1.202 demand analysis

[hyunjimoon]

1. 🎯 Understanding Customer Choice Dynamics

• Latent variable elicitation and measurement
• Behavioral mixture models and relaxing IIA assumptions
• Integration of GPT for measuring latent states
• Connection between structural and measurement relationships

2. 🔄 Population Synthesis & Calibration

• Synthetic population generation techniques
• Iterative Proportional Fitting (IPF)
• Calibration of model constants
• Handling aggregate vs. disaggregate data
• MCMC and sampling approaches

3. 📊 Hierarchical Choice Modeling

• Nested logit model implementation
• Multiple level decision structures
• Correlation patterns across choice levels
• Automation of aggregation/hierarchy approaches

These new components should be coherently added to the previous three modules in #100, #224, #234. Ideal contribution of four modules are:

🧭🗺️ Bayes.Entrep: Bridges probabilistic computation with entrepreneurial decision-making, leveraging hierarchical structures to understand market dynamics, operational capabilities, and pivoting strategies. It provides a Bayesian framework for experimental design and ecosystem mapping, integrating theory-driven approaches with practical business insights. NO HIERARCHICAL MODELING NOR NESTED LOGIT.

🛠️ Probabilistic Programming: Focuses on implementing scalable auto-modeling through Bayesian synthesis and domain-specific languages, combining inference controllers with population models. It emphasizes efficient posterior sampling algorithms while balancing robustness and generality in automated inference systems.

🧬⚙️ Evolutionary Entrepreneurship: Examines value chain evolution through the lens of industry clockspeed and modular systems, incorporating concepts like entropy and exaptation. It uses various tools (Value Chain, Double Helix, Bullwhip, Gear, Triple S-Curve) to understand organizational adaptation and industry dynamics.

🛠️ Discrete Choice Analysis for Founders: Empowers founders to operationalize demand analysis by combining advanced choice modeling with practical business applications. It integrates synthetic population generation, hierarchical decision structures, and latent variable measurement to create robust demand forecasting systems. This framework leverages modern tools like GPT for measuring latent states while maintaining statistical rigor through calibration and validation processes.

[hyunjimoon]

setting above table as input, ergodic markovian cld

"This paper challenges Arrow's impossibility theorem in entrepreneurial settings. While Arrow proved that perfectly combining different preferences is mathematically impossible, we show how entrepreneurs can achieve practical solutions through statistical learning. Studying ventures like Tesla and Waymo reveals two key challenges: managing conflicting motivations within teams (profitability vs. safety vs. environmental impact) and evaluating contrasting approaches across ventures (camera-only vs. multi-sensor autonomous driving). Our framework shows how statistical testing and simulation can turn these apparent conflicts into opportunities for innovation. Rather than adding complexity, considering team and market context through systematic testing actually simplifies decision-making by revealing hidden patterns. For example, when Tesla tests self-driving technology, each road test not only validates technical assumptions but also reveals how different stakeholders define success. These patterns help identify unexplored possibilities that satisfy multiple stakeholder needs. Through case studies of successful ventures, we demonstrate how entrepreneurs can navigate complex stakeholder preferences not by seeking perfect solutions, but by building better learning processes that reveal integration opportunities in seemingly impossible trade-offs."

[hyunjimoon]

2. latent variable elicitation

jeff confirmed the relevance of hybrid class model with our LLM-based equity valuation algorithm but now with GPT, we can measure latent states. now the question is how does ability to measure latent states affect elicitation of latent states

Q1. example of opportunities that can be measured that mosche gives example?
Q2. algorithms for discovering latent variables?
Q3. difference btw structural and measurement relationship?
Q4. how’d the forecasting latent variable look like?
Q5. implication of relaxing IIA assumption with behavioral mixture model (integrated choice + latent variable) for Arrow's impossibility theorem?

