Computing Reserve Prices and Identifying the Value Distribution in Real-World Auctions with Market Dynamics

Engineering and Applied Science

A Free Exchange e-Marketplace for Digital Services

The digital era is witnessing a remarkable evolution of digital services. While the prospects are countless, the e-marketplaces of digital services are encountering inherent game-theoretic and computational challenges that restrict the rational choices of bidders. Our work examines the limited bidding scope and the inefficiencies of present exchange e-marketplaces. To meet challenges, a free exchange e-marketplace is proposed that follows the free market economy. The free exchange model includes a new bidding language and a double auction mechanism. The rule-based bidding language enables the flexible expression of preferences and strategic conduct. The bidding message holds the attribute-valuations and bidding rules of the selected services. The free exchange deliberates on attributes and logical bidding rules for automatic deduction and formation of elicited services and bids that result in a more rapid self-managed multiple exchange trades. The double auction uses forward and reverse generalized second price auctions for the symmetric matching of multiple digital services of identical attributes and different quality levels. The proposed double auction uses tractable heuristics that secure exchange profitability, improve truthful bidding and deliver stable social efficiency. While the strongest properties of symmetric exchanges are unfeasible game-theoretically, the free exchange converges rapidly to the social efficiency, Nash truthful stability, and weak budget balance by multiple quality-levels cross-matching, constant learning and informs at repetitive thick trades. The empirical findings validate the soundness and viability of the free exchange

Comparing Open and Sealed Bid Auctions: Theory and Evidence from Timber Auctions

We study entry and bidding patterns in sealed bid and open auctions with heterogeneous bidders. Using data from U.S. Forest Service timber auctions, we document a set of systematic effects of auction format: sealed bid auctions attract more small bidders, shift the allocation towards these bidders, and can also generate higher revenue. We propose a model, which extends the theory of private value auctions with heterogeneous bidders to capture participation decisions, that can account for these qualitative effects of auction format. We then calibrate the model using parameters estimated from the data and show that the model can explain the quantitative effects as well. Finally, we use the model to provide an assessment of bidder competitiveness, which has important consequences for auction choice.Auctions, Timber

Approaches to monetary policy revisited - lessons from the crisis, 6th ECB Central Banking Conference, 18-19 November 2010

This volume contains a collection of papers, commentaries and speeches that review the strategic and operational decisions taken by the central banks to combat the crisis and that reflect on the lessons for the future. The contributions are grouped around five broad topics: monetary policy strategy, lessons from historical experiences, challenges for macroeconomic and finance theory, the international dimension of the crisis, and operational frameworks for monetary policy.monetary policy strategy, monetary policy operational framework

Broadbasing and Deepening the Bond Market in India

At the time of its independence in 1947 India had only the traditional commercial banks, all with private sector ownership. Like the typical commercial banks in other parts of the world, all banks in India were also not keen to provide medium and long-term finance to industry and other sectors for their fixed asset formation. The banks were willing to fund basically the working capital requirements of the credit-worthy borrowers on the security of tangible assets. Since the government was keen to stimulate setting up of a wide range of new industrial units as also expansion/diversification of the existing units it decided to encourage setting up of financial intermediaries that provided term finance to projects in industry. Thus emerged a well-knit structure of national and state level development financial institutions (DFIs) for meeting requirements of medium and long-term finance of all range of industrial units, from the smallest to the very large ones. Reserve Bank of India (the central banking institutions of the country) and Government of India nurtured DFIS through various types of financial incentives and other supportive measures. The main objective of all these measures was to provide much needed long-term finance to the industry, which the then existing commercial banks were not keen to provide because of the fear of asset-liability mismatch. Since deposits with the banks were mainly short/medium term, extending term loans was considered by the banks to be relatively risky. The five-year development plans envisaged rapid growth of domestic industry even in the private sector to support the import substitution growth model adopted by the national planners. To encourage investment in industry, a conscious policy decision was taken that the DFIs should provide term-finance mainly to the private sector at interest rates that were lower than those applicable to working capital or any other short-term loans. In the early years of the post-Independence period, shortages of various commodities tended to make trading in commodities a more profitable proposition than investment in industry, which carried higher risk. Partly to correct this imbalance, the conscious policy design was to increase attractiveness of long-term investment in industry and infrastructure through relatively lower interest rates. To enable term-lending institutions to finance industry at concessional rates, Government and RBI gave them access to low cost funds. They were allowed to issue bonds with government guarantee, given funds through the budget and RBI allocated sizeable part of RBI's National Industrial Credit (Long Term Operations) funds to Industrial Development Bank of India, the large DFI of the country. Through an appropriate RBI fiat, the turf of the DFIs was also protected, until recently, by keeping commercial banks away from extending large sized term loans to industrial units. Banks were expected to provide small term loans to small-scale industrial units on a priority basis.

Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles

IEEE This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions