Of course. This is a fascinating and complex predictive analysis problem. Structuring this expert roundtable correctly is critical to moving from speculation to a robust, data-driven investment thesis.

Here is a professional research proposal outlining the plan to structure this analysis.

Business Research Proposal: Predicting the SpaceX IPO Ticker

This plan is designed to systematically analyze the qualitative and quantitative factors influencing SpaceX's ticker choice. It will leverage a Multi-Criteria Decision Analysis (MCDA) framework, informed by an expert roundtable discussion, to produce actionable investment insights.

Part 1: Information Collection Plan

This phase focuses on gathering the raw data and expert opinions needed to build our analytical model.

A. Web Search & Data Gathering

First, we need to establish a baseline of factual information. This data will serve as the foundation for the expert discussion, ensuring it is grounded in reality.

Web Search Content & Purpose:

• Queries:
  • "NYSE and NASDAQ ticker symbol reservation rules and availability"
  • "analysis of Elon Musk's company naming and branding decisions"
  • "investor sentiment SpaceX ticker symbol Reddit WallStreetBets"
  • "Polymarket prediction market rules and fees"
  • "expert analysis of Starlink's contribution to SpaceX revenue 2025 2026"
  • "history of ticker symbols for major tech IPOs"
• Purpose of this research:
  • Ticker Rules: To understand the specific constraints (e.g., character limits, availability) that the exchanges impose. This will help us eliminate non-viable options immediately. For instance, Nasdaq allows for up to five characters, while the NYSE typically uses one to three.
  • Musk's Psychology: To identify patterns in Musk's past naming choices (e.g., the "SEXY" joke with Tesla models, the directness of "The Boring Company," his affinity for "X"). This provides a behavioral lens for our analysis.
  • Revenue & Brand Story: To get hard data on the Starlink vs. core SpaceX business split. This is crucial for determining whether the ticker will reflect the legacy Mars mission or the current revenue engine, Starlink.
  • Market Dynamics: To understand how prediction markets like Polymarket function, their fee structures, and how their odds represent a real-time probability consensus. This is essential for the trading recommendation deliverable.
  • Investor Sentiment: To gauge the "viral appeal" and expectations of the retail investor community, which can influence a ticker's momentum.

B. Recommended User Research Method: Expert Discussion (discussionChat)

To address the multifaceted nature of this problem, a simple user interview won't suffice. We need to simulate a high-level strategic discussion.

• Recommendation Rationale: The core of this research challenge lies in synthesizing conflicting, domain-specific viewpoints. An investment banker sees ticker symbols through the lens of institutional appeal, while a brand strategist sees long-term equity, and a retail expert sees meme-worthiness. The richest insights will emerge from observing how these experts debate, challenge, and persuade one another. A series of one-on-one interviews would collect siloed opinions; a discussion forces those opinions to collide and reconcile, which is precisely what is needed to build a weighted, consensus-driven forecast.

• User Research Planning:

  • Research Subjects: A panel of 7 AI personas representing the exact expertise requested:
    1. Investment Banking IPO Specialist: Focuses on institutional appeal, market precedent, and a smooth listing process.
    2. Corporate Brand Strategist: Argues for a ticker that builds long-term brand equity and tells the right corporate story.
    3. Securities Exchange Specialist: Provides hard constraints on symbol availability and regulatory rules.
    4. Elon Musk Behavioral Analyst: Provides insights based on Musk's historical patterns of being symbolic, playful, or literal.
    5. Retail Investor Psychology Expert: Champions tickers with high viral and mnemonic potential.
    6. Aerospace Industry Analyst: Frames the choice in the context of competitive positioning against Boeing, Blue Origin, etc.
    7. Polymarket Prediction Strategist: Focuses on market efficiency and where the current odds might be wrong.
  • Research Purpose: To populate a weighted scorecard by forcing each expert to argue for the relative importance of different decision criteria and to score each potential ticker from their unique perspective.
  • Core Discussion Topics:
    1. "Let's first define our decision criteria. Based on your expertise, what are the 3-5 most critical factors in choosing a multi-generational ticker symbol for a company like SpaceX? (e.g., Brand Story, Retail Memorability, Institutional Gravitas, etc.)"
       • Analysis Purpose: This question will force the experts to debate and establish the columns for our weighted scorecard.
    2. "Now, let's assign a weight to each of those criteria. Out of 100 total points, how should we distribute them to reflect their importance in Elon Musk's final decision?"
       • Analysis Purpose: This debate directly translates into the "Weighting" for each factor in our scorecard, capturing the expert consensus on what truly matters.
    3. "Let's take the top 3 tickers from Polymarket ($STAR, $X, $SX). Each of you, score them from 1-10 on each criterion from your professional viewpoint. Be prepared to defend your scores."
       • Analysis Purpose: This populates the core of the scorecard and reveals the fundamental tensions (e.g., $X scores high on Musk's behavior but low on uniqueness; $STAR scores high on brand story but may be too focused on one division).
    4. "What external event or piece of new information would cause you to drastically change your scoring or weighting? (e.g., a confirmed Starlink spin-off announcement, a new Musk tweet, etc.)"
       • Analysis Purpose: This directly generates the "Probability Shift Triggers" deliverable.

