Scenario Template: The Framework for Evaluating Decision-Making

This structured template was designed to evaluate decision-making processes in a controlled and uniform manner across both human participants and AI systems. By simulating real-world challenges, it ensures consistency, reliability, and objectivity throughout the research process.

Scenario Overview

Scenario Title: A concise title summarizing the scenario, e.g., "Strategic Resource Allocation for Market Entry."

Objective:
Define the specific decision to be made, such as:
“Allocate limited resources to maximize ROI for market expansion.”

Description:
Provide a contextual narrative for the problem. For example:
“A company must allocate a $2 million budget to enter a competitive market, balancing staffing, marketing, and operational costs within a six-month timeframe.”

Key Variables:

• Inputs: Budget constraints, competitor data, operational timelines, and customer demand projections.
• Decision Criteria: ROI, risk mitigation, timeline adherence, and stakeholder alignment.

Uniform Parameters:
The same inputs, constraints, and objectives were presented to both humans and AI, ensuring that tasks followed a consistent sequence across all participants.

Stages of Decision-Making

1. Problem Recognition

• Task: Identify the core issue or opportunity.
• Input Data: Key performance indicators, e.g., declining sales or customer churn rates.
• Metrics Captured: Time taken and accuracy in recognizing the problem.

2. Information Gathering

• Task: Collect and analyze relevant data.
• Input Data: Market analysis reports, competitor benchmarks, and customer surveys.
• Constraints:
  • Humans: Allowed 24–48 hours.
  • AI: Processed all inputs simultaneously.
• Metrics Captured: Sources consulted, time spent, and depth of analysis.

3. Risk Assessment

• Task: Evaluate the potential downsides of decisions.
• Criteria: Financial, reputational, and operational risks.
• Process:
  • Humans: Relied on experience and intuition.
  • AI: Used predictive algorithms to score risks quantitatively.
• Metrics Captured: Accuracy and effectiveness of risk mitigation.

4. Stakeholder Consultation (Humans Only)

• Task: Collaborate with team members or external experts.
• Constraints: Limited to input from 3–7 stakeholders.
• Metrics Captured: Time spent, diversity of perspectives, and their influence on the final decision.

5. Scenario Simulation (AI Only)

• Task: Simulate and evaluate potential decision paths.
• Process: Generate over 10,000 scenarios, varying key variables like budgets and timelines.
• Metrics Captured: Number of scenarios, confidence levels, and alignment with success metrics.

6. Alternative Exploration

• Task: Develop multiple viable solutions.
• Process:
  • Humans: Collaborative brainstorming.
  • AI: Algorithmically generated and ranked options.
• Metrics Captured: Number, diversity, and creativity of proposed solutions.

7. Final Decision

• Task: Select the optimal course of action.
• Output Format:
  • Humans: Provided narrative justifications.
  • AI: Delivered recommendations with confidence scores.
• Metrics Captured: Time required, decision quality, and alignment with success criteria.

Evaluation Metrics

To ensure uniformity, the following metrics were captured for both humans and AI:

• Speed: Total time taken for each stage and overall decision-making.
• Accuracy: Adherence to predefined success criteria like ROI or timeline.
• Creativity: Quality and innovation of proposed solutions.
• Bias:
  • Humans: Measured emotional and cognitive influences.
  • AI: Assessed potential algorithmic limitations or data quality issues.

Controlled Scenarios

Each scenario replicated real-world complexities, ensuring consistent challenges for both humans and AI. For example:

Scenario Title: Crisis Management for Supply Chain Disruption
Description: A global company faces a major supply chain disruption. Participants must allocate resources to minimize customer impact while maintaining operational efficiency.
Input Data: Inventory levels, shipping delays, demand projections, and financial constraints.
Constraints: $2 million budget and a 72-hour resolution window.
Tasks:

• Problem recognition and framing.
• Data collection and analysis.
• Risk assessment and scenario simulation.
• Generation and selection of optimal solutions.

Data Collection and Validation

To ensure data integrity and actionable insights, the following methods were employed:

• Timing Metrics: Recorded the duration of each stage for humans via observation and for AI through automated logs.
• Outcome Metrics: Evaluated decisions against success indicators like ROI or risk mitigation.
• Cross-Validation: Analyzed datasets to identify patterns, strengths, and opportunities for improvement.

Key Insights

This template enabled a direct and meaningful comparison of decision-making processes, revealing strengths and limitations across both approaches. By applying this structured framework, the study identified areas where humans and AI excel—and opportunities to integrate their strengths for more effective outcomes.

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