"This paper challenges Arrow's impossibility theorem in entrepreneurial settings. While Arrow proved that perfectly combining different preferences is mathematically impossible, we show how entrepreneurs can achieve practical solutions through statistical learning. Studying ventures like Tesla and Waymo reveals two key challenges: managing conflicting motivations within teams (profitability vs. safety vs. environmental impact) and evaluating contrasting approaches across ventures (camera-only vs. multi-sensor autonomous driving). Our framework shows how statistical testing and simulation can turn these apparent conflicts into opportunities for innovation. Rather than adding complexity, considering team and market context through systematic testing actually simplifies decision-making by revealing hidden patterns. For example, when Tesla tests self-driving technology, each road test not only validates technical assumptions but also reveals how different stakeholders define success. These patterns help identify unexplored possibilities that satisfy multiple stakeholder needs. Through case studies of successful ventures, we demonstrate how entrepreneurs can navigate complex stakeholder preferences not by seeking perfect solutions, but by building better learning processes that reveal integration opportunities in seemingly impossible trade-offs." using ergodic markovian cld

[hyunjimoon]

from mosche's new textbook three things are relevant to #234's first topic on hierarchy

🙉 embargo IIA

IIA and exchangeability violations in entrepreneurship reveal similar patterns of insight through their breakdowns. When Lyft enters a market, its disproportionate effect on Uber versus walking demonstrates how hierarchical structures in transportation emerge - users first choose between walking versus car-based options, then those selecting car options further choose between Uber or Lyft. Similarly in entrepreneurship, both the violation of assumed exchangeability (when "identical" EV batteries show different charging times) and the violation of assumed non-exchangeability (when distinct market segments show similar product acceptance patterns) reveal critical business insights. These systematic deviations from assumptions - whether in choice substitution patterns (IIA) or observational equivalence (exchangeability) - provide actionable intelligence for decision-making, from manufacturing quality control to market positioning strategies. The key isn't just acknowledging these deviations but leveraging them as signals that illuminate underlying business structures. (using exploring iia-exbl parallel cld)

nested logit model is motivated by the need to relax this assumption in some circumstances for the sake of realism, while trying to maintain the nice properties of logit for the sake of simplicity.

Algorithm

Synthetic population and Iterative Proportional Fitting (IPF)
chp. 9.2.2 Synthetic population
Iterative Proportional Fitting is to consider each marginal one at a time, and to adjust the contingency table of the sample to match the aggregate property of the population

relevant with System dynamic's "Automation of aggregation/hierarchy" on @tomfid's todo list

using gpt, among optimization approach of bayesian calibrated choice, i find synthetic population from aggregate forecasting and calibration/testing useful.

