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When both a buyer and a seller have private information about their valuations of the same object, they trade the object less efficiently. A better-informed agent than the buyer and the seller can facilitate trade more efficiently by advising them with the agent's information. However, the agent's interest affects the validity of the agent's advice and thus trade outcomes. When the agent's interest is to maximize the trade probability rather than the expected transaction price, the agent is more likely to share the agent's information with both the buyer and the seller. The information provided by the agent raises not only the trade probability but also the expected transaction price. Therefore, the attempt of the agent to raise the expected transaction price may fail, whereas the one to increase the trade probability succeeds. [ABSTRACT FROM AUTHOR]

We introduce a dynamic network formation model with incomplete information and asymmetric players who are partitioned into ordered groups. At each stage, a pair of players is randomly selected to update the link connecting them. The feature of the model is that the cost of a link depends on the distance between the labels of the groups players belong to. As a result, information about the other player's label is significant in the decision to form a link. Assuming incomplete information, players initially do not know each other's labels, but learn them upon being connected. Comparing the topologies of stable networks for cases of complete and incomplete information indicates that the presence of the latter can have deep implications. Furthermore, our simulation experiments are focused on three issues: the stabilization time, the heterogeneity index, and the impact of the group size gap. [ABSTRACT FROM AUTHOR]

The problem of guaranteed closed-loop guidance to a given set at a given time is studied for a linear dynamic control system described by differential equations with a fractional derivative of the Caputo type. The initial state is a priori unknown, but belongs to a given finite set. The information on the position of the system is received online in the form of an observation signal. The solvability of the guidance problem for the control system is analyzed using the method of Osipov–Kryazhimskii program packages. The paper provides a brief overview of the results that develop the program package method and use it in guidance problems for various classes of systems. This method allows us to connect the solvability condition of the guaranteed closed-loop guidance problem for an original system with the solvability condition of the open-loop guidance problem for a special extended system. Following the technique of the program package method, a criterion for the solvability of the considered guidance problem is derived for a fractional-order system. In the case where the problem is solvable, a special procedure for constructing a guiding program package is given. The developed technique for analyzing the guaranteed closed-loop guidance problem and constructing a guiding control for an unknown initial state is illustrated by the example of a specific linear mechanical control system with a Caputo fractional derivative. [ABSTRACT FROM AUTHOR]

We study binary opinion formation in a large population where individuals are influenced by the opinions of other individuals. The population is characterised by the existence of (i) communities where individuals share some similar features, (ii) opinion leaders that may trigger unpredictable opinion shifts in the short term (iii) some degree of incomplete information in the observation of the individual or public opinion processes. In this setting, we study three different approximate mechanisms: common sampling approximation, independent sampling approximation, and, what will be our main focus in this paper, McKean–Vlasov (or mean-field) approximation. We show that all three approximations perform well in terms of different metrics that we introduce for measuring population level and individual level errors. In the presence of a common noise represented by the major influencers opinions processes, and despite the absence of idiosyncratic noises, we derive a propagation of chaos type result. For the particular case of a linear model and particular specifications of the major influencers opinion dynamics, we provide additional analysis, including long term behavior and fluctuations of the public opinion. The theoretical results are complemented by some concrete examples and numerical analysis, illustrating the formation of echo-chambers, the propagation of chaos, and phenomena such as snowball effect and social inertia. [ABSTRACT FROM AUTHOR]

In some actual decision-making problems, experts may be hesitant to judge the performances of alternatives, which leads to experts providing decision matrices with incomplete information. However, most existing estimation methods for incomplete information in group decision-making (GDM) neglect the hesitant judgments of experts, possibly making the group decision outcomes unreasonable. Considering the hesitation degrees of experts in decision judgments, an approach is proposed based on the triangular intuitionistic fuzzy numbers (TIFNs) and TODIM (interactive and multiple criteria decision-making) method for GDM and consensus measure. First, TIFNs are applied to handle incomplete information due to the hesitant judgments of experts. Second, considering the risk attitudes of experts, a decision-making model is proposed to rank alternatives for GDM with incomplete information. Subsequently, based on measuring the concordance between solutions, a consensus model is presented to measure the group's and individual's consensus degrees. Finally, an illustrative example is presented to show the detailed implementation procedure of the proposed approach. The comparisons with some existing estimation methods verify the effectiveness of the proposed approach for handling incomplete information. The impacts and necessities of experts' hesitation degrees are discussed by a sensitivity analysis. [ABSTRACT FROM AUTHOR]

