Keywords

Backcasting, Agent-Based Modelling, Decision Trees, Social Networks

Location

Session H4: Modeling for Low Carbon Economies

Start Date

19-6-2014 9:00 AM

End Date

19-6-2014 10:20 AM

Abstract

We report on agent-based modelling work in the LOCAW project (Low Carbon at Work: Modelling Agents and Organisations to Achieve Transition to a Low Carbon Europe). The project explored the effectiveness of various backcasting scenarios conducted with case study organisations in bringing about pro-environmental change in the workforce in the domains of transport, energy use and waste. The model used qualitative representations of workspaces in formalising each scenario, and decision trees learned from questionnaire responses to represent decision-making. We describe the process by which the decision trees were constructed, noting that the use of decision trees in agent-based models requires particular considerations owing to the potential use of explanatory variables in model dynamics. The results of the modelling in various scenarios emphasise the importance of structural environmental changes in facilitating everyday pro-environmental behaviour, but also show there is a role for psychological variables such as norms, values and efficacy. As such, the topology of social interactions is a potentially important driver, raising the interesting prospect that both workplace geography and organisational hierarchy have a role to play in influencing workplace pro-environmental behaviours.

 
Jun 19th, 9:00 AM Jun 19th, 10:20 AM

Empirical Agent-Based Modelling of Everyday Pro­environmental Behaviours at Work

Session H4: Modeling for Low Carbon Economies

We report on agent-based modelling work in the LOCAW project (Low Carbon at Work: Modelling Agents and Organisations to Achieve Transition to a Low Carbon Europe). The project explored the effectiveness of various backcasting scenarios conducted with case study organisations in bringing about pro-environmental change in the workforce in the domains of transport, energy use and waste. The model used qualitative representations of workspaces in formalising each scenario, and decision trees learned from questionnaire responses to represent decision-making. We describe the process by which the decision trees were constructed, noting that the use of decision trees in agent-based models requires particular considerations owing to the potential use of explanatory variables in model dynamics. The results of the modelling in various scenarios emphasise the importance of structural environmental changes in facilitating everyday pro-environmental behaviour, but also show there is a role for psychological variables such as norms, values and efficacy. As such, the topology of social interactions is a potentially important driver, raising the interesting prospect that both workplace geography and organisational hierarchy have a role to play in influencing workplace pro-environmental behaviours.