A Global Database of Domestic and International Tourist Numbers at National and Subnational Level

We present a new, global data base on tourist destinations. The data base differs from other data bases in that it includes both domestic and international tourists; and it contains data, for the most important destinations, data at national level as well as at lower administrative levels. Missing observations are interpolated using statistical models. The data are freely accessible on the internet.Tourism, Data

Competition and Collusion in Grain Markets: Basmati Auctions in North India

Many small wholesale grain markets in India are characterized by large numbers of sellers, and a relatively small number of buyers, thereby lending the price formation process open to manipulation through collusion. Government intervention limits the extent of such manipulation by instituting regulated markets where the rules of exchange are clearly spelled out. The key institutional features of these markets are (a) sales through open ascending auctions; (b) the presence of "commission agents" representing both buyers and sellers. We present simple models of noncooperative and collusive behavior in auctions incorporating the above, and some more market specific, assumptions. We exploit data from a primary survey of a market for basmati paddy in North India. The main findings are (i) the collusive model explains the data better; (ii) the incentives of sellers and a subset of the large buyers are aligned; (iii) this, along with a Principal-Agent slack between millers and commission agents who buy for them, facilitates the form that collusion takes, and (iv) due to (ii) and (iii), the impact of collusion on market prices is not necessarily adverse. Insofar as the features of the market we study are common to grain markets in North India, we believe that these findings may be of much wider significance.

Applying revenue management to agent-based transportation planning

We consider a multi-company, less-than-truckload, dynamic VRP based on the concept of multi-agent systems. We focus on the intelligence of one vehicle agent and especially on its bidding strategy. We address the problem how to price loads that are offered in real-time such that available capacity is used in the most profitable way taking into account possible future revenues. We develop methods to price loads dynamically based on revenue management concepts.\ud We consider a one leg problem, i.e., a vehicle travels from i to j and can wait at most τ time units in which it can get additional loads from i to j. We develop a DP to price loads given a certain amount of remaining capacity and an expected number of auctions in the time-to-go. Because a DP might be impractical if parameters change frequently and bids has to be determined in real-time, we derived two approximations to speed up calculations. The performance of these approximations are compared with the performance of the DP. Besides we introduce a new measure to calculate the average vehicle utilisation in consolidated shipments. This measure can be calculated based on a limited amount of data and gives an indication of the efficiency of schedules and the performance of vehicles

Online Auctions

The economic literature on online auctions is rapidly growing because of the enormous amount of freely available field data. Moreover, numerous innovations in auction-design features on platforms such as eBay have created excellent research opportunities. In this article, we survey the theoretical, empirical, and experimental research on bidder strategies (including the timing of bids and winner's-curse effects) and seller strategies (including reserve-price policies and the use of buy-now options) in online auctions, as well as some of the literature dealing with online-auction design (including stopping rules and multi-object pricing rules).

The Unsupportable Support Price: The Government in Paddy Auctions of Northern India

In most developing countries, there is an active debate on the changing role of the government in mediating market outcomes. In grain markets in India, this debate assumes a renewed significance, given the excessive accumulation of food stocks in recent years. For example, the wisdom of maintaining a 'high' Minimum Support Price has been called to question. Auction theory provides a powerful and hitherto unused tool not only for analysing the structure of grain markets and the process of price formation, but also for analysing implications of alternative government policies. Our results for a small, regulated market for parmal paddy in Northern India, where grain sales occur through the open ascending auction, suggest that (a) the government's inability to support the minimum price in the market has less to do with the poor quality of grain offered for sale, and more to do with a reluctance to accumulate stocks. (b) a lower but credibly-enforced minimum support price will not have the desired effect on government purchases. (c) a lowering of the percentage that millers are required to sell as levy to the government is consistent with a credible support price and effective management of stocks.

