Under the idea of ¡§Land ownership means wealth ownership¡¨, people believe that owning land is the ultimate symbol of wealth. Area of Taiwan¡¦s state-owned land goes up to 3.6 million hectares. Government agencies at all levels that require official or public use of land must apply for appropriation of the public land, which creates fewer disputes. This is not within the scope of this study. The rest of the non-public land would be treated in accordance with the provisions of Article 53 of the State-Owned Property Law, whereby ¡§The vacant house and land belonging to non-public use property shall be sold by tendering, if no predeterminated purpose.¡¨ However, after the land value skyrocketed due to cases of land auction, the public gradually became concerned with whether the state-owned land auctions are driving up the land prices. According to the principle of public land management and process, it expressly states that state-owned land is ¡§for rent only and not for sale¡¨. However, there are many exceptions, causing the current state-owned land policy to be still primarily auction-based. Therefore, there is still room for discussion regarding the impact of the ¡§for rent only and not for sale¡¨ policy on the development of the state-owned land. ¡@¡@ However, we are currently faced with limited resources and infinite demands, while the government regards land tendering as one of the optimal operating methods. Strengthened management for issues such as illegal occupation and idleness of state-owned land should thus be simultaneously implemented considering the rare and irreplaceable nature of state-owned land resources. As a result, in conforming to the principles of making reasonable and optimal adjustments in the utilization of land resources, this study explored the domestic state-owned land management system within the administrative law frameworks and examined whether any amendment is required. The ultimate aim of this paper is to examine the problems one by one under the five major frameworks: basic principles of the administrative law, administrative organization, administrative authority, administrative relief, and administrative supervision, while providing a discourse and analysis of the existing system. Concrete suggestions were then given to correct the existing state-owned land management system in Taiwan, in an attempt to provide a standard for policy makers or for administrative agencies when setting administrative rules.

Successful strategy of E-commerce is an important research topic in the passed decades. Understanding the relationship between order of entry and competitive advantage is the main purpose of this research. In this research, it collected a lot of survey reports, press releases, financial information and market research data from well-known online research companies, consultant companies and the Securities and Exchange Commission (SEC). This research focus on three kinds of internet business models such as search engine service, online shop and e-auction. After analyzing the data, the market pioneers have better performance in present than the market followers. It can conclude that there is relative pioneer advantage in the Internet business. However the entry of order has different influence on different business models. For search engine service and e-auction, they have the advantage of taking first. But there is no apparent preponderating first in the on-line shop. The types of online shop and product characteristics also affect the performance under different entry of order. Keywords¡GPioneer Advantage¡Bonline shop¡BSearch engine¡Be-auction¡B E-commerce¡Be-business¡Be-business model

Basically, to evaluate the operational performances of online auction is according to seller¡¦s transaction and investment in the auction website. Thus, how to maintain good relationship with the existing seller switching and attract new seller is the most attention and major care for auction service provider. However, there were few prior researches focused on the topic of online auction switching and examined from real case. In this study, we tried to develop a measuring model and explore the factors that relate to auction seller¡¦s switching. In July 2006, Yahoo! Kimo auction, the largest auction website in Taiwan, announced a new fee scheme which would charge 3 percent transaction handling fee on its sellers from Aug. 10. Many users expressed objection to the new charge scheme. During the same period, Ruten, a new joint venture auction website that combined eBay and Taiwanese portal operator PChome is nearly ready and makes some responding strategic approaches to attract new user. Many sellers of Yahoo! Kimo auction thought to switch auction provider. Base on the background, the study aim to explore auction seller¡¦s switching behavior through switching costs, including procedure costs, financial costs, and relational costs. Moreover, we examined how website quality, fee, anti-lock-in, and anti-switching affect switching intention and behavior. A total of 292 usable questionnaires were gathered through online surveys from auction discussion board and BBS. The data were analyzed by partial least squares (PLS) to test the hypotheses. We find that switching costs, website quality, fee have negative effect on seller¡¦s switching intention. Anti-lock-in and anti-switching have moderating effect in the model. According to the finding, we provide useful guidance for auction seller and auction service provider.

