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Port digital transformation requires substantial investments. However, the benefits after transformation are difficult to accurately predict due to incomplete information available to ports. Balancing digital investments with benefits poses a key challenge for ports. We propose a game model based on revenue sharing between the port and the technology company to determine the digital level in equilibrium under incomplete information. Our study reveals that the incomplete information influences digital level of ports. When information is incomplete and ambiguous, the digital level of ports may be lower. However, significant ambiguous does not necessarily decrease the digital level due to risk pooling and agent cost effects. Additionally, revenue sharing reduces the incentive for technology company to withhold private information, and specific sharing ratios are identified. The study also finds that under incomplete information, digital level of port initially increases and then decreases with increasing optimism or the ratio of port's retained revenue. By considering these factors, our study provides guidance for ports in optimal investment strategies for digital transformation. • When information is significant ambiguous, the digital level of ports may not necessarily decrease due to risk pooling and agent cost effects. • Under incomplete information, digital level of ports initially increases and then decreases with increasing optimism or the ratio of port's retained revenue. • The revenue sharing ratio should also be carefully determined, with the port retention no less than 40%. [ABSTRACT FROM AUTHOR]

This study considers zero-sum risk-sensitive discrete-time stochastic games with incomplete reward information on one side. We show the existence of the value function, derive a new Shapley equation, and prove that the value function solves the Shapley equation, which is used to construct an optimal policy for the informed player. For the uninformed player, we construct an optimal policy by introducing a dual game. Finally, we give an example to illustrate the effects of the reward information and risk-sensitive parameters on the value function and optimal policies. [ABSTRACT FROM AUTHOR]

This paper develops a sovereign debt model proposing that a debt issuance can be a credible signaling channel between a sovereign government and foreign creditors. The government has private information regarding the future economy. The one with a good economic outlook would like to find a credible way to disclose it to obtain a high bond price. Foreign creditors are interested in inferring the government's private information to assess sovereign default risk precisely. The government's private information is imperfect, so the precision of information matters. We study how the interaction of the prior, the signal, and its precision affects the equilibrium and the resulting welfare. We propose a unique separating signaling equilibrium demonstrating the feature that a more precise signal of good economic outlook leads to a greater extent of fiscal austerity. As the information becomes more precise, the signaling cost for a government with a good economic outlook increases. Interestingly, unless the prior is very pessimistic, a highly precise signal harms the sovereign because a resulting strong signaling motive drives it to reduce bond issuance excessively (paradox of highly precise information). [ABSTRACT FROM AUTHOR]

This paper revisits classical work of Rauch et al. (1965) and develops a novel statistical method for maximum likelihood (ML) recursive state estimation in general state–space models. The new method is based on statistical estimation theory for incomplete information, which has been well developed primarily for ML parameter estimation (Dempster et al., 1977). Distributional identities for the posterior score function and information matrix of state are established. Using these identities, a fast convergent EM-gradient algorithm is proposed, extending the Lange (1995) algorithm for ML recursive state estimation. It revisits and provides an improvement to the EM-algorithm of Ramadan and Bitmead (2022). An explicit form of the information matrix is developed to provide empirical estimates of the standard errors. Sequential Monte Carlo method is used for the valuation of the score function, information and posterior covariance matrices. Some numerical examples are discussed to exemplify the main results. [ABSTRACT FROM AUTHOR]

Utilization of observers in social networks for rumor source estimation is quite common. However, existing observer-based techniques have several drawbacks. Mostly existing approaches use the information available (such as t i m e s t a m p s) with many observers to locate the source node, which degrades their performance. Estimation of exact t i m e s t a m p s of non-observer nodes also poses an added challenge. To overcome these issues, we introduced a Radial Observer Source Estimator (ROSE) algorithm to estimate the source node in the network. The ROSE algorithm utilizes s h a r e s of a particular message instead of t i m e s t a m p s , and it considers the information available with only those observers that have minimum s h a r e s. The overall complexity of the algorithm is O (l o g (N) ∗ N 2) , where N is the number of nodes in a network. We evaluate our algorithm on small-scale, large-scale, and artificial networks. Empirical results show that the proposed algorithm is highly efficient in terms of execution time compared to four other state-of-the-art rumor source identification algorithms. Also, it mostly performs well in accurately discovering a rumor source node, with a higher success rate and fewer distance errors. • Developed an algorithm called ROSE to locate single rumor source in social networks. • Introduced a novel partial radial observer-based technique in the ROSE algorithm. • The time complexity of ROSE algorithm is O (l o g (N) ∗ N 2). • ROSE outperforms baseline algorithms in terms of distance error and execution time. [ABSTRACT FROM AUTHOR]

In this paper, two kinds of distributed Nash equilibrium seeking strategies based on Kalman filter are proposed in non-cooperative games with incomplete information. In the discrete-time system with process and measurement noises, each player, selfish and only considering its own profit, utilizes the gradient method to maximize the benefit. Since the payoff function is related to all players' states, Kalman filter and leader-following consensus are used to estimate the states in the network. Furthermore, considering the trade-off between strategy precision of Nash equilibrium and communication rate, another Nash equilibrium seeking method is proposed by introducing an event-based scheduler. The convergence of both Nash equilibrium seeking strategies is analyzed based on Lyapunov method. It is proved that both strategies are bounded in the mean square sense. Simulation examples are given to verify the efficiency. • Noises are added for privacy protection in noncooperative games. • Kalman filter is introduced in the distributed Nash equilibrium seeking strategy. • To balance accuracy against communication rate, an event-based scheme is proposed. [ABSTRACT FROM AUTHOR]

