Research Article
Legal Transplants in Indonesia: Bridging Tradition and Modernity
Ratno Lukito*
Issue:
Volume 14, Issue 2, April 2025
Pages:
60-77
Received:
28 January 2025
Accepted:
20 February 2025
Published:
6 March 2025
DOI:
10.11648/j.ss.20251402.11
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Abstract: Legal transplants, the adoption of foreign legal principles into a domestic legal system, have significantly influenced Indonesia’s legal development. As a country characterized by legal pluralism—comprising state law, customary law (adat), and Islamic law—Indonesia has historically relied on legal transplants to modernize its legal system, bridge legal gaps, and harmonize conflicting traditions. This study examines key cases of legal transplantation in Indonesia, including the adoption of anti-money laundering laws based on FATF recommendations, the integration of constitutional injury principles into the Constitutional Court’s procedural norms, and the application of the proportionality principle in judicial review. Furthermore, the incorporation of Islamic family law through the Compilation of Islamic Law, the regulation of endowments under Law No. 41 of 2004, and the development of Shariah economics through KHES illustrate how traditional Islamic concepts have been adapted to align with modern legal frameworks. While some legal transplants involve direct adoption with minimal changes, others require modifications to fit Indonesia’s socio-cultural and legal context. Despite challenges such as potential conflicts with local traditions and legal inconsistencies, legal transplants remain essential for Indonesia’s legal evolution. By carefully integrating foreign legal norms while preserving local values, Indonesia can continue to strengthen its legal system to meet the needs of its diverse and dynamic society.
Abstract: Legal transplants, the adoption of foreign legal principles into a domestic legal system, have significantly influenced Indonesia’s legal development. As a country characterized by legal pluralism—comprising state law, customary law (adat), and Islamic law—Indonesia has historically relied on legal transplants to modernize its legal system, bridge ...
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Research Article
Adaptive Representation: A Moderate Stance on Predictive Processing
Zhichao Gong,
Yidong Wei*
Issue:
Volume 14, Issue 2, April 2025
Pages:
78-86
Received:
27 April 2024
Accepted:
13 May 2024
Published:
11 March 2025
DOI:
10.11648/j.ss.20251402.12
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Abstract: Predictive processing (PP), emerging as a novel research paradigm in contemporary cognitive science, offers a departure from both traditional computational representation views and 4E+S cognition perspectives. This theory advocates that the brain is a hierarchical prediction model based on Bayesian inference, which aims to minimize the difference between the predicted world and the actual world to prediction error minimization. In recent years, the problem of representation has emerged as a focal point in the philosophical examination of PP. This article introduces two primary strands of PP theories: conservative predictive processing (CPP) and radical predictive processing (RPP). Building upon these frameworks, it outlines three distinct positions regarding the representation problem within PP: representationalism, anti-representationalism, and a moderate stance on representations. Lastly, the article proposes a new perspective on representation: Adaptive Representation. Adaptive representation highlights the fact that generative processes are adaptive processes, and that adaptation is not necessarily optimal, whether based on natural selection or natural drift; and that generation is at the same time a representational process. By advocating for a form of weak representationalism grounded in adaptive processes, this perspective supports a moderate stance on representations within PP.
Abstract: Predictive processing (PP), emerging as a novel research paradigm in contemporary cognitive science, offers a departure from both traditional computational representation views and 4E+S cognition perspectives. This theory advocates that the brain is a hierarchical prediction model based on Bayesian inference, which aims to minimize the difference b...
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