Why Surveys?

Understanding Observer Behavior

The Power of Surveys

Surveys are essential tools for collecting data and insights from diverse participants. They enable researchers and decision-makers to map expected observer behavior, preferences, and opinions in a structured yet flexible manner.

Common survey types include:

1. Cross-sectional: Conducted at a single point in time to measure prevalence of behaviors or attitudes. Bitmason uses cross-sectional surveys to capture a snapshot of observer behavior in decentralized networks every month starting in 2025.

2. Longitudinal: Conducted over an extended period to track changes in behavior or attitudes.

3. Panel: Conducted with the same group at multiple points to study effects of specific events or interventions.

4. Cohort: Focused on a specific group sharing a common characteristic or experience.

Survey methods vary (in-person, telephone, online, mail), each with its own advantages and limitations.

The AI Challenge: Distorting Observable Behavior

The proliferation of machine learning models and AI systems has complicated our understanding of individual motivations and behaviors. This is particularly evident in decentralized networks like Bitcoin, where pseudonymous transactions obscure observer behavior while traditional network systems lose signal to noise with the rise of agentic AI.

Consider:

• An estimated 11% of Twitter accounts are bots (2022 research cited by Elon Musk)
• Approximately 5% of Twitter activity is bot-generated and increasing
• AI-generated content is becoming increasingly difficult to distinguish from human-generated content

The rise of models trained on non-human data creates a feedback loop, potentially disconnecting AI predictions from actual human behavior or beliefs. This degradation of the human signal in data is a critical yet often overlooked issue.

Surveys: Bridging the Gap Between AI and Human Behavior

Surveys provide a direct line to human participants via aligned incentives, capturing nuances and complexities often lost in AI-generated content. They offer insights into motivations, preferences, and attitudes that are difficult to glean through other means.

A shift towards human-generated data is crucial for ensuring models accurately reflect not just expected human opinions, but the actual state of human intention and behavior.

Baysian approaches are enriching the incentive structure of survey participant and survey designer relationships through breakthroughs such as the Baysian Truth Serum and Baysian Persuasion methods.

Enhancing Cryptographic Network Analysis

Surveys can complement blockchain metadata by providing a more comprehensive picture of observer behavior. This is particularly valuable in decentralized networks like Bitcoin, where the lack of central authority complicates data gathering.

Surveys offer:

• Insights into motivations behind pseudonymous transactions
• A degree of accuracy and uncertainty regarding expected observer behavior
• A deeper understanding of the challenges decentralization poses for data collection and analysis

Measuring Uncertainty in Decentralized Systems

Survey data collection in this context serves two primary purposes:

1. Measuring the expected behavior and uncertainty of observers (nodes) in a decentralized network

2. Assessing the state of the network itself

By providing a more complete picture of the network and its participants, surveys enable more accurate predictions and analysis in complex, decentralized systems while also reducing the noise introduced by agentic and autonomous AI systems.