We propose a novel distributed user-car matching method based on a contract between users to mitigate the imbalance problem between vehicle distribution and demand in free-floating car sharing. Previous regulation methods involved an incentive system based on the predictions of origin-destination (OD) demand obtained from past usage history. However, the difficulty these methods have in obtaining accurate data limits their applicability. To overcome this drawback, we introduce contract-based coordination among drop-off and pick-up users in which an auction is conducted for drop-off users' intended drop-off locations. We theoretically analyze the proposed method regarding the upper bound of its efficiency. We also compare it with a baseline method and non-regulation scenario on a free-floating car-sharing simulator. The experimental results show that the proposed method achieves a higher social surplus than the existing method.

This paper proposes a new budget allocation method for crowdsourced sequential tasks. Complex tasks are decomposed into interdependent subtasks that can be executed cooperatively by individual workers. In the case of sequential tasks, the output of a task becomes the input to another task, and the quality of the final artifact thus depends on the qualities of the preceding tasks. In crowdsourcing, the abilities of workers are often difficult to learn in advance. Thus, a fixed budget allocation for the component subtasks cannot deal with a dynamic situation. Also, it is often difficult for a requester to accurately evaluate the quality of intermediate artifacts, which can result in budget misallocation and waste. To overcome these difficulties, we formalize the budget allocation problem as a partially observable Markov decision process (POMDP) by introducing quality evaluation actions and developing a contingent budget allocation method, which generates a conditional plan given uncertainty about the intermediate states and action effects. Experimental simulation results show that the proposed method can find a solution in a reasonable time and improve the quality of the final artifact.

How can human forecasts and a machine forecast be combined in inflation forecast tasks? A machine-learning-based forecaster makes a forecast based on a statistical model constructed from past time-series data, while humans take varied information such as economic policies into account. Combination methods for different forecasts have been studied such as ensemble and consensus methods. These methods, however, always use the same manner of combination regardless of the situation (input), which makes it difficult to use the advantages of different types of forecasters. To overcome this drawback, we propose an ensemble method for estimating the expected error of a machine forecast and dynamically determining the optimal number of humans included in the ensemble. We evaluated the proposed method by using the seven datasets on U.S. inflation and confirmed that it attained the highest forecast accuracy for four datasets and the same accuracy as the highest one of traditional methods for two datasets.

Identity and the different roles identities play in social question-answering communities have long been discussed from both a theoretical and empirical standpoint. Identity is usually analysed in ways that emphasise a transaction, and many studies use third-party raters to assign value judgements to these factors, which may not be relevant to a community. In this article, we examine identity in Stack Overflow, a question and answer site dedicated to solving computer-programming problems, which has a competitive community run reputation system. Profiles on Stack Overflow are highly customizable, allowing users to choose the level of personal information they want to share: from extensive to none. We develop a categorisation scheme using grounded theory to develop definitions of identity, and analyse behaviour based on these definitions. We find that the choice of identity is diverse within the community and that there is a correlation with identifiers and increased reputation earning among the general population. An analysis of elite users, however, indicates that identity is closely tied to membership length, but not to performance. The results indicate that allowing identity choices does affect system efficacy or user outcomes.

This paper analyzes the effect of introducing preference signaling into the matching process of services (service providers) and consumers. This problem can be formalized as a two-sided matching problem in the game theory, and the Gale-Shapley algorithm is known as effective in solving the problem. It is implicitly assumed that service providers can learn the preference order over all consumers, but the service providers have to examine consumers before starting the Gale-Shapley algorithm, especially in the service-consumer matching market. Due to the different popularity of services, a congestion problem arises, that is, some service providers may receive many applications that are difficult to deal with. As a method to overcome this drawback, preference signaling has been proposed. However, the previous studies are not sufficient in that (1) they do not consider the difference in popularity on consumers, (2) they do not consider that a consumer can apply for more than one services simultaneously. To solve this problem, we build two models of the matching process (sequential one and parallel one) and examine the optimal strategies of consumers. The experimental results show that the introduction of preference signaling is effective to reduce the burden of service providers/consumers.

