The Twist in Creative Problem Solving: Objective, Facts, Claims, and AI cautions
Overview of the session
Focus: creative problem solving with a concrete, personal example to illustrate the process (the “squat with the twist”).
Aim: apply the creative problem solving framework step by step, and connect to a business case analysis.
Core idea: start with the problem from the perspective of the person or organization, not immediately jumping to a solution.
Note on method: use a twist on the usual SWOT approach to reveal deeper insights and avoid surface-level fixes.
The twist: using creative problem solving to tackle a problem
The twist (the twist tool) is a method to tackle a problem by moving through objective planning and rigorous inquiry rather than leaping to solutions.
The twist helps you begin with the problem’s context, not just the supposed solution.
It can be applied to personal problems first and then extended to business cases (e.g., business case analysis).
Typical SWOT analyses often miss key connections when you start with strengths and opportunities without grounding them in a clear problem.
The speaker emphasizes starting at the objective plan and using the twist as a tool to organize thinking rather than presenting a full SWOT on slides.
Starting from the objective: what is the objective?
Objective finding is the core of the twist: define what “solved” looks like.
Example from the speaker: objective = find a doctor who can actually help and specializes in something connected to the speaker’s health issue.
Structure:
Objective: state what solving the problem would look like.
Then proceed to fact finding to determine how to reach that objective.
The objective should be specific and measurable in the context of the problem.
Fact finding: gathering information to support the objective
The process involves asking what kinds of information you need to verify and locate.
You compare current situation (e.g., current doctor) to the objective (specialized doctor).
The speaker emphasizes the importance of having a real contact within the organization (not a hypothetical scenario).
Confidentiality and real information: sometimes people hide information; to address this, consider confidentiality agreements when needed.
The speaker warns against superficial conclusions that come from surface observations; you must dig for supporting facts.
Practical notes:
Track relevant details (e.g., doctor availability, specialization areas).
Identify limiting variables that might affect information gathering (e.g., appointment wait times).
Example given: difficulty finding a doctor who specializes in a relevant cancer-related issue; four-month wait times for appointments.
Problem finding: diagnosing the real problem beyond the obvious
The process involves scrutinizing the problem to determine if the initial problem statement is correct or if there is a deeper issue.
The speaker recounts personal experience about health issues (e.g., hand pain) and how that led to rethinking the problem rather than just patching symptoms.
They discuss how stress, diet, and other confounding variables can cloud the actual cause.
It’s common to misidentify the root problem if you don’t continue digging and validating facts.
Six honest serving men: Who, What, Where, When, Why, How – used to frame the inquiry and avoid missing important angles.
Examples of misdirection: symptoms may point to weight, but the underlying problem could be stress management, sleep, or metabolic factors.
Important reminder: be honest about what the real problem is; avoid taking the first assumption as the whole truth.
Questions to explore the problem (Who, What, Where, When, Why, How)
Who is affected by the problem? Self, family, coworkers, customers, or employees in an organization.
What is affected? Health, productivity, relationships, financial costs, or organizational performance.
Where does the problem persist? Specific body areas, environments, or times of day.
When is the problem most evident? Seasonal patterns, stressful periods, or specific activities.
Why is this a problem? Health risks, performance decline, or negative ripple effects in the organization or personal life.
How could the problem be addressed? Through behavior changes, process changes, or systemic changes; consider trade-offs and feasibility.
The weight loss example is used to illustrate how to apply the Who/What/Where/When/Why/How questions to a problem: collecting data, identifying triggers, and understanding root causes.
Collecting facts: measuring and gathering data
For weight loss as an example, collect a broad set of data:
Height, BMI, weight, body composition, muscle mass, endurance indicators (e.g., mile time), etc.
Diet data: track what is eaten, caloric intake, macronutrient balance, and eating patterns.
Activity levels: daily steps, workouts, sedentary time.
Other factors: sleep quality, stress, hormones, thyroid function, hydration, and sleep-related eating patterns.
