Change is inevitable in this fast-moving world. As the environment and people’s needs continuously change, so must the project. In our previous work, we developed an agent-based model of human collaboration that incorporates individual personalities. In this work, we applied a genetic algorithm to select the optimal personality combinations of a team in order to cope with different types of project change. We studied change in the context of three types of tasks: disjunctive (team performance is the performance achieved by the best performing individual), conjunctive (team performance is the performance achieved by the worst performing individual), and additive (team performance is the total performance of the group). Results reveal that different compositions of team personalities are suitable for different dynamic problems and task types. In particular, optimal personalities found for static problems differ from optimal personalities found for dynamic problems.

Collaboration is an essential aspect of human interaction. Despite being mutually beneficial to everyone involved, it often fails due to behaviour differences as individuals process information, form opinions, and interact with each other, especially when their task contains uncertainty. Thus, to understand collaboration on noisy problems effectively, it is necessary to consider the psychology of the individuals involved. We propose an agent-based model of collaboration that incorporates human psychology. We abstract the shared goal as a shared optimisation task, and model personality differences as strategies for moving within, interpreting and sharing information about the solution space. Although used to explore a specific hypothesis here, the model is psychology theoryagnostic and problem-independent and can also be used to investigate other tasks and different psychology theories.

Abstract concepts are rules about relationships such as identity or sameness. Instead of learning that specific objects belong to specific categories, the abstract concept of same/different applies to any objects that an organism might encounter, even if those objects have never been seen before. In this paper we investigate learning of abstract concepts by computer, in order to recognize same/different in novel data never seen before. To do so, we integrate recursive self-organizing maps with the data they are processing into a single graph to enable a brain-like self- adaptive learning system. We perform experiments on simple same/different datasets designed to resemble those used in animal experiments and then show an example of a practical application of same/different learning using the approach.