Aspect	Synthetic Population	Calibration of Constants
Definition	Creating an artificial set of individuals (with attributes) that collectively match known aggregate totals of the real population.	Adjusting the alternative-specific constants (intercepts) in a choice model so that predicted aggregate shares match observed shares.
Purpose	- Enables application of disaggregate choice models when only partial (aggregate) data are known about the real population.- Fills in missing individual-level data so that each “synthetic person” has all relevant attributes.	- Ensures the base-case predictions from the choice model align with real-world market shares.- Preserves the behavioral interpretation of other estimated coefficients while re-aligning the overall utility levels of each alternative.
Key Steps / Methods	1. Contingency Table Setup: Identify categories (age, income, etc.) and known totals for each.2. Iterative Proportional Fitting (IPF): Repeatedly scale rows/columns to match marginal counts.3. Gibbs Sampling: Use Markov Chain Monte Carlo to generate individuals whose attributes reproduce those same marginal distributions.4. Validation: Check that synthetic totals match the real population’s aggregate data.	1. Start with an estimated model P(i∣x; c1,…,cJ−1)P(i \mid x;, c_1, \ldots, c_{J-1}).2. Compute current predicted shares W(i)W(i).3. Compare with observed shares WiobsW_i^\text{obs}.4. Update constants (e.g., via a heuristic formula ci←ci+ln⁡(Wiobs)−ln⁡(W(i))c_i \leftarrow c_i + \ln(W_i^\text{obs}) - \ln(W(i))).5. Repeat until predicted shares match observed shares.
Data Requirements	- At least some population-level marginal data (e.g., from censuses, administrative records).- Potentially a small sample of microdata (individual records) to seed or guide the initial table.	- Observed aggregate market shares for the base situation.- An already estimated choice model with known parameter estimates.
When to Use	- You lack full individual-level data but know how many people fall into certain categories.- You want to perform sample enumeration or microsimulation with individual attributes.	- Your disaggregate model (estimated on a sample) under- or over-predicts actual usage shares.- You need forecasts where correct baseline shares are important for credibility.
Outcome	A fully populated “synthetic” individual-level dataset that is consistent with aggregate (real) demographics and totals.	A set of updated constants ensuring the model replicates real-world aggregate shares, thus improving forecast realism.
Typical Applications	- Travel demand forecasting (constructing synthetic households, travelers, etc.).- Demographic or market-behavior modeling in policy studies.	- All discrete choice contexts (transportation, marketing, etc.) where observed overall shares must be matched before testing future/policy scenarios.
Benefits	- Enables disaggregate simulation without observing every actual person.- Maintains individual variability via microdata-like structures.	- Simple to implement: only the constants are tweaked.- Retains the originally estimated behavioral sensitivities (time, cost, etc.).
Caveats	- IPF can struggle with sparse tables or “zero cells.”- Gibbs Sampling requires careful tuning (warm-up, convergence checks).	- Over-calibrating can mask model mis-specification.- Assumes the structural part of the utility is otherwise correct; only constants shift.

Use these techniques in tandem when you (a) need a detailed population for disaggregate modeling and (b) want to ensure the model’s predicted base-case shares match real-world data before you project future scenarios.

[hyunjimoon]

moshe's take on beyond rationality - connection with psychological stock

Aspect	Discrete Choice Models	Behavioral Theory
🎯 Primary Focus	Operational and quantitative modeling of choices	Understanding cognitive processes and psychological factors
⚡ Approach	Mathematical prediction and measurement of choice behavior	Analysis of decision-making processes, emotions, and context
💡 Key Assumptions	Traditional focus on rational decision-making with stable preferences	Recognition of cognitive limitations and varying decision protocols
🔄 Variables Considered	Measurable, quantifiable factors that influence choice	Context, experience, knowledge, emotions, social norms, attitudes
🔀 Decision Protocol	Utility maximization based on available alternatives	Problem-solving, rule-driven processes, non-utility maximizing decisions
🧪 Methodology	Statistical formulation and mathematical modeling	Experimental psychology, behavioral observation, qualitative analysis
⏱️ Time Perspective	Often static or snapshot-based	Process-oriented, considering behavioral stages and changes
💻 Application Focus	Predicting choices and market behavior	Understanding and influencing behavioral change

using moshe's take on DCA vs BehTheory cld

[hyunjimoon]

using stated preference.pdf principle, i designed below using adjusting case study requirements cld and https://claude.ai/chat/64ccafe7-f9a1-4026-b2d0-87a31fbed256 to create below choices:

• Credibility and Realism: Pages 123-124 emphasize making choices realistic so respondents can relate them to real-world contexts. This increases credibility and makes responses more likely to match actual behavior.
• ⭐️Efficiency in Design: Pages 119-121 discuss designing choices that maximize information gathered. Presenting options that force meaningful trade-offs (rather than dominated alternatives) improves statistical efficiency.
• Clear Attribute Description: Pages 101 and 112 highlight the importance of fully describing alternatives to prevent respondents from "filling in" missing information with assumptions, which can bias results.

