Presence Of Incomplete Information Research Articles

On the relationship between lemon effect and worker characteristics: evidence from Korea

ABSTRACT Layoffs can negatively affect a worker’s wage in the presence of incomplete information on the worker’s productivity (‘lemon effect’). This study examines whether such an effect is present in Korea by utilizing the Korea Labor and Income Panel Study (KLIPS) data from 1998 to 2021 with a two-way fixed effects model. We find supporting evidence for the lemon effect: The drop in wage is generally greater for workers who experience layoff than those displaced due to firm closure. This observation is more evident for those (1) who worked at a small-to-medium firm, (2) who got re-employed in a different industry and occupation, and (3) who were laid off during economic booms at which information asymmetry is more pronounced.

Repeated Games in the Presence of Incomplete Information

Repeated Games in the Presence of Incomplete Information

Stable outcomes and information in games: An empirical framework

Empirically, many strategic settings are characterized by stable outcomes in which players’ decisions are publicly observed, yet no player takes the opportunity to deviate. To analyze such situations in the presence of incomplete information, we build an empirical framework by introducing a novel solution concept that we call Bayes stable equilibrium and computationally tractable approaches for estimation and inference. Our framework allows the researcher to be agnostic about players’ information and the equilibrium selection rule. In an application, we study the strategic entry decisions of McDonald’s and Burger King in the US. While the Bayes stable equilibrium identified set is always (weakly) tighter than the Bayes correlated equilibrium identified set, our results show that the former can be substantially tighter in practice. In a counterfactual experiment, we examine the impact of increasing access to healthy food on the market structures in Mississippi food deserts.

A novel GRU-TCN network based Interactive Behavior Learning of multi-energy Microgrid under incomplete information

The interactive operation of multi-energy Microgrid (MEMG) in energy market is facing more and more incomplete information decision-making requirements due to the enhanced privacy of users, which brings great challenges to the participation flexibility of MEMG in demand response programs. Meanwhile, considering the uncertainty of the external MEMG unit combination, the randomness of the environmental meteorology, the interactive characteristics of the external MEMGs are complex and time-varying. To solve these problems, a novel GRU-TCN network based Interactive Behavior Learning method for MEMG interactive operation is proposed. The proposed Interactive Behavior Learning framework needs merely externally available input information and historical interaction power data to efficiently predict the interaction power, which protects the data privacy of users. Besides, the GRU-TCN network also leverages the strengths of Recurrent Neural Network and Convolutional Neural Networks while incorporating the self-attention mechanism. This combination enables the GRU-TCN network to effectively capture the relationship between interactive power and the available resources data beyond the MEMG. The example analysis and test are carried out on a typical MEMG system, and the case study turns out that the proposed GRU-TCN network has the capability to capture coupling mechanisms even in the presence of incomplete information and present more accurate prediction of multi-energy interactive data.

A navigability entropy model for street networks

In this paper, we propose novel local and global models of street network entropy that measure levels of navigability given only limited local directional information. These models are defined for individual locations and entire street networks. Both models are derived using a generalised model of entropy from the field of game theory, which considers a decision-maker attempting to perform a task in the presence of incomplete information. We argue that the proposed models are more interpretable and useful than existing models of street network entropy since they measure the uncertainty of navigation, which is the task street networks are intended to facilitate. We demonstrate this utility by performing an empirical analysis of the entropy properties of UK city street networks.

Tactile Routing for Location Privacy Preservation in Wireless Sensor Networks: A Game Theoretic Approach.

