Incomplete Information Model Research Articles

Two-Stage Games of Complete but Imperfect Information: Application in International Trade with Tariff

Trade relationships are very complicated especially given the battles between Keynesian and Neo-liberal economic views on the issue. Game theory and its application in international trade can provide a simplistic but important method for quantifying the benefits of trading. In this paper, we intend to use two-stage games of complete but imperfect information in the trading context among two and three countries and compare it with the non-trading country. The purpose of this work is to apply a two-level game model of complete but incomplete information in game theory and apply it to international economics in the context of customs trading. The basic literature for this work is “Game Theory for Applied Economists” by Robert Gibbons. Glenn W. Harrison and E. E. Rustrom on Trade War, Trade Negotiation, and Applied Game Theory. We provide mathematical models and eventually provide the interpretation in a broader context.

Game Analysis of Digital Inclusive Financial Platform and Family Entrepreneurial Choice

The difficulty and cost of financing for family entrepreneurship are becoming increasingly prominent, and the issue of how to alleviate the financing constraints of family entrepreneurship has attracted widespread social attention. The financial services provided by digital inclusive finance platforms are beneficial in addressing the negative impact of financing constraints on households' entrepreneurial behaviour. They can contribute to the inclusive growth of China's economy. Thus the financial policies of digital inclusive finance platforms influence households' entrepreneurial choices. This paper constructs a dynamic game model of incomplete information between the platform and families based on the game perspective of micro subjects and explains the impact of the decision of the digital inclusive finance platform on households' choice of entrepreneurship by solving the equilibrium solution in the game tree, and explores the effects of factors such as the government subsidy system, costs and benefits, and the risk of platform loans on the decision-making behaviour of households and the platform by analysing the conditions required for the decision-making subjects to change their decisions in each sub-game. This essay explores the impact of government subsidies, costs, benefits, and the risk of lending to the platform on the decision-making behaviour of households so that the digital inclusive finance platform and families choose the "no support" and "no business" decisions. According to the dynamic game analysis, the government and the digital inclusive finance platform should increase subsidies to the platform, reduce the cost of household entrepreneurship and improve the guarantee system for household entrepreneurship; the digital inclusive finance platform should make use of digital information technology to enhance the platform construction, alleviate information asymmetry, reduce the cost of platform services, actively support household entrepreneurship and promote the increase of household entrepreneurship.

A pipeline for the retrieval and extraction of domain-specific information with application to COVID-19 immune signatures

BackgroundThe accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging when the information itself resides beyond titles or abstracts. For emerging science, with a limited set of known papers of interest and an incomplete information model, this is of pressing concern. A timely example in retrospect is the identification of immune signatures (coherent sets of biomarkers) driving differential SARS-CoV-2 infection outcomes.ImplementationWe built a classifier to identify papers containing domain-specific information from the document embeddings of the title and abstract. To train this classifier with limited data, we developed an iterative process leveraging pre-trained SPECTER document embeddings, SVM classifiers and web-enabled expert review to iteratively augment the training set. This training set was then used to create a classifier to identify papers containing domain-specific information. Finally, information was extracted from these papers through a semi-automated system that directly solicited the paper authors to respond via a web-based form.ResultsWe demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. The type of immune signature (e.g., gene expression vs. other types of profiling) was also identified with a positive predictive value of 74%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate.ConclusionsOur results demonstrate the efficacy of using a SVM classifier with document embeddings of the title and abstract, to retrieve papers with domain-specific information, even when that information is rarely present in the abstract. Targeted author engagement based on classifier predictions offers a promising pathway to build a semi-structured representation of such information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats.

Value of information with manufacturer encroachment

We study the value of information in a supply chain where the manufacturer introduces a direct‐selling channel to encroach in the retailer's market. Based on different information structures regarding the direct‐selling cost for the manufacturer, we analyze three models: incomplete information model, asymmetric information model, and full information model. By comparing the firms' profits in these models, we derive the value of the manufacturer's information acquisition and the value of sharing that information with the retailer. We find that the manufacturer's information acquisition may hurt the manufacturer but benefit the retailer simultaneously. However, we also find a win–win outcome for the manufacturer and the retailer due to the manufacturer's learning of its direct‐selling cost. Further, in expectation, the manufacturer always benefits from sharing its private cost information with the retailer, while the retailer does not. We also show that the direct‐selling channel is always valuable to the manufacturer, while the retailer's preference will depend on the information structure. Finally, we consider a supply competition model where the manufacturer competes with a spot supply source to sell to the retailer. In contrast to the case of no supply competition, the manufacturer sells less using direct selling. However, direct selling can become more valuable as it may contribute higher profit for the manufacturer due to the reduced wholesale price charged to the retailer. We find that supply competition can completely reverse the manufacturer's disincentive into incentive for information acquisition and facilitate information sharing with the retailer.