Internet auctions have become an integral part of electronic commerce (EC) and a promising field for applying agent technologies. Although the Internet provides an excellent infrastructure for large-scale auctions, we must consider the possibility of a new type of cheating, i.e., a bidder trying to profit from submitting several bids under fictitious names (false-name bids). Double auctions are an important subclass of auction protocols that permit multiple buyers and sellers to bid to exchange a good, and have been widely used in stock, bond, and foreign exchange markets. If there exists no false-name bid, a double auction protocol called PMD protocol has proven to be dominant-strategy incentive compatible. On the other hand, if we consider the possibility of false-name bids, the PMD protocol is no longer dominant-strategy incentive compatible. We develop a new double auction protocol called the Threshold Price Double auction (TPD) protocol, which is dominant strategy incentive compatible even if participants can submit false-name bids. The characteristics of the TPD protocol is that the number of trades and prices of exchange are controlled by the threshold price. Simulation results show that this protocol can achieve a social surplus that is very close to being Pareto efficient.

An ascending-bid auction protocol with a fixed end time has been widely used at many Internet auction sites. At these sites, we can observe bidders engaging in the behavior called last minute bidding, namely, a large fraction of the bids for a good are submitted in the closing seconds of the auction. This can cause problems such as information revelation failure as well as server overload and network congestion. When bidders behave in this way, each of them cannot obtain information about the good from the other bidders' bidding behaviors, and this spoils the advantages of open-bid auctions. The result is an inefficient allocation of the good. To solve this problem, we propose a new protocol that gives each bidder an option to fix the maximum bid of a proxy agent and an incentive to submit a high bid at an early stage of an auction by paying compensation money. We examine the property of the protocol based on the game theory and clarify what situations our protocol outperforms the ordinary ascending-bid auction protocol by computer simulation.

Pricing goods properly is critical for the further growth of electronic commerce. One price discrimination technique drawn from microeconomics theory has shown promise as regards the trading of information services. This technique, however, has a serious drawback in that it assumes that a seller knows the distribution of buyers' preferences. Unfortunately, obtaining such data is not always easy. We can incorporate agent technologies into the technique, namely, by gathering sales data and updating information about buyers, and thus improve the performance. However, if a naive method for revising prices is employed, this adaptive pricing can end in failure because of buyer collusion. If collusion does occur, the rationality of any price revision is lost. To solve this problem, we have developed a pricing mechanism that can withstand buyer collusion. We provide a concrete method for calculating quality and price combinations based on a theoretical analysis of the mechanism, and then show its effectiveness by using computer simulation.

The purpose of this paper is to analyze a collective decision making problem in an open, dynamic environment, such as the Internet. More specifically, we study a class of mechanism design problems where the designer of a mechanism cannot completely identify the participants (agents) of the mechanism. A typical example of such a situation is Internet auctions. The main contributions of this paper are as follows. We develop a formal model of a mechanism design problem in which false-name declarations are possible, and prove that the revelation principle still holds in this model. When false-name declarations and hiding are possible, we show that there exists no auction protocol that achieves Pareto efficient allocations in a dominant strategy equilibrium for all cases. We show a sufficient condition where the Clarke mechanism is robust against false-name declarations (the concavity of the maximal total utility of agents).

We have developed a social meeting system called CommunityBoard which visually displays the discussion structure. Discussions on networks, e.g., by a mailing list, Usenet newsgroup, or online forum, are becoming more and more popular. However, many people have difficulty following and participating in discussions on networks, and one of the reasons is due to a failure to grasp the context of discussions. Previous tools have been insufficient in helping to avoid this failure. In order to solve this problem, we have developed CommunityBoard. Users can avoid confusion and achieve informative discussion. This system provides one integrated view of discussions from three aspects: the speakers, topics and time. The integrated view enables users to easily obtain various information about these aspects at one glance and this leads to lively and productive discussions. An example illustrates the efficiency of CommunityBoard.