• CONFLICT
  • DRIFT
  • RISK
  • HUMAN

How to analyze stakeholder incentive alignment with your AI

Connect your reward structures to your actual goals. Your AI identifies the friction points where incentives push people in the wrong direction.

Ask AI about this page

Short answer

How do I check if my reward structures are working?

You feed your proposed compensation or reward rules into your AI client. The AI uses mental models to spot where those rewards actually encourage behaviors that conflict with your intended outcomes. You get a breakdown of where your incentives fail.

The outcomes.

Where the friction shows up.

The AI categorizes the risks it finds in your incentive models.

CONFLICT

Direct goal clashes

The AI flags where a specific reward directly incentivizes someone to ignore your primary objective. You see exactly which payout triggers the wrong action.

DRIFT

Slow incentive decay

This identifies where rewards encourage people to focus on easy, low-value tasks instead of the hard, high-impact work you actually need.

RISK

Systemic vulnerabilities

The AI highlights structural flaws that could lead to widespread gaming of the system. You get a list of potential points of failure.

HUMAN

Manual review needed

The AI points out complex social dynamics or cultural nuances it can't model. These are the areas where you must step in and make the final call.

The workflow

What your AI does when the data arrives.

The AI processes your incentive documents through a specific logical framework.

  1. Ingest reward structures

    Your AI reads through your compensation spreadsheets, contract terms, or bonus descriptions to understand the rules of the game.

    validate_munger_latticework

  2. Map incentive paths

    The AI traces how a specific dollar or reward moves through the system to influence individual choices.

    validate_munger_latticework

  3. Identify misalignments

    The agent compares the intended outcome against the likely behavior triggered by the reward structure.

    validate_munger_latticework

  4. Flag edge cases

    The AI isolates specific scenarios where the incentive model breaks down or encourages extreme, unintended behavior.

    validate_munger_latticework

Try it

Copy these to start.

Use these prompts to kick off the analysis.

Starting points

Swap out the bracketed text for your actual documents or specific plan names.

Munger Latticework Prover Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_bm5eaupy69UlUkNfa4VBoomvZ5LR8GRbI3QTdssk/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Munger Latticework Prover capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "munger-latticework-prover-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_bm5eaupy69UlUkNfa4VBoomvZ5LR8GRbI3QTdssk/mcp"
    }
  }
}
Copy into chat04

  • Analyze this compensation spreadsheet for any incentives that might encourage short-term thinking over long-term growth.

  • Look at the bonus structure in this PDF and tell me if it creates a conflict of interest for the sales team.

  • Review the reward rules in this Google Doc and find where they might drive people to game the system.

  • Check this project incentive plan against our core company values to see if they align or clash.

  • Claude
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  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Munger Latticework Prover once, then ask.

One connection gives your AI access to these mental models. You can then use any compatible client to run these checks immediately.

Connect Munger Latticework Prover to your AI

FAQ

How this task behaves.

  • 01

    Can the AI change my compensation plans?

    No. The AI only analyzes the data you provide. You must manually edit your plans in your own documents.

  • 02

    Does the AI understand the social politics of my office?

    No. The AI focuses on the logical relationship between rewards and behaviors. It cannot account for unwritten social rules or office politics.

  • 03

    What kind of files can I use for this?

    You can use any text-based data your AI client can read, such as spreadsheets, PDFs, or text documents containing your incentive rules.

  • 04

    Will the AI automatically send these reports to my manager?

    No. The AI only performs the analysis within your chat session. You decide what to share and with whom.

  • 05

    Can it predict exactly how much money people will make?

    No. It identifies behavioral risks and misalignments, not precise financial forecasting.

  • More questions about Munger Latticework Prover? The Connector page answers them. See everything the Munger Latticework Prover Connector can do

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