Robust Predictions for DSGE Models with Incomplete Information

We provide predictions for DSGE models with incomplete information that are robust across information structures. Our approach maps an incomplete-information model into a full-information economy with time-varying expectation wedges and provides conditions that ensure the wedges are rationalizable by some information structure. Using our approach, we quantify the potential importance of information as a source of business cycle fluctuations in an otherwise frictionless model. Our approach uncovers a central role for firm-specific demand shocks in supporting aggregate confidence fluctuations. Only if firms face unobserved local demand shocks can confidence fluctuations account for a significant portion of the US business cycle. (JEL D82, D83, E13, E31, E32)

Continuous‐time optimal unbiased FIR filter for input‐delayed systems

ABSTRACTMost existing finite impulse response (FIR) filters are restricted to models without delays. This paper proposes an continuous‐time optimal unbiased FIR filter for input‐delayed systems (CTOUFFID). A new integral transformation relation was introduced to derive the FIR filter. By applying this relation, the CTOUFFID problem is represented as an optimal control problem with zero terminal state. The filter gain function is obtained by solving two coupled matrix differential equations using initial conditions. The paper also offers discussion on the horizon size and a few special cases. The main benefit of the proposed solution is that it provides the maximum likelihood estimate with no requirements for the initial values. Finally, an application with the ‐404 turbofan engine model is presented to demonstrate the highly robust nature of the proposed FIR filer against incomplete noise information and unspecified model uncertainties.

The Choquet integral-based Shapley function for n-person cooperative games with probabilistic hesitant fuzzy coalitions

The use of probabilistic hesitant fuzzy elements (PHFEs) to represent the alliance status in cooperative games can describe the player's participation levels and associated probability. This paper proposes an effective Shapley function for probabilistic hesitant fuzzy cooperative games based on generalized Choquet integral. We first define the probabilistic hesitant fuzzy coalition of cooperative games. After that, a supplementary model of incomplete probability information is established. Without losing any probability information, an adjustment algorithm is developed to derive the consistent probability of coalitions. Then the characteristic function of probabilistic hesitant fuzzy cooperative games is constructed based on generalized Choquet integral. The Choquet integral-based Shapley function is presented according to the proposed characteristic function. Some desirable properties of Shapley function are discussed in detail. The CIG algorithm is established for generating consistent imputation of cooperative games. Finally, the proposed algorithm is applied to the cooperative production of alloy materials.

Target allocation decision of incomplete information game based on Bayesian Nash equilibrium

Aiming at the incomplete information of AUV cooperative confrontation, the incomplete information game theory is used to study the confrontation behavior of AUVs. Taking the remaining survival probability and weapon consumption as the evaluation indicators, adding the position error factor, an AUV game confrontation target allocation model for incomplete information is established. In terms of the Bayesian Nash equilibrium theory, the prior probabilities of the offensive and defensive strategy types are set by the virtual participant "Nature" in advance. Then the types of AUVs to be allocated are selected, and the judgment on the types of the target assignment strategies adopted by the other party are modified through the posterior probability. An algorithm for solving incomplete information target assignment based on the multi-target discrete particle swarms is proposed, and the Bayesian Nash equilibrium target assignment strategies of the two sides are obtained, which provides strategic choice help for the commander's combat command.