In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models and are thus having an undesirable (and possibly illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such an approach requires full knowledge of policyholders' protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics and produces prices that are free from proxy discrimination. We demonstrate the proposed method on both synthetic data and a real-world motor claims dataset, in which proxy discrimination can be observed. In both examples we find that the predictive accuracy of the multi-task network is comparable to a conventional feed-forward neural network, when the protected information is available for at least half of the insurance policies. However, the multi-task network has superior performance in the case when the protected information is known for less than half of the insurance policyholders. [ABSTRACT FROM AUTHOR]

We study the problem of inspection and maintenance planning of capital goods based on observations of the capital good's degradation state. However, the observations are imprecise, and their quality depends on the environment. For example, when performing maintenance for Heating, Ventilation, and Air-Conditioning units (HVACs) in trains, the health of the cooling component of an HVAC can be assessed from temperature readouts of the car in which the HVAC is mounted. Temperature information is useful in the summer when high car temperatures can indicate a failed cooling component, but this information has limited value during the winter. We model the problem as a partially observable Markov decision process with a fully observed environment. We analytically show that an environment-dependent monotonic at-most-4-region policy is optimal. Furthermore, we numerically analyze an example motivated by HVAC maintenance at Dutch Railways. This analysis shows that, in many cases, including the environment in the model can lead to cost savings of more than 10%. In a broad numerical experiment, we show that a simple policy cannot always substitute an optimal policy. [ABSTRACT FROM AUTHOR]

We prove the existence of a behavioral-strategy Bayesian Nash equilibrium in all-pay auctions with statistically interdependent types (signals) under quite general assumptions on the values, costs and tie-breaking rules. Moreover, the set of equilibria is shown to be the same for any tie-breaking rule used in the auction. [ABSTRACT FROM AUTHOR]

It is well known that in the absence of transfers, stable matchings are positive assortative when agents' preferences are strictly monotonic in each other's types and types are commonly known. Instead, monotonicity is consistent with the existence of stable matchings that exhibit negative sorting when types on one side of the market are private information. This paper sheds light on the scope and meaning of this consistency by showing that within familiar monotonic markets we cannot conclude that a stable matching is negative, and not positive, assortative. [ABSTRACT FROM AUTHOR]

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management. [ABSTRACT FROM AUTHOR]

When states fight wars, they choose between multiple military strategies, e.g. whether to place boots on the ground or restrict efforts to an air campaign. We modify the standard crisis bargaining model to account for this by endogenizing a state's war strategy. Intuitively, states choose the more powerful strategy when the additional strength gained covers the additional costs of doing so. However, there is a counterintuitive second-order consequence of this. When stronger campaigns are expensive but still credible, states are more likely to reach a negotiated settlement to avoid the corresponding high costs. As the cost of the more powerful option increases, states substitute their actions with cheaper but weaker alternatives. Because of these lower costs, states become less likely to reach a settlement. In some cases, both parties may be worse off as a result. [ABSTRACT FROM AUTHOR]

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]

A growing body of empirical evidence suggests the existence of a Paradox of Choice, whereby a larger choice set leads to a lower expected payoff to the decisionmaker. These empirical findings contradict traditional choice-theoretic results in microeconomics and even social psychology, suggesting profound yet unexplained aspects of choice settings/behavior. The Paradox remains largely as a theoretically-rootless empirical phenomenon. We neither understand the mechanisms and conditions that generate it, nor whether the Paradox stems from choice behavior, choice setting, or both. We present a general search model of consumer choice under incomplete information to examine the theoretical existence and conditions for the Paradox. The model departs from traditional microeconomic choice theory in assuming no "cognitive free lunches." Search and evaluative efforts require time, focus, and opportunity cost in the model, as in real life. The model shows that conditions for Paradox readily exist, diminishing returns to potential value of the choice in choice set size is a necessary condition for a Paradox outcome, that a Paradox outcome can occur with or without a "choice overload" response by the decisionmaker, and that avoidance of a Paradox outcome is an impossibility in some choice settings (primitive to setting regardless of choice behavior). Lastly, we develop a pareto-improving choice set reduction algorithm that eliminates weakly dominated choice set elements and presents the restricted choice set to the decisionmaker. As such, it is shown that the algorithm acts to treat or mitigate the Paradox of Choice, as well as to pareto-improve choice-supporting evaluation more generally. Like established AI recommender systems, the novel algorithm filters choices. In the present case, the filtering is based upon identification of weakly dominated choice set elements. [ABSTRACT FROM AUTHOR]

