Scenario generation and scenario quality using the cone of plausibility

Bibliographic Data

ID9845313
AuthorsMandeep K Dhami (0000-0001-6157-3142, Middlesex University, corresponding author), Lars Wicke (London School of Economics and Political Science), Dilek Önkal (0000-0003-3544-5934, Northumbria University)
Year2022
Volume142
Pages102995
Publication date2022-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFutures (JOURNAL)
Journal identifiersISSN: 0016-3287 • E-ISSN: 1873-6378
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.futures.2022.102995
OpenAlexW4285500451
LanguageEN
Citations received6
References cited35

The intelligence analysis domain is a critical area for futures work. Indeed, intelligence analysts’ judgments of security threats are based on considerations of how futures may unfold, and as such play a vital role in informing policy- and decision-making. In this domain, futures are typically considered using qualitative scenario generation techniques such as the cone of plausibility (CoP). We empirically examined the quality of scenarios generated using this technique on five criteria: completeness, context (otherwise known as ‘relevance/pertinence’), plausibility, coherence, and order effects (i.e., ‘transparency’). Participants were trained to use the CoP and then asked to generate scenarios that might follow within six months of the Turkish government banning Syrian refugees from entering the country. On average, participants generated three scenarios, and these could be characterized as baseline, best case, and worst case. All scenarios were significantly more likely to be of high quality on the ‘coherence’ criterion compared to the other criteria. Scenario quality was independent of scenario type. However, scenarios generated first were significantly more likely to be of high quality on the context and order effects criteria compared to those generated afterwards. We discuss the implications of these findings for the use of the CoP as well as other qualitative scenario generation techniques in futures studies

Algorithm · Cone (formal languages · Epistemology · Quality (philosophy · Cognitive Science and Mapping · Complex Systems and Decision Making · Computer Science · Decision-Making and Behavioral Economics · Philosophy