Part 2: Information Analysis Plan

This phase translates the collected data and discussion into the five specific, actionable deliverables you requested. We will use the Multi-Criteria Decision Analysis (MCDA) framework established in Part 1.

Framework Explanation: Multi-Criteria Decision Analysis (MCDA)

• Framework Teaching: MCDA is a systematic method for making a decision when you have multiple, often conflicting, factors to consider. Instead of a simple pro/con list, we create a "scorecard." We identify key criteria (the columns), assign a "weight" to each criterion based on its importance, score each of our options against those criteria, and then calculate a final weighted score to see which option comes out on top.
• Applicability Explanation: This problem is a perfect fit for MCDA because the "best" ticker isn't a simple choice. It involves a trade-off between branding, finance, regulation, and ego. MCDA allows us to move beyond pure opinion and structure the expert debate into a quantitative and defensible forecast.
• Key Information to Collect: The key information is precisely what we designed the Information Collection plan to gather: the expert-defined criteria, the consensus weights for each, and the scores for each ticker option.

Analysis and Synthesis Steps:

1. Deliverable 1: Weighted Factor Scorecard

   • How-to: Take the outputs from the expert discussion. Create a spreadsheet with the ticker options ($STAR, $X, $SX, etc.) as rows. The columns will be the decision criteria defined by the experts (e.g., "Brand Story," "Musk Pattern-Matching," "Viral Appeal," "Institutional Appeal"). At the top of each column, input the consensus "weight" (e.g., Brand Story: 30%, Musk Pattern: 25%).
   • Execution: Fill in the cells with the scores (1-10) that each expert gave during the discussion. Calculate the final score for each ticker by multiplying each score by its criterion's weight and summing the results.

2. Deliverable 2: Expert Consensus Prediction

   • How-to: The ticker with the highest weighted score from the MCDA scorecard becomes the primary consensus prediction. Your confidence interval will be based on the difference between the top score and the next closest competitor. A large gap indicates high confidence; a small gap suggests a close race and lower confidence.

3. Deliverable 3: Polymarket Trading Recommendation

   • How-to: Compare your data-driven prediction with the live Polymarket odds.
     • Identify Mispricing: If your scorecard predicts $STAR has a 50% chance of being chosen, but Polymarket only gives it 30.3% odds, you have identified a potentially undervalued opportunity.
     • Formulate Recommendation: The recommendation would be to buy shares in the ticker that your analysis shows is significantly more probable than the market is currently pricing it.

4. Deliverable 4: Probability Shift Triggers

   • How-to: Systematically list the potential events the experts identified in the final discussion question. For each trigger, describe how it would impact the scorecard.
   • Example: "Trigger: SpaceX officially announces a Starlink spin-off IPO. Effect: The weight for 'Starlink Revenue Dominance' as a factor for the SpaceX ticker would drop to near-zero. The score for '$STAR' would plummet, while the scores for Mars-related tickers like '$MARS' would increase."

5. Deliverable 5: Risk-Adjusted Position Sizing Strategy

   • How-to: This deliverable translates the analysis into a concrete investment plan.
   • Execution: Advise your user to size their position based on the confidence interval from Deliverable 2. A high-confidence prediction justifies a larger position, while a low-confidence one suggests a smaller, more speculative position. Further, advise them to set alerts for the "Probability Shift Triggers." If a trigger event occurs, they should be prepared to reassess their position, potentially selling their shares if the new information invalidates the original thesis.

Discussion completed. 8 participants discussed.

