Meetings are important activities that influence the achievement of team objectives. The characteristics of creative meetings must be clarified to enhance creativity. This study conducted two analyses with the aim of identifying participant behaviors that contribute to creativity by quantitatively analyzing creativity in actual meeting data. First, utterances in meetings were annotated using 11 categories, and frequently occurring dialogue patterns were examined both overall and for each team through pattern analysis. Second, regression analysis was conducted to examine the relationship between the frequency of these frequent patterns and a quantitative creativity index constructed based on text sentiment. In the first analysis, the most frequently observed type of interaction in meetings was the exchange of opinions. Additionally, communication characteristics specific to each team were identified. However, no significant relationships were found between the creativity index and the dialogue patterns in the second analysis. Further analysis, including a reconsideration of the method of aggregating conversation patterns, is required in future work.

Modeling and computer simulation are effective tools for predicting system behavior. However, simulation software (i.e., source codes), particularly those on social systems, tends to be complicated and un-reusable owing to the complexity, diversity, and ambiguity of the target systems. This makes it difficult to develop, validate, and explain simulation software. One possible solution is to formalize a mathematically rigorous simulation theory and incorporate it directly into a functional programming language. If the mathematical counterpart of the code is clear, coders can easily explain, validate, and extend it. In this study, we define "monoidal system" as the basis to describe the physical and social system that will be modeled. We also conducted an experiment to demonstrate that the introduction of a monoidal system could reduce program complexity in software development.

During the Covid-19 pandemic, more guidelines were created to teach people how to facilitate meetings online, but few were designed from a cognition-oriented perspective. Additionally, solving complex problems is essential in many occupations. However, the influence of online and face-to-face discussion formats on the performance in complex problem-solving tasks is unclear, even though remote working has become common over the past several few years. Hence, this study aims to answer two research questions: (a) Does problem-solving performance differ between online and face-to-face meetings? and (b) Does facilitation improve problem-solving performance when different formats are used? We conducted experiments with 40 groups using a 2 × 2 factorial design, which were controlled for both facilitation and format. Each group comprised two randomly selected participants, and each problem-solving discussion lasted between 1.5–2 h. The obtained evidence showed that format can influence the performance of balancing intercorrelated factors in a complex scenario, but it does not affect the performance of achieving a predefined goal. Instead, it we found that facilitation is helpful for achieving a predefined goal. Based on the results obtained, we propose future design directions for problem-solving centric computer-supported cooperative work systems from a cognition-oriented perspective.

During the pandemic, people gradually realized the limitations of remote problem-solving collaboration. Thus far, the impact of online platforms on problem-solving cognitive processes has not been thoroughly investigated, despite the active use of such processes in remote work. This study analyzes the differences in cognitive patterns between online and offline problem-solving meetings. Discussion data containing approximately 5,000 utterances were subjected to entropy and content analysis. The results showed a distinct difference in cognitive patterns in discussions conducted on various platforms, as online discussions require more cognitive effort for technological equipment operation. Online meeting solutions were found to be less developed than those generated in offline meetings. According to the results obtained, we propose a series of high-level platform-neutral steps for small-scale problem-solving. The findings not only contribute to building platform-specific facilitation guidelines, but they also aid research on human-computer interaction from the perspective of online discussion platform designs and methods for cognitive process evaluation.

One of the typical causes of errors in team cooperative activities, such as in central control rooms of power plants and cockpits in aircrafts, is conflicts among team members' intentions. If mutual awareness and communication were perfectly established and maintained, conflicts could be detected and recovered by team members; however, this does not happen in practice. In this paper, we provide a framework for detecting conflicts among team members' intentions based on team intention inference, aiming to make machines function as a coordinator for cooperative activities. In previous work, we developed a method for team intention inference based on a definition of 'we-intention'. We-intention is other-regarding intentions relating to situations in which some agents act together, and is represented as a set of individual intentions and mutual beliefs. In this framework, a conflict can be defined as a set of individual intentions and false beliefs (undesired procedures), and detected by searching for such combinations. We applied the proposed method to the operation of a plant simulator operated by a two-person team, and it was confirmed through an experiment that this method could list candidates for conflicts by type and set the actual conflict high in priority in the tested context.