• We model subsidy policies on transport facility adaptation under incomplete information and ambiguity. • Incomplete information and ambiguity make the facility output, adaptation investment and social welfare downwardly distorted. • Two countervailing effects, the "pooling effect" and the "agency cost effect," jointly determine the downward distortions. • When the ambiguity is sufficiently high (or low, respectively), a complete pooling (or partial pooling, respectively) strategy is adopted. • Under ambiguity, a more optimistic government leads to a larger distortion on the regulation outcomes. This paper models subsidy policies on transport facility's adaptation to climate change-related disasters under the government's incompleteness and ambiguity of disaster information. We consider a transport facility (an airport or a seaport) that is in a vertical structure with an upstream facility operator and a few downstream carriers. The exact probability of disaster damage level is privately known by the facility operator, while the government has incomplete information and has to form a probability distribution. If the government cannot accurately perceive such probability (misjudgment), ambiguity emerges. This is the first modeling work to integrate the incomplete information and ambiguity in one framework to analyze the transport investment under uncertainty. Modeling results suggest that such incomplete information and ambiguity in many cases allow the facility operator to secure information rent, and make the facility output, adaptation investment and social welfare downwardly distorted (i.e., less than the levels under complete information). The ambiguity can bring two countervailing effects under incomplete information, namely the "pooling effect" and the "agency cost effect," which jointly determine the downward distortions on market outcomes and the welfare-maximizing subsidy policy. When its degree is low, the ambiguity causes a more dominant "pooling effect" that can help alleviate the distortion, improving social welfare. In presence of ambiguity, the increasing optimism of the government would deteriorate the distortion, likely to damage social welfare. Our analytical results provide support to subsidy on adaptation and policies to promote adaptation effectiveness and carrier competition in downstream market. To alleviate possible distortion, the government also needs to be cautious to reduce ambiguity through more information acquisition, and not be too optimistic on the disaster damage. [ABSTRACT FROM AUTHOR]

This paper considers the river sharing problem with incomplete information. We ask whether the efficient allocation of water in a river network is possible when agents have private information on their satiation levels. First, we introduce a trading model on a tree network with incomplete information. Then, we focus on the river sharing problem for a three-agent river network in which water flows from two upstream agents to one downstream agent. We assume that each agent has a satiation point and this satiation point is his private information. We are interested in allocation mechanisms that map each profile of the satiation points to an allocation of water and a list of monetary transfers between the agents. We show that, if each upstream agent's endowment of water is sufficiently larger than the downstream agent's endowment, then there exists an incentive compatible, individually rational, and budget balanced mechanism that allocates the water efficiently. • We consider the river sharing problem in which agents' satiation levels are private information. • We focus on a nonlinear river network with two upstream agents and one downstream agent. • We employ the theory of mechanism design to investigate the existence of efficient trading mechanisms for sharing water. • We find a sufficient condition based on water endowments for efficient trading mechanisms. [ABSTRACT FROM AUTHOR]

We study a quickest detection problem where the observation rate of the underlying process can be increased at any time for higher precision, but at an observation cost that grows linearly in the observation rate. This leads to a problem of combined control-and-stopping with incomplete information , with a two-dimensional sufficient statistic comprised of the current observation rate together with the conditional probability that disorder has already happened. The problem is shown to have a semi-explicit solution, where for some parameter values it is too costly to exert control at all, whereas for other parameter values the optimal strategy is to increase the observation rate in such a way that the sufficient statistic reflects at a certain boundary until the optimal stopping time. In both cases we fully characterise the optimal strategy with the help of appropriate smooth fit conditions. [ABSTRACT FROM AUTHOR]

Recent research shows that while the creditors' interests determine an issuer's hedging policy, creditors observe incomplete information regarding the cash flow. This study examines hedging effects with incomplete information through a real-option model, thereby deriving associated implications. We show that the difference in firm values between hedged and unhedged policies increases with more incomplete information. The higher the hedging efficiency, the larger the difference in hedging values across the degrees of incomplete information. The hedging effect decreases with shareholders' bargaining power in renegotiations. Our findings imply that creditors are more likely to impose hedging requirements when facing more incomplete information. • Incomplete information raises the disparity between hedged and unhedged firm values. • Hedging efficiency is crucial for creditors with incomplete information. • Hedging value is lower for firms with higher shareholders' bargaining power. • Incomplete information increases creditors' incentives to impose hedging requirements. [ABSTRACT FROM AUTHOR]

With the development of prosumers, peer-to-peer (P2P) energy transactions have become possible and emerged as a research focus. However, global information cannot be obtained in the P2P energy market, which results in the inability to consider network physical constraints during the trading process. As a result, the operational risks may exist in the system due to the opportunistic behaviors. To this end, this paper proposes a dual-layer game optimization method between the distributed system operator (DSO) and prosumers involving the risk price guidance mechanism in the P2P energy market. Specifically, the upper and lower layers meet the privacy requirements of each participant through limited information sharing. Then, the concept of risk price is introduced to guide prosumers' P2P energy transactions. The upper and lower layers continuously adjust their strategies, and the risk of congestion is mitigated when the game achieves equilibrium. This ensures a balanced consideration of system security and the economic interests of all participants. Additionally, a C-ADMM method with adaptive penalty factor is introduced for solving the equilibrium of the distributed P2P energy market, promoting supply-demand balance and enhancing energy efficiency while protecting privacy. Moreover, the solution efficiency is improved compared to standard ADMM methods. Case studies on an modified IEEE 33-node system validate the effectiveness of the proposed model and method. • A DSO-prosumers dual-layer game framework based on pricing guidance in the P2P energy transaction is presented. • A definition for the risk price in P2P energy transactions is provided to guide prosumers' trading behaviors. • C-ADMM with adaptive penalty factor is employed to expedite convergence in solving the P2P energy transaction model. • A method of progressively contracting the operating boundaries is introduced for accelerating the solution of game model. [ABSTRACT FROM AUTHOR]

