The exp-UIV for Markets with Partial Information and Complete Information

In distributed wireless networks, nodes often do not have access to complete network information (e.g. network topology, channel gains, etc.). As a result, they have to execute their transmission and reception strategies with partial information about the network, in a distributed fashion. Thus, the key question is how good are the distributed decisions in comparison to the optimal decisions based on full network knowledge. In this paper, we formalize the concept of partial-information sum-capacity by defining normalized sum-capacity, which is defined as the maximum achievable fraction of full-information sum-capacity with a given amount of partial information. We then examine four deterministic networks, multiple access, multiuser Z-channel chain, one-to-many and many-to-one interference channel, and characterize the normalized sum-capacity. For each network, two cases of partial network information are analyzed: (a) each transmitter only knows the channel gains to its receiver, and (b) transmitters knows the channel gains of all links which are no more than two hops away. Quite interestingly, we show that in all eight cases (4 networks × 2 forms of partial information), the normalized sum-capacity is achieved by scheduling subnetworks for which there exist a universally optimal distributed strategy with the available partial information. Furthermore, we show that while actual sum-capacity is not known in all cases, normalized sum-capacity can be in fact be exactly characterized.

Mobile communication has become the preferred choice of personal communication today. With the rapid proliferation of wireless network technologies, heterogeneous wireless network environments increase more and more. Such environments provide multiple access networks simultaneously for mobile users. Each mobile user wishes to get connected to Internet using the access technology that best suits his needs at any point in time. Always Best Connected is considered as a special concept for mobile users to select their suitable access networks. In this paper, we have presented two scenarios which are based on Always Best Connected concept to provide, for a smart vehicle moving in a road network installed with multiple access technologies, an always best connected service at any point in time. In the first scenario, we assume that the mobile user (vehicle) has complete information on the road network (all access technologies installed in the road network). Whilst the second scenario is the same as the first one except that the mobile user periodically receives a partial information on the road network (only the access technologies installed in the edges which are immediately accessible by mobile user). For both scenarios, we have proposed a graph model to characterize the observed mobility pattern. Also, two algorithms are proposed to find the optimal path that provides always best connected service for vehicles in both scenarios.

Nonlinear dynamics of discrete time multi-level leader–follower games

Many markets are characterized by competitive settings and incomplete information. While offer prices of sellers are often observable, the competitors’ inventory levels are mutually not observable. In this paper, we study stochastic dynamic pricing models in a finite horizon duopoly model with partial information. To be able to derive effective pricing strategies when the competitor’s inventory level is not observable, we use a Hidden Markov Model. Our approach is based on feedback pricing strategies that are optimal, if the competitor’s inventory level is observable. Optimized price reactions are balancing two effects: (i) to slightly undercut the competitor’s price to sell more items, and (ii) to use high prices to promote a competitor’s run-out. For the case that a competitor’s strategy is unknown, we derive robust heuristic strategies. Comparing duopolies with different information structures, we find that expected sales results are quite similar as long as the firms’ information is symmetric. By evaluating asymmetric pairs of strategies, we study to which extent the value of additional information is affected by the consumers’ price sensitivity or the competitors’ price response times.

Tobit models with social interactions: Complete vs incomplete information

Designing mobile robots that navigate indoor environments autonomously is known to be a difficult problem. A critical issue is in the formulation of robust motion control algorithms capable of reliably sensing partial or incomplete information from the environment and using this information to choose appropriate actions to achieve its designed goals. As an example, suppose we wish to deploy a mobile robot that autonomously patrols defined locations at a hazardous high-security facility. The robot must maintain accurate knowledge of its location, while using sensory data to recognize objects and obstacles in its immediate vicinity. Its task is to inspect the desired locations within a defined time period and provide real-time data in the event of an incident. The problem is thus to choose appropriate actions that result in accomplishing the patrol in a minimal amount of time in the partially structured environment. To solve this problem we adopt the Partially Observable Markov Decision Processes (POMDP) formalism to find near-optimal and efficient policies that provides a description the robot's motion in environments with incomplete state information. POMDP is a generalization of Markov Decision Processes (MDPs). It models a system as a coupling of an agent/decision maker (robots in our case) and an environment. We also present a methodology called Goal-Specific Representation (GSR) to reduce the size of the state-space for computational efficiency and propose an extension to the methodology.