We extend the classical setting of an optimal stopping problem under full information to include problems with an unknown state. The framework allows the unknown state to influence (i) the drift of the underlying process, (ii) the payoff functions, and (iii) the distribution of the time horizon. Since the stopper is assumed to observe the underlying process and the random horizon, this is a two-source learning problem. Assigning a prior distribution for the unknown state, standard filtering theory can be employed to embed the problem in a Markovian framework with one additional state variable representing the posterior of the unknown state. We provide a convenient formulation of this Markovian problem, based on a measure change technique that decouples the underlying process from the new state variable. Moreover, we show by means of several novel examples that this reduced formulation can be used to solve problems explicitly. [ABSTRACT FROM AUTHOR]

AbstractIn the financial market with both observable and unobservable market states, this article explores the equilibrium investment strategy for a DC pension plan with the mean-variance criterion in a discrete-time setting. The dynamics of the partially observed market state are described by a discrete-time finite-state hidden Markov chain. There is a riskless asset and a risky asset in the financial market, where the return rate of the risky asset depends both on the observable and unobservable market states. Meanwhile, the stochastic salary process is also modulated by the observable and unobservable market states. Under the framework of non cooperative game, we first define the equilibrium investment strategy for the multi-period mean-variance DC pension plan. By adopting the sufficient statistics method, the investment problem for the mean-variance DC pension plan with incomplete information is transformed into the one with complete information. The closed-form equilibrium investment strategy is derived by solving the extended Bellman equation. Finally, numerical results show that the incomplete information has significant impacts on the equilibrium investment strategy and the equilibrium efficient frontier. Neglecting the reality of incomplete information in the financial market will reduce the investment benefit of the DC pension plan. The longer the investment horizon, the greater the investment benefit loss for the DC pension plan. [ABSTRACT FROM AUTHOR]

We consider the problem of numerically approximating the solutions to a partial differential equation (PDE) when there is insufficient information to determine a unique solution. Our main example is the Poisson boundary value problem, when the boundary data is unknown and instead one observes finitely many linear measurements of the solution. We view this setting as an optimal recovery problem and develop theory and numerical algorithms for its solution. The main vehicle employed is the derivation and approximation of the Riesz representers of these functionals with respect to relevant Hilbert spaces of harmonic functions. [ABSTRACT FROM AUTHOR]

This paper proposes a method of ranking discrete alternatives when attribute weights are incompletely known. There are a variety of situations in which it is reasonable to consider incomplete attribute weights and several techniques have been developed to solve such multi-attribute decision making problems. Most frequently, a linear programming (LP) problem subject to a set of incomplete attribute weights is solved to identify dominance relations between alternatives. In this paper, we explore a dual problem to find a closed-form solution and determine the extreme points of a set of (strictly) ranked attribute weights. A simple investigation of the dual optimal solution often leads to a preferred alternative and permits to find the optimal attribute weights that can be applied to the primal, based on the primal-dual relationship. Furthermore, we extend the approach to several examples of incomplete attribute weights and to linear partial information expressed as linear inequalities that satisfy some predefined conditions. Finally, we present a case study to demonstrate how the dual approach can be used to establish dominance between alternatives, when preference orders are specified for a subset of alternatives. [ABSTRACT FROM AUTHOR]

Forest tickets refer to a type of forest resource usufruct certificate characterized by "cooperative operation, quantification of rights and interests, free circulation, and guaranteed dividends". It is an important means to build a market-oriented mechanism for realizing the value of ecological resources. Incomplete information, based on field survey data from thirteen villages in eight townships (towns) in Sanming City, Fujian Province, China, and a binary logit model were used to explore the moderating effects of factors affecting farmers' willingness to participate in forest ticket trading, the heterogeneity of farmers, and social capital. We found the following: In an environment with incomplete information, farmers' willingness to participate in forest ticket trading is influenced by heterogeneity expectations, social capital, government propaganda, and individual family characteristics. There are certain differences in the influencing factors and degree of farmers' willingness to participate in forest ticket trading among different groups of farmers with different levels of education and part-time employment. Social capital can strengthen the positive impact of income expectations and policy sustainability expectations, and alleviate the negative impact of risk expectations. [ABSTRACT FROM AUTHOR]

