An Actor's Knowledge and Intent Are More Important in Evaluating Moral Transgressions Than Conventional Transgressions

An actor's mental states—whether she acted knowingly and with bad intentions—typically play an important role in evaluating the extent to which an action is wrong and in determining appropriate levels of punishment. In four experiments, we find that this role for knowledge and intent is significantly weaker when evaluating transgressions of conventional rules as opposed to moral rules. We also find that this attenuated role for knowledge and intent is partly due to the fact that conventional rules are judged to be more arbitrary than moral rules; whereas moral transgressions are associated with actions that are intrinsically wrong (e.g., hitting another person), conventional transgressions are associated with actions that are only contingently wrong (e.g., wearing pajamas to school, which is only wrong if it violates a dress code that could have been otherwise). Finally, we find that it is the perpetrator's belief about the arbitrary or non‐arbitrary basis of the rule—not the reality—that drives this differential effect of knowledge and intent across types of transgressions.     

Inference in the Wild: A Framework for Human Situation Assessment and a Case Study of Air Combat

Situation awareness is a key construct in human factors and arises from a process of situation assessment (SA). SA comprises the perception of information, its integration with existing knowledge, the search for new information, and the prediction of the future state of the world, including the consequences of planned actions. Causal models implemented as Bayesian networks (BNs) are attractive for modeling all of these processes within a single, unified framework. We elicited declarative knowledge from two Royal Australian Air Force (RAAF) fighter pilots about the information sources used in the identification (ID) of airborne entities and the causal relationships between these sources. This knowledge was represented in a BN (the declarative model) that was evaluated against the performance of 19 RAAF fighter pilots in a low‐fidelity simulation. Pilot behavior was well predicted by a simple associative model (the behavioral model) with only three attributes of ID. Search for information by pilots was largely compensatory and was near‐optimal with respect to the behavioral model. The average revision of beliefs in response to evidence was close to Bayesian, but there was substantial variability. Together, these results demonstrate the value of BNs for modeling human SA.     

A Fuzzy Topsis Method for Prioritized Aggregation in Multi‐Criteria Decision Making Problems

Aggregation in a decision making environment requires the fusion of opinions of a group of decision makers. The group of decision makers are required to analyse a set of interrelated criteria that are usually measured on a linguistic scale. This process requires, in many instances, to capture experts experience, intuition and thinking that are traditionally expressed in a linguistic fashion rather than a numerical fashion. Furthermore, the necessity of considering the relationship between the criteria to the overall decision must be considered by the group of decision makers. This paper extends the application of fuzzy numbers, fuzzy relative importance scores (FRIS), fuzzy relative weights (FRW) and the fuzzy technique of order preference by similarity to ideal solution (TOPSIS) in prioritized aggregation. This extension provides a mean to systematically aggregate a group of decision makers' views for a set of interrelated criteria that are measured on a linguistic scale. First, an overview of the application of fuzzy numbers and the characteristics of aggregating fuzzy numbers in multi‐criteria decision making problems are presented. Then, the application of TOPSIS in fuzzy environments is presented. Next, past research is highlighted to present prioritized aggregation and the different aggregation operators' classes. Subsequently, a new prioritized aggregation method is presented. This method utilizes fuzzy TOPSIS with prioritized aggregation in fuzzy environments. Finally, the fuzzy prioritized aggregation method presented in this paper is applied on an actual case study. According to the results, the method presented in this paper provides a systematic approach to capture the uncertainty and imprecision associated with quantifying linguistic measurements in multi‐criteria decision making problems. Furthermore, it considers the relationship between the set of linguistically measured criteria undergoing prioritized aggregation in a fuzzy environment. Lastly, findings, conclusions and future work are presented. Copyright © 2016 John Wiley & Sons, Ltd.     

An Approach for Determining the Mix between Human and Automated Service Providers

With the growing popularity of self‐service technologies (SST), businesses have to determine how to combine traditional human service with SST technologies. These mix problems have not received adequate attention in the research literature. This paper offers a decision model for solving mix problems between human service channels and SSTs. The decision problem cannot be determined as a traditional max or min problem and it is fuzzy. In fuzzy problems, the decision maker (DM) is considered as the focal point with the relevant knowledge and preferences and therefore, the objective is to maximize the DM's preferences. This paper develops a decision support system for eliciting a DM's value function when the service channels are considered to be substitutes, but when the rate of substitution may change and then uses a linear‐fractional model to fit the DM's data and optimize the DM's preferences. A service mix problem for a grocery store with human service providers and SSTs is used to demonstrate how the decision model can be employed to analyze and solve service mix decision making problems. Copyright © 2014 John Wiley & Sons, Ltd.     

