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EconEvals: Benchmarks and Litmus Tests for Economic Decision-Making by LLM Agents

Sara Fish, Julia Shephard, Minkai Li, Ran I. Shorrer, Yannai A. Gonczarowski · Mar 24, 2025 · Citations: 0

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How to use this paper page

Coverage: Stale

Use this page to decide whether the paper is strong enough to influence an eval design. It summarizes the abstract plus available structured metadata. If the signal is thin, use it as background context and compare it against stronger hub pages before making protocol choices.

Best use

Background context only

Metadata: Stale

Trust level

Low

Signals: Stale

What still needs checking

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Signal confidence: 0.35

Abstract

We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs. First, we develop benchmarks derived from key problems in economics -- procurement, scheduling, and pricing -- that test an LLM's ability to learn from the environment in context. Second, we develop the framework of litmus tests, evaluations that quantify an LLM's choice behavior on a stylized decision-making task with multiple conflicting objectives. Each litmus test outputs a litmus score, which quantifies an LLM's tradeoff response, a reliability score, which measures the coherence of an LLM's choice behavior, and a competency score, which measures an LLM's capability at the same task when the conflicting objectives are replaced by a single, well-specified objective. Evaluating a broad array of frontier LLMs, we (1) investigate changes in LLM capabilities and tendencies over time, (2) derive economically meaningful insights from the LLMs' choice behavior and chain-of-thought, (3) validate our litmus test framework by testing self-consistency, robustness, and generalizability. Overall, this work provides a foundation for evaluating LLM agents as they are further integrated into economic decision-making.

Use caution before copying this protocol

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Extraction flags indicate low-signal or possible false-positive protocol mapping.
  • Extraction confidence is 0.35 (below strong-reference threshold).
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit Score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What This Page Found In The Paper

Each field below shows whether the signal looked explicit, partial, or missing in the available metadata. Use this to judge what is safe to trust directly and what still needs full-paper validation.

Human Feedback Types

missing

None explicit

Confidence: Low Not found

No explicit feedback protocol extracted.

Evidence snippet: We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.

Evaluation Modes

partial

Automatic Metrics

Confidence: Low Direct evidence

Includes extracted eval setup.

Evidence snippet: We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.

Quality Controls

missing

Not reported

Confidence: Low Not found

No explicit QC controls found.

Evidence snippet: We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Not found

No benchmark anchors detected.

Evidence snippet: We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.

Reported Metrics

partial

Coherence

Confidence: Low Direct evidence

Useful for evaluation criteria comparison.

Evidence snippet: Each litmus test outputs a litmus score, which quantifies an LLM's tradeoff response, a reliability score, which measures the coherence of an LLM's choice behavior, and a competency score, which measures an LLM's capability at the same task when the conflicting objectives are replaced by a single, well-specified objective.

Rater Population

missing

Unknown

Confidence: Low Not found

Rater source not explicitly reported.

Evidence snippet: We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Unknown
  • Expertise required: General
  • Signal basis: Structured extraction plus abstract evidence.

Evaluation Lens

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Signal confidence: 0.35
  • Known cautions: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

coherence

Research Brief

Metadata summary

We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.
  • First, we develop benchmarks derived from key problems in economics -- procurement, scheduling, and pricing -- that test an LLM's ability to learn from the environment in context.
  • Second, we develop the framework of litmus tests, evaluations that quantify an LLM's choice behavior on a stylized decision-making task with multiple conflicting objectives.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Simulation environment) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.
  • First, we develop benchmarks derived from key problems in economics -- procurement, scheduling, and pricing -- that test an LLM's ability to learn from the environment in context.
  • Second, we develop the framework of litmus tests, evaluations that quantify an LLM's choice behavior on a stylized decision-making task with multiple conflicting objectives.

Why It Matters For Eval

  • We develop evaluation methods for measuring the economic decision-making capabilities and tendencies of LLMs.
  • First, we develop benchmarks derived from key problems in economics -- procurement, scheduling, and pricing -- that test an LLM's ability to learn from the environment in context.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: coherence

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Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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