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Algorithms for Decision-making
Select Committee on Science and Technology called for evidence

Problem Solving Algorithm
An algorithm that is able to simulate and enhance human problem-solving capabilities

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Features

A window on the world of O.R.?
The “invisibility cloak” of science fiction is now fact, albeit with limitations. O.R. could claim to have had the power of invisibility for years, though not by desire; what we want is the opposite - a high-visibility jacket! Indeed, part of the mission of the OR Society is to help make our presence more visible. But perception involves both the observed and the observer. And all of us have open and hidden parts.

YOR18 – OR – A Twenty Twenty Vision
The 18th Young [to] OR Conference got off to a great start with the plenary session given by the President of the OR Society, Dr Geoff Royston. Antuela Tako, the chair of the organising committee, began the proceedings by telling the audience what had been planned for them and how to find out more about streams.

The Education & Research Committee
- Roles and Responsibilities: Brian Dangerfield (Liaison with ESRC)
Ruth Kaufman, Inside OR February 2013

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Posted on 12 February 2018

Artificial Intelligence

Algorithms for Decision-making

When the House of commons Select Committee on Science and Technology first called for evidence for its enquiry into Algorithms for Decision-making in spring 2017, the OR Society’s response flagged up demand management as an example of OR’s use of algorithms. It specifically mentioned an EPSRC funded interdisciplinary workshop to identify future research directions in demand management (EP/ P026117/1) and was due to be led by OR Society members Dr Arne Strauss, Warwick University, and Dr Christine Currie, University of Southampton.
The workshop took place in September 2017 at The Shard in London and brought together academics and practitioners in demand management and related disciplines, including law, computer science, digital marketing, service manufacturing and operational research. A range of topics was identified as being of considerable interest to industry and academia, including: demand modelling, dynamic pricing under demand model uncertainty, parameter fitting under strategic customers, and quantification and modelling of behavioural pricing effects.