Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with ‘bottom-up’ simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.

We are increasingly exposed to applications that embed some sort of artificial intelligence (AI) algorithm, and there is a general belief that people trust any AI-based product or service without question. This study investigated the effect of personality characteristics (Big Five Inventory (BFI) traits and locus of control (LOC)) on trust behaviour, and the extent to which people trust the advice from an AI-based algorithm, more than humans, in a decision-making card game. One hundred and seventy-one adult volunteers decided whether the final covered card, in a five-card sequence over ten trials, had a higher/lower number than the second-to-last card. They either received no suggestion (control), recommendations from what they were told were previous participants (humans), or an AI-based algorithm (AI). Trust behaviour was measured as response time and concordance (number of participants' responses that were the same as the suggestion), and trust beliefs were measured as self-reported trust ratings. It was found that LOC influences trust concordance and trust ratings, which are correlated. In particular, LOC negatively predicted beyond the BFI dimensions trust concordance. As LOC levels increased, people were less likely to follow suggestions from both humans or AI. Neuroticism negatively predicted trust ratings. Openness predicted reaction time, but only for suggestions from previous participants. However, people chose the AI suggestions more than those from humans, and self-reported that they believed such recommendations more. The results indicate that LOC accounts for a significant variance for trust concordance and trust ratings, predicting beyond BFI traits, and affects the way people select whom they trust whether humans or AI. These findings also support the AI-based algorithm appreciation.

Games are played for entertainment and have the ability to stimulate a variety of moods during gameplay, including happiness. Serious or applied games are created and used to serve a specific purpose rather than for pure entertainment. The relationship between mood and task efficiency has been investigated in psychology with contrasting results, and it also appears that there is a relationship between mood and learning. Players' mood and learning efficiency as a consequence of playing two serious games involving navigation in a virtual environment as the main action of gameplay, but with different learning objectives, have been investigated. The first game taught the route to a real world destination, while the second trained players to perform a religious ritual. The pre- and post-gameplay mood of 52 players were noted. It was found that both serious games helped the players developing a pleasant overall mood and significantly increased the self-reported happiness score in the post-questionnaire. It was also discovered that players who felt happier spent more time learning and that women performed better when they were happier. Besides, younger learners tend to obtain a higher learning performance score than other age categories. Navigation-based serious games induced positive overall mood in players.There was a significant increase in self-reported happiness.Happier players spent more time learning.Happier women gained higher learning performance score after playing.Younger players obtained higher learning performance score with serious gameplay.

Within the planning process, there is a recent trend to engage with stakeholders. Traditionally, visualisation media such as plans, sections, and physical models have been used to aid communication of planning proposals, yet technological advances now allow for virtual interactive, three-dimensional visualisations of proposals to be created. It is unclear whether these new technologies will usurp or augment the more traditional media. In an experimental setting, a set of participants in a collaborative planning workshop were introduced to a range of different visualisation media for a riverside flood-defence proposal and asked to discuss the proposal. Video recordings were made of the workshop and a questionnaire was completed by each participant. A method for capturing the time usage of each media type is presented. This shows that plans and interactive eye-level walkthroughs were the most used media types, but that user-based assessment of media might not match observed usage. These results lead to a discussion about visualisation media that should be created and further research to support the participatory planning process.

Virtual Reality (VR) has been used for some time for training various skills. The results obtained are generally very reassuring, suggesting that Virtual Environments (VEs) are an effective new kind of educational tool. There are some, however, who argue that there are cases in which a 2D approach would achieve the same training effect. The literature suggests that the key features that distinguish VR from other training approaches is the sense of presence, which provides a first-person experience of the world. Usually, real world learning is multisensory and gives ownership and control over the experience, increases learner motivation, and triggers the construction of knowledge. Despite technical limitations, a VE is the most effective form of information technology for providing multisensory experience including visual, auditory, and to some extend haptic and tactile cues. The sense of presence ensures that the perceived experience is interpreted as being real and makes it likely that skills learned in the VE will be transferred to the real world. We argue that for training time-limited decision-making skills, the learner should also have an opportunity to reflect on actions/strategies to improve performance. Therefore a virtual environment for training should also provide support for students to reflect on their performance. This paper describes a prototype training system that aims to support both construction of knowledge and cognitive learning. It is also intended to trigger a sense of presence as well as provide support mechanisms, not available in the real world, that the students can exercise to reflect on the training experience.