Robust design, Taguchi methods, Genetic algorithms, Desirability functions,

It is generally believed that concepts can be characterized by their properties (or features). When investigating concepts encoded in language, researchers often ask subjects to produce lists of properties that describe them (i.e., the Property Listing Task, PLT). These lists are accumulated to produce Conceptual Property Norms (CPNs). CPNs contain frequency distributions of properties for individual concepts. It is widely believed that these distributions represent the underlying semantic structure of those concepts. Here, instead of focusing on the underlying semantic structure, we aim at characterizing the PLT. An often disregarded aspect of the PLT is that individuals show intersubject variability (i.e., they produce only partially overlapping lists). In our study we use a mathematical analysis of this intersubject variability to guide our inquiry. To this end, we resort to a set of publicly available norms that contain information about the specific properties that were informed at the individual subject level. Our results suggest that when an individual is performing the PLT, he or she generates a list of properties that is a mixture of general and distinctive properties, such that there is a non-linear tendency to produce more general than distinctive properties. Furthermore, the low generality properties are precisely those that tend not to be repeated across lists, accounting in this manner for part of the intersubject variability. In consequence, any manipulation that may affect the mixture of general and distinctive properties in lists is bound to change intersubject variability. We discuss why these results are important for researchers using the PLT.

No abstract

In a previous article, we presented a genetic algorithm (GA), which finds solutions to problems of robust design in multivariate systems. Based on that GA, we developed a new GA that uses a new desirability function, based on the aggregation of the observed variance of the responses and the squared deviation between the mean of each response and its corresponding target value. Additionally, we also changed the crossover operator from a one-point to a uniform one. We used three different case studies to evaluate the performance of the new GA and also to compare it with the original one. The first case study involved using data from a univariate real system, and the other two employed data obtained from multivariate process simulators. In each of the case studies, the new GA delivered good solutions, which simultaneously adjusted the mean of each response to its corresponding target value. This performance was similar to the one of the original GA. Regarding variability reduction, the new GA worked much better than the original one. In all the case studies, the new GA delivered solutions that simultaneously decreased the standard deviation of each response to almost the minimum possible value. Thus, we conclude that the new GA performs better than the original one, especially regarding variance reduction, which was the main problem exhibited by the original GA.

Organizations that make use of computer information systems (CIS) are prototypical complex adaptive systems (CAS). This paper shows how an approach from Complexity Science, exploratory agent-based modeling (ABM), can be used to study the impact of two different modes of use of computer-mediated communication (CMC) on organizational culture (OC) and performance. The ABM includes stylized representations of (a) agents communicating with other agents to complete tasks; (b) an OC consisting of the distribution of agent traits, changing as agents communicate; (c) the effect of OC on communication effectiveness (CE), and (d) the effect of CE on task completion times, that is, performance. If CMC is used in a broad mode, that is, to contact and collaborate with many, new agents, the development of a strong OC is slowed, leading to decreased CE and poorer performance early on. If CMC is used in a local mode, repeatedly contacting the same agents, a strong OC develops rapidly, leading to increased CE and high performance early on. However, if CMC is used in a broad mode over longer time periods, a strong OC can develop over a wider set of agents, leading to an OC that is stronger than an OC which develops with local CMC use. Thus broad use of CMC results in overall CE and performance that is higher than is generated by local use of CMC. We also discuss how the dynamics generated by an ABM can lead to a deeper understanding of the behavior of a CAS, for example, allowing us to better design empirical longitudinal studies.