Behavioural OR

The main aim of the SIG is to foster interest in behavioural science and its usefulness for OR and to organise events which will be of interest to practitioners and academics alike. A secondary aim is to increase public knowledge and awareness of OR in general. Just as behavioural work from the likes of Simon, Kahneman and Gigerenzer has made contributions to economics, healthcare and policy, there is potential for similar benefits from behavioural OR.

The current growth in interest in behavioural OR stems from a long observed gap in 'people issues' in a wide sense, coupled with the emergence of a set of methods that promise the potential of being able to address such issues (Mingers and Rosenhead; Gary et al). The recognition of this gap is not new, nor does it represent a revolution of the field of OR, but what is perhaps new is the emergence of a set of methods and structured areas of study from behavioural science that may allow a more rigorous and systematic approach to behavioural issues within the OR field. For example, developments in OR could occur through integrating insights from cognitive modelling into human judgment and decision-making under uncertainty or from social experiments into the study of group behaviour. Equally, advances in computing as applied to the social sciences, particularly the increasing interest in agent-based modelling, is opening up new avenues for research which behavioural OR can explore.

Committee Details

Konstantinos Katsikopoulos Chair
Jonathan Malpass Secretary
Katharina Burger Committee Member
Valentina Ferretti Committee Member
Richard Underwood Committee Member

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Come to our next event

BOR B2S2 VIII: The behavior of demand planners: their effectiveness in using diverse information.

Date: 9 February 2023, 12.00 - 12.40
We have the great pleasure to invite you to the BOR B2S2 VIII, on 9 February 2023, 12.00 - 12.40 GMT
Robert Fildes (presenting author), Distinguished Professor (Emeritus) and Founding Director, Centre for Marketing Analytics and Forecasting, Lancaster University Management School, UK
Paul Goodwin, Professor Emeritus, School of Management, University of Bath; UK

Most private and many public organizations employ ‘demand planners’ whose job it is to forecast the sales (or activities) arising in their organization. The processes, which the planners undertake, are usually complex, often involving interactions with many colleagues, information from external sources, and ‘advice’ from forecasting software. In this presentation we describe a typical demand planning process, highlighting the information used and misused through the lens of the ‘heuristics and biases’ literature. Research in this area, based on both experiment and field studies, has been limited. In the current study we integrate 6 data sources to highlight commonalities in the process by which a final demand forecast is reached. The conclusions are striking and underline the importance of a forecasting system that limits the damage arising from the biases of the participants.
Why should you join?
Demand forecasting is ubiquitous. Understanding the processes by which information is interpreted in order to produce a final forecast, which is then used in various operational decisions, is important in practice – poor forecasting has serious financial consequences. But it is also of great current theoretical interest as it asks and partially answers the question of how users interpret the information arising from increasingly complex AI type models. Methodologically, successful research requires a ‘mixed method’ approach.

Zoom details
Meeting-ID: 891 6049 7274
Kenncode: 857585
Comment: In case of technical problems, please visit
before the start of the meeting.

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