Using groups to support judgmental parameter estimation VISCONS: 'Eyeballing' to capture a quantified group consensus

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.08.015 ⇑ Corresponding author. Tel.: +44 (0) 1752 585635; E-mail addresses: [email protected], ca [email protected] (J. Moizer), shaofeng.liu@ply Whilst the concept of a learning curve is well established, within organisational settings the coordinates of points on the curve often remain unmeasured. Devon and Cornwall Police are a UK police force that is seeking to better allocate officer resource to high volumes of crime demand through understanding the relationship between levels of officer learning and development and the time required to resolve crimes. Devon and Cornwall Police subscribed to building a system dynamics simulation model of officer resourcing, within which an aggregate learning curve would convert levels of available officer experience into time required to resolve volume crime. Fragmented learning curve data meant that judgmental data needed to be elicited from expert practitioners in order to establish a consensus based measure of bivariate relationships. VISCONS is a judgmental method that was developed to help Devon and Cornwall Police quantify these relationships in a group setting. Three components are synergised into the VISCONS approach, namely: visual or graphical elicitation, parameterisation and consensus through group model building. The method brings together expert practitioners to arrive at a single shared view of the quantified bivariate relationship from a set of individual perspectives. Experts are involved in sketching their views onto acetates before overlaying them into a set of agreed parameter values. Experts do not require any knowledge of system dynamics simulation techniques, as they are facilitated in a single workshop to represent and amalgamate different views and perspectives on the parameterisation problem under consideration. The group is able to validate resulting bivariate parameter values using an efficient graphical method of data capture. VISCONS can be used for parameter capture beyond the scope of learning curves or system dynamics. 2012 Elsevier Ltd. All rights reserved.