This study aims to improve transportation held between six auction centers, Inter-Auction Transportation, of FloraHolland. FloraHolland serves ninety eight percent of the Dutch market and is the largest auction in floriculture industry. The company wants to give the best sale opportunities with the costs as low as possible and this is the main initiative of this study. In this line of thought, FloraHolland wants to have a improvement on its current routing and scheduling mechanism. Exact models do not work due to the complexity and the size of the problem. Therefore, we developed a two-stage approach specific to this study. With this approach, we split exact approach into two, a mathematical model followed by a heuristic. In the exact approach, trucks are routed and scheduled at the same time. On the other hand, our solution approach first determines most efficient routes to be followed with Cycle Assignment Model and then, with Scheduling Heuristic, trucks are assigned to the routes, so within day transportation is planned in detail. Overall, each stage of this approach works in harmony and brings good solutions in a short CPU time.

A scenario where multiple entities interact with a common environment to achieve individual and common goals either co-operatively or competitively can be classified as a Multi-Agent System. In this thesis, we concentrate on the situations where the agents exhibit selfish, competitive and strategic behaviour, giving rise to interesting game theoretic and optimization problems. From a computational point of view, the presence of multiple agents introduces strategic and temporal issues, apart from enhancing the difficulty of optimization. We study the following natural mathematical models of such multi-agent problems faced in practice: a) combinatorial optimization problems with multi-agent submodular cost functions, b) combinatorial auctions with partially public valuations and c) online vertex-weighted bipartite matching and single bid budgeted allocations. We provide approximation algorithms, online algorithms and hardness of approximation results for these problems.

Past studies have confirmed the importance of reputation in online auction. However, the marginal return to sellers’ feedback rating may be declined after some point in which many sellers have been cumulating certain feedback, and the diagnostic role of reputation for buyers to diminish asymmetric information may lessen. Therefore, besides reputation signal, what potential signals that bidders tend to rely on making purchasing decision in online scenario may need further exploration. Moreover, the heterogeneity of auctioned items may strengthen information asymmetry therein, bidders may perceive higher value for the signals provided by sellers which efficiently reduce transaction risk and facilitate their bidding decision. The purposes of our research are then to investigate the formation process of sellers’ auction strategy and the impacts of four types of signals delivering on the web-page on auction outcomes. The data sets used for empirical estimates were coded from eBay auction site United States and Taiwan. Results show that signals such as sellers’ reputation, auction format, product condition, and arguments presented on the web page are significantly related to three outcome variables: number of bids, possibility of auction success and willingness to pay. In addition, we also exhibited that the formation of bidders auction strategy depends upon how many value signals that they possess. Finally, there is evidence in support of the degree of signals on bidders’ need for information is different under product usage status classified. Bidders are inclined to reward more for the signals when being an auction for the used products which have greater uncertainty on its actual performance. We conclude with a discussion of the practical implications of our research findings for online auction and suggestion of future study.

Economics is the study of the allocation of resources. Since Arrow's Fundamental Welfare Theorems, we know that competitive-markets achieve Pareto allocations when governments correct market failures. Thus, it has largely been the mission of economists to serve as 'Market Engineers': To identify and quantify market failures so the government can implement Pareto-improving policy (make everyone better without making anyone worse). Do Pareto- improving policies get implemented? How does policy become implemented? Achieving a Pareto efficient allocation of a nation's resources requires studying the implementation of policy, and therefore studying the allocation of political resources that influence policy. Policy implementation begins with the electoral process. In this dissertation, I use auction analysis, econometrics, and game theory to study political resource allocations in the electoral process. This dissertation consists of three research papers: Finance-Augmented Median-Voter Model, Vote Empirics, and Colonel Blotto Strategies. The Finance-Augmented Median-Voter Model postulates that candidates' campaign expenditures are bids in a first-price asymmetric all-pay auction in order to explain campaign expenditure behavior. Vote Empirics empirically analyzes the impacts of campaign expenditures, incumbency status, and district voter registration statistics on observed vote-share results from the 2004 congressional election. Colonel Blotto Strategies postulates that parties' campaign allocations across congressional districts may be a version of the classic Col Blotto game from Game Theory. While some equilibrium strategies and equilibrium payoffs have been identified, this paper completely characterizes players' optimal strategies. In total, this dissertation solves candidates' optimal campaign expenditure strategies when campaign expenditures are bids in an all-pay auction. The analysis demonstrates the need for understanding exactly the impacts of various factors, including strategic expenditures, on final vote results. The research uses econometric techniques to identify the effects. Last, the research derives the complete characterization of Col Blotto strategies. Discussed extensions provide testable predictions for cross-district Party contributions. I present this research not as a final statement to the literature, but in hopes that future research will continue its explanation of political resource allocation. An even greater hope is that in time this literature will be used to identify optimal "policy-influencing policies"; constitutional election policies that provide for the implementation of Pareto-improving government policies. / text

