Biased Probability Assignment for A/B Test Region Balance
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Solution Overview
Problem
A/B testing in online concierge systems faces challenges such as network effects and spillover effects, making it difficult to determine the effectiveness of system updates and leading to sub-optimal versions remaining in production, which can result in unnecessary resource consumption.
Innovation Solution
Implementing a random sequential algorithm that assigns geographic regions to treatment or control groups for A/B testing, using biased probability based on previous assignments to maintain balance and reduce conflict and swarming, ensuring fair comparisons and accurate analysis of system updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geographic regions are randomly assigned to treatment or control groups in A/B testing, then the assignment is simple and fast, but network effects and spillover effects cause imbalance and conflict between regions
Solution Approach 1:
The system maintains a log of previous assignments for each geographic region and uses this feedback to dynamically adjust assignment probabilities. The biased probability for each region is calculated based on the frequency of past assignments, ensuring that recently assigned regions are less likely to be assigned again soon, thereby reducing network effects and spillover while maintaining balanced treatment-control distribution.
2Measurement precision
If biased probability is used based on previous assignments, then balance and conflict reduction are improved, but the assignment process becomes more complex
Solution Approach 1:
The system automatically maintains the assignment log and calculates biased probabilities without manual intervention. The complexity is encapsulated within the automated assignment module, which reads from the log, computes the biased probability for each region based on previous assignments, and performs the assignment independently, reducing the perceived complexity for users.
3Productivity
If multiple experiments run simultaneously in the same geographic region, then resource utilization is improved, but conflict and swarming between experiments increase
Solution Approach 1:
The system introduces an intermediary assignment log that tracks assignments across multiple experiments and regions. This log acts as a mediator that coordinates assignments between simultaneous experiments, adjusting biased probabilities to reduce conflict and swarming effects while allowing multiple experiments to run in parallel across different geographic regions.
Data Source
AI summary
Test periods for an A/B test to be run in one or more geographic regions are set. Each test period in each geographic region is assignable to a treatment or control group. For each of plural test periods other than a first test period and for each geographic region, a biased probability indicating a probability of the test period being assigned to the treatment group of the A/B test is set. The biased probability is set based on a log of previous assignments for the geographic region indicating respective assignments for each previous test period including the first test period. The test period of the geographic region is assigned to one of the treatment and control groups of the A/B test based on the set biased probability. The A/B test is run in the geographic region and during the test period based on the assignment.