Skip to content
#federated learning Open access

Replication package for "How Technical Architecture Shapes Platform Emergence: A Multi-Agent Simulation of Data Collaboration Strategies"

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

Replication package for the HICSS-60 paper "How Technical Architecture Shapes Platform Emergence: A Multi-Agent Simulation of Data Collaboration Strategies" (Aoki & Yoshioka-Kobayashi, 2027). The paper formalises self-reliance, raw-data pooling and federated learning as profiles over two architectural primitives — integration capacity (Cap) and the preservation of competitive distinctiveness (Scarcity dynamics) — and studies their population dynamics in an agent-based model of competing firms. This archive contains everything the paper undertakes to release: the simulation code and analysis pipeline; the configuration for every simulation cell; run manifests recording parameters, commit hashes and software versions; replication-level output for the twelve cells reported in Table 3 (30 replications each); a full model description following the ODD protocol (Grimm et al., 2020); the project's pre-specification together with a note reconciling it with the published paper; and the literature-search protocols behind the paper's two scoped novelty claims. No new simulation was run for the revision: every number in the published paper derives from the output archived here. Section 5.4 of the published paper retracts a size-ordered adoption cascade reported in the submitted version; the superseded analysis and the code that produced it are retained in this archive so that the earlier reading can be reproduced and checked. Two archives are provided. The code archive(under 1 MB) holds code, configurations, notebook and documentation; the results archive (~90 MB) holds the simulation output. Unpack the results archive into the package root. Start from README.md, and from docs/ODD.md for the model itself. Code is licensed under the MIT License; simulation output and documentation under CC BY 4.0, as stated in LICENSE-DATA.md inside the archive.

View source

Similar papers

#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#machine learning Review Open access Jun 2014

Why Early-Stage Software Startups Fail: A Behavioral Framework

This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.

Carmine Giardino, Xiaofeng Wang, P. Abrahamsson · 175 citations · ⚡19
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#machine learning Review Open access May 2016

Key Challenges in Software Startups Across Life Cycle Stages

It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.

Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al. · 62 citations · ⚡6

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

MIT News · Artificial Intelligence Oct 7, 2026

Discovering the value of humanistic inquiry

Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.

Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.