The Role of Informational Asymmetry in Interfaith Communication During Conflict: A Game Theoretical Approach

Interfaith dialogues are platforms to address the issues of common concern for different faiths and beliefs. In this article, we use game theory to draw attention to the tensions between representing one’s community and reaching out to the ‘other’ side in pursuit of a common goal. We investigate the role of uncertainty and trust in interfaith communications, especially in times of political conflict. We propose four dynamic game models of incomplete information classified into two categories. Through our model, we find that even if one participant would prefer a scenario where the other party is cooperative while she herself remains defiant for reputational purposes, as long as she values a mutual solution/cooperation to mutual defection/conflict, the interfaith interaction will be more likely to be successful. Our models also show that the parties will be more tolerant of a ‘defiant’ looking behaviour if they believe they are dealing with a partner who is sincere and not prone to peer pressure, rather than one who cares more about his reputation than the communication itself. We demonstrate the findings of our models by using the case of the interfaith interactions during the Troubles Period in Northern Ireland.

From Conflict Avoidance to Peaceful Path: The US-DPRK Denuclearization Interaction under the Frame of Incomplete Information Game

The North Korean nuclear crisis is not only a major security issue but also a defense strategic issue as well. Whatever future U.S. policy toward North Korea, the game between the two countries will have to figure out what kind of Pyongyang Washington is dealing with. Starting from the extreme hypothesis that the United States may take a military strike against the DPRK, this article creatively constructs the game model of incomplete information between the U.S. and the DPRK, and establishes the payoff matrix for two types of the DPRK: tough and weak. Then, the article tries to answer why the United States does not have the conditions for military strikes against North Korea through the analysis of different payoffs of the United States towards the DPRK. Also, North Korea s tactics at the negotiating table, as well as the trends of US-DPRK relations, are important and will be analyzed in the article.

Towards Personalized Privacy-Preserving Incentive for Truth Discovery in Mobile Crowdsensing Systems

Incentive mechanisms are essential for stimulating adequate worker participation to achieve good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of existing incentive mechanisms only consider compensating workers’ sensing cost, while the cost incurred by potential privacy leakage has been largely neglected. Moreover, none of existing privacy-preserving incentive mechanisms has incorporated workers’ different privacy preferences to provide personalized payments for them. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery. The basic idea is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses to sign a contract which specifies a privacy-preserving degree (PPD) and the corresponding payment the worker will receive if she submits perturbed data with that PPD. Specifically, we respectively design a set of optimal contracts analytically under both full and incomplete information models, which maximize the truth discovery accuracy under a given budget, while satisfying the individual rationality and incentive compatibility properties. The feasibility and effectiveness of Paris-TD are validated through experiments on both synthetic and real-world datasets.

Pain-FL: Personalized Privacy-Preserving Incentive for Federated Learning

Federated learning (FL) is a privacy-preserving distributed machine learning framework, which involves training statistical models over a number of mobile users (i.e., workers) while keeping data localized. However, recent works have demonstrated that workers engaged in FL are still susceptible to advanced inference attacks when sharing model updates or gradients, which would discourage them from participating. Most of the existing incentive mechanisms for FL mainly account for workers’ resource cost, while the cost incurred by potential privacy leakage resulting from inference attacks has rarely been incorporated. To address these issues, in this paper, we propose a contract-based personalized privacy-preserving incentive for FL, named Pain-FL, to provide customized payments for workers with different privacy preferences as compensation for privacy leakage cost while ensuring satisfactory convergence performance of FL models. The core idea of Pain-FL is that each worker agrees on a customized contract, which specifies a kind of privacy-preserving level (PPL) and the corresponding payment, with the server in each round of FL. Then, the worker perturbs her calculated stochastic gradients to be uploaded with that PPL in exchange for that payment. In particular, we respectively derive a set of optimal contracts analytically under both complete and incomplete information models, which could optimize the convergence performance of the finally learned global model, while bearing some desired economic properties, i.e., budget feasibility, individual rationality, and incentive compatibility. An exhaustive experimental evaluation of Pain-FL is conducted, and the results corroborate its practicability and effectiveness.

Incomplete information model of credit default of micro and small enterprises

AbstractThis research constructs a credit default estimation model for micro and small enterprises (MSEs) under the condition of changing information asymmetry. The model is established through the refinement, abstraction and portrayal of the core elements contained in the default trigger mechanism; this mechanism is based on the cash flow, the default boundary and the distribution of actual cash flow in the framework of the incomplete information model. By relaxing the assumption that the bank can fully observe the customer's initial information, this work constructs a theoretical model with practical application value that can effectively describe the default risk of MSEs. Numerical simulation shows that the model can accurately estimate the default probability of MSEs by quantitatively modelling the mechanism of default risk management and control which is applicable to the risk characteristics of MSEs. The model is also suitable for the continuous dynamic estimation and forecast of individual customer credit application and post‐lending risk.

