On automated e‐business negotiations: Goal,policy, strategy,and plans of decision and action

In recent years, there has been increasing interest in automated e‐business negotiations. The automation of negotiation requires a decision model to capture the negotiation knowledge of policymakers and negotiation experts so that the decision‐making process can be carried out automatically. Current research on automated e‐business negotiations has focused on defining low‐level tactics (or negotiation rules) so that automated negotiation systems can carry out automated negotiation processes. These low‐level tactics are usually defined from a technical perspective, not from a business perspective. There is a gap between high‐level business negotiation goals and low‐level tactics. In this article, we distinguish the concepts of negotiation context, negotiation goals, negotiation strategy, and negotiation tactics and introduce a formal decision model to show the relations among these concepts. We show how high‐level negotiation goals can be formally mapped to low‐level tactics that can be used to affect the behavior of a negotiation system during the negotiation process. In business, a business organization faces different negotiation situations (or contexts) and determines different sets of goals for different negotiation contexts. In our decision model, a business policymaker sets negotiation goals from different perspectives, which are called goal dimensions. A negotiation policy is a functional mapping from a negotiation context to some quantitative measures (or goal values) for the goal dimensions to express how competitive the policymaker wants to reach that set of goals. A negotiation expert who has the experience and expertise to conduct negotiations would define the negotiation strategies needed for reaching the negotiation goals. Formally, a negotiation strategy is a functional mapping from a set of goal values to a set of decision‐action rules that implement negotiation tactics. The selected decision‐action rules can then be used to control the execution of an automated negotiation system, which conducts a negotiation on behalf of a business organization.     

Model uncertainty and liquidity

Extreme market outcomes are often followed by a lack of liquidity and a lack of trade. This market collapse seems particularly acute for markets where traders rely heavily on a specific empirical model such as in derivative markets like the market for mortgage backed securities or credit derivatives. Moreover, the observed behavior of traders and institutions that places a large emphasis on “worst-case scenarios” through the use of “stress testing” and “Value-at-Risk” seems different than Savage expected utility would suggest. In this paper, we capture model-uncertainty using an Epstein and Wang [Epstein, L.G., Wang, T., 1994. Intertemporal asset pricing under Knightian uncertainty. Econometrica 62, 283–322] uncertainty-averse utility function with an ambiguous underlying asset-returns distribution. To explore the connection of uncertainty with liquidity, we specify a simple market where a monopolist financial intermediary makes a market for a propriety derivative security. The market-maker chooses bid and ask prices for the derivative, then, conditional on trade in this market, chooses an optimal portfolio and consumption. We explore how uncertainty can increase the bid–ask spread and, hence, reduces liquidity. Our infinite-horizon example produces short, dramatic decreases in liquidity even though the underlying environment is stationary. We show how these liquidity crises are closely linked to the uncertainty aversion effect on the optimal portfolio. Effectively, the uncertainty aversion can, at times, limit the ability of the market-maker to hedge a position and thus reduces the desirability of trade, and hence, liquidity.     

Schedule evaluation in unstable manufacturing environments

This paper provides a new approach to validate the feasibility of schedules of multiple-step mixed-model unstable manufacturing environments with different changeover times. Traditionally, continuous lines in a manufacturing process run the same sequence in order to minimize WIP inventory and lead time. Nevertheless, multiple reasons such as different product mixes or setup times can lead managers to run different sequences in continuous lines. Unfortunately, lack of reliability of supply and demand in these environments makes it difficult to manage product inventories and often leads to starvation due to the discoordination between the schedules of both lines. This approach is based on a new type of visual representation of schedules and an estimate the probability of starvation. It assumes stochastic supply and demand and a predefined schedule sequence based on batches of different sizes.     

Dynamics and Long-run Structure in U.S. Meat Demand

Empirical analysis, based on a general dynamic Almost Ideal Demand System, shows the commonly used autoregressive and partial adjustment processes are restrictive to meat demand data. This study derives a linear specification in levels form to investigate dynamics in a general framework. Merging a long-run steady state structure with short-run dynamics results in consistent and robust long-run demand elasticities Une analyse empirique, basée sur un systéme dynamique général de demande quasi optimale, montre que les mécanismes courants d'ajustement autorégressif et d'ajustement partiel ont un effect restrictif sur l'évaluation des données de la demande de viande. Les auteurs proposent une spécifcation linéaire par niveaux pour examiner la dynamique du cadre général. La combinaison d'une structure stable de longue durée avec une dynamique de courte période a produit des élasticités cohérentes et solides de la demande à long terme     

The Foreclosure–House Price Nexus: A Panel VAR Model for U.S. States, 1981–2009

Despite housing's economic importance, little has been written on how foreclosures and home prices interact in a framework that includes macroeconomic and housing variables such as employment, permits or sales. Panel VAR results for quarterly state‐level data indicate that price–foreclosure linkages run both ways. Foreclosures negatively impact home prices. The negative impact of prices on foreclosures, however, is much larger. These results suggest the low‐frequency association observed between foreclosures and prices is mostly driven by the endogenous adjustment of foreclosures to prices via the strategic choices of homeowners and lenders, rather than through the effects of foreclosures on home prices.     

Publication Selection Bias in Minimum‐Wage Research? A Meta‐Regression Analysis

Card and Krueger's meta-analysis of the employment effects of minimum wages challenged existing theory. Unfortunately, their meta-analysis confused publication selection with the absence of a genuine empirical effect. We apply recently developed meta-analysis methods to 64 US minimum-wage studies and corroborate that Card and Krueger's findings were nevertheless correct. The minimum-wage effects literature is contaminated by publication selection bias, which we estimate to be slightly larger than the average reported minimum-wage effect. Once this publication selection is corrected, little or no evidence of a negative association between minimum wages and employment remains.