The metaverse, constructed through digital technology, serves as a virtual realm intertwining with reality. Within this context, the challenge of evaluating data from diverse sources arises, and the application of large-scale group decision-making (LSGDM) methods emerges as a viable solution. Handling incomplete information and reducing dimensionality for large-scale decision-makers (DMs) is crucial in addressing complex decision-making problems. Moreover, addressing missing data is a fundamental and pivotal concern in tackling real-world decision challenges, given the ubiquitous presence of information gaps that cannot be straightforwardly integrated into decision models. Besides, the intricacies of LSGDM amplify this challenge by introducing a wealth of DMs, thereby augmenting the complexity and diversity of decision-related information. This paper proposes an approach to supplement missing data by double-dimensions. This paper explores various facets of similarity relationships within the data to enhance data completeness. Additionally, this paper categorizes DMs into clusters based on their relevance and establishes a two-stage consensus-reaching process (CRP) that takes into account both group sizes and individual consensus contributions. These CRPs play a crucial role in enhancing the overall consistency and consensus within the decision group. Subsequently, this paper applies a robust decision-making method rooted in MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the complete MULTIplicative form) to rank decision objects. Finally, this paper employs this proposed methodology in a practical case study that involves evaluating the operational status of a metaverse's urban construction metro system. Following these considerations, a comprehensive stability analysis of relevant parameters is conducted to guarantee the robustness and reliability of the decision-making process. • A similarity-based double-dimensional patching method for absent values is proposed. • A clustering-based dimensionality reduction for decision-makers is developed. • A two-stage consensus-reaching process based on weight adjustments is constructed. • A robust decision-making method based on MULTIMOORA and TODIM is explored. [ABSTRACT FROM AUTHOR]

We study the emergence of norms and their enforcement in a public goods game with private information about endowments. Subjects were randomly assigned a Low or High endowment and, across treatments, endowments were either Observed or Unobserved. In both treatments subjects could enforce contributions with peer punishment. We use punishment decisions to estimate contribution norms and then estimate the expected costs of noncompliance. In Observed , both Low and High types enforce a "contribute-your-endowment" norm, adjusting the costs of noncompliance to account for each type's endowment. In Unobserved we find that groups adapt to incomplete information by adopting a "contribute-the- Low -endowment" norm, and our expected cost calculations suggest that the enforcement of this norm balances the benefits of cooperation with the risk of misguided punishment. When at least one High type pools with Low types (by contributing less than or equal to the Low endowment), contributions of zero are punished as if they come from a High type, while contributions equal to the Low endowment are not punished in expectation (in case they come from a cooperative Low type). This enforcement strategy prevents cooperation from unraveling, but it also enables High types to hide behind the Low endowment. Our results dovetail with results from bargaining games and suggest that in settings with incomplete information, norms can attenuate but not eliminate non-cooperative behavior. [ABSTRACT FROM AUTHOR]

We study the role of information about the multiplier in a finitely repeated investment game. A high multiplier increases the reputational incentives of a trustee, leading to more repayments. Our perfect Bayesian equilibrium analysis shows that if the trustee is privately informed about the multiplier, both the expected frequency of investments and repayments as well as the expected payoffs of both players are higher compared to a situation where the multiplier is public knowledge. We test this result in a laboratory experiment. The data cannot confirm the predicted welfare dominance of private information about the multiplier. We discuss potential reasons for the deviation between theory and experimental data. [ABSTRACT FROM AUTHOR]

Aiming at the problem of spacecraft terminal intercept game with incomplete-information, the multiple mode adaptive intercept strategy is proposed. In the game, both sides try their effort to pursuit or evade with minimal cost. Associated to the saddle point equilibrium, the spacecraft game strategy pair with the complete-information assumption is derived through differential game theory. Whereas, the scenario where the interceptor cannot obtain the target information is formulated as the incomplete-information game. Assume that the target information belongs to a finite set and is fixed throughout the game. A multiple mode adaptive identification scheme, with a bank of filters matching different target information modes in parallel, is proposed to identify the unknown information and derive the estimation-dependent strategy. In addition, the probability-weighted adaptive strategy is derived through the multiple mode adaptive control approach, and the estimation is input into a controller that matches the specific mode. Finally, the effectiveness of the proposed strategies is demonstrated by numerical simulations. • The interceptor cannot obtain the target complete information in a two-sided interception game. • The multiple model adaptive estimator is used to identify the target strategy. • The probability-fusion-based strategy is derived via the multiple model adaptive control. • The adaptive interception can be guaranteed using the proposed scheme. [ABSTRACT FROM AUTHOR]

We introduce a variant of Milgrom and Weber's (1985) model of n -person games with incomplete information (games for short) and define a correspondence that maps each game to its α -core (the α -core correspondences). Our main objective is to prove such a correspondence to be generically lower semicontinuous. For a multi-valued solution correspondence, the lower semicontinuity is relevant as a theoretical base for predicting outcomes using game-theoretic models. We introduce a family of games parametrized by both payoff functions and information structures (common priors), which allows simultaneous perturbations in those two parameters. We then appeal to Fort's (1951) theorem to conclude that generic games are essential relative to the parameter space. • One can apply Fort's theorem to study the stability of the alpha core solutions of games with incomplete information. • The alpha core correspondence of games with incomplete information is stable on a residual set in a suitably chosen parameter space. • Lower semi-continuity is the adequate notion of continuity for game-theoretic predictions of outcomes. [ABSTRACT FROM AUTHOR]