Vertical Mergers in a Model of Upstream Monopoly and Incomplete Information

We examine the role of private information on the impact of vertical mergers. A vertical merger can improve the information that is available to an upstream monopolist because, after the merger, the monopolist can observe the cost of its downstream merger partner. In the pre-merger world, because the costs of the downstream firms are private information, the monopolist has incomplete information and cannot implement the monopoly outcome: The expected pre-merger equilibrium price of the downstream product is lower than the monopoly price. After a vertical merger, the equilibrium input price that is charged to the downstream rival can either increase or decrease—depending on whether the downstream merger partner’s cost is low or high, respectively. However, in all cases the equilibrium price of the downstream product increases to the monopoly price. Therefore, the merger leads to consumer harm even when it leads to a reduction in the input price. The merged firm, however, cannot extract all of the monopoly profit: The merger causes production inefficiency (when the downstream rival has a relatively small cost advantage) and the downstream rival still earns an information rent (when it has a relatively large cost advantage). These results also have implications for vertical merger policy.

Deterrence in the Cyber Realm: Public versus Private Cyber Capacity

Abstract Can cyber deterrence work? Existing scholarly works argue that deterrence by punishment using cyberattacks is ineffective because the difficulty of attributing the origin of cyberattacks makes the threat of future attacks less credible. However, these works have told us relatively little about the deterrence ability of public cyberinstitutions (PCIs), defined as publicly observable proactive efforts aimed at signaling a country’s level of cyber offensive and defensive capability. This research shows that middle powers (that have scarce cyber arsenals) can use PCIs to deter cyber attacks that cause significant damage to their economy and prosperity; however, this deterrent capability is rather limited. Using an incomplete-information model, we demonstrate that PCIs only deter adversaries that are susceptible to the costs created by these institutions. Despite this limited deterrence ability, middle powers tend to over-invest resources in these cyberinstitutions: Weak cyber states tend to over-invest to convince strong cyber adversaries that they are strong, whereas strong cyber states over-invest so that adversaries do not believe that they are weak states pretending to be strong. By doing so, states reduce their overall cybercapacity. We establish the empirical plausibility of these results using election interference campaigns as examples of strategic attacks. Our focus on the strategic use of PCIs as a deterrent represents a departure from existing literature—which has focused only on cyberoperations—and has important policy implications.

Vertical Mergers in a Model of Upstream Monopoly and Incomplete Information

We examine the role of private information on the impact of vertical mergers. A vertical merger can improve the information that is available to an upstream monopolist because, after the merger, the monopolist can observe the cost of its downstream merger partner. In the pre-merger world, because the costs of the downstream firms are private information, the monopolist has incomplete information and cannot implement the monopoly outcome: The expected pre-merger equilibrium price of the downstream product is lower than the monopoly price. After a vertical merger, the equilibrium input price that is charged to the downstream rival can either increase or decrease -- depending on whether the downstream merger partner’s cost is low or high, respectively. However, in all cases the equilibrium price of the downstream product increases to the monopoly price. Therefore, the merger leads to consumer harm even when it leads to a reduction in the input price. The merged firm, however, cannot extract all of the monopoly profit: The merger causes production inefficiency (when the downstream rival has a relatively small cost advantage) and the downstream rival still earns an information rent (when it has a relatively large cost advantage). These results also have implications for vertical merger policy.

