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  <doc>
    <id>7781</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-10-31</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">ChatGPT for futures: how large language models can support the development of future scenarios using the Cone of Plausibility</title>
    <abstract language="eng">Recently, large language models (LLMs), such as the “Chat Generative Pre-trained Transformer”, commonly known as ChatGPT, are substantially impacting the world of (big) data analytics and artificial intelligence. This paper explores how ChatGPT-4 can be used to support the development of future scenarios using the Cone of Plausibility method. The most recent version ChatGPT 4.0 differs from its predecessors in that it can now access real-time data to generate its responses. This makes it an attractive tool for intelligence analysts, who are faced with the permanent challenge of having to sift through large amounts of data whilst also providing actionable products in a timely manner. Previous versions of ChatGPT were prone to errors such as making up information. But what if, using the right prompts, version 4.0 could become a useful sparring partner for developing future scenarios? In this study, we will ask ChatGPT 4.0 to generate sets of plausible future scenarios for “Russia 2035+” based on previously specified key drivers and assumptions. The LLM’s potential for error is reduced because it is not working with hypotheses that it has generated itself. This could turn ChatGPT into a quick-thinking sparring partner for imagining varieties of future scenarios, supporting out-of-the box thinking and further minimising cognitive biases. To evaluate the reliability and accuracy of the results generated by ChatGPT, we will use the VV&amp;A concept developed by the US Department of Defence. According to this concept, credibility in an intelligence product is rooted in the verification, validation and accreditation of its analysis. This process is routinely followed by every intelligence analyst. We will have found out to which extent and under which circumstances ChatGPT 4.0 can credibly support the development of future scenarios.</abstract>
    <parentTitle language="eng">CCEW SYMPOSIUM 2024: Predictive Synergies: Crisis Early Warning &amp; Foresight, 19. und 20. September 2024, München</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-77811</identifier>
    <identifier type="doi">10.35096/othr/pub-7781</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Bresinsky</author>
    <author>Eva Hager</author>
    <author>George Xanthos</author>
    <collection role="institutes" number="FakANK">Fakultät Angewandte Natur- und Kulturwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7781/Bresinsky_ChatGPT_2024.pdf</file>
  </doc>
</export-example>