In many procurement auctions, entrants determine whether to participate in auctions accounting for their roles of intermediaries who search for the best (or the cheapest) input suppliers. We build on a procurement auction model with entry, combining with intermediary search for suppliers. The novel feature is that costs of bidders are endogenously determined by suppliers who strategically charge input prices. We show the existence of an equilibrium with price dispersion for inputs, generating cost heterogeneity among bidders. Interestingly, the procurement cost may rise as the number of potential bidders increases. (author's abstract) / Series: Department of Economics Working Paper Series

Unit-demand auctions have been well studied with applications in several areas. In this dissertation, we discuss new variants of the unit-demand auction that are motivated by practical applications. We design mechanisms for these variants that have strong properties related to truthfulness, efficiency, scalability, and privacy. The main contributions of this dissertation can be divided into two parts. In the first part, we introduce a new variant of the classic sealed-bid unit-demand auction in which each item is associated with a put option; the put option of an item gives the seller the right to sell the item at a specified strike price to a specified bidder, regardless of market conditions. We motivate our unit-demand auction setting by discussing applications to the reassignment of leases, and to the design of multi-round auctions. For the classic sealed-bid unit-demand framework, the VCG mechanism provides a truthful auction with strong associated guarantees, including efficiency and envy-freedom. For an item in our auction, the strike price of the associated put imposes a lower bound on the auction price. Due to these lower bound constraints on auction prices, we find that the VCG mechanism is not suitable for our setting. Instead, our work draws on two fundamental techniques, one from the realm of mechanism design for numerical preferences -- the dynamic unit-demand approximate auction of Demange, Gale, and Sotomayor -- and one from the realm of mechanism design for ordinal preferences -- the Top Trading Cycles algorithm -- to obtain a natural auction that satisfies the lower bound constraints on auction prices. While we cannot, in general, achieve either efficiency or envy-freedom in our setting, our auction achieves suitably relaxed versions of these properties. For example, this auction is envy-free for all bidders who do not acquire an item via the exercise of a put. We provide a polynomial time implementation of this auction. By breaking ties in an appropriate manner, we are able to prove that this auction is truthful. In the second part, we specify rules for a dynamic unit-demand auction that supports arbitrary bid revision. In each round, the dynamic auction takes a tentative allocation and pricing as part of the input, and allows each bidder -- including a tentatively allocated bidder -- to submit an arbitrary unit-demand bid. Each round of our dynamic auction is implemented via a single application of the sealed-bid unit-demand auction proposed in the first part. We show that our dynamic auction satisfies strong properties related to truthfulness and efficiency. Using a certain privacy preservation property of each round of the auction, we show that the overall dynamic auction is highly resistant to shilling. We present a fast algorithm for implementing the proposed auction. Using this algorithm, the amortized cost of processing each bidding operation is upper bounded by the complexity of solving a single-source shortest paths problem on a graph with nonnegative edge weights and a node for each item in the auction. We also propose a dynamic price adjustment scheme that discourages sniping by providing bidders with incentives to bid early in the auction. / text