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In this paper, we consider a shortest path network interdiction problem with incomplete information and multiple levels of interdiction intensity. The evader knows the attacker's decision on the network arcs that have been interdicted. However, the extent of damage on each arc depends on the interdiction intensity and the amount of budget spent for interdiction. We consider two cases in which the evader has incomplete information about both the intensity of attack on the interdicted arcs and the additional cost imposed for traversing those arcs. In the first case, the evader's perception of this cost falls in an interval of uncertainty. In the second case, it is assumed that the evader estimates a relative frequency for each level of interdiction intensity. This gives rise to multiple uncertainty sets for the evader's estimates of the additional cost. To handle the uncertainty that arises in both cases, a robust optimization approach is employed to derive the mathematical formulation of underlying bilevel optimization problem. For each case, we first take the well-known duality-based approach to reformulate the problem as a single-level model. We show that this method does not always end up with an integer solution or fails in achieving a solution within the time limit. Therefore, we develop an alternative algorithm based on the decomposition approach. Computational results show that the proposed algorithm outperforms the duality-based method to obtain the optimal solution. Last, a real case study is presented to show the applicability of the studied problem. [ABSTRACT FROM AUTHOR]

Data analytics generally helps businesses or entities to make better and efficient decision making. But in the face of growing volume of data or information, it becomes challenging to achieve these goals. One of which is on classification of information with high accuracy. Furthermore, when the information is incomplete, definitely it is more challenging in order to classify the information with high accuracy. Although incomplete information is well discussed using rough set theory for data classification, such as based on tolerance and similarity relations, there are still issues on accuracy to evaluate data classification. The main objective of this paper is to introduce a new similarity approach with semantically justified based on possible equivalent value-set related to incomplete information systems. It is based on a classification of three semantics types of incomplete information i.e., “any value”, “maybe value” and “not applicable value” for modelling similarity. Subsequently, the similarity precision between objects in incomplete information systems is considered. The comparative studies and simulation results between the proposed approach in terms of accuracy on synthetic data, four well-known classification datasets and one real marine dataset are presented. The proposed approach improves the accuracy up to two orders of magnitude and, thus verifying its data classification accuracy. [ABSTRACT FROM AUTHOR]

This paper presents the development of computing tools for finding the optimal structures of multi-currency deposits in terms of guaranteed risk under uncertain exchange rates. The approach utilizes Savage's minimax regret concept to calculate risk and guaranteed risk functions explicitly, assuming only the limits of possible changes in uncertain parameters are known. The Excel environment implements the algorithm for calculating the optimal solution that minimizes income loss due to incomplete information. Computational experiments analyzed the dependence of the optimal guaranteed risk on problem parameters, such as interest rates of currencies and boundaries of uncertain factors. The results can be used to analyze financial management problems in conditions of incomplete information. [ABSTRACT FROM AUTHOR]

Efficiency plays a crucial role in portfolio optimization. This notion is formulated by means of stochastic optimization techniques. Very often this problem is subject to partial uncertainty or incomplete information on the probability distribution and on the preferences expressed by means of the utility function. In this case both the objective function and the underlying probability measure are not known with precision. To address this kind of issues, we propose to model the notion of incomplete information by means of set-valued analysis and, therefore, we propose two different extensions of the classical model. In the first one we rely on the notion of set-valued function while the second one utilizes the notion of set-valued probability. For both of them we investigate stability properties. These results are also linked to the notion of robustness of the aforementioned problem. Finally we apply the obtained results to portfolio theory and stochastic dominance. [ABSTRACT FROM AUTHOR]

I propose a notion of Rationalizability, called Incomplete Preference Rationalizability, for games with incomplete preferences. Under an appropriate topological condition, the incomplete preference rationalizable set is non-empty and compact. I argue that incomplete orderings can be used to model incomplete information in strategic settings. Drawing on this connection, I show that in games with private values the sets of incomplete preference rationalizable actions, of belief-free rationalizable actions (Battigalli et al., 2011 ; Bergemann and Morris, 2017), and of interim correlated rationalizable actions (Dekel et al., 2007) of the universal type space coincide. [ABSTRACT FROM AUTHOR]

