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Problem-Solving with Theory and Hypothesis Creation - Coggle Diagram
Problem-Solving with
Theory
and
Hypothesis
Creation
Steps
Theory Creation
For strategic and systemic problem-solving
, theory creation serves as a robust foundation.
Problems are complex
or multifaceted.
Insights need to connect
multiple hypotheses
cohesively.
Generalizability
and scalability are important.
Can be skipped If the goal is merely
to address specific problems quickly
Inputs:
Hypotheses, patterns in data, domain knowledge.
Develop a unifying explanation that connects hypotheses and provides a broader understanding of the system.
Tools:
Conceptual modeling, causal diagrams.
Outputs:
A generalizable, falsifiable theory.
Example Theory:
“Driver engagement is driven by the perceived balance of effort, reward, and predictability. Optimizing these factors improves trip acceptance and platform loyalty.”
Problem
Definition
Inputs:
Stakeholder feedback, performance metrics, business context.
Clearly articulate the problem in terms of symptoms, goals, and stakeholders.
Tools:
Problem statements, SMART goal frameworks.
Outputs:
A concise problem statement.
Symptoms
Goals
Stakeholders
Example:
“Drivers are disengaging with the platform, leading to a 15% drop in trip acceptance rates in NYC during peak hours.”
Problem
Decomposition
Inputs:
The problem statement, relevant data, and domain knowledge.
Break the problem into variables and relationships (e.g., drivers’ pay and trip acceptance rates). Break the problem into smaller components to understand root causes and contributing factors.
Tools:
Fishbone diagrams, 5 Whys, systems thinking.
Outputs:
A structured map of variables, such as independent, dependent, and control variables.
Dependent Variables:
Acceptance rates, hours logged.
Control Variables:
Traffic, time of day, region.
Independent Variables:
Earnings per trip, distance, app usability.
Hypothesis
Generation
(Conceptualization)
Inputs:
Decomposed variables, prior observations, stakeholder insights.
Propose how variables interact. Formulate
specific, testable statements
based on the problem decomposition.
Tools:
Logic, hypothesis templates.
Outputs:
A set of hypotheses linking variables to measurable outcomes.
H1: Higher pay → Higher acceptance.
H2: Transparency → Higher trust.
H3: More offers → Higher acceptance.
H4: We belive bonuses during peak hours affect acceptance rates
H5: We belive that app workflows affects session duration
Communication
and Implementation
Inputs:
Validated hypotheses and theories.
Share findings and implement validated solutions.
Tools:
Dashboards, reports, presentations.
Outputs:
Actionable plans, stakeholder buy-in.
Finding:
Bonuses increase engagement only in high-traffic areas.
Implementation:
Roll out bonuses selectively in NYC high-demand zones.
Example:
Implement regional bonus schemes and driver training for transparency features.
Iteration and Refinement
Inputs:
Experiment results, stakeholder feedback.
Use test results to refine hypotheses, theories, and operational strategies.
Tools:
Post-mortem analysis, feedback loops, root cause analysis.
Outputs:
Updated hypotheses or a revised theory.
Refinement:
Investigate regional factors influencing driver behavior.
Result:
Bonus increases worked in NYC but not in LA.
Testing
Inputs:
Operationalized hypotheses, testing plans.
Conduct experiments or collect observational data to validate or refute hypotheses.
Tools:
A/B testing, regression analysis, randomized controlled trials.
Outputs:
Validated or rejected hypotheses, data insights.
A/B test in NYC: Group A gets a bonus; Group B doesn’t. Measure acceptance rates in both groups.
Operationalization
Inputs:
Hypotheses and theories.
Translate abstract ideas into measurable or testable actions.
Tools:
Metrics frameworks, experimental design, data collection methods.
Outputs:
Clear metrics, testing criteria, and experimental designs.
Metric for "reward": Earnings per trip in dollars.
Experimental plan: Increase bonuses for trips >10 miles; measure acceptance rates.
Metric for "effort": Average trip distance in miles.