Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.24706

Enabling the 1 1 Promise AI Enabled Fairness for Indonesia Retail IPO Allocation

Anchal Gupta·Gleb Papyshev·James T Kwok
View original source ↗Read source document ↗Export metadata ↓

Abstract

Indonesia OJK Circular, effective November 2025, requires that half of every IPO pooled allocation go to retail investors. Uniquely amongst major capital markets, the regulation places all non-institutional participants, regardless of wealth, into a single competing pool rather than separating them by monetary thresholds. The rule is structurally sound; however, enforcement and measurement of effectiveness require methods beyond rule-based detection alone. Well-resourced participants operating within the retail pool can distribute bids across networks of synthetic retail accounts, each individually compliant, allowing them to capture the retail quota without triggering standard concentration heuristics. Without a mechanism to detect such networks before allotment closes, the statutory guarantee of equal access becomes procedurally hollow: the quota is formally reserved for retail households while the economic benefit flows elsewhere. We present a framework for identifying such nominee networks using eleven statutory fields already collected by Indonesia e-IPO infrastructure, requiring no new surveillance powers or personal data. The paper shows that when a Global South regulator prioritizes genuine inclusion over administrative convenience, algorithmic enforcement becomes both feasible and necessary.

Explore related subjects

cs.CE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anchal Gupta, Gleb Papyshev, James T Kwok. 2026-10-17. Enabling the 1 1 Promise AI Enabled Fairness for Indonesia Retail IPO Allocation. https://doi.org/10.1609/aies.v9i2.43214

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

EBCC v0.2: Error bounded compression for weather and climate applications

As the resolution of weather and climate simulations increases, the amount of data produced is growing rapidly from hundreds of terabytes to tens of petabytes. These data volumes limit broader adoption, and their continued growth will soon exhaust available storage capacity. To address this problem, we present EBCC v0.2 (Error Bounded Climate-data Compressor). EBCC follows a two-layer compression approach: a base layer using JPEG2000 to capture the bulk of the data at high compression ratios, and a residual layer storing sparse wavelet coefficients to eliminate long-tail extreme errors efficiently. It supports three-dimensional chunks through a multicomponent transform (MCT) in the base layer. To achieve error bounded compression, we introduce error-bound-aware rate allocation in the JPEG2000 base layer. It selects coding passes to approximately satisfy the error target while optimizing the trade-off between compression ratio and mean squared error. We evaluate EBCC alongside established compression methods on benchmarks relevant to weather and climate science, including error statistics, sequential and parallel throughput tests, case studies of primitive and derived variables near a tropical cyclone, an analysis of global energy-budget closure, and a Lagrangian air-parcel trajectory simulation. EBCC either outperforms or closely follows the strongest existing baseline across these benchmarks at relative error targets ranging from 0.01% to 10%, achieving compression ratios from 15x to more than 300x. In the energy-budget-closure and Lagrangian-trajectory benchmarks, EBCC achieves more than 100x compression while keeping compression-induced errors below an assimilation-uncertainty reference derived from the ERA5 uncertainty ensemble. These results demonstrate that EBCC can produce heavily compressed weather and climate datasets suitable for downstream applications.

cs.CE↗
arXiv2026

HP-GSBS: Accelerating greedy state boundary search with reusable interval statistics

Greedy State Boundary Search (GSBS) identifies transitions between relatively stable neural activity patterns, but repeated candidate evaluation makes large analyses expensive. HP-GSBS is an open-source Python package that reorganizes the search around reusable interval contributions and correlation statistics. Streaming candidate reduction and local refinement further reduce repeated work; CPU, PyTorch CUDA and Triton backends share sequential segmentation decisions. Across seven controlled workloads with two realizations each, median same-input speedups range from 154 to 1730 times. All 168 optimized executions reproduce original boundaries and selected state counts, with maximum absolute t-distance difference $1.88\times10^{-11}$. Four frozen EEG recordings provide complementary validation, including a complete input-to-segmentation application example. Batch processing, versioned result persistence and optional low-memory execution support recording collections and longer inputs. By removing redundant computation, HP-GSBS makes established neural-state segmentation faster while preserving its scientific outputs.

cs.CE↗
arXiv2026

Surface matching for image-derived geometries with a projection-based separation of rigid body modes

The large diffeomorphic deformation metric mapping (LDDMM) framework offers numerous techniques for analyzing shapes and images. Surface matching computes a non-rigid, smooth, and invertible mapping that registers one surface to another. The computed deformation may contain rigid body motion, which frequently does not represent meaningful shape differences but is due to external factors like imaging modalities, such as the object's positioning during image acquisition. We present a simple extension to the LDDMM framework's surface matching that uniquely decomposes the computed deformation into rigid body modes (RBM) and RBM-free motion by introducing a suitable projection. This enables a focused analysis of each component individually and additionally helps to preserve the surface discretization quality. Three sets of examples illustrate our approach. A small-scale example demonstrates the proposed method's effects, followed by an example using a single pair of heart geometries, and finally an example involving a cohort of 98 heart shapes.

cs.CE↗