Emphasize that there are many potential contributing factors (stress, metabolism, hormones, sleep, hydration, etc.).
The goal is to build a full information set to understand what is really driving the problem, not to rush to a simplistic conclusion.
The speaker notes that many people mistakenly blame weight gain solely on diet or lack of exercise without considering other variables.
The role of confounding variables: stress, sleep deprivation, diet quality, activity levels – all interact, making it hard to tease apart causes.
The outcome of this phase should lead you to a redefined problem statement that reflects what the data show.
What is the real problem? Reframing the problem
The investigation may reveal that the original problem was not the actual driver; the true problem could be something like weight management being tied to stress or sleep rather than weight per se.
The problem could shift to a more practical or different objective (e.g., feeling better, improving energy, or health risk mitigation) rather than simply losing weight.
The clinician/organization context: some issues require more fundamental changes (e.g., access to specialized care, or changing a process within an organization).
It’s important to consider why you should address the problem in the first place (health benefits, cost savings, or achieving a desired outcome such as better energy or compatibility with life goals).
The role of perception, biases, and ethics in problem solving
Perception issues: self-deception and bias can distort objectivity when diagnosing problems.
Social desirability bias: the tendency to present oneself or an organization in a favorable light, which can mask the true situation.
If information is potentially biased or hidden, confidentiality agreements or insistence on transparency may be needed to obtain real data.
Contrast effects: perception depends on surrounding cues, which can lead to misjudgments (e.g., comparing sizes or values in a misleading way).
Real-world example: perceiving pain relief after a visit to a chiropractor or misinterpreting improvement due to lingering expectations.
Caution about over-claiming or misrepresenting information to executives or stakeholders; present the findings honestly and selectively (show the outcome, not every calculation).
The practical caveat: avoid slides that dump the entire SWOT with the twist
Guidance on presenting: never put a full SWOT analysis on a slide while presenting the problem.
If you share the SWOT, consider a hidden slide or a separate document that shows how the twist informed the decision.
The key is to explain how the twist affected decision making and highlight the conclusions without overwhelming the audience with raw analyses.
The caution about AI in analysis and integrity
The speaker warns that AI struggles with this kind of analytical task and is not reliable for performing the core problem-finding and claim-dissection work.
It may help as a supplement, but it cannot replace careful, human-driven analysis.
If AI is used, be prepared to defend or adjust its outputs; do not rely on it to replace your critical thinking.
Some students may use AI to attempt assignments; the instructor emphasizes that AI-based work will not meet the assignment requirements and may result in a zero.
Takeaway: the skill of dissecting claims, separating the components of information, and building strong, defensible conclusions remains a human-centric task.
Assessing claims: identifying, dissecting, and evaluating claims
Core goal: gain a clear understanding of what is actually being claimed and how solid the claim is.
Strengthen or dismantle claims by examining the individual components of information.
Step-by-step method for assessing claims:
Identify a single, discrete claim (one sentence or a clearly defined element).
If sentences contain quotes, split them into separate claims while preserving original meaning.
Distinguish between statements of fact and opinions; treat opinions as subjective and not easily refutable by data.
Use a labeling system (e.g., claim numbers or names) to track individual claims that exist within longer sentences or quotes.
Avoid conflating multiple claims in a single sentence; break them down to isolated units for evaluation.
If a sentence contains ambiguous terms like "and so on" or vague descriptors, note them as uncertain and seek explicit clarification.
When a claim references where something or someone said something, verify the source (e.g., did the manager actually make that claim, or is it a paraphrase?).
Example: split a long sentence with multiple clauses into separate claims (e.g., Claim 1: The product is fast; Claim 2: The product is reliable; Claim 3: The product is easy to use).
If a claim is satisfied only by untestable or undefined terms, mark it as weak and seek measurable criteria.
Practical tips:
Use tools like Word to manage sentence boundaries and ensure claims don’t cross sentence lines unexpectedly.
