My vision is to bridge [bayesian entrepreneurship](https://www.entrepreneurial-strategy.net/) (BE) with [probabilistic computing](http://probcomp.csail.mit.edu/) (PC). This presents a novel approach towards navigating the complexities of startup ventures through data-driven decision-making and strategic planning. Bayesian Entrepreneurship, drawing from the research of Scott Stern and Charlie Fine, emphasizes strategic decision-making in startups across four critical domains: Customer, Technology, Organization, and Competition, with a focus on selecting among disruptor, architectural, value chain, and IP strategies for a phase-based, learning-driven path to success. This strategic framework is complemented by Probabilistic Computing, which, based on Vikash Mansinghka's research, aims to [democratize data science](https://streaklinks.com/B5izAmKxMQ0z7-9b_g_e_gJr/https%3A%2F%2Fnews.mit.edu%2F2019%2Fnonprogrammers-data-science-0115) by enabling non-experts to generate and use sophisticated statistical models for data analysis through user-friendly tools. By integrating PC's capabilities to automatically model data and predict trends with BE's strategic insights for startups, this research envisions a data-informed platform that not only guides startups through personalized strategic decisions but also offers adaptive paths to achieve long-term success. This confluence of data science and strategic entrepreneurship could significantly enhance the decision-making processes in startups, making them more resilient and adaptable in the face of uncertainties. | Prob.Comp X BayesEntrep | theory 🟩 (desirability) | tool 🔴 (tech feasibility) | input | output | | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----- | ------ | | 🧬 genome | **statistics X probabilistic computing**<br><br>- Bayesian Learning Approach to Entrepreneurial Strategy (s7.1) | - autoGP which learns the covariance structure of Gaussian process time series models<br><br> - [bayesDB](http://probcomp.csail.mit.edu/software/bayesdb/) AI-assisted inferential statistics: “what genetic markers, if any, predict increased risk of suicide given a PTSD diagnosis? and how confident can we be in the amount of increase, given uncertainty due to statistical sampling and the large number of possible alternative explanations?”<br> | | | | 🗺️ map | **business**<br>- knowledge transfer/reconfiguration between industries (with different clockspeed and hardwareness, charlie's expertise<br>- Camuffo, A., Gambardella, A. & Messinese, D. (2023b). “Bayesianism and Unforeseen Events: Empirical Evidence from Two Field Experiments”. Academy of Management Proceedings 2023 (1).<br><br>- sec.5.2 (Experimenting With Alternative Theories): "Therefore, entrepreneurs run experiments against alternative theories. 10 If entrepreneurs have an alternative theory with expected value Q (for example, an alternative opportunity, idea or strategy), then important changes in V – such as those induced by updating priors µ Θ pθq or priors on priors ω – are more likely to imply V 1 ă Q and thus a switch to the alternative theory Q. Besides, experiments that generate unexpected (“surprising”) outcomes – for instance observations that represent unforeseen contingencies (Karni & Vierø, 2013 and 2017), or regarding low probabilities states (Ortoleva, 2012) – may nudge the entrepreneur to consider novel attributes and causal links and, hence, be conducive of novel theories. Therefore, not only do entrepreneurs use experiments to test their prior beliefs about a future state space, aiming at higher V through a more favorable µ Θ pθq or higher ω, but also as a potential source of “anomalous” observations that can give rise to alternative theories (Mullainathan & Rambachan, 2023)."<br>- decomposition "founder is “optimistic” about this theory, i.e. they believe in it and set a high V Θ, defined by (3) and high V , defined by (5). The founder’s strong prior belief (”optimism”) derives from a high ppx h \| θ h q. At the same time, they think they have good technology and are confident that it can be put to good use, so they expect ppx e \| θ e q also to be high. The conditional probabilities associated with the causal links ppx d \| θ des , x e , x s q and ppx s \| θ sh , x h q are also high because the available information on GAI adoption converges on indicating that the parameters are positive"<br> | - JB's help on version control tools + visualization + evolution<br> | | | | 🧭 compass | **simulation X entrepreneurial decision making**<br><br>- "entrepreneurs change their prior beliefs over (1) new states that may emerge when they obtain awareness about previously unforeseen actions or consequences and (2) over previously null states that had been previously considered as not possible. Reverse Bayesianism enables entrepreneurs to adjust the state space they envision (their theory/opportunity) for unknown events. This process is consistent with the methodic doubt of scientists who acknowledge there might be other explanations/theories to what they study, which they do not know (Camuffo et al., 2023b). We can think of “Reverse Bayesianism” as “forward-looking” Bayesianism.<br><br>- Scientific Approach to Entrepreneurial Decision Making as Bayesian Learning (s7.2)<br>- "Bayesian learning occurs for entrepreneurs adopting the scientific approach at two levels. First, “scientific” entrepreneurs form, test and update their beliefs about the success or value of their ideas. Second, they form, test and update their beliefs about the generating mechanisms underlying such ideas." - First part is forward looking, Second part is about past (DGP) | - [gen-finance](https://probcomp.github.io/gen-finance/) | | | - A, T should be more relatively defined (process:product e.g. apple manufacturing chain/iphone1-3) - b2b, b2c may differ as b2b sells process how to komodos: health map company - - testing the belief that doctors as distributers matters and the value of visualization would rise - [[🌙amoon()]] revolves around this vision, with mechanism of 🧠iai(🌙o4s(🏳️‍🌈bae()) OR 🧠iai(o4s(🏳️‍🌈bae(🌏o4e()) Dec.W4 a: go through the following and summarize behavior-relevant literature https://www.connectedpapers.com/main/8f19f27dbb75367af9578948dbea02ab9cbfcf95/graph?utm_source=share_popup&utm_medium=copy_link&utm_campaign=share_graph Jan.W1 --- - so many things could go wrong with signal from customer (segmentation, technology; production and market fit; easy to pivot) - structure learning in fast clockspeed market than moving to slower clockspeed market with heightened capability (reach frontier for any atom, bit, customer then moving on) ![[🗄️n2s2n]] experiment - assign with ai wo ai; monetize [posterior-db](https://github.com/stan-dev/posteriordb?tab=readme-ov-file#posteriordb-a-database-of-bayesian-posterior-inference) hardware, data, software ref. [FOW time, energy, money ](https://www.pinterest.com/pin/71916925269425974/)