Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
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| Μορφή: | Recurso digital |
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Zenodo
2025
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| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.5281/zenodo.15367708 |
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Πίνακας περιεχομένων:
- <p>Legal decision-making has long been studied through deterministic and probabilistic models, but these approaches fail when deception is introduced. In the real world, legal deception creates entangled networks of uncertainty, making resolution increasingly elusive. Consider a high-profile criminal trial where both prosecution and defense strategically manipulate evidence, delay proceedings, and create ambiguity. Traditional models cannot fully capture these dynamics, but by integrating entropy, game theory, graph theory, and AI-driven models, we can mathematically quantify deception and predict its effects on legal outcomes. This chapter introduces the Ultimate Legal Deception Model, an empirically validated framework designed to:</p> <p>Measure deception complexity through entropy functions. Model deception loops using graph theory and Markov processes. Predict deception strategies through game-theoretic equilibria. Break deception cycles using quantum decision theory. Validate deception analytics using AI and real case datasets.</p> <p> </p>