Traffic congestion has been placing an extremely high burden on the development of modern cities. Congestion can be alleviated by either increasing road capacity, or by reducing traffic demand. For decades, increasing capacity by building more roads and lanes has been the major solution applied to accommodate the ever-growing traffic demand. However, it turns out to be of limited effect due to some well-known phenomenon such as latent demand. Controlling and managing traffic demand has in turn been viewed as a cost-effective alternative to increasing road capacity, as has been demonstrated many successful applications all around the world. Within the concept framework of Traffic Demand Management (TDM), Active Transportation and Demand Management (ATDM) is the dynamic management, control, and influence of traffic demand and traffic flow of transportation facilities. ATDM strategies attempt to influence traveler behavior and further manage traffic flow in a time-dependent manner within the existing infrastructure Successful ATDM applications include congestion pricing, adaptive ramp metering, dynamic speed limits, dynamic lane use control, etc. Singapore stands out to be an excellent success story of ATDM, as the implementations of "Cap and Trade" license plates and electronic road pricing make motoring a high cost privilege for citizens of Singapore, making the public relies on transit. Monetary leverage is an effective instrument to facilitate ATDM. Examples of ATDM applications adopting monetary instrument includes dynamic congestion pricing, "Cap and Trade" of car licenses, etc. Taking congestion pricing as an example, policy makers are inducing travelers' behavior and alternating their preferences towards different behavior decisions by levying price tags to different choices. As an important underpinning of rationing choice theory, an individual assigns an ordinal number over the available actions and this ordinal number is calculated by their utility function or payoff function. The individual's preference is expressed as the relationship between those ordinal assignments. In the implementation of congestion pricing, policy makers are imposing an additional high disutility to congested roads and therefore pushing some of the travelers to take alternative routes or shift to alternative departure times or even cancel the trips. However, congestion pricing suffers from public aversion as it creates burden on the motoring of low-income people and therefore doesn't help to alleviate social inequality. The concept of Tradable Mobility Credit (TMC) has been proposed by a group of researchers as another innovative application to facilitate dynamic traffic demand management and solve social inequality issues using pricing instruments. The concept of TMC is borrowed from carbon trading in environmental control. A limited quota of personal auto usage is issued to eligible travelers and credits can be traded in a free market fashion. This guarantees that the roadway usage does not exceed capacity while avoiding the negative effects of shortages normally associated with quotation systems. TMC is literally not a market-ready policy as the integration of the supporting infrastructures, including the trading market, the credit assignment component, and the credit charging component, has not been fully explored yet. Existing TMC research focuses on explaining and exploring the equilibrium condition through analytical methods such as mathematical modeling. Analytical models produce perfect convergence curves and deterministic equilibrium traffic flow patterns. Analytical models provide influential guidance for further works but the solution procedure may encounter problems when dealing with larger real world networks and scenarios. Meantime, current analytical models don't consider the microstructure of the credit trading market sufficiently while it's actually the most unique component of TMC system. Motivated by those concerns, an integrated TMC evaluation platform consisting of a policy making module and traveler behavior modules are proposed in this research. The concept of Agent-Based Modeling and Simulation (ABMS) is extensively adopted in this integrated platform as each individual traveler carries his/her personal memory across iterations. The goal of establishing this framework is to better predict a traveler's route choice and trading behavior if TMC is imposed and further provide intelligence to potential policy makers' decision making process. The proposed integrated platform is able to generate results at different aggregation levels, including both individual level microscopic behavior data as well as aggregated traffic flow and market performance data. In order to calibrate the proposed integrated platform, an online interactive experiment is designed based on an experimental economic package and a human research element with 22 participants has been conducted on this experiment platform to gather field data regarding a real person's route choice behavior and credit trading behavior in an artificial TMC system. Participants are recruited from forum, listserve, social media, etc. The calibrated platform is proved to have the ability to predict travelers' behavior accurately. A prototype market microstructure is proposed in this research as well and it is proved to be a cost-effective setting and resulted to a vast amount of economic saving given the fact that travelers would behave similar to the prediction generated by traveler behavior module. It's also demonstrated that the principle of Pareto-improving is not achieved in the proposed ABMS models.