Location Privacy Preservation (LPP) in Wireless Sensor Networks (WSNs) during the era of the Internet of things and smart systems is a critical element in the success of WSNs. LPP in WSN can be stated as: given a WSN with an adversary aiming to unravel the location of critical nodes of a WSN, the goal of the WSN manager is to enshroud the location of the critical nodes via routing and/or encryption mechanisms. Typical research in the LPP of WSN routing involves developing and/or estimating the performance of a fixed routing protocol under a given attack mechanism. Motivated by advancements in network softwarization, in this work, we propose an approach where the WSN manager as well as the WSN adversary can deploy multiple routing and attack mechanisms, respectively. Initially, the proposed approach is formulated as a repeated two-player zero-sum game. The formulation is further extended to handle multiple objectives and incomplete information in the game matrix. In this work, the multiple objectives are handled via the epsilon constraint method. The presence of incomplete information in the formulation is modeled as interval based uncertainty. To sum, the proposed formulation ultimately boils down to linear programming problems, which can be efficiently solved. Numerical case studies to showcase the applicability of the proposed approach are illustrated in this work. Finally, discussion on obtaining the required data from any given WSN, discussion and interpretation of the formulation’s results, and future research direction of the current work is presented.

Using Rough Set Theory for Reasoning on Vague Ontologies

Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. A prototype of Tableau's extended algorithm is developed and tested on examples where encouraging results are given by this method to demonstrate that unlike other methods, it is possible to answer queries even in the presence of incomplete information.

Signaling Strength with Handicaps

In the presence of incomplete information, strong states have an incentive to invest in costly signals that can differentiate them from weaker states. I argue that states can signal strength by handicapping themselves, deliberately reducing their combat effectiveness. In an ultimatum crisis bargaining model, I show that strong states can reduce the risk of war by making themselves weaker without reducing their demands. The key to this result is a comparative advantage that allows stronger types to fight more effectively with handicaps. This allows for an equilibrium where (1) stronger states adopt larger handicaps, thereby revealing their strength to the receiver, (2) larger handicaps are more likely to deter the receiver, and, (3) the positive risk of war precludes weaker types from imitating handicap signals. The ability to reveal strength peacefully has important ramifications for theories of mutual optimism, war termination, and the relationship between parity and war incidence.

Scarcity-aware spam detection technique for big data ecosystem

To expand their business, companies in the industry use the big data ecosystem for handling enormous amounts of information. For this purpose, text data must be analyzed while ensuring data security and organizing authenticated and valuable data using spam filters. Several methods are available such as Word2Vec, bag-of-words, BERT, and term frequency-inverse document frequency (TF-IDF). However, none of these resolve the data scarcity issue that may result in the presence of incomplete information in collected documents. A technique that groups each document by subject and applies approximation using statistical methods is required to effectively solve this problem. This study proposes a natural language processing-based technique for spam detection that alters topics using a least-squares model and uses gradient-descent and altering-least-squares (AMALS) models to estimate missing data through TF-IDF and uniform-distribution. A performance evaluation demonstrates that the proposed technique outperforms 98% than the existing industrial TF-IDF model in predicting spam in big data ecosystems.

Handling Censoring and Censored Data in Survival Analysis: A Standalone Systematic Literature Review

The study recognized the worth of understanding the how’s of handling censoring and censored data in survival analysis and the potential biases it might cause if researchers fail to identify and handle the concepts with utmost care. We systematically reviewed the concepts of censoring and how researchers have handled censored data and brought all the ideas under one umbrella. The review was done on articles written in the English language spanning from the late fifties to the present time. We googled through NCBI, PubMed, Google scholar and other websites and identified theories and publications on the research topic. Revelation was that censoring has the potential of biasing results and reducing the statistical power of analyses if not handled with the appropriate techniques it requires. We also found that, besides the four main approaches (complete-data analysis method; imputation approach; dichotomizing the data; the likelihood-based approach) to handling censored data, there were several other innovative approaches to handling censored data. These methods include censored network estimation; conditional mean imputation method; inverse probability of censoring weighting; maximum likelihood estimation; Buckley-Janes least squares algorithm; simple multiple imputation strategy; filter algorithm; Bayesian framework;β-substitution method; search-and-score-hill-climbing algorithm and constraint-based conditional independence algorithm; frequentist; Markov chain Monte Carlo for imputed data; quantile regression; random effects hierarchical Cox proportional hazards; Lin’s Concordance Correlation Coefficient; classical maximum likelihood estimate. We infer that the presence of incomplete information about subjects does not necessarily mean that such information must be discarded, rather they must be incorporated into the study for they might carry certain relevant information that holds the key to the understanding of the research. We anticipate that through this review, researchers will develop a deeper understanding of this concept in survival analysis and select the appropriate statistical procedures for such studies devoid of biases.