Social Networking Sites (SNS) have entailed a revolution for society. They have given a say to everyone regardless of their status and this has been translated into loads of data. The task of profiling users constitutes a way to learn from this data in order to show users only the content that is relevant to them. Several recommendation system techniques have been used to address this problem, being mainly based on what the user explicitly says about themselves, on what the user publishes in the SNS and on the similarities between users. However, in social media context, it is also possible to use relations between users. Considering basic relations like follower or followee to extract information from them may result in noise, since they do not imply that users share interest or even ideas. In this work, we present a fuzzy framework to enrich user profiles with complex properties in order to have an even better representation of them. We use basic relations defined by SNSs to complete the information available in user profiles with topics of interest and ideas towards them and to define deep relations that will enable new ways of analysis. We use these deep relations to create clusters of similar users that, ultimately, will allow the expansion of properties from known users to the rest of the cluster. We tested our proposal with a dataset of Tweets in Spanish related to a political event. Our experiments prove the potential that this approach has for a lot of applications in microblogging context. [ABSTRACT FROM AUTHOR]

In this paper, we study the solution concept of value in transferable utility (TU) games with asymmetric information. In our model contingent contracts are required to be incentive compatible, and thus utility might not be not fully transferable. Our approach differs from the standard methodology of TU games with complete information, which summarizes the cooperative possibilities through the characteristic function. Instead, we consider a model in which monetary transfers are modeled as additional sidepayments in a non-transferable utility (NTU) game. Our main result states that [18] generalization of the Shapley NTU value and Salamanca (2020) extension of the Harsanyi NTU value are interim utility equivalent in our model with sidepayments. As a consequence of this result, we obtain a generalization of the Shapley TU value to games with incomplete information. Its formula, however, cannot be described by a simple closed form expression as in the case of complete information. • We study the Shapley value of transferable utility games with asymmetric information. • Monetary transfers are modeled as additional sidepayments in an NTU game. • In our model contracts are required to be incentive compatible. • Monetary transfers may be bounded because of incentive compatibility. • The value cannot be described by a simple formula as under complete information. [ABSTRACT FROM AUTHOR]

This paper investigates optimal concession contracts of the port authority when terminal operators have private information about their own operation costs. A two-stage game is constructed to characterize the interactions between the port authority and one terminal operator, and the port authority can select among the fixed-fee, the unit-fee and the two-part tariff contracts. We derive the pooling and the separating equilibria, and find that the separating equilibria are always preferred. In addition, incomplete information is at least as important as the minimum throughput requirement in affecting optimal contract types of the port authority. If the operator's marginal service costs are low (high), the port authority will face expected efficiency losses (gains) under incomplete information. However, the throughput requirement can mitigate (enhance) the expected efficiency losses (gains) under specific conditions. Moreover, requiring throughput may also lessen output distortions and information rents. Our findings provide insights and practical policy implications to port authorities and governments. • This paper analyzes the optimal concession contracts of port authorities when operators have private information about their costs. • We construct a two-stage game, and derive both pooling and separating equilibria. • Port authority prefers separating to pooling equilibrium, and face expected efficiency losses (gains) under incomplete information if operator's marginal costs are low (high). • Imposing minimum throughput requirements can mitigate (enhance) the expected efficiency losses (gains), and lessen output distortions and information rents. • Incomplete information is at least as important as minimum throughput requirements in affecting optimal contract types of port authorities. [ABSTRACT FROM AUTHOR]

The problem of stopping a Brownian bridge with an unknown pinning point to maximise the expected value at the stopping time is studied. A few general properties, such as continuity and various bounds of the value function, are established. However, structural properties of the optimal stopping region are shown to crucially depend on the prior, and we provide a general condition for a one-sided stopping region. Moreover, a detailed analysis is conducted in the cases of the two-point and the mixed Gaussian priors, revealing a rich structure present in the problem. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is the inevitable path to achieving energy transition. To promote renewable energy investment, many countries have subsidized renewable energy generation alongside auction mechanisms. The bidding strategy for renewable energy auctions under different forms of government subsidies is not well studied. Based on the constructed preemption game model, this paper studies the bidding strategy for renewable energy auctions under government subsidies and further discusses the impact of different subsidy forms on the investor's bidding strategy. Our results demonstrate that with incomplete information on competitors' investment income or investment cost, an investor can refer to the bidding strategy without competitors to guide decisions. In addition, the higher the government subsidy, the lower the bidding price of the investor. In the fixed amount subsidy form, the most critical factor influencing the bidding strategy is the annual power production after the project investment. • Examining bidding strategy under government subsidies • Bidding without competitors can guide decisions under incomplete information • The higher the government subsidy, the lower the bidding price of the investor • In the fixed amount subsidy form, key factor is the annual power production [ABSTRACT FROM AUTHOR]

We investigate whether including the text-based communicative value of annual report increases the predictive power of four machine learning models (Logistic regression, Random Forest, XGBoost, and Support Vector Machine) for corporate bankruptcy prediction using U.S. firm observations from 1994 to 2018. We find that the overall prediction effectiveness of these four models (e.g. accuracy, F1-score, AUCs) significantly improves, especially true in the performance of XGBoost and Random Forest models. In addition, we find that annual report text-based communicative value variables significantly reduce models' Type II error and keep the Type I error at a relatively small level, especially for the short-term bankruptcy forecast. The results reveal that annual report text-based communicative value effectively mitigates the model misidentification of a non-bankrupt firm as a bankrupt firm. Our results also suggest that annual report text-based communicative value is helpful for bank's corporate loan underwriting decisions. Finally, our findings still hold when considering different testing periods and random state settings, replacing by another publicly available bankruptcy dataset, and introducing neural network models. [ABSTRACT FROM AUTHOR]

