Bargaining over waiting time in ultimatum game experiments

While preference-based explanations play an increasing role in economics and sociology, the accurate measurement of social preferences deserves more attention. Most laboratory experiments measure social preferences by studying the division of “a cake that nobody had to bake” (Güth and Kliemt, 2003). This article reports results of the first ultimatum game experiment with bargaining over waiting time. The experiment was created to avoid effects of windfall gains. In contrast to donated money, time is not endowed by the experimenter and implies a natural loss to subjects. This allows for a better measurement of the inherent conflict in the ultimatum game. We implemented three anonymity conditions; one baseline condition, one condition with anonymity among subjects and one double-blind condition in which the experimenter did not know the division of waiting time. While we expected to observe less other-regarding behavior in ultimatum game bargaining over time, our experimental results rather confirm previous ultimatum game experiments, in which people bargained over money. The modal offer was half of the waiting time and only one offer was rejected. Interestingly, anonymity did not change the results significantly. In conclusion, our experiment confirms other-regarding behavior in the ultimatum game.     

Bauer on Aid

Book reviewed in this article: Reality and Rhetoric. Studies in the Economics of Development. By P.T. Bauer     

An Analytical Investigation of the Bullwhip Effect

The Bullwhip Effect is problematic: order variability increases as orders propagate along the supply chain. The fundamental differential delay equations for a retailer's inventory reacting to a surge in demand are solved exactly. Much of the rich and complex inventory behavior is determined by the replenishment delay. The analytical solutions agree with numerical integrations and previous control theory results. Managerially useful ordering strategies are proposed. Exact expressions are derived for the retailer's orders to the manufacturer, and the Bullwhip Effect arises naturally. The approach is quite general and applicable to a wide variety of supply chain problems.     

Solving the Labour Problem Among Professional Workers in the UK Public Sector: Organisation Change and Performance Management

This paper draws together evidence from across the UK public services to present an analysis of public sector organisational change that has been observed and experienced since the 1980s. The argument is that the pattern of persistent change, packaged as a myriad of reform measures, has been centrally concerned with solving the labour problem—low worker productivity and managers not being able to manage—in what are labour intensive services. The now familiar managerial discourse of empowerment and high commitment working practices is highlighted and express linkages are drawn between such themes and the day-to-day practices of performance and human resource management, seen here to be key levers in solving the same labour problem through a reduction in the quality of working lives of many public service workers.     

Probabilistic Inversion in Priority Setting of Emerging Zoonoses

This article presents methodology of applying probabilistic inversion in combination with expert judgment in priority setting problem. Experts rank scenarios according to severity. A linear multi‐criteria analysis model underlying the expert preferences is posited. Using probabilistic inversion, a distribution over attribute weights is found that optimally reproduces the expert rankings. This model is validated in three ways. First, consistency of expert rankings is checked, second, a complete model fitted using all expert data is found to adequately reproduce observed expert rankings, and third, the model is fitted to subsets of the expert data and used to predict rankings in out‐of‐sample expert data.     

Ranked Items Auctions and Online Advertisement

We study auctions for a set of commonly‐ranked items where each buyer has unit demand. This setting has promising applications in areas such as keyword auctions in the search engine advertising industry, the sale of quality‐ranked raw materials, etc. An auction mechanism suitable for this setting is the simultaneous pooled auction (SPA), where each bidder simultaneously submits a single bid and is allocated an object based on the rank of his bid among all the bids. We study how to improve the seller's expected revenue by enforcing a reserve price in an SPA. We find that the use of an appropriate reserve price may significantly increase the seller's revenue, especially when the number of items for sale is relatively large compared to the number of participating bidders. One inherent problem in the SPA is that some bidders may incur ex post losses; that is, they pay more than what they value the received objects. We propose a tailored VCG mechanism that generates the same expected revenue as the SPA does, while bidders do not incur any ex post loss. We also discuss the potential applications of this research to keyword auctions.     

The Use of Simulation to Reduce the Domain of “Black Swans” with Application to Hurricane Impacts to Power Systems

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.