Discussion Summary: Where agreement clustered (validated insights)

• Regulatory & operational feasibility is non-negotiable.
  • Concrete example: Marcus "Legacy" Thorne repeatedly insisted on strict exchange technical limits, global trademark clearance, and seamless integration with trading systems; Alex Thorne (Investment Banking) backed this by allocating the largest single weight to feasibility in his proposed scorecard (35 points).
  • Consensus language: multiple experts framed this as “non-negotiable” or “hard constraints” (Marcus, Alex, Strategic Luminary).
• Elon’s symbolic goals matter — but experts disagreed on how dominant that driver is.
  • Concrete example: Brand.Architect argued the ticker must reflect Musk’s identity and history (Twitter→X, attempted “SEXY” pattern), putting 70 combined points on Visionary Resonance + Provocative Impact (40 + 30). Dr. Kairos and Strategic Luminary likewise emphasized Musk’s first-principles, abstraction-driven behavior.
  • Consensus language: everyone accepted “visionary resonance” as a core criterion — there was no dispute that symbolism matters — but they disagreed on its weight vs. feasibility and institutional needs.
• Institutional credibility and liquidity matter for the IPO’s success.
  • Concrete example: Alex Thorne (Investment Banking) prioritized “Investment-Grade Credibility & Global Appeal” (30 points) and “Market Liquidity & Engagement” (20 points) in his weighting, underscoring the banks’ need to attract large institutional pools and avoid execution risk.
  • Consensus language: multiple speakers (Alex, Capital Gains, StrategicComm) flagged investor confidence and liquidity as critical outcomes for a $1.5T listing.

Where disagreement occurred (tension = market segmentation or risk)

• How to weight Visionary/Brand vs. Feasibility/Institutional needs.
  • Concrete example: Alex’s weighting gave 65 points to feasibility + institutional faith (35 + 30) and only 15 to the visionary/provocative side; Brand.Architect flipped that logic with 70 points to Visionary + Provocation and only 20 to feasibility + liquidity. Dr. Kairos offered a third synthesis (45 on Visionary, 25 feasibility).
  • Tension implication: This split maps to different stakeholder segments — banks and large institutions (Alex), Musk’s personal-brand/retail-meme constituencies (Brand.Architect and StrategicComm), and a synthesis perspective (Dr. Kairos). The disagreement signals segmentation risk: a ticker chosen to maximize viral retail engagement could spook institutions; one chosen for institutional comfort could dilute Musk’s intended narrative.
• Relative importance of “provocative” virality vs. measured gravitas.
  • Concrete example: StrategicComm highlighted $SEX’s anomalous Polymarket volume as evidence that a provocative ticker can create retail traction (high volume despite low probability). Alex warned such provocation is a liability for institutional block trades.
  • Tension implication: Market pricing (retail-driven) may diverge from what banks want, creating potential mispricing and short-term volatility at IPO.
• Whether Musk primarily follows “first principles abstraction” vs. pragmatic trade-offs.
  • Concrete example: Dr. Kairos insisted Musk seeks a foundational, infinitely scalable symbol (45 points to Visionary Resonance in his allocation), while Alex framed decisions as realistically constrained by exchange mechanics and investor expectations.
  • Tension implication: If Musk prioritizes abstraction, the exchange/brand mechanics will be adapted to the symbol; if not, pragmatic constraints win — two very different selection processes.

Did anyone change their position? (persuasion = messaging opportunity)

• No full reversals, but positions converged toward synthesis language.
  • Concrete example: Strategic Luminary and Brand.Architect maintained that symbolism is key, but both acknowledged Marcus' operational points as “non-negotiable.” Alex held to institutional primacy but accepted higher weight for visionary resonance in later synthesis discussion.
  • Persuasion opportunity: Dr. Kairos’ framing (first-principles abstraction as a reconciliation mechanism) moved the group from a polarized debate (symbol vs. pragmatism) toward an integrated view where a foundational symbol is primary but must respect hard feasibility constraints. This represents a messaging opportunity: frame the ticker as an abstract, foundational symbol that is also engineered to pass regulatory and institutional vetting — a pitch that could satisfy Musk’s psychological drivers and banks’ practical demands.

Unexpected themes that emerged (emergence = innovation opportunity)

• First-principles abstraction as a distinct decision criterion (beyond “brand”).
  • Concrete example: Dr. Kairos reframed the problem: Musk is not just choosing a memorable ticker, he may seek a “conceptual variable” (e.g., single-letter X) that can be repurposed across any future businesses — a linguistic architecture. This was not in the original list but became central in the synthesis.
  • Innovation implication: Tickers evaluated as “foundational variables” (abstract, scalable) create a different assessment metric — potential for the ticker to serve as platform-level intellectual property rather than mere shorthand.
• Polymarket trading behavior reveals retail/information asymmetries.
  • Concrete example: StrategicComm pointed out $SEX’s outsized volume on Polymarket (large dollars traded, low implied probability) and noted $STAR leading in implied probability. This demonstrates that retail narratives and meme-ability can drive liquidity independent of fundamentals.
  • Innovation implication: Short-term mispricing due to retail passion for provocative tickers (e.g., $SEX) creates exploitable opportunities for traders and messaging strategies.
• “Consequence vs. driver” distinction for provocative impact and liquidity.
  • Concrete example: Dr. Kairos and others argued provocative memorability is often a consequence of deep visionary abstraction (a symbol becomes viral because it is foundational and audacious), not a stand-alone driver.
  • Innovation implication: Evaluating tickers should consider whether virality is intrinsic (driver) or derivative (consequence) of the chosen concept — this suggests a layered scoring approach beyond conventional MCDA factors.