Incentive-based demand response (IBDR), as an important measure to encourage the users to participate in the demand-side management, is commonly modeled as the Stackelberg game with the complete information. However, it is difficult to acquire the users' complete information due to privacy protections. In this paper, a Markov decision process (MDP) game model is proposed to address IBDR under the incomplete information, which is based on a deep deterministic policy gradient algorithm (DDPG). Considering differences on the users' load, the K-means method is used to classify different users according to the daily load rate and the peak-to-valley difference, such that different types of user load will have different incentive prices to participate in demand responses. The proposed DDPG algorithm can improve the calculation efficiency of the Nash equilibrium solution of the IBDR under the incomplete information, as it can deal with the multi-dimensional continuous state and action spaces. Simulation results show that the proposed approach can achieve the Nash equilibrium under the incomplete information and has the higher calculation accuracy and the lower calculation time in comparison to the Q learning method. • An incentive-based demand response game strategy is proposed based on the deep reinforcement learning algorithm. • A Markov decision process model considering the benefit gambling is established. • A DDPG algorithm is proposed to achieve the Nash equilibrium under incomplete information. • The discount factor representing the load aggregator's foresight degree is analyzed. [ABSTRACT FROM AUTHOR]

Networked markets are prevalently seen in different industries. Current research mainly focuses on settings with a transparent network. However, in many applications, firms choose their actions without fully knowing their competitors' information. This study examines oligopolies in networked markets when firms' production costs are incomplete information. We characterize the unique Bayesian equilibrium supply quantities for any given network under incomplete information. Our results connect the equilibrium outcome with the underlying competition and information structures. We find that when the production cost of a firm is high, incomplete information helps increase its total production quantity. In contrast, the impact is the opposite in the presence of low production costs. However, the impact of incomplete information on a firm's supply allocation on each edge depends not only on its production cost but also on the network position of the edge. We also find that, unlike the case in single-market, the low-cost firm always suffers from incomplete information, while the high-cost firm benefits from incomplete information only under certain conditions. Moreover, we show that changes in global network structure that do not directly relate to a firm may alter its information preference. We study several extensions and find that not only do firms with cost advantage prefer complete information, but those high-cost firms with smaller cost disadvantages are also more likely to prefer a transparent competition environment. The case of heterogeneous logistic costs is also considered, and we show that for firms of high-cost type, the logistic cost advantage may hedge its benefit from incomplete information. Our findings imply that a firm has to consider not only its own cost type and local competition but also the global network structure to determine its preference for information structure. Moreover, our results may be helpful to the market regulator that, when promoting information transparency, firms with better production technology are more likely to support the movement. • Identify the unique Bayesian equilibrium supply quantities in a supply network under incomplete information. • Under incomplete information, firms of high (low) production cost produce more (less). • Information effect on firm's supply allocation depends on both the production cost and the network structure. • Low-cost firms suffer from incomplete information; high-cost firms suffer only under certain conditions. • Changes in the global network structure that do not directly relate to a firm still may alter its information preference. [ABSTRACT FROM AUTHOR]

Abstract Data-driven methods for direct prognostic map the relationship between monitored parameters and equipment Remaining Useful Life (RUL). They typically require the availability of a set of run-to-failure degradation trajectories for model training. Yet, in many industrial applications, equipment is often replaced before they fail to avoid catastrophic consequences on production and safety. Then also, incomplete degradation trajectories are available. In this work, we develop a method for predicting equipment RUL, and the related uncertainty based on both complete and incomplete degradation trajectories. The method is based on the combined use of a similarity measure and Evidence Theory (EvT). Application of the method on two case studies shows that it provides accurate RUL predictions, also in comparison with a similarity-based regression method of literature. [ABSTRACT FROM AUTHOR]

Clean energy is an important source to mitigate the pressure of resources shortage and environmental effects. While multiattribute reverse auction is frequently adopted for the procurement efficiency of clean energy devices, the associated winner determination problem is explicitly studied for a utility company/buyer to select the most appropriate supplier in the clean energy market. Considering the buyer's loss-averse behavior with incomplete information and suppliers' conflicting attributes in different types evaluated by multiple experts, we incorporate an I U L O W A f (induced uncertain linguistic ordered weighted averaging) operator and a revised TODIM (an acronym in Portuguese of Interactive and Multi-attribute Decision Making) measure that is consistent with prospect theory into the "benefits, opportunities, costs and risks" (BOCR) framework to develop a novel BOCR-uRTODIM solution method for multi-attribute decision making. Numerical experiments illustrate the effectiveness and robustness of the proposed method by comparing it with some known methods. An interesting result indicates that the risk aversion degree of the buyer increases as the variance of suppliers' attributes expands. The BOCR-uRTODIM could be a useful tool for utility companies to avoid losses and for associated suppliers to improve their attributes for a win in the clean energy industry. • Reverse auction for the clear energy device procurement is studied. • Utility companies' loss-averse behavior and incomplete information are considered. • Prospect theory is integrated into analytic hierarchy process for decision making. • A novel, effective, and robust method is proposed for determining the winner. • Managerial insights are drawn for utility companies in the clean energy industry. [ABSTRACT FROM AUTHOR]