[hyunjimoon]

using activity sequencing and discrete choice analysis gpt

Row ↓ / Column →	Definition	Discrete Choice Analysis (DCA) Mapping	Entrepreneurial Planning Mapping	Example
Characteristics	Describe fixed traits of the decision-maker that affect utility but do not vary across alternatives	Socioeconomic attributes (e.g., income, education, age)	Team traits and traction signals (e.g., founder experience, patents/IP, advisory board)	A repeat founder may have an easier time raising funding compared to a first-time founder
Alternatives	Set of discrete options available at each choice instance	Set of actions or modes (e.g., drive, take train, fly)	Operational actions (Segment, Capitalize, Collaborate) chosen at each step	Startup must decide between doing a customer segmentation study or pitching to investors
Attributes	Action-specific properties that determine their utility	Travel time, cost, comfort of each transport mode	Expected uncertainty reduction, monetary cost, and time/resource consumption of each operational action	Customer segmentation might cost $10k, take 3 weeks, but reduce market uncertainty by 40%
Outcome Updating	Process by which past outcomes revise future choice behavior	Bayesian updating of β parameters (taste coefficients) after observed choices	Bayesian inference over the effectiveness of actions based on realized state transitions (improved investor trust, customer insight, product fit confidence)	After failing to raise funding twice, founder updates beliefs that Capitalize is lower ROI and shifts toward Segmenting actions

Here’s the Markovian prompt you asked for — crafted precisely according to the instructions in markovian prompting.md:

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📄 Markovian Prompt for Revisiting "Connecting with Discrete Choice Analysis"

🥅 Goal

You are tasked with restarting and advancing the project that connects entrepreneurial action planning under uncertainty with Discrete Choice Analysis (DCA) modeling frameworks.
The user’s deeper goal is to model entrepreneurial operations as a sequential discrete choice problem where choices (actions) are influenced by characteristics (team traits, traction) and attributes (action-specific expected effects like uncertainty reduction, cost, and delay).

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📚 Essential Background

Concept	Current Mapping to Entrepreneurship Modeling
Decision-maker characteristics	Team characteristics and traction indicators (e.g., previous startup experience, IP, advisory board)
Choice alternatives	Operational actions: Segment, Capitalize, Collaborate
Attributes of alternatives	Action-specific expected uncertainty reduction, cost, and execution delay
Systematic utility	Weighted function of attributes, possibly moderated by team characteristics
Random utility component	Noise in perceived effectiveness or unexpected execution challenges
Observations	State transitions (e.g., improvement in investor relationship, customer understanding, product-market fit belief) after actions
Updating rule	Bayesian updating of β parameters (i.e., responsiveness to attributes) based on observed outcomes

🧠 The table must satisfy row-wise (A:B = C:D) and column-wise (A:C = B:D) coherence to enable a relational understanding of the concepts.

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📑 Top 3 Documents to Consult

Please prioritize studying the following three foundational documents:

1. 1.202_s25_Lecture13_IntroDCM.pdf — Introduces Random Utility Models, discrete vs. continuous choice, attributes vs. characteristics .

2. 1.202_s25_Lecture20_MixtureModels.pdf — Details mixture models, Bayesian updating of β parameters, random taste heterogeneity .

3. jeff__angie_empirical outcomes+qual exam feedback_otter_ai.txt — Clarifies the user's goal of defining a semantically expressive but syntactically restrictive entrepreneurial operations model .

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🚨 Important

If you are a new LLM agent reading this prompt:

• DO NOT assume any unstated information from prior chats.

• Start from the definitions, mathematical structure, and relational table you create.

• Rebuild the conceptual bridge between Discrete Choice Analysis and Entrepreneurial Decision Modeling from this basis.

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Would you like me to now also pre-build the requested database table for you as an example? 📄✨ (It would make the handoff even smoother!)

1.202_s25_Lecture13_IntroDCM.pdf

1.202_s25_Lecture20_MixtureModels.pdf
jeff__angie_empirical outcomes+qual exam feedback_otter_ai.txt

[hyunjimoon]

Each action is a probe: it helps clarify how your specific venture responds to uncertainty.

Utility maximization = targeted experimentation to refine your posterior over:

Action effectiveness (empirical β)

World state (θ, e.g. market size, investor norms)

Over time, you refine a personalized, founder-specific model that becomes less reliant on population priors.