The energy harvesting of unmanned aerial vehicle (UAV) has been researched extensively in recent years. However, the existing energy harvesting between the base station and UAVs does not consider the information asymmetry factor, which means the base station provides the radio frequency (RF) energy for UAVs in the context of UAVs’ partial private information. In order to maximize the base station’s utility or payoff, it is crucial for the base station to motivate more UAVs to harvest RF energy. In the paper, we propose an effective incentive energy harvesting mechanism in UAV communication network, which is a challenging problem since there exist interest conflicts that the base station and UAVs are rational individuals who maximize their utilities. Our objective is to make the base station’s utility maximum via balancing the tradeoff between transmit power cost and charged price benefit, while incentivizing UAVs to purchase transmit power. We design a series of optimal energy harvesting contract with different price discounts targeting different types of UAVs by contract theory. Owing to information asymmetry, we analyze two different information scenarios: complete and incomplete information. We suppose the base station knows each UAV’s type in complete information, then we analyze the practical case that the base station is aware of incomplete information of UAV’s private information. The base station aims to maximize its utility by providing contract. The UAVs choose the contract meeting the individual rationality (IR) and incentive compatibility (IC) rules while maximizing their utilities. Our simulation shows that the energy harvesting mechanism maximizes the base station’s utility and stimulates UAVs to purchase RF energy transmit power in different scenarios. Compared with other methods, our proposed optimal contract can improve the utility of the base station while maximizing the utility of UAVs.

Under market frictions like illiquidity or transaction costs, contingent claims can incorporate some inevitable intrinsic risk that cannot be completely hedged away but remains with the holder. In general, they cannot be synthesized by dynamical trading in liquid assets and hence cannot be priced by no‐arbitrage arguments alone. Still, an agent (she) can determine a valuation with respect to her preferences toward risk. The utility indifference value for a variation in the quantity of illiquid assets held by the agent is defined as the compensating variation of wealth, under which her maximal expected utility remains unchanged.

A Stackelberg security Markov game based on partial information for strategic decision making against unexpected attacks

The Comparison of Information Structures in Games: Bayes Correlated Equilibrium and Individual Sufficiency

This study suggests an attention economy perspective on incomplete information, asserting that people use partial information as basis for action, hence an effective information system should provide them just with the most useful partial information, and should avoid redundant information that wastes their limited attention resources. These assertions were empirically examined via simulation of a speech-reading process that measured the real and perceived value of two levels of partial speech-reading support. The findings indicate that additional partial information improved performance, but there was practically no significant difference between one and two signals support levels. High correlation was found between the different methods of evaluation, suggesting that perceived value may be used as substitute to real value measurement after considering the circumstances of the system involved. This research may provide important insights for designing information systems in general, as well as speech-reading support systems that will improve communication opportunities of hearing impaired people.

Purpose Portfolio management in the construction sector represents a key issue with an impact on strategic development and competitiveness, involving the consideration of a large number of items, increasing its complexity. Permeated not only by financial issues, but also by ecological ones, the selection and management of portfolios should consider long-term organizational strategic objectives, so traditional measures based on value alone become inadequate. Thus, integrating partial information and the benefit-to-cost heuristic, this paper proposes a Multi-Criteria Decision Making/Aiding (MCDM/A) model to support Decision-Makers (DMs) in such problems, making companies less susceptible to risks despite the higher number of items. Design/methodology/approach In this study, a framework with two phases is applied. In the first, the problem is structured, defining the main objectives for the problem and the related criteria. In the second phase, the MCDM/A model is built through the FITradeoff method to obtain a portfolio of items in accordance with preference information and the budget. A case study illustrates the application of both the framework and the proposed model. Findings Based on objectives that encompass not only financial aspects but also ecological and strategic ones, six evaluation criteria were defined. Using these criteria, 147 projects from a construction company in Brazil were assessed. A portfolio with 68 alternatives was then developed, utilizing a simplified heuristic structure that ensured process agility and required only partial information about the DM’s preferences, which contributed to the simplification of the process. At the same time, this approach maintained a solid axiomatic foundation, providing robustness to the developed model. The results achieved were satisfactory for the company and were validated by the DM. Originality/value This work is the first to demonstrate the feasibility and success of the benefit-to-cost heuristic approach with partial information applied to a portfolio problem with a large number of projects. Additionally, the advantage of using incomplete information is highlighted, as this allows DMs to obtain partial results from the FITradeoff method. In addition, this study contributes by offering a model that provides suggestions for criteria that are consistent with the context in which the construction sector operates, extending beyond the purely economic perspective.

This paper presents three types of learning behaviors with complete information, partial information and no information respectively, and thus models travelers’ route choice for investigating the impact of memory on the evolution of transportation system. Simulation results on a three-parallel-route network show that it is uncertain whether the information released by advanced traveler information systems (ATIS) can make the flow pattern evolve to a logit stochastic user equilibrium, which depends on the length of memory. If memory length is larger than a threshold value, the provided information drives the flow pattern to a stochastic user equilibrium state; otherwise, the logit stochastic user equilibrium is unattainable and the system evolution can not converge to a stable state. Furthermore, it is also shown that the fluctuation of traffic flow under full information exceeds that under partial information on the same route, when the length of memory remains unchanged.

A new Cauchy-based black-box technique for uncertainty in risk analysis

Effects of risk information on pedestrian evacuation during fire emergencies: Virtual experiments and survey