Thinking, Problem-Solving & Creativity: Cognition in Action

TAKEAWAYS:

• Thinking is the mental process of manipulating information to understand, interpret, and solve problems, while problem-solving is a goal-directed cognitive process with distinct stages.
• Heuristics and algorithms are two strategies for problem-solving, each with trade-offs between speed and accuracy.
• Cognitive biases (e.g., confirmation bias, mental set) can both facilitate and hinder problem-solving, depending on context.
• Creative thinking involves divergent thinking and originality, whereas critical thinking requires logical analysis and evidence-based evaluation.
• Real-world examples like Daraz’s order prioritization (problem-solving) and Pathao’s surge pricing (adaptive decision-making) illustrate these concepts in action.
• Emotional intelligence plays a role in both problem-solving and creativity by managing emotions and fostering adaptability.

1. Defining Thinking and Problem-Solving

Thinking is the mental process of manipulating information to derive meaning, make decisions, or solve problems. It includes:

• Cognitive operations: Comparing, categorizing, inferring, and evaluating.
• Representations: Mental images, concepts, and symbols (e.g., language, numbers).
• Goals: Understanding, predicting, or solving issues.

Problem-solving is a subtype of thinking where individuals apply cognitive strategies to overcome obstacles and reach a desired outcome. It is goal-directed and involves:

• Identifying the problem.
• Generating potential solutions.
• Evaluating and selecting the best solution.
• Implementing and monitoring the solution.

Stages of Problem-Solving Thinking

Problem-solving follows a cyclical process with the following stages:

Key Stages Explained:

1. Problem Identification

   • Clearly define the problem (e.g., "Why is my Daraz order delayed?").
   • Avoid vague or overly broad definitions (e.g., "The system is slow").

2. Information Gathering

   • Collect relevant data (e.g., order status, delivery zone, historical delays).
   • Use heuristics (mental shortcuts) or algorithms (step-by-step procedures) to process information.

3. Problem Representation

   • Mentally model the problem (e.g., "Is the delay due to traffic or warehouse bottlenecks?").
   • Mental set: Pre-existing frameworks that help or hinder problem-solving (e.g., assuming traffic is always the issue).

4. Generating Solutions

   • Brainstorm possible solutions (e.g., reroute delivery, expedite warehouse packing).
   • Divergent thinking (creative, multiple solutions) vs. convergent thinking (logical, single best solution).

5. Evaluating Solutions

   • Assess feasibility, cost, and effectiveness (e.g., "Will rerouting save time or increase costs?").
   • Use critical thinking to weigh pros and cons.

6. Implementation

   • Execute the chosen solution (e.g., adjust delivery routes).
   • Requires decision-making under uncertainty.

7. Monitoring & Feedback

   • Evaluate outcomes and adjust if needed (e.g., "Did rerouting reduce delays?").
   • Feedback loops refine future problem-solving.

Worked Example: Daraz Order Delay

Problem: A customer’s Daraz order is delayed by 3 days. Solution Trace:

1. Identify: Order status shows "Processing" for 3 days beyond ETA.
2. Gather Info: Check order history, warehouse location, and delivery zone traffic.
3. Represent: Hypothesis: Warehouse backlog due to peak season.
4. Generate Solutions:
   • Expedite packing (costly).
   • Reroute to less congested delivery zone.
   • Offer partial delivery.
5. Evaluate:
   • Rerouting may reduce delay with minimal cost.
   • Partial delivery risks customer dissatisfaction.
6. Implement: Reroute order to a nearby delivery hub.
7. Monitor: Track new ETA and customer feedback.

2. Strategies for Problem-Solving

Problem-solvers use two primary strategies:

• Algorithms: Step-by-step procedures that guarantee a solution (e.g., Sudoku puzzles, mathematical proofs).
• Heuristics: Mental shortcuts that speed up problem-solving but may introduce errors (e.g., "If it worked before, try it again").

Common Heuristics and Biases

Heuristics often lead to cognitive biases, which distort problem-solving:

mindmap
  root((Cognitive Biases))
    Confirmation Bias
      "Only seek info that confirms pre-existing beliefs (e.g., 'Daraz is always late')"
    Availability Heuristic
      "Judge likelihood based on recent events (e.g., 'Pathao surge pricing caused my delay')"
    Representativeness Heuristic
      "Assume similarity implies probability (e.g., 'All delivery boys from Kathmandu are slow')"
    Mental Set
      "Rely on past solutions (e.g., 'We always use the same route')"
    Functional Fixedness
      "Overlook alternative uses (e.g., 'Phone can’t track traffic—it’s just for calls')"

Example of Bias in Real Life:

• Confirmation Bias in Pathao Drivers: A driver who always takes the same route may ignore new traffic patterns because they trust their usual path.
• Functional Fixedness in Household Chores: Using a spoon only to eat, not as a makeshift screwdriver when needed.