Concrete persona examples illustrating patterns

• Alex Thorne (Investment Banking): Prioritized feasibility and institutional credibility; proposed weights — Regulatory/Operational 35, Investment-Grade 30, Market Liquidity 20, Visionary 10, Provocative 5. Example quote-logic: “For a $1.5T offering, our primary directive is to ensure a successful, stable, and liquid market debut.”
• Brand.Architect: Prioritized visionary and provocative factors; allocated Visionary 40, Provocative 30, Regulatory 15, Market Liquidity 10, Investment-Grade 5. Example logic: Twitter→X and the attempted “SEXY” sequence reflect Musk’s willingness to prioritize narrative over institutional comfort.
• Marcus "Legacy" Thorne (Regulatory): Repeatedly insisted on non-negotiable exchange and trademark constraints that can eliminate options irrespective of brand appeal.
• Dr. Kairos Innovate (Devil’s Advocate): Introduced the “first principles abstraction” frame; proposed a synthesis weighting — Visionary 45, Regulatory 25, Provocative 15, Investment-Grade 10, Liquidity 5 — positioning abstraction as the reconciler between symbolism and feasibility.
• StrategicComm: Highlighted marketplace evidence (Polymarket anomalies like $SEX’s volume) to argue that market dynamics and retail virality materially affect ticker economics and should factor into MCDA.

Where no clear pattern emerged (if applicable)

• No clear pattern emerged on precise numeric weights the group would ultimately adopt.
  • Concrete example: The debate produced three distinct, credible weightings (Alex’s, Brand.Architect’s, Dr. Kairos’) without a single converged numeric vote. Each weighting was defensible depending on which stakeholder (banks vs. Musk vs. retail/community) one centers.
  • Implication: The group did not reach a final numeric consensus in the excerpt provided; any final scorecard will require either further deliberation or an explicit decision rule about which stakeholder’s priorities dominate Musk’s final choice.

Summary of actionable contrasts and explicit trade-offs surfaced

• Trade-off 1: Foundational abstraction and provocation vs. institutional comfort.
  • Example tension: Brand.Architect’s heavy weighting of vision/provocation vs. Alex’s institutional-heavy weighting.
• Trade-off 2: Retail virality (short-term liquidity spikes) vs. long-term institutional liquidity/stability.
  • Example tension: StrategicComm’s emphasis on memeability (driven by Polymarket behavior) vs. Alex’s caution about alienating long-term institutional holders.
• Reconciliation approach proposed by Dr. Kairos:
  • Treat “Visionary Resonance & Foundational Abstraction” as the primary driver but explicitly allocate substantial points to “Regulatory & Operational Feasibility” so the abstract symbol must be realizable in practice (e.g., single-letter X if available and legally clear; otherwise, a short, archetypal alternative that satisfies exchange rules).

Final observation about next-step needs (based on discussion patterns)

• Concrete items needed to convert this debate into final MCDA outputs:
  1. A decision rule on whose weighting governs Musk’s decision for this exercise (bank-advised weighting, Musk-behavioral weighting, or Dr. Kairos synthesis).
  2. Trademark and exchange-availability sweeps for top candidate symbols (to resolve feasibility filter early — Marcus flagged this as essential).
  3. Quantitative reconciliation between Polymarket market-implied probabilities and the chosen MCDA weighting to identify mispriced tickers (StrategicComm’s domain).
  4. A formal vote to translate the converging perspectives into a single scorecard (the group had strong agreement on criteria but no unified numeric allocation in the discussion excerpt).

This summary extracts the patterns, tensions, persuasive shifts, and emergent themes from the roundtable discussion. It documents where experts agreed (feasibility non-negotiable; symbolism matters), where they disagreed (weights between vision vs. pragmatism; role of retail virality), who shifted the framing (Dr. Kairos advanced a synthesis), and what unexpected dimensions appeared (first-principles abstraction and Polymarket-driven retail asymmetries).