To answer database queries over incomplete data, the gold standard is finding certain answers: those that are true regardless of how incomplete data is interpreted. Such answers can be found efficiently for conjunctive queries and their unions, even in the presence of constraints. With negation added, the problem becomes intractable however. We concentrate on the complexity of certain answers under constraints and on effficiently answering queries outside the usual classes of (unions) of conjunctive queries by means of rewriting as Datalog and first-order queries. We first notice that there are three different ways in which query answering can be cast as a decision problem. We complete the existing picture and provide precise complexity bounds on all versions of the decision problem, for certain and best answers. We then study a well-behaved class of queries that extends unions of conjunctive queries with a mild form of negation. We show that for them, certain answers can be expressed in Datalog with negation, even in the presence of functional dependencies, thus making them tractable in data complexity. We show that in general, Datalog cannot be replaced by first-order logic, but without constraints such a rewriting can be done in first order. [ABSTRACT FROM AUTHOR]

Developing effective inspection processes at border crossings in order to identify violators within large groups of mostly innocent people is an important and difficult task. Passenger profiling is a tool used to deal with this task, but it raises many public concerns and ongoing debates about its usefulness. In this article, we study whether profiling is helpful, how it should be used to maximize its effectiveness, and how social utility is affected by its use. We consider two game models that take place at a crowded border crossing, where passengers are divided into different groups based on their risk profile. The models involve three players: the defender, the attacker, and the passenger recruited to engage in a violation. The defender decides on an inspection policy for every group of passengers. The attacker decides from which group to recruit a single passenger as a violator. The recruited passenger has private knowledge about the likelihood that she/he will engage in the violation or not. We solve the game models and compare their solutions with those of a no‐profiling inspection policy. We then study a model with four players, including a social planner who is concerned about social utility, and chooses the defender's inspection resources and policy. We show that the announced profiling policy provides the highest payoffs to the defender. Regarding social utility, profiling is always better than no profiling. However, there are cases where the unannounced profiling policy provides the most social utility. [ABSTRACT FROM AUTHOR]

In order to solve the problem of emergency decision-making with incomplete information and deal with the accident information in different time series at the scenes of major accidents, this paper proposes a method of sequential decision-making by utilizing the relevant knowledge of D-S evidence theory and game theory. Firstly, we took an oil tank fire accident as an example and sorted out historical cases and expert experiences to establish a logical relationship between key accident scenes and accident scene symptoms in the accident. Meanwhile, we applied the logistic regression analysis method to obtain the basic probability distribution of each key accident scene in the oil tank fire, and on this basis, we constructed an evidence set of the fire. Secondly, based on the D-S evidence theory, we effectively quantified the knowledge uncertainty and evidence uncertainty, with the incomplete and insufficient information taken as an evidence system of the development of key accident scenes to construct a situation prediction model of these accident scenes. Thirdly, based on the game theory, we viewed emergency decision-makers and major accidents as two sides of the game to compare and analyze accident states at different time points and solve the contradiction between loss costs of decision-making and information collection costs. Therefore, this paper has provided a solution for the optimization of accident schemes at different time stages, thus realizing the sequential decision-making at the scenes of major accidents. Furthermore, we combined the situation prediction model with sequential decision-making, with the basic steps described below: (1) We drew up an initial action plan in the case of an extreme lack of information; then, we (2) started to address the accident and constructed a framework of accident identification, (3) collected and dealt with the continuously added evidence information with the evolution of the accident, (4) calculated the confidence levels of key accident scenarios after evaluating different evidence and then predicted the accident state in the next stage, and (5) calculated the profit–loss ratio between the current decision-making scheme and the decision-making scheme of the next stage. Finally, we (6) repeated steps (3) to (5) until the accident completely vanished. We verified the feasibility of the proposed method with the explosion accident of the Zhangzhou P.X. project in Fujian on 6 April used as an example. Based on the D-S evidence theory, this method employs approximate reasoning on the incomplete and insufficient information obtained at the scenes of major accidents, thus realizing the situation prediction of key scenes of these accidents. Additionally, this method uses the game theory to solve the contradiction between decision-making loss costs and information collection costs, thus optimizing the decision-making schemes at different time stages of major accidents. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimal regulation performance of networked time delay systems with limited bandwidth and interference signals. Communication networks are primarily influenced by parameters including bandwidth, packet dropouts, coding and decoding, interference signals, and channel noise. For a given system, non-minimum phase zeros, unstable poles, and time delay are considered. The corresponding regulation performance expressions are derived using coprime decomposition, spectral decomposition techniques, and norm correlation theory in the frequency domain. Results indicate that regulation performance is dependent on the location and direction of non-minimum phase zeros and unstable poles of a given system, as well as the internal time delay of the controlled plant. In addition, network communication parameters such as bandwidth, channel noise, packet dropouts, and external interference signals influence the performance of the regulation. Finally, simulation examples are provided to demonstrate the theory's validity. [ABSTRACT FROM AUTHOR]

