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Modelling strategic management with multiple intelligent agents

This project is no longer listed in the FindAPhD
database and may not be available.

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  • Full or part time
    Prof A Hopgood
    Dr J Ries
    Dr A Tait
  • Application Deadline
    No more applications being accepted
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Start date: 1st October 2018
Application deadline: 15 June 2018
Interview date: week commencing the 25th June 2018

Project
This project concerns the development of artificial intelligence (AI) for high-level strategic management. Typical AI projects to date have been narrowly constrained and highly operational, so the project will require a technological and intellectual step forward.

As strategic management requires a broad range of perspectives, with balanced oversight, it is ideally suited to modelling with a multiagent approach using the blackboard framework. The different aspects of strategic management can be modelled by independent specialised software agents that inform the overall judgements. The established academic models typically include stages such as analysis, strategy formulation, goal setting, structure, and control/feedback.

The use of the balanced scorecard is proposed as a complementary strategic planning and management decision-making tool. Individual optimizations may use genetic algorithms with a performance function derived from the balanced scorecard, while the multi-agent framework will draw together the multiple strategic perspectives.

You may also wish to consider the linked project, "The Next Generation Strategist: exploring the competences required for engaging in strategy". https://www.findaphd.com/search/ProjectDetails.aspx?PJID=95906

Candidate specification
Applications from candidates with a background in computer science, software engineering, information systems, or a related subject area are welcomed. The candidate will also need a keen interest in business and management. We are seeking to appoint an enthusiastic and committed candidate with excellent interpersonal and organisational skills as well as an understanding of quantitative research methods.


Enquiries and application
Informal enquiries are encouraged and can be made to Professor Adrian Hopgood at [Email Address Removed] (02392 84 2946) or Dr Jana Ries at [Email Address Removed] (02392 844023).

You can apply online by submitting your CV, two references and copies of any relevant qualifications. Please quote the project code - CCTS4290618 when prompted. In your application, please indicate your motivation for applying for the post and also outline how your experience and skill-set will contribute to the project. If English is not your first language, please provide evidence of IELTS (score of 6.5, with no component falling below 6.0).


Funding Notes

Applicants must be able to self-fund their studies. The successful candidate will receive full access to the University’s Graduate School Development Programme, research training, and internal qualifications that enable applications for Associate Fellowship of the Higher Education Academy.

References

Levine, S.S., Bernard, M., & Nagel, R. (2017). Strategic Intelligence: The Cognitive Capability to Appreciate Competitor Behavior. Strategic Management Journal, 38 (12): 2390-2423.

Hopgood, A.A. (2012). Intelligent Systems for Engineers and Scientists, 3rd edition, CRC Press.

Khmeleva, E., Hopgood, A.A., Tipi, L. and Shahidan, M. (2017). "Fuzzy-Logic Controlled Genetic Algorithm for the Rail-Freight Crew-Scheduling Problem", KI - Künstliche Intelligenz.

How good is research at University of Portsmouth in Computer Science and Informatics?

FTE Category A staff submitted: 13.00

Research output data provided by the Research Excellence Framework (REF)

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