Why so serious? Theorising playful model-driven group decision support with situated affectivity

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.An integrative approach to theorising behavioural, affective and cognitive processes in modeldriven group decision support (GDS) interventions is needed to gain insight into the (micro-)processes by which outcomes are accomplished. This paper proposes that the theoretical lens of situated affectivity, grounded in recent extensions of scaffolded mind models, is suitable to understand the performativity of affective micro-processes in model-driven GDS interventions. An illustrative vignette of a humorous micro-moment in a group decision workshop is presented to reveal the performativity of extended affective scaffolding processes for group decision development. The lens of situated affectivity constitutes a novel approach for the study of interventionist practice in the context of group decision making (and negotiation). An outlook with opportunities for future research is offered to facilitate an integrated approach to the study of cognitive-affective and behavioural micro-processes in model-driven GDS interventions.This work was supported in part by the EU FP7-ENERGY- SMARTCITIES-2012 (314277) project STEEP (Systems Thinking for Comprehensive City Efficient Energy Planning

Strategic Dialogical Argumentation using Multi-Criteria Decision Making with Application to Epistemic and Emotional Aspects of Arguments

Participants in dialogical argumentation often make strategic choices of move, for example to maximize the probability that they will persuade the other opponents. Multiple dimensions of information about the other agents (e.g., the belief and likely emotional response that the other agents might have in the arguments) might be used to make this strategic choice. To support this, we present a framework with implementation for multi-criteria decision making for strategic argumentation. We provide methods to improve the computational viability of the framework, and analyze these methods theoretically and empirically. We finally present decision rules supported by the psychology literature and evidence using human experiments