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Human strategic decision making in parametrized games

Sam Ganzfried · Apr 30, 2021 · Citations: 0

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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic to assume that a human can solve the game in real time. We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers. We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

This paper looks adjacent to evaluation work, but not like a strong protocol reference.
The available metadata is too thin to trust this as a primary source.
The abstract does not clearly describe the evaluation setup.
The abstract does not clearly name benchmarks or metrics.

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Should You Rely On This Paper?

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

Background context only.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

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

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Many real-world games contain parameters which can affect payoffs, action spaces, and information states."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Many real-world games contain parameters which can affect payoffs, action spaces, and information states."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Many real-world games contain parameters which can affect payoffs, action spaces, and information states."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Many real-world games contain parameters which can affect payoffs, action spaces, and information states."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Many real-world games contain parameters which can affect payoffs, action spaces, and information states."

Human Feedback Details

Uses human feedback: No
Feedback types: None
Rater population: Not reported
Expertise required: General

Evaluation Details

Evaluation modes:
Agentic eval: None
Quality controls: Not reported
Evidence quality: Low
Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

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

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Many real-world games contain parameters which can affect payoffs, action spaces, and information states.

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

Key Takeaways

Many real-world games contain parameters which can affect payoffs, action spaces, and information states.
For fixed values of the parameters, the game can be solved using standard algorithms.
However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance.

Researcher Actions

Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
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

However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance.
We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers.
We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information.

Why It Matters For Eval

However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance.
We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers.

Researcher Checklist

Gap: Human feedback protocol is explicit

No explicit human feedback protocol detected.
Gap: Evaluation mode is explicit

No clear evaluation mode extracted.
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.
Gap: Metric reporting is present

No metric terms extracted.

Related Papers

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

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