Product Greenness and Pricing Strategy of Supply Chain Incorporating Asymmetric Heterogeneous Preferences

The decision making of product greenness and pricing strategy is important to achieve sustainable competitiveness of supply chain systems. Behavioral studies find that individual preferences, such as altruism and fairness, can affect the decision and performance of the supply chain significantly. However, the previous widely adopted assumption contradicts the private, asymmetric, and diverse behaviors in reality. This paper considers a green supply chain with asymmetric heterogeneous preferences, in which the manufacturer does not know whether the behavioral type of the retailer is self-interest or altruistic. The optimal decisions on product greenness and pricing strategy of the supply chain under asymmetric heterogeneous preferences are obtained with a game analytic model of incomplete information. The impact of asymmetric preferences on product greenness, price, and supply chain performance is attained by comparing the results under symmetric and asymmetric scenarios. How heterogeneous preferences change the impact of asymmetric preferences is presented with sensitivity analysis. The case study illustrates and verifies the impact of asymmetric preferences, and its sensitivity with respect to heterogeneous preferences. The findings show that asymmetric preferences will reduce the product greenness and wholesale price decided by the manufacturer, will enhance the retail price decided by the self-interest retailer, and meanwhile reduce that decided by the altruistic retailer. Moreover, asymmetric preferences will decrease the manufacturer's profit, reduce the self-interest retailer's profit, and enhance the altruistic retailer's profit while reducing the altruistic retailer's utility. Their influence extents will increase in the heterogeneity of preferences with few exceptions in cases of the self-interest retailer's profit and the altruistic retailer's utility. The theoretical findings generate some managerial implications, by which for example it is necessary to adjust promptly the estimation about the behavior types of supply chain partners.

Capacity Model of Exclusive Right-Turn Lane at Signalized Intersection considering Pedestrian-Vehicle Interaction

In high density urban areas, pedestrians have a great influence on the capacity of intersections. This paper studies the influence of pedestrians on road capacity and proposes an exclusive right-turn lane capacity model considering pedestrian-vehicle interaction (PV-RTC). Firstly, a pedestrian-vehicle interaction (PVI) model is proposed based on the logit model and static games theory of incomplete information. Through this model, the probability of 6 kinds of pedestrian-vehicle interaction situations (vehicles yield to pedestrians, pedestrians yield to vehicles, etc.) in the crosswalk can be obtained. Then, based on the basic idea of the stop line method and the probabilities of above situations, the PV-RTC model is established, and the sensitivity analysis of the important factors (pedestrian arrival rate, yielding rate, and green time ratio) affecting the model is carried out to clarify the mechanism of the proposed model. Finally, a pedestrian-vehicle interaction model of cellular automata for the exclusive right-turn lane is established and its simulation results are compared with the results of the PV-RTC model. The results show that the relative error between the microscopic simulation model and PV-RTC model is less than 15% overall, which verifies the validity of the PV-RTC model. This study provides references for a more precise estimation method of pedestrian impact on road capacity.

A Minimum Trust Discount Coefficient Model for Incomplete Information in Group Decision Making with Intuitionistic Fuzzy Soft Set

This article proposes a framework to deal with incomplete information in multiple criteria group decision making with intuitionistic fuzzy soft set. To do that, the weighted sum method for choice values and simple mathematical expectation method are extended to the case of obtained fuzzy soft set, and they are proved to have the same result in estimating the incomplete information. In order to reduce the system error in the estimating process cause by multiple decision information, a minimum trust discount coefficient model is established according to the relevant methods of evidence theory. Then, a new definition of entropy for intuitionistic fuzzy sets is introduced to determine the weights of group experts. Therefore, individual decision-making matrices are integrated into a comprehensive decision-making matrix by the integration operation formula of intuitionistic fuzzy soft matrix. The decision making is realized according to the difference of the score values of objects. Finally, the steps of this method are concluded, and one example is given to explain the application of this method.

Sequential Bargaining in the Field: Evidence from Millions of Online Bargaining Interactions*

AbstractWe study patterns of behavior in bilateral bargaining situations using a rich new data set describing back-and-forth sequential bargaining occurring in over 25 million listings from eBay’s Best Offer platform. We compare observed behavior to predictions from the large theoretical bargaining literature. One-third of bargaining interactions end in immediate agreement, as predicted by complete-information models. The majority of sequences play out differently, ending in disagreement or delayed agreement, which have been rationalized by incomplete information models. We find that stronger bargaining power and better outside options improve agents’ outcomes. Robust empirical findings that existing models cannot rationalize include reciprocal (and gradual) concession behavior and delayed disagreement. Another robust pattern at odds with existing theory is that players exhibit a preference for making and accepting offers that split the difference between the two most recent offers. These observations suggest that behavioral norms, which are neither incorporated nor explained by existing theories, play an important role in the success of bargaining outcomes.