We analyze the patent licensing contracts offered by an insider innovator that has private information about the quality of innovation that can be transferred to two downstream firms. When information is complete, the first‐best choice is a pure‐royalty contract which is accepted by both firms (i.e., is nonexclusive). When information is incomplete, however, no nonexclusive contract can be supported as a separating equilibrium; it can only be the case where the innovator sells an exclusive contract to only one firm or a nonlicensing contract where no license is sold. In particular, when the gap in the innovation between the efficient and inefficient type is sufficiently small, there does not exist any separating equilibrium. It is sharply different from the case of an outsider innovation, in which a separating equilibrium always exists. [ABSTRACT FROM AUTHOR]

Patent pricing is fundamental for patent industrialisation. This article aims to capture the patent price under incomplete information with game theory approaches. The results show first, patent industrialisation with a high technology level promotes outputs and reduces the prices of final goods. Second, competition decreases the price of patents, while asymmetric information deters patent industrialisation. Finally, a new type of risk function is established to merit the risk of cheap information, and we show that exaggerated technology amplifies the risks of both cost and profit. The policy implication is to strengthen information disclosure and supervision about patents. Finally, numerical examples are illustrated with the sensitivity analysis of the key parameters to justify the proposed model. [ABSTRACT FROM AUTHOR]

We study collective action under adverse incentives: each member of the group has a given budget ('use-it-or-lose-it') that is private information and can be used for contributions to make the group win a prize and for internal fights over this very prize. Even in the face of such rivalry in resource use, the group often succeeds in overcoming the collective action problem in the non-cooperative equilibrium. In one type of equilibrium, all group members jointly contribute; in the other type of equilibrium, volunteers make full standalone contributions. Both types of equilibrium exist for larger and partially overlapping parameter ranges. [ABSTRACT FROM AUTHOR]

An equilibrium is communication-proof if it is unaffected by new opportunities to communicate and renegotiate. We characterize the set of equilibria of coordination games with pre-play communication in which players have private preferences over the coordinated outcomes. The set of communication-proof equilibria is a small and relatively homogeneous subset of the set of qualitatively diverse Bayesian Nash equilibria. Under a communication-proof equilibrium, players never miscoordinate, play their jointly preferred outcome whenever there is one, and communicate only the ordinal part of their preferences. Moreover, such equilibria are robust to changes in players' beliefs and interim Pareto efficient. [ABSTRACT FROM AUTHOR]

I investigate the design of effort‐maximizing mechanisms when agents have both private information and convex effort costs, and the designer has a fixed prize budget. I first demonstrate that it is always optimal for the designer to utilize a contest with as many participants as possible. Further, I identify a necessary and sufficient condition for the winner‐takes‐all prize structure to be optimal. When this condition fails, the designer may prefer to award multiple prizes of descending sizes. I also provide a characterization of the optimal prize allocation rule for this case. Finally, I illustrate how the optimal prize distribution evolves as the contest size grows. [ABSTRACT FROM AUTHOR]

This paper studies optimal relational contracts when the value of the relationship between contracting parties is not commonly known. I examine a simplified, two-period version of the principal–agent model of Halac (2012), in which the principal has persistent private information about her outside option. The results capture the main lessons, showing why inefficiencies necessarily arise and how they depend on the parties’ bargaining powers. When parties care enough about the future, revealing the value of the relationship requires the informed principal to have bargaining power, and to sometimes renege on her promises. [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]

