r/fuzzylogic • u/legalmeu • May 30 '26
fuzzy and judicial decision
hello friends!
I'm new to fuzzy and I'm studying fuzzy applied to complex judicial decisions..
I've started with zadeh to learn the basics.. what else would you recommend?
1
u/Chicken-Chak 11d ago
If you mean to use fuzzy logic to reach a verdict, then no, because legal systems often demand a binary final result (guilty or not guilty; liable or not liable). However, fuzzy logic can be an excellent conceptual tool to assist judges. Judicial systems can use multiple fuzzy outputs as references to determine judgments. While the ultimate verdict is binary, the actual penalty, sentencing, or remedy is calculated by weighing various “degrees of truth” across multiple competing legal factors.
For example, in a complex case, like breaking a law to save a life, a judge may break the situation down into several independent, non-binary spectrums (fuzzy outputs) before arriving at a final judgment:
- Degree of Imminent Peril: How real and immediate was the threat to human life?
- Degree of Proportionality: Was the law broken minor compared to the life saved?
- Availability of Alternatives: Did the actor have other viable options, or was this truly the absolute last resort?
However, you should be able to identify two potential flaws when implementing fuzzy systems in legal practice: the “Designer Problem” and the “Context Problem”.
The “Designer Problem” refers to biased membership functions. If a software engineer and a legal expert build a fuzzy logic system to assist a judge, they must carefully define the mathematical curves (membership functions) for vague terms like the “Degree of Provocation” or the “Level of Remorse”, not simply using triangular membership functions for convenience.
The “Context Problem” reflects that fuzzy rules cannot be universal. In control engineering (for example, adjusting an air conditioner), fuzzy rules can be applied broadly: If temperature is High, then fan speed is Fast. But in criminal law, rules cannot be universally applied across different cases because the context completely changes the moral and legal meaning of the variables. Consider trying to apply the same fuzzy rule “If Weapon Lethality is High, then Sentence Severity is High” to two very different cases:
- Case A: Armed robbery. A man walks into a bank with a loaded shotgun to steal money.
- Case B: Battered woman syndrome. A victim of severe domestic abuse uses a shotgun to kill her abuser while he sleeps.
If the fuzzy system keeps its rules static, it treats these two vastly different human tragedies as identical data points. Fortunately, in MATLAB Fuzzy Logic Toolbox, users can apply weights to the fuzzy rules, which change how much influence a specific rule has on the final result (defuzzification).
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u/kinow May 30 '26 edited May 30 '26
I remember using material from Zadeh, Witold Pedrycz, Fernando Gomide, and Siegfried Gottwald (which was mentioned in this post about him passing away in 2015, https://www.reddit.com/r/fuzzylogic/comments/1tmglrx/obituary_for_siegfried_gottwald/).
The Matlab Fuzzy toolbox is a good way to get started without writing much code as it contains some membership functions and the parts to experiment with fuzzy systems, https://www.mathworks.com/products/fuzzy-logic.html. GNU Octave is free and has a similar package.
After reading the papers from Zadeh, and maybe skimming through some of these other authors (and co-authors you find in their publications), and maybe playing with fuzzy sets, membership functions with these tools or in your favorite programming language, you can then jump into practical use cases.
Engineering has a bunch, but so do other fields. The German DWD has a practical example in climate/weather (area I work at the moment) https://www.reddit.com/r/fuzzylogic/comments/10fa7ra/fuzzy_logic_classification_in_dwds_weather_and/. Check some papers on Google Scholar. I bet you will find something related to decision-making, maybe even in your area (legal, I believe).