We live in an age of information and forecast bombardment. Given the role played by social media platforms as important and instant communication channels, we are flooded with a continuous flow of all kinds of information, including false/inaccurate material (misinformation) as well as information/predictions that are deliberately deceitful (disinformation). We know that not every piece of information or forecast can be trusted but how do we actually separate misinformation/disinformation from those predictions we can rely on? The overall research aim of this study would be to examine the dynamics of trust in forecasts. In so doing, this work would investigate the effectiveness of scenarios and would address issues around misinformation/disinformation, information source and forecast combination. Important questions would include:
- What are the forecasting implications for detecting misinformation versus disinformation?
- How do we choose which predictions to trust and which predictions to adjust?
- Are there differences in how we trust and use forecasts from human experts versus those from automated systems?
- How do we distinguish among individuals with varying claims to expertise?
- What role does the choice of communication channel play in gaining/losing trust?
A mixed method study approach would be utilised. Data would be collected through surveys and interviews, as well as through behavioural experiments. This data would be analysed using qualitative and quantitative methods. Findings could be expected to yield implications on forecasting and misinformation/disinformation detection and management for individuals and organisations.
Eligibility and How to Apply
Please note eligibility requirement:
- Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
- Appropriate IELTS score, if required.
- Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.
For further details of how to apply, entry requirements and the application form, see: https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF20/…) will not be considered.
Deadline for applications: Friday 24 January 2020.
Start Date: 1 October 2020.
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.
Önkal, D., M.S. Gönül, S. DeBaets (2019). Trusting Forecasts. Futures & Foresight Science, https://doi.org/10.1002/ffo2.19.
Wicke, L, M.K. Dhami, D. Önkal, I.K. Belton (2019). Using Scenarios to Forecast Outcomes of the Syrian Refugee Crisis, International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2019.05.017.
Goodwin, P., M.S. Gönül, D. Önkal (2019). When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions. European Journal of Operational Research, 273, 992-1004.
Goodwin P., D. Önkal, M.S. Gönül, M.Thomson and E. Öz, (2017) “Evaluating expert advice in forecasting: Users’ reactions to presumed vs experienced credibility”, International Journal of Forecasting, 33, 280-297, DOI:10.1016/j.ijforecast.2015.12.009.
Önkal, D., K. Z. Sayım, and M.S. Gönül (2013). Scenarios as channels of forecast advice, Technological Forecasting & Social Change, 80, 772-788.
Goodwin, P., M.S. Gönül, and D. Önkal (2013). Antecedents and effects of trust in forecasting advice. International Journal of Forecasting, 29, 354-366.
Önkal, D., P. Goodwin, M.E. Thomson, M.S. Gönül, A.C. Pollock (2009) “The Relative influence of advice from human experts and statistical methods on forecast adjustments”, Journal of Behavioral Decision Making, 22(4), 390-409.