An Investigation of Regulatory Decision Making by Automated Decision Makers
Lead Research Organisation:
University of Nottingham
Department Name: School of Computer Science
Abstract
In this proposal we will investigate if we can simulate regulatory group decision making utilising an environment of cooperating and automated decision makers. This ambitious project comprises a multi-disciplinary, inter-institutional team drawn from Computer Science, Psychology and Industrial and Manufacturing Science. The four investigators will be supported by a three year PhD student, based at Cranfield, and a two year Nottingham based research assistant.The first phase of the project will define the regulatory decision contexts (environmental planning, permitting, policy development), individual roles and personality influences. These factors will be incorporated in the simulation environment in the second phase of the project and the resulting system will be analysed in the final phase.The second phase of the project will take the outputs from phase one and create a simulation environment. This environment will initially be built using agent based technology, but other machine learning approaches will be investigated as appropriate.The final phase will involve all members of the team in analysing the resulting system and calibrating its parameters in order to emulate group decision making processes as closely as possible.A key feature of this proposal are 2-day workshops at nine monthly intervals in order for the whole team (investigators, PhD student, RA and project partners) to work together in a focused way.
Publications
Davies G
(2010)
Regulators as 'agents': power and personality in risk regulation and a role for agent-based simulation
in Journal of Risk Research
Davies GJ
(2014)
Regulators as agents: modelling personality and power as evidence is brokered to support decisions on environmental risk.
in The Science of the total environment
Li J
(2012)
Evidence and belief in regulatory decisions - Incorporating expected utility into decision modelling
in Expert Systems with Applications
Li J
(2009)
Optimising risk reduction: An expected utility approach for marginal risk reduction during regulatory decision making
in Reliability Engineering & System Safety
Description | Complex regulatory decisions about risk rely on the brokering of evidence between providers and recipients, and involve personality and power relationships that influence the confidence that recipients may place in the sufficiency of evidence and, therefore, the decision outcome. We explore these relationships in an agent-based model; drawing on concepts from environmental risk science, decision psychology and computer simulation. |
Exploitation Route | The research we have conducted is applicable to anybody who is faced with complex regulatory decisions, where an element of risk is involved. Therefore, it might be of interest to government agencies, regulatory bodies, large/complex organisations, as well agencies that have to decide on policy given a range of stakeholders. |
Sectors | Communities and Social Services/Policy Environment Transport Other |
Description | This project produced five high quality journal papers, with the latest paper being published in 2014, demonstrating that the research team was still working together long after the research program has been completed (from a funding point of view). We are pleased to see that the papers we produced are starting to attract citations, indicating that other reseachers are finding our work is useful. In particular, the 2009 paper has received 11 citations (as at 12 Nov 2014) which are speard a variety of disciplines, including medicine, the environment and logistics. We believe this shows that the research is applicable to many different disciplines. |
First Year Of Impact | 2009 |
Sector | Environment,Transport |
Impact Types | Economic Policy & public services |
Description | Dept for Env Food & Rural Affairs DEFRA |
Organisation | Department For Environment, Food And Rural Affairs (DEFRA) |
Country | United Kingdom |
Sector | Public |
Start Year | 2006 |
Description | University of Oxford |
Organisation | University of Oxford |
Country | United Kingdom |
Sector | Academic/University |
Start Year | 2006 |