototype DHR system based on dynamic evolution was constructed. We collect key configuration information and modified them randomly. It ensures that the information scanned by the attacker is different each time. Finally, the effectiveness of the physical system is verified by nmap and metasploit tools.

A method for incremental updating three-way decision with incomplete information system

In recent years, the three-way decisions theory has been developed in both theoretical and practical applications. In fact, data are often incomplete and often change over time. To solve this problem, a method of updating the three-way decisions in the dynamic incomplete information system is proposed. First, we consider the relationship between the change of conditional probabilities for the change of the three regions. Then, we consider the change of conditional probability when objects and attribute values of the object change. From that change, we propose a method for updating three-way decisions for two situations, namely, the objects vary, and the attribute values of an object vary. Finally, we give an example to illustrate this method.

MotiLearn: Contract-Based Incentive Mechanism for Heterogeneous Edge Collaborative Training

Due to the computation capacity improvement of edge devices and the popularization of artificial intelligence, there has been a dramatic increase in topic of edge intelligence. In order to motivate edge users to participate in task release and collaborative training, it is crucial to design a feasible incentive mechanism so that the task publisher (Principal) can maximize his interests while guaranteeing the income of co-trainers (Agent). In this paper, we first characterize the computation model, agent's benefits, and principal's profits model by considering collaborative training features and user heterogeneity. Furthermore, through the asymmetry of information, we divide the edge intelligent environment into complete information and incomplete information scenarios, in which the principal only knows the distribution of the agents’ private information rather than the specific information. Subsequently by discussing the above two situations in a static environment, we obtain the basic laws of market operation and reveal the significance of introducing a dynamic environment. Then a two-period contract-based incentive mechanism (MotiLearn) is proposed to overcome the ratchet effects of long-term contracts under dynamic incomplete information environment. Finally, theoretical proof and numerical results show that the proposed incentive mechanism is feasible and can motivate both the principal and agents positively.

Learning from Monetary and Fiscal Policy

We present a dynamic incomplete information model where monetary and fiscal policy instruments serve as endogenous signals for the private sector. We highlight a novel information channel of policy interactions, and show the general equilibrium (GE) information feedback between policies largely shapes the economy's response to policy shocks. We document a non-monotone signaling effect of policies with respect to the policy rule parameters. Our analysis shows the GE information feedback is quantitatively significant, and the model provides a unified explanation of the various policy impacts on inflation, the dynamics of survey expectations, and the missing inflation after the Great Recession.

An experimental study of the generalized second price auction

We experimentally investigate the Generalized Second Price (GSP) auction used to sell advertising positions in online search engines. Two contrasting click through rates (CTRs) are studied, under both static complete and dynamic incomplete information settings. Subjects consistently bid above the Vikrey–Clarke–Grove's (VCG) like equilibrium favored in the theoretical literature. However, bidding, at least qualitatively, satisfies the contrasting outcomes predicted under the two CTRs. For both CTRs, outcomes under the static complete information environment are similar to those in later rounds of the dynamic incomplete information environment. This supports the theoretical literature that uses the static complete information model as an approximation to the dynamic incomplete information under which advertising positions are allocated in field settings.

A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems

With the rapid growth of data sets nowadays, the object sets in an information system may evolve in time when new information arrives. In order to deal with the missing data and incomplete information in real decision problems, this paper presents a matrix based incremental approach in dynamic incomplete information systems. Three matrices (support matrix, accuracy matrix and coverage matrix) under four different extended relations (tolerance relation, similarity relation, limited tolerance relation and characteristic relation), are introduced to incomplete information systems for inducing knowledge dynamically. An illustration shows the procedure of the proposed method for knowledge updating. Extensive experimental evaluations on nine UCI datasets and a big dataset with millions of records validate the feasibility of our proposed approach.

Analysis of Multi-Stage and Dynamic Incomplete Information Game Model for EIA System in China

Environmental impact assessment (EIA) system has gradually developed in China while several problems still exist in the ecological environment protection implementation as the obstacles. This paper summarized the regular EIA process of China and picked up the stakeholders basis of case analyzing. A multi-stage and dynamic incomplete information game model was established to investigate the interactions among government, enterprises and EIA organizations and explain strategy selection of EIA participants according to perfect Bayesian equilibrium theory and scenario analysis. Furthermore, a suggestion was proposed to improve effectiveness of EIA system in China after discussing the present demerit. Results showed that in a short term, government and enterprises tended to choose evading EIA for their private benefits. But in a long run, they would be willing to take EIA because environmental deterioration would seriously affect their economic benefits. Thus they had to pursuit social benefits firstly instead. The public played a silent role as a victim in EIA system of China currently, caused by present beneficial balance of government, enterprises and EIA organizations. Some countermeasures must be brought out to develop the public participation regulation in the management system in order to break that balance and improve the effectiveness of EIA.

On the Informational Role of Quantities: Durable Goods and Consumers' Word- of-Mouth Communication

A dynamic incomplete information game is set up to study the introduction of a durable good when consumers can learn its quality from previous buyers. High sales today imply fewer potential buyers tomorrow, but each buyer will have better information about the good. Consumers are fully rational and can update their beliefs, even when they do not directly receive information; in equilibrium, no news is bad news. A low-quality firm follows a 'fly-by-night' strategy, randomizing over the timing of sales. A high-quality firm spreads out its sales more smoothly, solving a stochastic dynamic programming problem. Copyright 1997 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.