Abstract We often try to predict others' actions by obtaining supporting information that shows a preference index of surrounding people. In order to reproduce these situations, we propose a game named "One-sided Preference Game with Reference Information (OSPG-R)." We conducted experiments in which players who have similar preferences compete for objects in OSPG-R. In the experiment, we used three different types of objects: boxes, faces, and cars. Our results show that the most frequently selected object was not the most popular one. In order to gain deeper insights into the experiment's results, we constructed a decision-making model based on two assumptions: (1) players are rational and (2) are convinced that the other players' preference orders are equivalent to the preference index for the group. Compared to the choice behavior of the model, the experiment's results show that there was a tendency to take risks when the objects were faces, or the priority of that particular player was low. Highlights • We propose a multiplayer decision-making game with reference information. • We conducted experiments where players competed for objects: boxes, faces, and cars. • The most frequently selected object was not the most popular one. • When the objects were faces, players tended to choose to take risks. • Choice behavior between risk and safe were not symmetrical. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the optimal power schedule is studied for the wireless communication network under Denial-of-Service (DoS) attacks. Different from Nash Equilibrium (NE), a Stackelberg Equilibrium (SE) framework is proposed to analyze the game between the defender and the attacker under two different types of incomplete information. In the first scenario, the defender only knows the statistical characteristics about whether the attacker exists or not, which means that the defender knows the probability that there is only background noise on the environment. In the second situation, the defender does not know the total power of the attacker exactly and only acquires to know the total power with certain probability. Moreover, the detailed steps of designing both players' optimal strategies are given and analyzed. Adaptive Penalty Function (APF) approach and Differential Evolution (DE) algorithm are combined to deal with the corresponding nonlinear and non-convex optimization issues. Finally, examples are provided to illustrate the results proposed in this work. [ABSTRACT FROM AUTHOR]

Abstract A major application of rough set theory is concept analysis for deciding if an object is an instance of a concept based on its description. Objects with the same description form an equivalence class and the family of equivalence classes is used to define rough set approximations. When deriving the decision rules from approximations, the description of an equivalence class is the left-hand-side of a decision rule. Therefore, it is useful to retain structural information of approximations, that is, the composition of an approximation in terms of equivalence classes. However, existing studies do not explicitly consider the structural information. To address this issue, we introduce structured rough set approximations in both complete and incomplete information tables, which serve as a basis for three-way decisions with rough sets. In a complete table, we define a family of conjunctively definable concepts. The structured three-way approximations are three structured positive, boundary and negative regions given by three sets of conjunctively definable concepts. By adopting a possible-world semantics, we introduce the notion of conjunctively definable interval concepts in an incomplete table, which is used to construct the structured three-way approximations. The internal structure of structured approximations contributes to sound semantics of rough set approximations and is directly and explicitly related to three-way decision rules. [ABSTRACT FROM AUTHOR]

Highlights • Best response analysis is a powerful tool to study quantum duopolies. • Complete analysis of the perfect Bayesian equilibria is carried out for maximal entanglement. • The second mover gains advantage in the quantum Stackelberg duopoly with incomplete information. Abstract The Li–Du–Massar quantum duopoly model is one of the generally accepted quantum game schemes. It has applications in a wide range of duopoly problems. Our purpose is to study Stackelberg's duopoly with incomplete information in the quantum domain. The result of Lo and Kiang has shown that the correlation of players' quantities caused by the quantum entanglement enhances the first-mover advantage in the game. Our work demonstrates that there is no first-mover advantage if the players' actions are maximally correlated. Furthermore, we proved that the second mover gains a higher equilibrium payoff than the first one. [ABSTRACT FROM AUTHOR]

• A criteria clustering and weighting approach based on three-way decision making. • Uncertain evaluation information is modeled objectively based on clustering results. • An incomplete information fusion approach based on interval D-S evidence theory. • Consideration of decision risk can improve the objectivity of evaluation results. • Consideration of information conflicts can improve the reliability of results. Concept evaluation plays a pivotal role in complex mechatronic product development, and its results greatly influence the subsequent design and manufacturing. However, the inclusion of large-scale criteria introduces complex calculations and coupling relationships, which will affect the evaluation. Moreover, the limited availability of early design knowledge, risk attitudes and cognitive differences of decision makers (DMs) lead to incompleteness and uncertainty in evaluation information. To address these challenges, a concept evaluation approach based on incomplete information with considering large-scale criteria and risk attitudes is proposed. Initially, an evaluation criteria clustering approach based on interval three-way decision-making is proposed, which considers DMs' cognitions and risk attitudes. Second, the uncertainty model of evaluation information is established according to the criteria cluster result, and the incomplete evaluation information is fused using the interval Dempster-Shafer evidence theory which takes conflicting evidence into consideration. Third, the optimal concept is selected using the combined criteria weighting approach and the interval acronym in Portuguese for interactive and multi-criteria decision making (I-TODIM). Finally, a pipe inspection robot is utilized as an engineering case to verify the effectiveness of the proposed approach. Approach comparison and sensitivity analysis demonstrate the reliability and robustness of the proposed approach when applied to large-scale criteria concept evaluation. [ABSTRACT FROM AUTHOR]

The integrated energy system (IES) can improve energy efficiency and promote the use of renewable energy. It is of great significance to find the optimal alternative from many IES alternatives. However, most studies focused on the IES evaluation with complete information. Given this background, this paper proposes an evaluation method based on generalized regression neural network (GRNN) and probabilistic neural network (PNN) to address the issue of IES evaluation with incomplete information, which takes the running speed of the algorithm and the dynamic updating of the indexes into account. Firstly, the missing data is predicted via GRNN, and a complete evaluation matrix is obtained. Then, the evaluation indexes are clustered by affinity propagation algorithm, and the evaluation matrix of the cluster centers is determined, which can reduce the dimension of the initial matrix and improve the speed of the IES evaluation method. For the newly added evaluation indexes, the PNN is used to classify them, and the new cluster center is calculated, which can address the issue of the indexes updating. Based on the data of a hospital in Henan, case studies were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than the existing IES evaluation method. • GRNN is used to address the IES evaluation problem with incomplete information. • The accuracy of GRNN is verified by comparing with common prediction methods. • It is the first work to deal with the issue of indexes updating in IES evaluation. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

