Url https://cimne.com/sgp/rtd/Project.aspx?id=108
LogoEntFinanc LogoPlan LogoSubPrograma
Acronym xDELIA
Project title Excellence in Public and Professional Decision Making: Boosting Deliberate Practice and Handling biases through inmersive cognitive and emotional reinforcement strategies & tools
Official Website https://webgate.ec.europa.eu/nef/
Reference 231830
Principal investigator Gilbert PEFFER - gilbert@cimne.upc.edu
Start date 01/03/2009 End date 31/05/2012
Coordinator CIMNE
Consortium members
  • FZI
  • OU
  • EUR
  • UNIVBRIS
  • SAXO
  • BTH
Program FP7 (2007-2013) Call FP7-ICT-2007-3
Subprogram COOPERATION Category Europeo
Funding body(ies) EC Grant $323,803.01
Abstract Focusing on a broad range of subjects from traders and private investors to ordinary members of the public, xDelia will exploit new and emerging technologies to explore financial decision-making processes, including the role of emotion in people’s decisions. Much of financial training has, to date, focused purely on imparting knowledge and increasing people’s understanding. However, people often may have appropriate knowledge, but despite this they go on to be ruled by their attitudes, habits, or emotional states. Investigating this, the project is to develop new, technologically supported approaches to training and support for non-formal and informal learning in real-world settings to tackle the challenges faced by people and businesses when they make financial decisions. Spanning three years, the project will use cutting-edge gaming and sensor technologies throughout. Game based technologies are becoming proven as a method of learning, particularly as they can place people in virtual situations, and the xDelia project will be employing these to analyse behavioural patterns and to support the non-formal and informal learning process. Alongside this, wearable sensor equipment detecting pulse rates and skin-inductance will help build a picture of a person’s emotional state at the point of, and in the run up to, a financial decision. Moreover, eye tracking and automated event logging completes this picture with behavioural profile data. This technology will have three principal roles. First behavioural and emotion state data will be used to extend our understanding of judgmental biases or emotion regulation in novice and expert decision-makers. Second, the collected data will provide evidence of engagement with learning game approaches developed in the project. Third, such data can provide an important source of feedback in learning support technologies where the learning goal concerns improvements in emotion regulation or reduction in the impact of decision-biases.