See Scoop in action
Bring your data to life with AI-powered presentations—start your free trial of Scoop.
The only AI that does real data science. It finds patterns, segments, and predictors in your data — Try it now →
.jpg)
This case study matters for behavioral research and game design teams seeking to quantify how individuals engage with competitive tasks and adapt strategies over time. In an era where understanding micro-decision behaviors can power smarter digital engagement, Scoop’s AI reveals not just what people do—but why. With unsupervised analytics, pattern mining, and automated narrative generation, teams can move beyond superficial win/loss reporting to unlock actionable, experiment-driven insights for product and research advancement.
Scoop’s end-to-end automation transformed raw experimental data into actionable insights, fundamentally accelerating behavioral research velocity. Machine learning models quickly pinpointed the conditions most predictive of continued engagement and high win rates—offering research teams an interpretable ruleset for designing future experiments and interventions.
Crucially, the analysis: uncovered major gender differences in post-minimum engagement, quantified the nuanced impact of early wins/losses on behavioral adaptation, and revealed how ‘habitual’ (non-adaptive) play drives results. These findings provide a roadmap for predicting, segmenting, and influencing participant performance in competitive tasks.
Participants played an average of 4.4 rounds, exceeding the minimum, indicating substantial experiment-driven engagement.
Scissors was the dominant opening move (chosen by 64% of players), suggesting a strong initial selection bias.
Scissors was the dominant opening move (chosen by 64% of players), suggesting a strong initial selection bias.
Players who maintained the same move pattern (non-adaptive) achieved higher win rates, with 75% accuracy.
When no modifying conditions were present, players changed strategy after a loss 71% of the time, quantifying a general adaptive tendency.
Behavioral science teams have long struggled to dissect nuanced engagement and adaptation patterns from participant-level game data. Traditional BI tools and dashboards often provide only surface-level outcome statistics, failing to unravel the deeper psychological influences behind play. In competitive or experimental game settings, such as Rock-Paper-Scissors, uncovering how gender, experience, and adaptation drive engagement or success has required labor-intensive manual analysis. Researchers need to segment outcomes by participant traits, experiment conditions, and in-game choices to accurately model human decision strategies. However, data fragmentation, irregular round counts, and subtle response patterns make this difficult. The field needs tools that can synthesize detailed, round-by-round behavior—surfacing not just overall win rates, but the hidden logic behind when players persist, adapt, or disengage.
Automated Dataset Scanning & Metadata Inference: Scoop parsed complex, round-stacked records, inferred player-level and round-level fields, and classified key dimensions (gender, move patterns, outcome types), eliminating manual data wrangling.
This comprehensive AI-driven approach delivered depth and speed unattainable through manual analytics or traditional BI platforms.
.jpg)
Scoop’s ML-driven analysis surfaced behavioral dynamics often invisible to static reporting. For example, while dashboards might show overall engagement, only agentic automation unraveled that females not only engaged more beyond required rounds but also reacted differently to ties—being more likely to adapt strategies post-tie and post-loss. Automated rule extraction revealed subtle cause-and-effect: early success leads to confidence and strategy inertia, as 64% of first-round winners continued the same approach even after losing subsequent rounds. On the other hand, participants with no early success and flexible play styles were nearly guaranteed to change tactics after a loss (100% of such cases).
Crucially, the relationship between adaptation and performance was non-linear and experience-dependent. While inexperienced adapters (≤3 games) performed worse, those who adapted only after gaining more experience (>4 games) sometimes achieved superior win rates. Such non-obvious transition points—where the benefit of adaptation flips—would typically require extensive manual cohort analysis.
Additionally, the AI identified a prevalence of ties in first rounds (56 out of 107), outnumbering wins or losses twofold, exposing a natural synchronization bias likely missed by summary win/loss rates. These high-resolution findings provide a toolkit for designing experiments and digital engagements that harness intrinsic behavioral tendencies.
Enabled by Scoop’s rapid insights, research teams can now segment participants more precisely for follow-up studies, targeting strategies to maximize engagement or test interventions for specific subgroups (e.g., early winners vs. adapters). The clarity around adaptation thresholds and gender-specific responses provides actionable hypotheses for future experimental design, such as varying reward structures or game complexity. Follow-up could include real-time adaptive experiments powered by Scoop’s rules, tracking participant behavior to validate and refine psychological models. By leveraging Scoop’s continuous pattern detection, teams are positioned to iterate experiment protocols rapidly—advancing both academic understanding and applied behavioral modeling.