Abstract In this note, a novel Kalman filter is developed in the Bayesian framework for linear dynamical systems whose outputs are measured by faulty sensors and transmitted to the filter through a lossy delaying channel. The main novelty of the proposed method is to modify the likelihood function of the common Kalman filter to cope with incomplete, delayed and lost measurements. The suggested modified likelihood filter can be interpreted as an adaptive Kalman filter, wherein weighting factors are tuned based on the characteristics of the received measurements. Estimation accuracy is assured provided that some conditions on the properties of the sensor and the channel are met. Simulation results are presented to demonstrate the superior performance of the introduced filter compared to some rival ones in the literature. [ABSTRACT FROM AUTHOR]

Highlights • Incomplete traffic information hinders an accurate evaluation of CO 2 emissions. • Technologies such as single estimators and combination models can overcome this issue. • TANINO is based on a combination model and a macro-model of CO 2 evaluation. • Along the ring road of Seville, TANINO improves the results of single estimators. • This monitoring allows a consistent evaluation of the efficiency of carbon policies. Abstract Carbon emissions from road transport are one of the main issues related to modern transport planning. To address them adequately, the acquisition of reliable data about traffic flow is an essential prerequisite. However, the large quantity and the heterogeneity of available information often cause problems; missing or incomplete data are one of the most critical aspects. This paper discusses how technology handles imperfect information in order to obtain more accurate quantification of CO 2 emissions. First, an analysis of single estimators and combination models is provided, highlighting their main characteristics. Then, the TANINO model (T ool for the A nalysis of N on-conservative Carbon Emissions I n Tra N sp O rt) is presented, jointly developed at the University of Seville and at the IUAV University of Venice. It consists of two different modules: the first is a combination model that optimizes the results of three traffic flow single estimators, while the second is a macro-model of carbon evaluation, which takes into account road infrastructure, vehicle type and traffic conditions. TANINO is then tested to calculate CO 2 emissions along the ring road of the Spanish city of Seville, showing its more efficient performance, compared to the single estimators normally adopted for such aims. Transport planning can benefit from the adequate knowledge of traffic flows and related CO 2 emissions, since it allows a more reliable monitoring of the progresses granted by specific carbon policies. [ABSTRACT FROM AUTHOR]

The Coalitional Manipulation problem has been studied extensively in the literature for many voting rules. However, most studies have focused on the complete information setting, wherein the manipulators know the votes of the non-manipulators. While this assumption is reasonable for purposes of showing intractability, it is unrealistic for algorithmic considerations. In most real-world scenarios, it is impractical to assume that the manipulators to have accurate knowledge of all the other votes. In this work, we investigate manipulation with incomplete information. In our framework, the manipulators know a partial order for each voter that is consistent with the true preference of that voter. In this setting, we formulate three natural computational notions of manipulation, namely weak, opportunistic, and strong manipulation. We say that an extension of a partial order is viable if there exists a manipulative vote for that extension. We propose the following notions of manipulation when manipulators have incomplete information about the votes of other voters. 1. Weak Manipulation : the manipulators seek to vote in a way that makes their preferred candidate win in at least one extension of the partial votes of the non-manipulators. 2. Opportunistic Manipulation : the manipulators seek to vote in a way that makes their preferred candidate win in every viable extension of the partial votes of the non-manipulators. 3. Strong Manipulation : the manipulators seek to vote in a way that makes their preferred candidate win in every extension of the partial votes of the non-manipulators. We consider several scenarios for which the traditional manipulation problems are easy (for instance, Borda with a single manipulator). For many of them, the corresponding manipulative questions that we propose turn out to be computationally intractable. Our hardness results often hold even when very little information is missing, or in other words, even when the instances are very close to the complete information setting. Our results show that the impact of paucity of information on the computational complexity of manipulation crucially depends on the notion of manipulation under consideration. Our overall conclusion is that computational hardness continues to be a valid obstruction to manipulation, in the context of a more realistic model. [ABSTRACT FROM AUTHOR]

The hesitant fuzzy preference relation (HFPR) is a useful tool for decision makers to elicit their preference information over a set of alternatives. In this paper, it is first proposed an approach to deriving a priority weight vector from an incomplete HFPR using the logarithmic least squares method. Based on the priority weight vector, the consistency index of an incomplete HFPR is defined, which calculates the average deviation between the priority weight vector and all elements of the incomplete HFPR. For an incomplete HFPR which is unacceptably consistent, an automatic algorithm is developed to improve the consistency. These results are then extended to propose a new procedure for group analytic hierarchy process to deal with multi-criteria group decision making problems. The feasibility and effectiveness of the proposed approaches are demonstrated by some numerical examples. [ABSTRACT FROM AUTHOR]

Higher wages reduce workplace harassment directly by raising the cost for harassers, indirectly by attracting agent types who file complaint if harassed. I show that low wage-high harassment combinations are neither compatible with effort objectives nor collusion-proof. Better internal compliance structures (lower cost of filing complaint, accurate and speedy investigations) reduce the wage bill and/or the frequency of harassment but also narrow the range of feasible anti-harassment targets. Wages and harassment risks should be negatively correlated across organizations with similar and effective compliance structures, whereas organizations with less effective compliance structures must pay higher wages to induce the same harassment level. [ABSTRACT FROM AUTHOR]

With the development of the Internet era, online medical community is increasingly favored by patients and health care workers. How to make a reasonable doctor-patient matching and meet patients' relevant needs through online medical platform is the key to realizing effective allocation of medical resources. This paper proposes an online doctor-patient dynamic stable matching model to deeply investigate the online doctor-patient matching problem under incomplete information. Firstly, we assign the score to each doctor based on the patient's attribute expectations and the doctor's evaluation information. Considering the difficulty of obtaining complete information in real life and different attribute representations, we propose incomplete information filling methods based on collaborative filtering algorithm. To make the filling result more reasonable, we introduce probabilistic linguistic information entropy to process the incomplete information. Secondly, considering psychological behavior, we use regret theory to calculate patients' satisfaction with doctors. Finally, with the definitions of rational matching and stable matching, we propose a doctor-patient dynamic stable matching model. This model can solve the problems like a temporary appointment and private matching and ensure the stability of matching. In addition, the model proposed in this paper considers not only patients' but also the doctors' needs. The case of online doctor-patient matching is based on "haodf.com", and through comparative analysis can prove the effectiveness and superiority of this method. • Introduce PLTS to describe evaluation attribute information of doctors. • Propose the filling method of incomplete information in three cases. • Construct the patient satisfaction function based on regret theory. • Establish a multi-stage doctor-patient stable matching model. • Apply methods on a real online doctor-patient matching case and illustrate their characteristics. [ABSTRACT FROM AUTHOR]

