MereSimplicity Research / Knowledge Foundations

Research overview

Established knowledge.
New questions. Better methods.

Systems thinking, operations research, economics and data science give us a place to begin. Explore the independent publications behind our questions, then read original MereSimplicity work when its methods and results are ready to share.

Explore the foundations

External Research Foundations

Independent work. Useful foundations.

Read the source. Understand the principle. See how it informs our approach. These publications belong to their original authors and publishers.

8 publications

Systems ThinkingFoundational essay1999

Leverage Points: Places to Intervene in a System

Donella H. Meadows

An essay on how the structure of a system shapes its behaviour, from feedback and information flows to rules, goals and underlying assumptions.

Informs our approachInforms business-system mapping and the questions we ask about delays, feedback, information access and potential points of improvement.

Publisher: Sustainability Institute
Source: The Donella Meadows Project
Original publication (opens in a new tab)
EconomicsPeer-reviewed study2013

Does Management Matter? Evidence from India

Bloom, Eifert, Mahajan, McKenzie & Roberts

A field experiment in Indian textile plants examined how changes in management practices affected productivity, quality and inventory.

Informs our approachInforms how we frame operational investigations, establish baselines and distinguish a measured change from a plausible improvement idea.

Publisher: Oxford University Press
Source: Oxford Academic
Original publication (opens in a new tab)
Process IntelligenceResearch manifesto2012

Process Mining Manifesto

Wil van der Aalst et al. · IEEE Task Force on Process Mining

A shared research agenda and set of principles for using event records to discover processes, compare observed behaviour with models and investigate improvements.

Informs our approachInforms evidence planning: which activities, case identifiers and timestamps a process investigation needs before analysis begins.

Publisher: Springer
Source: IEEE Task Force on Process Mining
Original publication (opens in a new tab)
Industrial EngineeringMathematical paper1961

A Proof for the Queuing Formula: L = λW

John D. C. Little

A mathematical relationship connecting the average number of items in a queueing system, its average arrival rate and the average time spent in that system.

Informs our approachInforms questions about waiting times, order queues, work in progress and the records needed to measure a consistent process boundary.

Publisher: INFORMS
Source: Operations Research
Original publication (opens in a new tab)
Data & AIPeer-reviewed study2021

“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI

Nithya Sambasivan and colleagues

Interview-based research examines how undervalued data work and unresolved data problems can lead to compounding failures in high-stakes AI projects.

Informs our approachInforms evidence-readiness checks, source provenance and the decision to improve data collection before selecting analytical models.

Publisher: Association for Computing Machinery
Source: Google Research
Original publication (opens in a new tab)
EconomicsPeer-reviewed study2002

Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence

Bresnahan, Brynjolfsson & Hitt

Firm-level evidence examines the relationship between information technology, workplace organisation, new products and services, and workforce skills.

Informs our approachInforms Build discovery: understand responsibilities, workflows and team capabilities before deciding what software or integration to introduce.

Publisher: MIT Press
Source: Oxford Academic
Original publication (opens in a new tab)
Data & AIGovernment framework2023

Artificial Intelligence Risk Management Framework (AI RMF 1.0)

National Institute of Standards and Technology

A voluntary framework for organising the governance, mapping, measurement and management of risks throughout the lifecycle of an AI system.

Informs our approachInforms human review, traceable outputs and validation requirements in the design of MereSimplicity Intelligence.

Publisher: National Institute of Standards and Technology
Source: NIST
Original publication (opens in a new tab)
Continuous ImprovementPractice frameworkYear not stated

Model for Improvement

Associates in Process Improvement · presented by IHI

A practical approach that combines an improvement aim, measures and proposed changes with small Plan–Do–Study–Act tests.

Informs our approachInforms intervention planning and the learning loop connecting an operational change with observed outcomes.

Publisher: Institute for Healthcare Improvement
Source: Institute for Healthcare Improvement
Original publication (opens in a new tab)

MereSimplicity Research & Technical Notes

Our work needs its own evidence.

Original methodologies, experiments, prototypes, benchmarks and validation reports belong here when their methods and results are documented and publication is authorised.

0 publications

No original research reports are listed in this collection yet.

Our evidence standard

A foundation for the method.
A standard for the result.

The external publications were created by independent authors and institutions. Our reading notes explain how they inform MereSimplicity’s approach; those connections are our editorial interpretation.

Published research does not establish that MereSimplicity’s developing engine works. That requires our own testing, validation and documented results. A peer-reviewed study, a mathematical result and a practice framework answer different kinds of questions.

We link to original sources, identify assumptions and distinguish external research from our own work. Inclusion does not imply endorsement or partnership. Papers remain with their authors and publishers.

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