When a source includes a pronoun that could refer to different subjects, redefine the claim with clear references.
If you encounter a quote you cannot fairly segment, provide commentary to preserve interpretation while showing understanding of the original meaning.
Examples of problematic constructions:
A sentence that mixes claims and generalities (e.g., "The new product is fast, reliable, easy to use, affordable, and so on.") should be broken into explicit claims and the "and so on" should be clarified or discarded.
Ambiguity in sources that makes a claim uncertain or unverifiable should be noted, and the claim may be treated as exploratory rather than conclusive.
The end goal: identify the strongest, most defensible claims and be able to explain why they are strong (see the Carrot and Diamond mnemonic).
The Carrot and Diamond mnemonic: crafting strong claims
Purpose: guide the strength and quality of claims you present or derive from evidence.
Elements of a strong claim (Carrot–Diamond concept):
Clarity: the claim is very clear and easy to understand.
Narrow application: applies to a small, well-defined set of situations or individuals.
Recognized measurement: uses a standard, verifiable metric or measurement.
Not prescriptive: is a factual statement, not a directive about what should be done.
Time reference: includes a time element (past, present, or future) to anchor the claim.
Practical use:
Aim for claims that meet these criteria to avoid vague, broad generalizations.
Broad claims (e.g., AI cannot do this for all information) are weaker and more vulnerable to exceptions.
Narrow claims are easier to defend and provide a clearer basis for action.
Example emphasis: a statement like "AI cannot perform this complex analysis across all contexts" is too broad; a stronger claim would specify the contexts and the limits of AI performance.
Cautions:
Avoid overly broad generalizations (e.g., all, none, always, never) unless you can prove them across all cases.
If a claim uses broad language, demand concrete scope, measures, and timeframes.
In discussions, show how you tested or validated claims; present sources and calculations selectively to support the strongest claims.
Strengthening claims: using precision and avoiding ambiguity
Criteria for strong claims (Carrot–Diamond):
Clear: unambiguous and precise in meaning.
Narrow: limited application to a defined subset.
Recognized measurement: relies on a standard metric or benchmark.
Not advice-driven: describes a fact or observed phenomenon rather than telling someone what to do.
Time-bound: specifies when the claim applies or was observed.
Practice tips:
Avoid ambiguous language and domain jargon that others may interpret differently.
Decide on a single, testable claim per sentence or per data point.
If needed, provide definitions for key terms to ensure consistent interpretation.
Ambiguity examples to avoid:
All bodies are beautiful (subjective and broad; lacks a measurable standard).
Statements that invite infinite interpretations (e.g., "and so on").
Absolute absolutes (e.g., all, none, always, never) unless proven across all contexts.
The role of audience interpretation:
People will mentally test strong claims against exceptions; strong claims withstand scrutiny better when they are narrow and well-defined.
Objective vs. subjective: how to handle measurements and truth values
Objective claims: have a truth value that can be evaluated using standard measures (degrees, inches, metrics, tests).
Subjective claims: depend on personal opinions and experiences; these can be debated but are not falsifiable in the same way as objective claims.
Evidence-based evaluation:
Use impersonal standards (quantitative measures) where possible to establish objectivity.
Recognize when a claim is inherently subjective (taste, aesthetics, personal experience) and avoid treating it as factual in decision-making.
Examples:
Objective: "The product reduces processing time by 25% within 4 weeks of use." (measurable, time-bound)
Subjective: "This flavor is the best flavor." (preference-based, not verifiable universally)
The moderator’s point about language:
Avoid broad or universally applied adjectives; seek measurable and observable criteria.
When making a claim about people (e.g., a person’s behavior), ensure that the description is based on observable evidence and clearly defined terms.
Ambiguity, definitions, and avoiding misinterpretation
The speaker emphasizes defining terms to avoid misinterpretation (e.g., what does "broad" mean in a claim? What does "beautiful" mean in a given context?).
Definitions should be explicit, especially in legal or formal documents; adopt consistent terminology for ongoing analysis.
