Decompositions of λ K v

In a series of 2 papers, Kang, Du and Tian solved the existence problem for G-decomposition of λ K _n when G is any simple graph with 6 vertices and 7 edges, except when G is the graph T=K ₄∪K ₂. Notice that a T-decomposition can be considered to be a Pairwise Balanced Design in which each block of size 4 has been matched to a block of size 2. In this paper we remove this exception for all λ≥2. The case when λ=1 is also addressed. This paper is written in honor of Frank Hwang on the occasion of his 65th birthday.     

An interactive three-stage model for mutual funds portfolio selection

The aim of this work is to be a useful instrument for helping finance practitioners on the selection of suitable mutual fund portfolios. The portfolio selection problem is characterized by imprecision and/or vagueness inherent in the required data and more generally, in the context where investors have to make decisions. In order to mitigate these problems, a three stage model has been proposed based on a multi-index model and considering several market scenarios described in an imprecise way by an expert. The proposed fuzzy model allows the Decision Maker to select, by means of an outranking method, a suitable portfolio taking into account the uncertainty related to the market scenarios and the imprecision and/or vagueness associated with the model data.     

Perceived Risks from Radiation and Nuclear Testing Near Semipalatinsk, Kazakhstan: A Comparison Between Physicians, Scientists, and the Public

Determining the difference in perception of risk between experts, or more educated professionals, and laypeople is important so that a potential hazard can be effectively communicated to the public. Many surveys have been conducted to better understand the difference between expert and public opinions, and often laypeople exhibit higher perceptions of risk to hazards in comparison to experts. This is especially true when health risk is due to radiation, nuclear power, and nuclear waste. This article focuses on one section of a risk perception survey given to two groups of individuals with a more specialized education (scientists and physicians) and laypeople (villagers) in the Semipalatinsk region of Kazakhstan. All of these groups live near the former Soviet nuclear test site. Originally, it was expected that the scientists and physicians would have similar perceptions of radiation risk, while the public perceptions would be higher, but this was not always the case. For example, when perceptions of risk pertain to the health impacts of nuclear testing or the dose-response nature of radiation exposure, the physicians tend to agree with the laypeople, not the scientists. The villagers are always the most risk-averse group, followed by the physicians and then the scientists. These differences are likely due to different frames of reference for each of the populations.     

AN ANALYSIS OF SHAREHOLDER AGREEMENTS

Shareholder agreements govern the relations among shareholders in privately held firms, such as joint ventures and venture capital‐backed companies. We provide an economic explanation for key clauses in such agreements—namely, put and call options, tag‐along and drag‐along rights, demand and piggy‐back rights, and catch‐up clauses. In a dynamic moral hazard setting, we show that these clauses can ensure that the contract parties make efficient ex ante investments in the firm. They do so by constraining renegotiation. In the absence of the clauses, ex ante investment would be distorted by unconstrained renegotiation aimed at (i) precluding value‐destroying ex post transfers, (ii) inducing value‐increasing ex post investments, or (iii) precluding hold‐out on value‐increasing sales to a trade buyer or the IPO market. (JEL: G34)     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Validation of the Learning Transfer System Inventory (LTSI) in the Korean corporate context

Lack of a valid instrument to measure learning transfer predictors has been the major obstacle hindering Human Resource Development (HRD) professionals from moving forward. This problem is one of the lingering HRD issues in South Korea, in which human resources have been strategically emphasized as a critical asset due to the scarcity of natural resources. To address this issue, this study aims at testing the validity and reliability of the data collected with the Korean Learning Transfer System Inventory (LTSI) Version 4. A sample of 753 managers from 16 Korean industries was divided into two subsamples for exploratory and confirmatory factor analyses. Reliability and the effect of common method variance on the factor structure were examined, with the results suggesting that the LTSI is valid for use in the Korean industry.     

Predicting the Risk of Biological Invasions Using Environmental Similarity and Transport Network Connectedness

Understanding the risk of biological invasions associated with particular transport pathways and source regions is critical for implementing effective biosecurity management. This may require both a model for physical connectedness between regions, and a measure of environmental similarity, so as to quantify the potential for a species to be transported from a given region and to survive at a destination region. We present an analysis of integrated biosecurity risk into Australia, based on flights and shipping data from each global geopolitical region, and an adaptation of the “range bagging” method to determine environmental matching between regions. Here, we describe global patterns of environmental matching and highlight those regions with many physical connections. We classify patterns of global invasion risk (high to low) into Australian states and territories. We validate our analysis by comparison with global presence data for 844 phytophagous insect pest species, and produce a list of high‐risk species not previously known to be present in Australia. We determined that, of the insect pest species used for validation, the species most likely to be present in Australia were those also present in geopolitical regions with high transport connectivity to Australia, and those regions that were geographically close, and had similar environments.     

Let's Call it Quits: Break‐Even Effects in the Decision to Stop Taking Risks

“Chasing” behavior, whereby individuals, driven by a desire to break even, continue a risky activity (RA) despite incurring large losses, is a commonly observed phenomenon. We examine whether the desire to break even plays a wider role in decisions to stop engaging in financially motivated RA in a naturalistic setting. We test hypotheses, motivated by this research question, using a large data set: 707,152 transactions of 5,379 individual financial market spread traders between September 2004 and April 2013. The results indicate strong effects of changes in wealth around the break‐even point on the decision to cease an RA. An important mediating factor was the individual's historical long‐term performance. Those with a more profitable trading history were less affected by a fall in cash balance below the break‐even point compared to those who had been less profitable. We observe that break‐even points play an important role in the decision of nonpathological risk takers to stop RAs. It is possible, therefore, that these nonpathological cognitive processes, when occurring in extrema, may result in pathological gambling behavior such as “chasing.” Our data set focuses on RAs in financial markets and, consequently, we discuss the implications for institutions and regulators in the effective management of risk taking in markets. We also suggest that there may be a need to consider carefully the nature and role of “break‐even points” associated with a broader range of nonfinancially‐focused risk‐taking activities, such as smoking and substance abuse.     

Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission

Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.