Auctions with Dynamic Populations: Efficiency and Revenue Maximization

We study a setting where objects and privately-informed buyers arrive stochastically to a market. A seller in this setting faces a sequential allocation problem with a dynamic population. We derive both efficient and revenue-maximizing incentive compatible direct mechanisms. Our main result shows that the sequential ascending auction is a simple indirect mechanism that achieves these desirable objectives. We construct equilibria in memoryless strategies where, in every period, bidders reveal all private information. These equilibria are outcome equivalent to the direct mechanisms. In contrast to static settings, sequential second-price auctions cannot yield these outcomes, as they do not reveal sufficient information

Lorenzo Market between Diversity and Mutation

The aim of this paper is to investigate the relationship between the diversity of groups of workers and the local division of labour in the San Lorenzo Market, Florence, Italy. This empirical research may help distinguish the demographic, migratory and occupational patterns of the workers in accordance with their national origin. It shows that geographical origin is the key to the differences, specialisations and oppositions between the Florentines and the various groups of migrants on the one hand, and between these immigrant groups on the other hand. It shows too the mutation made by the conflict between global dynamics and a local labour market.San Lorenzo Market, Immigrant workers, Diversity, Local division of labour, Migratory trajectories

Opportunity costs calculation in agent-based vehicle routing and scheduling

In this paper we consider a real-time, dynamic pickup and delivery problem with timewindows where orders should be assigned to one of a set of competing transportation companies. Our approach decomposes the problem into a multi-agent structure where vehicle agents are responsible for the routing and scheduling decisions and the assignment of orders to vehicles is done by using a second-price auction. Therefore the system performance will be heavily dependent on the pricing strategy of the vehicle agents. We propose a pricing strategy for vehicle agents based on dynamic programming where not only the direct cost of a job insertion is taken into account, but also its impact on future opportunities. We also propose a waiting strategy based on the same opportunity valuation. Simulation is used to evaluate the benefit of pricing opportunities compared to simple pricing strategies in different market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization and delivery reliability

PERFORMANCE AND ANALYSIS OF SPOT TRUCK-LOAD PROCUREMENT MARKETS USING SEQUENTIAL AUCTIONS

Competition in a transportation marketplace is studied under different supply/demand conditions, auction formats, and carriers' behavioral assumptions. Carriers compete in a spot truck-load procurement market (TLPM) using sequential auctions. Carrier participation in a TLPM requires the ongoing solution of two distinct problems: profit maximization problem (chose best bid) and fleet management problem (best fleet assignment to serve acquired shipments). Sequential auctions are used to model an ongoing transportation market, where carrier competition is used to study carriers' dynamic vehicle routing technologies and decision making processes. Given the complexity of the bidding/fleet management problem, carriers can tackle it with different levels of sophistication. Carriers' decision making processes and rationality/bounded rationality assumptions are analyzed. A framework to study carrier behavior in TL sequential auctions is presented. Carriers' behavior is analyzed as a function of fleet management technology, auction format, carrier bounded rationality, market settings, and decision making complexity. The effects of fleet management technology asymmetries on a competitive marketplace are studied. A methodology to compare dynamic fleet management technologies is developed. Under a particular set of bounded rationality assumptions, bidding learning mechanisms are studied; reinforcement learning and fictitious play implementations are discussed. The performance of different auction formats is studied. Simulated scenarios are presented and their results discussed

Reverse Auction in Pricing Model

Dynamic price discrimination adjusts prices based on the option value of future sales, which varies with time and units available. This paper surveys the theoretical literature on dynamic price discrimination, and confronts the theories with new data from airline pricing behavior, Consider a multiple booking class airline-seat inventory control problem that relates to either a single flight leg or to multiple flight legs. During the time before the flight, the airline may face the problems of (1) what are the suitable prices for the opened booking classes, and (2) when to close those opened booking classes. This work deals with these two problems by only using the pricing policy. In this paper, a dynamic pricing model is developed in which the demand for tickets is modeled as a discrete time stochastic process. An important result of this work is that the strategy for the ticket booking policy can be reduced to sets of critical decision periods, which eliminates the need for large amounts of data storage