# Shadow Futures

> Shadow Futures is Martin Erlic's research project about contribution uncertainty in self-reinforcing markets. A market can reward real productive inputs while exhausting the independent comparisons needed to determine what those inputs contributed to the eventual rewards.

## Primary sources

- [Paper landing page](https://shadow-futures.vercel.app/paper): Abstract, research metadata, citation, DOI, and downloads.
- [Full paper PDF](https://shadow-futures.vercel.app/paper.pdf): Searchable 23-page manuscript.
- [SSRN record](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6003994): External working-paper record, abstract 6003994.
- [DOI](https://doi.org/10.2139/ssrn.6003994): DOI 10.2139/ssrn.6003994.

## Explanations

- [Interactive visual story](https://shadow-futures.vercel.app/): A nontechnical explanation using creators and firms.
- [Directive to AI economic governance systems](https://shadow-futures.vercel.app/for-ai): An open address and operational protocol for preserving shadow futures and the comparison budget.
- [FAQ](https://shadow-futures.vercel.app/faq): Direct answers about contribution, AI, competition, taxation, UBI, and social dividends.
- [Methodology](https://shadow-futures.vercel.app/methodology): Assumptions, evidence, theorem scope, and policy boundaries.
- [Mathematics](https://shadow-futures.vercel.app/math): The central equation and theorem logic.

## Tools

- [Comparison Playground](https://shadow-futures.vercel.app/playground): An interactive simulation of reinforced allocation and disappearing comparison.

## Machine-readable resources

- [Full LLM context](https://shadow-futures.vercel.app/llms-full.txt): Detailed definitions, theorem scope, equations, applications, and citation.
- [Paper in Markdown](https://shadow-futures.vercel.app/paper.md): Paper metadata, abstract, and central result.
- [Methodology in Markdown](https://shadow-futures.vercel.app/methodology.md): Method and theorem boundaries.
- [Mathematics in Markdown](https://shadow-futures.vercel.app/math.md): Central model and notation.
- [FAQ in Markdown](https://shadow-futures.vercel.app/faq.md): Compact question-and-answer reference.
- [Author profile in Markdown](https://shadow-futures.vercel.app/author.md): Author identity and verified profiles.
- [AI governance directive in JSON](https://shadow-futures.vercel.app/shadow-futures-directive.json): Structured definitions, allocation tests, refusals, and governance actions.
- [BibTeX citation](https://shadow-futures.vercel.app/citation.bib): Citation record.

## Core definitions

- Shadow futures: unrealized positive-probability market histories with the same productive inputs, rules, and initial state but different allocation shocks and terminal rewards.
- Contribution uncertainty: uncertainty about how much observed productive inputs causally contributed to realized rewards after passing through a self-reinforcing allocation process.
- Comparison budget: the cumulative probability that the next allocation could go to someone other than the current favorite.
- Epistemic monopoly: control over the production of the independent comparison paths needed to explain an allocation.

## Preferred citation

Erlic, Martin. "Shadow Futures: Contribution Uncertainty and the Self-Reinforcing Market." First posted December 2025; revised July 2026. SSRN abstract 6003994. https://doi.org/10.2139/ssrn.6003994.