Due to the complexity of multi-attribute group decision-making(MAGDM) problems, decision-makers frequently provide incomplete and hesitant evaluation information. How to make a valid decision based on incomplete and hesitant information is an important issue in MAGDM problems. Firstly, we novelly introduce a sequential K-nearest neighbor(SKNN) interpolation method to estimate the missing values based on the order of missing information proportions. When seeking the k-nearest neighbors of a decision-maker, an improved similarity measurement is proposed by considering the fuzzy-value similarity and the hesitation similarity between decision-makers simultaneously. Secondly, a K-means clustering algorithm based on attribute weighting is proposed to make the collective evaluation information more representative, and the attribute weights are obtained by constructing a mathematical model based on the minimum discrimination principle which can synthesize the subjective and objective weights information. Finally, an illustrative example and comparison analysis are demonstrated to show the validity and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

This study models the emission regulation of a port's cargo-handling activities when the regulatory government agency lacks complete information on the cost of reducing emissions for the port. The goal is to identify rules for determining the optimal port charge and capacity to allow port emissions to be regulated in an environment with incomplete information. We evaluate the effect of introducing a risk-averse environmental monitor as a supervisor to provide the government with additional information (a signal) on the port operator's emission reduction cost. To prevent the environmental monitor from colluding with the port operator, we develop a collusion-proof regulation scheme based on the principal–agent theory. The scheme is modeled as a bi-level problem faced by the government and the monitor. We find that, compared to the case with complete information, collusion-proof regulation do not distort optimal port charges only when the port operator is efficient and has low emission reduction costs. When distortion does occur, it depends on the monitor's degree of risk aversion and the accuracy of the signal about emission reduction cost. Besides, information asymmetry leads to less cargo throughput, a lower emission level, and reduced port capacity. Such regulation-induced downward distortion can be either alleviated or aggravated by the collusion-proof regulation, depending on the quality of the information received by the environmental monitor. Our theoretical models are tested using a case study based on container terminals in the Port of Shanghai. The numerical results suggest that a risk-averse environmental monitor can improve port user's social welfare in the presence of imperfect information. [ABSTRACT FROM AUTHOR]

Strategy Corporation Social Responsibility (CSR) has received a lot of attention lately and is mainly related to environmental protection in a single supply chain under the condition of the constant tax or subsidy imposed by government, this paper studies a price competition model of two environmentally friendly and harmful competing supply chains consisting of one manufacturer and one retailer respectively, adopting two-stage Stackelberg game under the condition of incomplete knowledge. Assume that the manufacturers don't know the retailers' knowledge, but they are aware of the prior probability of retailers about selling which type of product under the decentralized supply chain, and the higher the prior probability is, the more likely manufacturers tend to produce the environmentally friendly PV panels. This paper first quantifies the impact of products on the environment, studies the impact of which on competing supply chains' profit and the tariffs. This paper finds that only the tariffs set by government and the values of the environmental impact satisfy a three-dimensional area, can the firms ensure the stable competitive market. Results of this analysis show that government cannot impose extreme tax or subsidy on firms, and the environmental impact in the centralized supply chains is higher than that in the decentralized supply chains. This paper validates the results through a case of Photovoltaic (PV) industry from the perspective of CSR, and the numerical results show that there are significant impacts of different governmental policy of social responsibility and environment protection on the financial revenue and the profits of the competing supply chains and their members. [ABSTRACT FROM AUTHOR]

The mix of games theory with the real options has been a dynamic range of exploration in the most recent decade. The engaging quality of the specialists for displaying aggressive speculation choices by blending ideas from both hypotheses is on account of a venture choice in a focused business sector can be seen, in its substance, as a "game" between firms. In this paper, we expand a model to consider finite horizon real option games under incomplete information with various parameters. In incomplete information games, firms' actions express significant information about profitability to contestants. The encapsulation of this information proposed original blocks in models with strategic interactions. This is because of circularity where best exercise decisions are based on previous decisions taken, which at a given time are quiet to be mentioned because about the dynamic programming principle. We expand an extended version of the Least Squares Monte Carlo algorithm to confront these results. The model can aid in understanding the relation between strategic optionality and information besides how this influences the best decision policy and its value results. We find the informational feature is of great significance for firms' best decision policy and optimization of project values. [ABSTRACT FROM AUTHOR]

In many developing countries like China, the rising housing demand is accompanied by developers' strategic delay of land development. To explain this puzzle, this paper employs the framework of Option Games with incomplete information, and examines how incomplete information affects the timing and phasing strategy of real estate development. Based on a project-level dataset from Hangzhou city of China, we estimate a hazard model for development timing, and take into account the developer's self-selection of phasing strategies. Major findings of this research are: first, incomplete information undermines the acceleration effect of competition on development timing. Second, this delay effect disappears when the project is developed in multi-phases. Moreover, we show that the estimation can be severely biased without addressing developer's selection problem. Based on our results, we suggest the government should promote the establishment of a public housing transaction platform, to help the developers overcome the limitations of incomplete information and speed up the development cycle. [ABSTRACT FROM AUTHOR]