Examples of clarity pitfalls:
The phrase "All bodies are beautiful" is highly subjective and not universally verifiable.
Using colloquial or regional terms without definition can cause confusion (e.g., "soda" vs. "pop" vs. "soda fountain").
Practical approach:
Define key terms at the outset when writing claims or presenting analyses.
If a term is culturally dependent, provide a precise definition or measurement standard.
Practical presentation guidance: showing work without overloading the audience
When presenting your analysis, you should show results, not every step of the internal calculation:
Demonstrate the conclusions and the reasoning that led to them, not the entire raw analysis.
Use a secondary, hidden, or supplementary slide to show the full analysis if needed.
The aim is to convey how the twist changed the decision-making process and what the final recommendations are, not to relitigate every data point.
If you are discussing organizational decisions, be prepared to defend why a particular objective was chosen and how the information supported that choice.
Real-world, ethical, and practical implications
Personal accountability: the practitioner should be honest about what is known, what is uncertain, and what remains to be explored.
Confidentiality and data integrity: in workplace or clinical contexts, protect sensitive information while ensuring enough transparency to support sound decisions.
Ethical risk: misrepresenting information or manipulating claims can lead to poor decisions and harm; responsible disclosure and rigorous validation are essential.
Practical relevance: the weight-loss example shows how to connect personal data collection to problem framing, but the same framework applies to business problems (e.g., identifying root causes in operations or market strategy).
Relational and ripple effects: a problem in one area (e.g., weight management) can affect others (family dynamics, social activities); similarly, a business problem can impact suppliers, employees, customers, and partners.
Connections to prior learning and real-world relevance
The six honest serving men frame ties to classic investigative questioning methods used in research, journalism, and problem solving.
The critique of presenting full analyses on slides connects with best practices in management presentations: show outcomes, not every intermediate step.
The discussion of biases, perception, and social desirability bias links to core topics in psychology, behavioral science, and decision theory.
The emphasis on identifying and dissecting claims connects to critical thinking, logic, and evidence evaluation in academic and professional settings.
The weight loss example illustrates systems thinking: symptoms, root causes, and the interaction of multiple variables (diet, sleep, stress, activity, metabolism).
Summary of actionable takeaways
Start with a clear objective before diving into solutions; use the twist to frame the problem in terms of what a solved state looks like.
Use fact finding to gather relevant, verifiable information; seek internal champions or contacts to obtain real data; consider confidentiality when needed.
Use the six honest serving men to structure inquiry: Who, What, Where, When, Why, How.
Dig beyond surface issues to identify the real problem; be prepared to shift the problem statement as new information emerges.
Recognize biases (perception, self-deception, social desirability) and address them with transparent data collection and, if necessary, confidentiality agreements.
Use the weight loss example as a template for applying the framework: collect data (height, BMI, diet, activity, sleep, stress, hormones), ask Who/What/Where/When/Why, and evaluate root causes.
When presenting results, avoid dumping the entire analysis on slides; show the conclusions and the reasoning that supported them, with a separate place to access the full analysis if needed.
Be wary of AI-assisted work in this domain; AI is not reliable for producing rigorous problem-finding and claims-dissection; rely on disciplined human analysis for exam-grade work.
When forming claims, aim for Carrot–Diamond strength: clear, narrowly applied, measurement-based, non-prescriptive, and time-bound.
Avoid broad generalizations and ambiguous language; be specific about scope, definitions, and evidence.
Key terms to remember
The twist: a tool for structuring creative problem solving from objective to problem identification.
Six honest serving men: Who, What, Where, When, Why, How.
Claims assessment: identify, dissect, and evaluate individual claims.
Carrot and Diamond mnemonic: criteria for strong, testable claims.
Objective vs. subjective: measurable standards vs. personal opinions.
Biases: perception bias, social desirability bias, contrast effects.
Proper presentation: show outcomes and decision logic, not every calculation; maintain confidentiality when needed.