8.4 Cognitive Bias Detection

Detects systematic thinking errors and designs debiasing interventions to improve decisions. Launch on platform.

What is it?

Dragonfly's Cognitive Bias Detection Lens provides forensic analysis of decision-making processes to identify and quantify cognitive distortions that could undermine strategic effectiveness. By systematically detecting patterns across 40 documented cognitive biases, it prevents costly strategic errors and enhances decision quality through evidence-based debiasing interventions.

Why is it useful?

• Detect systematic thinking errors across 40 cognitive bias patterns before they manifest in strategic mistakes

• Quantify decision quality risks in concrete terms, including potential dollar impact of uncorrected biases

• Identify compound effects where multiple biases amplify each other to create exponentially greater risks

• Design targeted debiasing interventions tailored to specific cognitive distortions and organisational contexts

• Build organisational capability for systematic bias detection and prevention across all strategic decisions

• Compare biased versus debiased decision trajectories through scenario modelling to reveal hidden value protection

How does it work?

The Cognitive Bias Detection Lens applies systematic forensic analysis to identify cognitive distortions and design evidence-based interventions that improve strategic decision quality.

Comprehensive Bias Detection Engine

• Focus: Pattern recognition across 40 cognitive bias categories to identify active distortions in decision-making processes

• Example: Analysing a $50M acquisition decision to detect availability heuristic (recent IPO inflating success probability), confirmation bias (selective due diligence), and groupthink (no documented dissent despite clear risk factors)

Bias Interaction and Compound Effect Analysis

• Focus: Identify how multiple biases reinforce each other to create exponentially greater risks than individual biases alone

• Example: Revealing how overconfidence bias combines with sunk cost fallacy and anchoring bias in failing projects, creating 3x severity multiplier that prevents timely course corrections

Risk Scoring and Impact Quantification

• Focus: Calculate likelihood and impact scores for identified biases, translating cognitive risks into concrete business terms

• Example: Quantifying $30M value at risk over 3 years from unaddressed confirmation bias in market entry strategy, with 70% likelihood of materialization without intervention

Debiasing Architecture and Intervention Design

• Focus: Create specific, actionable interventions tailored to detected bias patterns and organisational context

• Example: Designing reference class forecasting protocols, mandatory red team reviews, and structured devil's advocate processes to counter identified overconfidence and groupthink patterns

Scenario Modelling and Trajectory Comparison

• Focus: Project decision outcomes under biased versus debiased conditions to reveal protected value and improved outcomes

• Example: Modelling 3-year scenarios showing biased path leading to 40% project failure rate versus debiased path achieving 85% success rate through systematic bias correction

Organisational Bias Prevention Framework

• Focus: Design systematic approaches that embed bias detection into standard decision-making processes

• Example: Creating decision checklists, mandatory cooling-off periods for major decisions, and rotating devil's advocate roles to institutionalize bias prevention across the organisation

Turning Cognitive Bias Detection into Action

• Implement systematic bias audits for high-stakes decisions rather than relying on intuition or standard due diligence processes

• Create organisational bias detection capabilities that prevent cognitive errors before they manifest as strategic mistakes

• Design decision-making frameworks that systematically counter identified bias patterns through evidence-based interventions and process improvements