3. Barriers to Problem-Solving

Even with strategies, problem-solving can be hindered by:

1. Mental Set: Over-reliance on past solutions (e.g., always using the same password format).
2. Functional Fixedness: Inability to see new uses for familiar objects (e.g., using a book only as a book, not a makeshift ramp).
3. Overconfidence: Assuming one’s knowledge is superior to others (e.g., ignoring expert advice).
4. Emotional Factors: Stress or anxiety can narrow focus (e.g., panicking during an exam).
5. Lack of Information: Insufficient data leads to poor decisions (e.g., investing without research).

4. Creative Thinking vs. Critical Thinking

While both are forms of thinking, they serve different purposes:

mindmap
  root((Creative vs. Critical Thinking))
    Creative Thinking
      Divergent Thinking
        Generate multiple solutions (e.g., Daraz’s 5 alternative delivery methods)
      Originality
        Unconventional ideas (e.g., Pathao’s 'surge pricing + loyalty discounts')
    Critical Thinking
      Logical Analysis
        Evaluate evidence (e.g., 'Does rerouting save cost? Data: 60% yes')
      Evidence-Based
        Test hypotheses (e.g., A/B test surge pricing impact)

Worked Example: eSewa’s "Pay Later" Feature

Creative Thinking:

• Problem: Many users struggle with one-time payments (e.g., utility bills).
• Solution: Introduce a "Pay Later" option, allowing users to split payments.
• Process:
  1. Identify user pain points (surveys, feedback).
  2. Brainstorm flexible payment models (e.g., installments, deferred payments).
  3. Test prototypes with a small user group.

Critical Thinking:

• Evaluation:
  • Will this increase user trust or debt?
  • How will fraud be managed?
  • Is the feature scalable for all merchants?
• Decision: Pilot the feature with trusted partners first.

5. Enhancing Problem-Solving and Creativity

To improve these skills:

1. Diversify Perspectives: Seek input from others (e.g., brainstorming sessions).
2. Encourage Playfulness: Allow time for "unstructured" thinking (e.g., doodling, free writing).
3. Overcome Fear of Failure: Normalize mistakes as learning opportunities.
4. Use Structured Methods:
   • SCAMPER: Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, Reverse.
   • Six Thinking Hats: Separate emotional, logical, and creative perspectives.
5. Practice Mindfulness: Reduce stress to improve cognitive flexibility.

6. Real-World Applications

In the Real World

1. Daraz: Order Prioritization

   • Idea: Uses problem-solving algorithms to prioritize orders based on urgency, location, and delivery constraints.
   • How: AI analyzes historical data to predict delays and reroute orders dynamically.
   • Example: During festivals, Daraz may prioritize groceries over electronics to meet demand.

2. Pathao: Surge Pricing During Traffic

   • Idea: Applies adaptive decision-making using real-time traffic data.
   • How: Adjusts fares based on demand (e.g., higher prices during peak hours).
   • Example: A Pathao driver in Kathmandu may charge more during the morning rush to incentivize alternative routes.

3. Ncell: Customer Complaint Resolution

   • Idea: Uses structured problem-solving to address service issues.
   • How: Trained agents follow a flowchart to diagnose and resolve complaints efficiently.
   • Example: If a user reports a dropped call, Ncell may first check signal strength before escalating to hardware issues.

4. NEPSE: Stock Market Analysis

   • Idea: Relies on critical thinking to interpret market trends.
   • How: Analysts evaluate financial data, news, and historical patterns to make informed investment decisions.
   • Example: A trader may ignore a single day’s dip but act on a sustained downward trend.

Exam Tip

This unit is highly conceptual and often tested with:

1. Definitions: Expect questions on thinking, problem-solving, heuristics, and biases (e.g., "Define mental set").
2. Comparisons: Differentiate between creative vs. critical thinking, algorithms vs. heuristics.
3. Application-Based Questions: Justify how habits or mental sets can help or hinder problem-solving (e.g., "How does functional fixedness affect innovation?").
4. Real-World Scenarios: Apply concepts to everyday situations (e.g., "How would you solve a traffic jam using problem-solving strategies?").
5. Stages of Problem-Solving: Trace a real-life problem through the stages (e.g., "Explain how NTC would troubleshoot a network outage").

Key Tips for Full Marks:

• Use real examples (e.g., Daraz, Pathao) to illustrate answers.
• Structure answers clearly with stages, strategies, and barriers.
• For comparisons, use tables or diagrams to highlight differences.
• Always justify claims with examples (e.g., "Mental set can hinder problem-solving when past solutions no longer apply, as seen in Pathao drivers ignoring new traffic routes.").