Customer In A Store

You are looking at a graph of customers entering and leaving a store. The question seems simple: when is the store most crowded? Most people answer confidently — and most people are wrong. Customer In A Store is not a test of mathematical ability. It is a structured encounter with the limits of your own intuition, designed to show you exactly where your reasoning breaks down and what it takes to improve it.

Authors:

Dr. T. T. Niranjan

Academic Partner:

LEVEL

Undergraduate, MBA, Executive Ed, Graduate

TYPE

Single Player

DURATION

60 mins

DISCIPLINE

Business Analytics / Decision Sciences

Introduction

Customer In A Store is a short, web-based behavioural simulation designed to examine how individuals interpret dynamic systems involving inflows, outflows, and accumulation over time. Participants act as independent decision-makers who analyse graphical time-series data representing customer entry and exit in a retail store, inferring stock levels visually and identifying moments of maximum and minimum accumulation — without being shown cumulative values explicitly.

The simulation is intentionally designed to surface intuitive but incorrect reasoning rather than to reward technical skill. It highlights two systematic cognitive errors — stock–flow failure and the correlation heuristic — and evaluates the effectiveness of three structured learning interventions in correcting flawed intuition. The pedagogical intent is not to teach formulas, but to confront participants with their own cognitive biases and demonstrate how decomposition and feedback can improve reasoning in dynamic systems.

Learning Objectives

Distinguish stocks from flows clearly
Recognise biases in dynamic reasoning
Infer accumulation from flow patterns
Evaluate interventions to correct intuition
Apply stock–flow logic to real contexts

Key Features

Four-phase structure — Cognitive Reflection Test warm-up, baseline stock–flow tasks, three learning intervention treatments, and a final assessment phase — designed to expose bias and measure correction
Graph-based decision environment requiring visual inference of stock accumulation from inflow and outflow curves, with no explicit numerical calculations provided
Three structured learning interventions mapped to cognitive correction mechanisms, allowing comparison of learning-by-doing, task decomposition, and feedback across participants
Crossover and non-crossover scenarios that systematically vary the structural conditions under which stock–flow errors occur
Performance evaluated exclusively on the accuracy of responses to graph-based stock–flow questions, with post-simulation analysis enabling comparison across participants and treatment groups

Educational Outcomes

Ability to distinguish clearly between stocks and flows and accurately infer accumulation from net flow data
Recognition of stock–flow failure as a pervasive cognitive limitation, not a gap in technical knowledge
Understanding of how the correlation heuristic causes individuals to incorrectly associate stock levels with flow rates
Capacity to evaluate the relative effectiveness of learning-by-doing, task decomposition, and feedback in correcting flawed dynamic reasoning
Readiness to apply stock–flow reasoning to operational and managerial contexts including inventory, sustainability, and analytics

Topics Covered

Stock–Flow Reasoning & Accumulation

Stock–Flow Failure & Correlation Heuristic

System Dynamics Fundamentals

Cognitive Biases in Decision-Making

Learning Interventions & Behavioural Correction