Reasoning on vague ontologies using rough set theory

Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. The extended Tableau algorithm by this rough set-based vagueness is intended to answer queries even with the presence of incomplete information.

Risk Assessment Framework for Outbound Supply-Chain Management

We developed a framework for the risk assessment of delaying the delivery of shipments to customers in the presence of incomplete information pertaining to a significant, e.g., weather-related, event that could cause substantial disruption. The approach was anchored in existing manual practices, but equipped with a mechanism for collecting critical data and incorporating it into decision-making, paving the path to gradual automation. Two key variables that affect the risk were: the likelihood of an event and the importance of the specific shipment. User-specified event likelihood, with elliptical spatial component, allowed the model to attach different probabilistic interpretations; uniform and Gaussian probability distributions were discussed, including possible paths for extensions. The framework development included a practical implementation in the Python scientific ecosystem. Although the framework was demonstrated in a prototype environment, the results clearly showed that the framework was quickly able to show scheduled and in-process shipments that were at risk of delay, while also providing a prioritized ranking of these shipments in order for personnel within the manufacturing organization to quickly implement mitigation actions and proactive communications with customers to ensure critical shipments were delivered when needed. Since the framework pulled in data from various business information systems, the framework proved to assist personnel to quickly identify potentially impacted shipments much faster than existing methods, which resulted in improved efficiency and customer satisfaction.

Probabilistic approach for characterising the static risk of ships using Bayesian networks

This paper proposes a probabilistic approach for characterising the static risk of individual ships based on Bayesian networks (BNs). The approach uses the Ship Risk Profile parameters of the New Inspection Regime of the Paris Memorandum of Understanding (MoU) on Port State Control (PSC), not as risk factors for ship selection in PSC inspections, but as risk variables for ship risk assessment and maritime traffic monitoring. The objectives of the proposed approach are threefold: the characterisation of the static risk profile of the maritime traffic crossing a given geographic area; the identification of the most likely circumstances under which a specific static risk profile is expected to occur; and the characterisation of the static risk profile of individual ships in the presence of incomplete information, such as that obtained from the Automatic Identification System. A dataset collected from the Paris MoU platform is used for the development of the BN model and its validity is assessed. A quantitative assessment for the predictive validity of the model is also conducted by a sensitivity analysis that shows the consistency of the model with the Ship Risk Profile criteria and with the results of other studies developed also from historical PSC inspection data.

Cumulative/dynamic ROC curve estimation under interval censorship

The receiver operating characteristic (ROC) curve is a graphical tool commonly used to assess the discriminatory ability of continuous markers in binary classification problems. Different extensions of the ROC curve have been proposed in the prognosis context, where the characteristics in the study are time-dependent events. Perhaps the most direct generalization is the so-called cumulative/dynamic (C/D) ROC curve. The main particularity when dealing with the C/D ROC curve estimation is the presence of incomplete information. Several approximation methods addressing this censoring problem have been suggested in the statistical literature, most of them focused on the right-censored case. Interval censorship arises naturally from those studies where subjects undergo periodical follow-ups. They may miss a scheduled appointment and the exact event times are only known to fall in a certain range. A new approach for estimating the C/D ROC curve under the particular scheme of interval censorship is presented in this work. Its finite-sample behaviour is studied via Monte Carlo simulations on two different scenarios. Results suggest that the proposed approximation is better than the existing one in terms of absolute error. Its direct application is illustrated in the real-world data set which motivated this research. The uniform strong consistency and a suitable R function for its practical implementation are provided as Appendices.

