Open Access Academic Publishing Using Game Theory and Graph Theory (2026-2027)

Background

The academic publishing system is widely regarded as unsustainable. Three structural problems dominate:

The metric problem
There is no accurate system for measuring scholarly contribution. As a result, authorship order becomes political, citation practices are inequitable and researchers — especially those from underrepresented groups — struggle to receive appropriate recognition.

The curation problem
Peer review is essential but poorly incentivized. Researchers provide almost all of the intellectual labor in publishing — writing, reviewing, editing and reading — while private publishers capture most of the revenue, costing universities and the public billions. Journal prestige often substitutes for true quality assessment.

The replication crisis
Scientific findings frequently go unverified because replication studies are unrewarded. Retractions rise, trust decreases and pressure to publish novelty continues to distort research incentives.

This project — building on the open-source platform Liberata — aims to reimagine academic publishing by aligning incentives, increasing transparency and using game theory and graph theory to create fair and measurable systems of credit, review and replication.

Project Description

Liberata replaces traditional ordinal authorship (1st, 2nd, etc.) with contribution shares, which reflect each researcher’s proportional contribution to a project. These shares can then be exchanged in a marketplace for peer review or replication services, creating a self-sustaining ecosystem of quality control.

The 2026-2027 team will expand and scale Liberata through four major workstreams:

Platform development

Build the full infrastructure for the peer review marketplace and replication marketplace
Integrate share-based bidding systems that match reviewers and replicators with manuscripts
Support ORCID login, manuscript submission pipelines and metadata tagging

Algorithm and metric design

Apply game theory and graph theory to analyze share distributions, citation networks and collaboration patterns
Detect nepotism, inequitable practices and contribution imbalances across research communities
Produce advanced, research-grade metrics that provide a more accurate picture of individual and team impact

AI integration

Build AI tools that help users search for collaborators, summarize manuscripts and recommend reviewers or replicators
Improve peer review quality and speed through responsible AI-assisted workflows

Adoption, outreach and institutional partnerships

Develop an ambassador program across universities
Conduct market research and user testing with academics and librarians
Build dashboards for institutions and funders to analyze impact metrics
Present at multiple open science and research integrity conferences

The project operates like a tech startup inside a research university — with agile workflows, cross-functional teams and mentorship from industry engineers at companies such as Apple, Meta, Google, Amazon, Autodesk and Lyft.

Anticipated Outputs

Ready-to-use Liberata platform with peer review and replication marketplaces
AI-assisted tools for manuscript summarization, reviewer matching and collaborator discovery
Institutional analytics infrastructure and dashboard prototypes
Ambassador program, outreach materials and early adopter partnerships
Research manuscripts on Liberata metrics and game-theoretic modeling of scientific ecosystems
Conference presentations and community-facing documentation

Student Opportunities

The team will include 10 graduate students and 25 undergraduate students, organized across five specialized subteams:

Software (CS, engineering; 12 undergraduates, 3 graduate students)
Design (graphic design; 4 undergraduates, 1 graduate student)
Product (marketing, strategy, finance; 5 undergraduates, 3 graduate students)
Algorithms (math, game theory, modeling; 4 undergraduates, 3 graduate students)
Operations (project management; 2 graduate students)

Students will gain experience in:

Cloud computing, networking, cybersecurity, database engineering
UI/UX design and Figma prototyping
Game theory, graph theory and mathematical modeling
Startup operations, fundraising and investor communications
Market research, communication strategy and product road mapping
Presentation skills, professional documentation and conference participation

Students will also develop deep structural understanding of academic publishing and its economic incentives — expertise that will serve them in future research careers.

Timing

Summer 2026 – Spring 2027

Summer 2026 (optional):

Beta testing with early users
Fundraising with open science organizations and early-stage investors
Build the replication marketplace and collaboration features
Survey-based modeling of share distributions across disciplines

Fall 2026:

Build and refine AI features
Port academic literature into Liberata’s metric engine
Launch ambassador program and academic outreach
Expand strategic partnerships

Spring 2027:

Continue platform development and refinement
Conduct user testing and integrate feedback
Present at conferences and prepare institutional pilot programs

Crediting

Academic credit available for fall and spring semesters