The Logic of Sentence Disparity in Court Rulings

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Table of Contents

Introduction
Discussion
Conclusion
References

Introduction

The book Noise: A Flaw in Human Judgment written by Kahneman, Sibony, and Sunstein (2021) explores two determinants of decision-making and how these factors impact final outcomes. The authors focus on the concept of noise and bias, but the emphasis of the book I on noise. The authors define noise as an unwanted variability in human judgment.

Discussion

The variability, according to Kahneman et al. (2021), is determined by hidden factors that cause decision-makers to make flawed judgments. After defining noise and bias in the first chapter, the authors dedicate the remaining five chapters to case studies and research studies that have been published in the past on the impact of noise on human judgment and decision-making processes. Most of the cited studies have been published and can be accessed in the reference section of the book. Three of these articles are discussed here and how they have been discussed and incorporated in the book.

One of the articles cited in the book to reinforce the topic of noise and flawed decision-making is Clancy et al. (1981). This was a case study investigating the logic of sentence disparity in court rulings. According to the authors of the article, an experimental study was carried out on a total of 208 judges to determine sentencing uniformity. The authors state that the results were stunning, considering the fact the judges agreed in only three out of sixteen cases. Clancy et al. (1981) further emphasize that even where the judges agreed on sentencing, the period was affected by noise. Kahneman et al. (2021) elaborate that different factors such as the emotions of judges as well as time of the day and weather can have impacts on the ruling outcomes The statement “if judges are hungry, they are tougher” summarizes how the fate of a person can be determined by weather or the mood of the judge. This article fits in the book because it explains the cause of noise and how it impacts judgments, which is the theme of the book. The example cited in the book helps the reader understand how noise can cause variations in judgments

Another academic article titled “Emotional Judges and Unlucky Juveniles” by Eren and Mocan (2018) and cited in Kahneman et al. (2021) further elaborates on the impact of noise on human judgment. The article explains how local environmental factors such as winning or losing of the local football team can be a determinant of outcomes in juvenile rulings. The authors contend that judges are harsh when local teams lose competitions, and they are lenient on days when they have birthdays or when local teams have won matches. This article suits in the book because it resonates with the theme of Kahneman et al.’s book on the impact of noise and how it flaws judgments. Kahneman et al. (2021) selected this article purposely because it reinforces the theme of the book and helps the reader to understand the causes of noise and how they can yield grave outcomes.

Conclusion

In conclusion, Kahneman et al. (2021) use the concept of noise as a random error in judgment often caused by bias. The examples cited in the book from real experimental studies indicate how errors in judgment can be life-changing, especially where judges fail to acknowledge the presence of noise in human judgment. Biases can contribute to noise and have ultimate impacts on judgments. One ought to detect and respond accordingly to avoid errors in decision-making.

References

Clancy, K., Bartolomeo, J., Richardson, D., & Wellford, C. (1981). Sentence decision-making: The logic of sentence decisions and the extent and sources of sentence disparity. Journal of criminal Law & Criminology, 72, 524. Web.

Eren, O., & Mocan, N. (2018). Emotional judges and unlucky juveniles. American Economic Journal: Applied Economics, 10(3), 171-205. Web.

Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. HarperCollins UK.

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The Logic of Sentence Disparity in Court Rulings

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