Distributed Demand Response for Multienergy Residential Communities With Incomplete Information

This article proposes distributed demand response (DR) approaches for a multienergy residential community, which is equipped with various energy conversion and storage devices to serve multiple residential loads (e.g., electricity, natural gas, and heating loads). In the proposed DR approaches, each of the energy devices and loads is an individual decision-maker and also a node in a randomly connected communication network. The DR approaches are tolerant to incomplete information which is caused by random inaction of nodes and links in the network. At first, in order to coordinate nodes’ behaviors in distributed DR, different information transmission mechanisms among nodes are employed. Particularly, Steiner tree broadcast, in which nodes are networked according to their energy types, is proposed to lower the nodes’ computational complexity and the network's communication overhead. Based on the information transmission mechanisms, the initial DR problem is transformed into network problems that are solvable in a random network. Then, based on the randomized alternating direction method of multipliers, distributed algorithms are designed to optimally solve the network problems in the presence of incomplete information. In simulation, real-world datasets of multiple energy loads and prices are used, and three proposed DR approaches are compared in terms of convergence performance and communication overhead.

Limiting Distributional Fixed Points in Systemic Risk Graph Models

We analyse the equilibrium behaviour of a large network of banks in presence of incomplete information, where inter-bank borrowing and lending is allowed, and banks suffer shocks to assets. In a two time period graphical model, we show that the equilibrium wealth distribution is the unique fixed point of a complex, high dimensional distribution-valued map. Fortunately, there is a dimension collapse in the limit as the network size increases, where the equilibriated system converges to the unique fixed point involving a simple, one dimensional distribution-valued operator, which, we show, is amenable to simulation. Specifically, we develop a Monte-Carlo algorithm that computes the fixed point of a general distribution-valued map and derive sample complexity guarantees for it. We numerically show that this limiting one-dimensional regime can be used to obtain useful structural insights and approximations for networks with as low as a few hundred banks.

Fuzzy Group Decision Making With Incomplete Information Guided by Social Influence

A promising research area in the field of group decision making (GDM) is the study of interpersonal influence and its impact on the evolution of experts’ opinions. In conventional GDM models, a group of experts express their individual preferences on a finite set of alternatives, then preferences are aggregated and the best alternative, satisfying the majority of experts, is selected. Nevertheless, in real situations, experts form their opinions in a complex interpersonal environment where preferences are liable to change due to social influence. In order to take into account the effects of social influence during the GDM process, we propose a new influence-guided GDM model based on the following assumptions: experts influence each other and the more an expert trusts in another expert, the more his opinion is influenced by that expert. The effects of social influence are especially relevant to cases when, due to domain complexity, limited expertise or pressure to make a decision, an expert is unable to express preferences on some alternatives, i.e., in presence of incomplete information. The proposed model adopts fuzzy rankings to collect both experts’ preferences on available alternatives and trust statements on other experts. Starting from collected information, possibly incomplete, the configuration and the strengths of interpersonal influences are evaluated and represented through a social influence network (SIN). The SIN, in its turn, is used to estimate missing preferences and evolve them by simulating the effects of experts’ interpersonal influence before aggregating them for the selection of the best alternative. The proposed model has been experimented with synthetic data to demonstrate the influence driven evolution of opinions and its convergence properties.

An intertemporal capital asset pricing model under incomplete information and short sales

This paper provides the inter-temporal capital asset pricing model with incomplete information and short sales constraints. We derive the general equilibrium market equation and the security market line of the “classical” capital asset pricing model in continuous time in the presence of incomplete information and short sales.

Intertemporal optimal portfolio choice based on labor income within shadow costs of incomplete information and short sales

This paper examines the optimal portfolio choice in the presence of risky labor income, retirement horizon and shadow costs of incomplete information and short sales. The optimal allocation to stocks is larger for employed investors than for retired investors within idiosyncratic labor income risk, incomplete information and short sales. Investors’ willingness to save is increased when increasing idiosyncratic labor income risk in the presence of incomplete information and short sales, while it is decreased when retired investors still keep partial of their income flow. Hence, they reduce their stock portfolio allocation towards the level of retired investors. The presence of a positive correlation between labor income and stock returns reduces stockholdings below the level of retired investors within the assumptions of the existence of shadow costs of incomplete information. Our results generalize previous findings in the literature by accounting for the additional impact of information costs and short sales constraints.

Using Ontology-Based Data Access to Enable Context Recognition in the Presence of Incomplete Information

Using Ontology-Based Data Access to Enable Context Recognition in the Presence of Incomplete Information