Coparenting and Children's School Readiness: A Mediational Model

We examined the long‐term direct and indirect links between coparenting (conflict, communication, and shared decision‐making) and preschoolers' school readiness (math, literacy, and social skills). The study sample consisted of 5,650 children and their biological mothers and fathers who participated in the Early Childhood Longitudinal Study‐Birth Cohort. Using structural equation modeling and controlling for background characteristics, we found that our conceptual model of the pathways from coparenting to child outcomes is structurally the same for cohabiting and married families. Controlling for a host of background characteristics, we found that coparenting conflict and shared decision‐making were negatively and positively, respectively, linked to children's academic and social skills and co‐parental communication was indirectly linked to academic and social skills through maternal supportiveness. Coparenting conflict was also indirectly linked to children's social skills through maternal depressive symptoms. The overall findings suggest that for both cohabiting and married families, the context of conflicted coparenting may interfere with the development of children's social competencies and academic skills, whereas collaborative coparenting promotes children's school readiness because mothers are more responsive to their children's needs. These findings have implications for programs aimed at promoting positive family processes in cohabiting and married families.     

Seeing is believing: Priors,trust, and base rate neglect

Kahneman and Tversky (1973) described an effect they called ‘insensitivity to prior probability of outcomes’, later dubbed base rate neglect, which describes people’s tendency to underweight prior information in favor of new data. As probability theory requires that prior probabilities be taken into account, via Bayes’ theorem, the fact that most people fail to do so has been taken as evidence of human irrationality and, by others, of a mismatch between our cognitive processes and the questions being asked (Cosmides & Tooby, 1996). In contrast to both views, we suggest that simplistic Bayesian updating using base rates is not necessarily rational. To that end, we present experiments in which base rate neglect is often the right strategy, and show that people’s base rate usage varies systematically as a function of the extent to which the data that make up a base rate are perceived as trustworthy.     

Reducing self‐control depletion effects through enhanced sensitivity to implementation: Evidence from fMRI and behavioral studies

Research suggests self-control relies on a limited set of resources that can be diminished by use. Recent theories posit that there are two stages of self-control: recognizing the need for control and implementing controlled responses. We conducted a functional magnetic resonance imaging experiment and an intervention experiment to investigate whether one or both stages were affected by the prior exercise of self-control. Results from both experiments indicated that only the implementation stage was affected. Further, we demonstrate that self-control can be increased by an intervention designed to boost implementation, as opposed to the recognition of the need to control one's responses.     

Integration of Analytic Hierarchy Process with Regression Analysis to Identify Attractive Locations for Market Expansion

Selection of new geographies in which to expand is a key decision for businesses aspiring to go beyond the opportunities in the existing markets. The conventional approaches of market selection can only provide a set of systematic steps for problem solving without considering the relationships between the decision factors. Decision models based on statistical techniques are able to examine the relationship between decision factors but are unable to effectively assist decision makers in identifying the most promising market, particularly in terms of prioritizing across decision factors. Analytic Hierarchy Process (AHP) is a commonly used approach for choosing alternatives by prioritizing across multiple decision factors. The typical AHP modelling requires knowledge of criteria and/or alternatives along with their relative weights, generally elicited from field experts. Quite often, firms encounter situations where decision makers are aware of only the overall objective and a set of earmarked geographies for setting up market locations while being relatively unaware of decision criteria and relative weights. This precludes using AHP to identify promising market locations. This paper conceptualizes a market selection decision model that integrates AHP with statistical modelling techniques to identify the attractive market locations for the purpose of expansion. The model first uses principal component analysis and multiple regression to determine significant decision criteria and their weights. Thereafter, it applies AHP to prioritize the market locations across the decision criteria. This integrative approach is illustrated for identifying the attractive locations in rural markets for a steel firm in India. The major advantage of this approach is that unlike the existing models, it works in situations when firms have not enough knowledge about factors for evaluating alternative market locations. Another key advantage of the proposed model is that of economizing resources for data collection on variables representing decision factors. Copyright © 2012 John Wiley & Sons, Ltd.