Split‐Award Procurement Auctions—Can Bayesian Equilibrium Strategies Predict Human Bidding Behavior in Multi‐Object Auctions?

We analyze if and when symmetric Bayes Nash equilibrium predictions can explain human bidding behavior in multi‐object auctions. We focus on two sealed‐bid split‐award auctions with ex ante split decisions as they can be regularly found in procurement practice. These auction formats are straightforward multi‐object extensions of the first‐price sealed‐bid auction. We derive the risk‐neutral symmetric Bayes Nash equilibrium strategies and find that, although the two auction mechanisms yield the same expected costs to the buyer, other aspects of the two models, including the equilibrium bidding strategies, differ significantly. The strategic considerations in these auction formats are more involved than in single‐lot first‐price sealed‐bid auctions, and it is questionable whether expected utility maximization can explain human bidding behavior in such multi‐object auctions. Therefore, we analyzed the predictive accuracy of our equilibrium strategies in the laboratory. In human subject experiments we found underbidding, which is in line with earlier experiments on single‐lot first‐price sealed‐bid auctions. To control for regret, we organize experiments against computerized bidders, who play the equilibrium strategy. In computerized experiments where bid functions are only used in a single auction, we found significant underbidding on low‐cost draws. In experiments where the bid function is reused in 100 auctions, we could also control effectively for risk aversion, and there is no significant difference of the average bidding behavior and the risk‐neutral Bayes Nash equilibrium bid function. The results suggest that strategic complexity does not serve as an explanation for underbidding in split‐award procurement auctions, but risk aversion does have a significant impact.     

An integrated environmental risk assessment framework for coal-fired power plants: A fuzzy logic approach

This study quantifies the environmental risk of a coal-fired thermal power plant during operation by using environmental monitoring data, site surveys, and documented evidence. The following criteria are assessed: emissions (CO, SO₂, NO_x, PM₁₀), impact on aquatic ecosystem (fish protection at cooling water intake and cooling water discharge temperature), and waste management (fly ash and bottom ash). Fuzzy sets were defined for each criterion, taking environmental regulatory context as an expert judgment. A survey was conducted with multiple stakeholders to determine the relative importance of risk factors. The survey results showed that the most concerned risks are SO₂ and NO_x emissions. The proposed method estimates the risk of each environmental criterion separately and then accumulates them into an environmental risk index (ERI). Accordingly, we assessed the Catalagzi coal-fired power plant, which has been in operation on the Black Sea coast in northwestern Turkey. For this case study, the ERI resulted in a value of 0.78 (on a scale of 0–1), showing high environmental risk to the facility. Moreover, the applicability of the proposed framework was tested in several existing coal-fired power plants using simultaneous measurements. All studied coal-fired power plants in Turkey have unacceptable pollutants (PM₁₀, SO₂, and NO_x) concentration levels indicating high health risk potential. The application of the integrated environmental risk assessment framework showed that new environmental regulations are needed in Turkey to specify more strict emission limits and to monitor CO₂, fine particulate matter emissions, cooling water discharge, and fish protection at cooling water intake.     

Effects of heart rate reduction with ivabradine on the international ındex of erectile function (IIEF-5) in patients with heart failure

Introduction: The presence of concomitant erectile dysfunction (ED) with heart failure (HF) is not surprising, because endothelial dysfunction is pathophysiologic signature of both ED and HF. ED significantly and adversely affects quality of life in patients with HF. It was demonstrated that ivabradine treatment can improve endothelial function and ED in experimental models. In this study, we aimed to determine the effect of ivabradine treatment on ED in patients with HF via International Index of Erectile Function (IIEF-5) questionaire.

Material and methods: Consequently, 29 patients, between 18 and 70?years of age, male with chronic HF known for at least 1?year, New York Heart Association functional class I–II, left ventricule ejection fraction less than 40%, in sinus rhythm with a resting HR of at least 70 beats per minute (b.p.m.), who were intended to be treated with ivabradine according to the decision of their physicians were evaluated to determine ED. We used the Turkish version of the IIEF-5 questionnaire to evaluate ED on the last 6-month period. Twenty-four of 29 patients who scored ≤21 were considered to have ED and included to the study. IIEF-5 scores for each question and domains were calculated for all responders at baseline and at 6-month follow-up visit in order to determine any effect of ivabradine treatment on ED in patients with HF.

Results: According to the data of survey, Cronbach's alpha coeffient for all of the patients who were included into the study were 0.84 and detected highly reliable. IEFF-5 questionnaire scores increased significantly (p?=?.003) after the ivabradine treatment, on the contrary, significant decrease in HR was revealed as expected. HR is decreased steadily after ivabradine treatment and mean decrease in HR was 11.5?±?9.4 in this study population. Likewise, negative correlation was demonstrated between decrease in HR (p?p?=?.003).

Conclusion: Although lack of patients with HF have been evaluated in this study population, initial results seem promising that ivabradine has favorable effects on ED. These findings were postulated to be dependent exclusively on HR reduction. As a sequel, cardiologist should avoid neglecting ED to improve medical compliance as well as quality of life in patients with heart failure. This pilot study provide some data for further randomized controlled studies.     

Social Stratification and Consumption Patterns in Turkey

In this article, by analyzing consumption practices of Turkish households, I attempted to identify whether there are distinctions between different social classes in Turkey. Stated another way, I assessed and explored the impact of socio-economic forces on consumption patterns, taste and lifestyle. In doing so, I tested emprically, two theoretical approaches, Bourdieu’s “reproduction theory” and Giddens “class Structuration thesis”. A total of eight dependent variables are analyzed in terms of the linkages between those selected consumption items and social structure. In general, the emprical findings indicated that the intersection and reinforcement of social class variables, such as income, education, occupation, sector, and neighborhood differentiation, determined consumption patterns and lifestyle differences in Turkey.     

On representation of monotone preference orders in a sequence space

In this paper we investigate the relation between scalar continuity and representability of monotone preference orders in a sequence space. Scalar continuity is shown to be sufficient for representability of a monotone preference order and easy to verify in concrete examples. Generalizing this result, we show that a condition, which restricts the extent of scalar discontinuity of a monotone preference order, ensures representability. We relate this condition to the well-known order dense property, which is both necessary and sufficient for representability.     

Using Real‐Time Decision Tools to Improve Distributed Decision‐Making Capabilities in High‐Magnitude Crisis Situations*

Multi‐organizational collaborative decision making in high‐magnitude crisis situations requires real‐time information sharing and dynamic modeling for effective response. Information technology (IT) based decision support tools can play a key role in facilitating such effective response. We explore one promising class of decision support tools based on machine learning, known as support vector machines (SVM), which have the capability to dynamically model and analyze decision processes. To examine this capability, we use a case study with a design science approach to evaluate improved decision‐making effectiveness of an SVM algorithm in an agent‐based simulation experimental environment. Testing and evaluation of real‐time decision support tools in simulated environments provides an opportunity to assess their value under various dynamic conditions. Decision making in high‐magnitude crisis situations involves multiple different patterns of behavior, requiring the development, application, and evaluation of different models. Therefore, we employ a multistage linear support vector machine (MLSVM) algorithm that permits partitioning decision maker response into behavioral subsets, which can then individually model and examine their diverse patterns of response behavior. The results of our case study indicate that our MLSVM is clearly superior to both single stage SVMs and traditional approaches such as linear and quadratic discriminant analysis for understanding and predicting behavior. We conclude that machine learning algorithms show promise for quickly assessing response strategy behavior and for providing the capability to share information with decision makers in multi‐organizational collaborative environments, thus supporting more effective decision making in such contexts.