How Future Narratives Improve ChatGPT’s Oscars Predictions

:::info Authors:

(1) Pham Hoang Van, Department of Economics, Baylor University Waco, TX, USA (Van [email protected]);

(2) Scott Cunningham, Department of Economics, Baylor University Waco, TX, USA (Scott [email protected]).

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Abstract and 1 Introduction

2 Direct vs Narrative Prediction

3 Prompting Methodology and Data Collection

4 Results

4.1 Establishing the Training Data Limit with Falsifications

4.2 Results of the 2022 Academy Awards Forecasts

5 Predicting Macroeconomic Variables

5.1 Predicting Inflation with an Economics Professor

5.2 Predicting Inflation with a Jerome Powell, Fed Chair

5.3 Predicting Inflation with Jerome Powell and Prompting with Russia’s Invasion of Ukraine

5.4 Predicting Unemployment with an Economics Professor

6 Conjecture on ChatGPT-4’s Predictive Abilities in Narrative Form

7 Conclusion and Acknowledgments

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A. Distribution of Predicted Academy Award Winners

B. Distribution of Predicted Macroeconomic Variables

\ References

Detailed figures illustrating the distribution of predicted winners for each Academy Award category, using the four prompting styles, are provided here. These figures showcase the improved accuracy of GPT-4 in predicting winners when prompted with a future narrative.

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:::info This paper is available on arxiv under CC BY 4.0 DEED license.

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