Nuclear and missile tests by North Korea, which directly threaten China's national interests, regional stability, and economic development, have consistently irritated China. Since the 1950s, China and North Korea have held high-level meetings aimed at discussing and improving their bilateral relationship. Using empirical analysis, this study attempts to examine the impact of these meetings on North Korea's missile and nuclear tests. The study argues that as the frequency of high-level meetings between China and North Korea increases, North Korea's provocative actions decrease. The high-level meetings serve to address the issue of incomplete information, create avenues for economic aid and cooperation, and reduce the likelihood of future nuclear and missile tests. The empirical findings indicate that while high-level meetings with or without the presence of top leaders can lead to a reduction in missile tests by North Korea, only summits between China and North Korea have a significant impact on the reduction of nuclear tests by North Korea. [ABSTRACT FROM AUTHOR]

We generalize the standard, private values voting model with single-peaked preferences and incomplete information by introducing interdependent preferences. Our main results show how standard mechanisms that are outcome-equivalent and implement the Condorcet winner under complete information or under private values yield starkly different outcomes if values are interdependent. We also propose a new notion of Condorcet winner under incomplete information and interdependent preferences, and discuss its implementation. The new phenomena in this paper arise because different voting rules (including dynamic ones) induce different processes of information aggregation and learning. [ABSTRACT FROM AUTHOR]

Information leakage is usually defined as the logarithmic increment in the adversary's probability of correctly guessing the legitimate user's private data or some arbitrary function of the private data when presented with the legitimate user's publicly disclosed information. However, this definition of information leakage implicitly assumes that both the privacy mechanism and the prior probability of the original data are entirely known to the attacker. In reality, the assumption of complete knowledge of the privacy mechanism for an attacker is often impractical. The attacker can usually have access to only an approximate version of the correct privacy mechanism, computed from a limited set of the disclosed data, for which they can access the corresponding un-distorted data. In this scenario, the conventional definition of leakage no longer has an operational meaning. To address this problem, in this article, we propose novel meaningful informationtheoretic metrics for information leakage when the attacker has incomplete information about the privacy mechanism--we call them average subjective leakage, average confidence boost, and average objective leakage, respectively. For the simplest, binary scenario, we demonstrate how to find an optimized privacy mechanism that minimizes the worst-case value of either of these leakages. [ABSTRACT FROM AUTHOR]

We study large‐population repeated games where players are symmetric but not anonymous, so player‐specific rewards and punishments are feasible. Players may be commitment types who always take the same action. Even though players are not anonymous, we show that an anti‐folk theorem holds when the commitment action is "population dominant," meaning that it secures a payoff greater than the population average payoff. For example, voluntary public goods provision in large populations is impossible when commitment types never contribute, even if monetary rewards can be targeted to contributors; however, provision is possible if noncontributors can be subjected to involuntary fines. A folk theorem under incomplete information provides a partial converse to our result. Along the way, we develop some general results on symmetric games with incomplete information and/or repeated play. [ABSTRACT FROM AUTHOR]

The present paper deals with the prediction of the fatigue life of a planar tubular truss, when geometrical parameters, material properties, and live loads are non-deterministic. A multi-level calculation uncertainty quantification framework code was designed to aggregate the finite element method and fatigue-induced sequential failures. Due to the incompleteness of the aleatory-type inputs, the maximum entropy principle was applied. Two sensitivity analyses were performed to report the most influencing factors. In terms of variance, the results suggest that the slope of the curve crack growth rate � stress intensity factor range is the most influencing factor related to fatigue life. Furthermore, due to the application of the entropy concept, the fatigue crack growth boundaries and fatigue crack semi-width boundaries obtained provide the most unbiased fatigue crack design mapping. These boundaries allow the designer to select the worst-case fatigue scenario, besides being able to predict the crack behavior at a required confidence level.

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management.

Forest tickets refer to a type of forest resource usufruct certificate characterized by “cooperative operation, quantification of rights and interests, free circulation, and guaranteed dividends”. It is an important means to build a market-oriented mechanism for realizing the value of ecological resources. Incomplete information, based on field survey data from thirteen villages in eight townships (towns) in Sanming City, Fujian Province, China, and a binary logit model were used to explore the moderating effects of factors affecting farmers’ willingness to participate in forest ticket trading, the heterogeneity of farmers, and social capital. We found the following: In an environment with incomplete information, farmers’ willingness to participate in forest ticket trading is influenced by heterogeneity expectations, social capital, government propaganda, and individual family characteristics. There are certain differences in the influencing factors and degree of farmers’ willingness to participate in forest ticket trading among different groups of farmers with different levels of education and part-time employment. Social capital can strengthen the positive impact of income expectations and policy sustainability expectations, and alleviate the negative impact of risk expectations.