process: [[visr🗄️(📝)]], [[visr⚙️(🗄️(📝))]] 2025-03-12 I need your input on the viability of pairing **NEED** and **SOLUTION 1,2** and using that as publication. Two papers that formed my loose prior on its venue are attached. **GOAL** > One impactful contribution I can and wish to make for BE field is writing a paper titled _Entrepreneurship - S__cience and E__ngineering of Defying Defaults_ with those who have shown collaboration intent (Charlie Fine, Andrew Gelman, Josh Tenanbaum, Vikash Mansinghka, Scott Stern) to increase the talent inflow of the field. I need your advice on which journal to target for this goal.  **SOLUTION1** You might find the sudden appearance of Andrew Gelman as a paper collaborator surprising. This is a recent progress.  Over the weekend, my intuition kept pushing me to connect my research question "**under what condition is go to market testing preferred to market viability testing**" with one of his conjectures on simulation based calibration (SBC) below.  > “Theorem” 2. Suppose we have a hierarchical model so that the parameter vector theta can be divided into a long vector of local or latent parameters, θ, and a short vector of hyperparameters, φ, so that the full prior is p(θ,φ) = p(φ)p(θ|φ). Now suppose we start by drawing φ_tilde from some alternative distribution, g(φ), then draw θ_tilde from p(θ|φ_tilde) and y_tilde from p(y|φ_tilde, θ_tilde). The theorem is that the posterior simulations will still be approximately calibrated for the parameters in θ in the limit as the dimension of θ increases. Here the idea is that we could have **weaker conditions** on g(φ), compared to theorem 1. The theorem's entrepreneurial relevance lies in modeling an idea's true market value φ and the entrepreneur's initial belief distribution g(φ) through prototyping, establishing a formal Bayesian framework for entrepreneurial decision-making under uncertainty. This hierarchical approach suggests that detailed implementation parameters θ can be reliably calibrated through extensive testing even when overarching market assumptions remain uncertain, providing theoretical justification for when rapid market entry might outperform incremental validation. By **formalizing the conditions** under which entrepreneurs can trust direct market feedback despite imperfect priors about an idea's overall viability, Andrew's conjecture directly addresses the research question of when go-to-market testing should be preferred over market viability testing. I trust Andrew's intuition on the criticality of this theorem as he suggested [here](https://0599faed.streaklinks.com/CWC8z3Hk5JxFcFyCeAvWs57z/https%3A%2F%2Fstatmodeling.stat.columbia.edu%2F2021%2F01%2F18%2Fsimulation-based-calibration-two-theorems%2F).  Andrew will have a high willingness to collaborate on **formalizing the condition** using our empirical settings. Measured evidence is [his comment](https://0599faed.streaklinks.com/CWC8z3HICE-1V5-_lwkTH95j/https%3A%2F%2Fstatmodeling.stat.columbia.edu%2F2025%2F01%2F17%2Fhow-far-can-exchangeability-get-us-toward-agreeing-on-individual-probability%2F%23comment-2387887) "adapt these thoughts to the specific problem at hand" from the exchangeability post we read together. Plus his prompt reply to my three mails on this topic last year. We can treat hierarchical models and exchangeability assumptions as similar animals. **SOLUTION2** Where does Vikash fit in? My intuition is, the attached slide Vikash uses that lists conditions where probabilistic inference can be automated can hint to the **conditions we seek.** **NEED** Predicted prior on Bayesian entrepreneurship's latent needs is becoming tighter as analyzed in [BE's need](https://0599faed.streaklinks.com/CWC8z3H9_UGDlf2FKgLKd0na/https%3A%2F%2Fgithub.com%2FData4DM%2FBayesSD%2Fdiscussions%2F234%23discussioncomment-12460980) with the reading group's transcripts.