preparing stancon #230
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summary of our last meeting Angie Moon, Tom Fiddaman, and Jair discussed Bayesian methods in various fields, including epidemiological modeling. Jair shared his experience of defending his PhD early February and moving to England two weeks ago. In the first part of the conversation, the speakers discussed a project proposal for an AUC conference and an applications-oriented paper highlighting the challenges and common features of their work. In the second part, they discussed workflows in system dynamics modeling, emphasizing the importance of visualizing the logic of the model to avoid errors and improve speed, and leveraging visualization tools and fostering interaction between stakeholders to improve workflows. Transcript https://otter.ai/u/Vtto_SL0AnYjCYyzRe3lInrQDK4?view=transcript Action Items This submission form asks only abstract so might be better submit after next week's meeting. @jandraor @tomfid |
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Tom, Jair, Angie Tom Fiddaman and Angie Moon discussed improving SD model parameters with GPT and HMC, emphasizing the importance of data framework and infrastructure. They also discussed Bayesian inference and modeling in AI, including Jair's work and Nader's paper. The speakers also exchanged comments on optimization methods for Bayesian inference and prior modeling. Later, Angie and Tom discussed reality checks in modeling work, highlighting the need for replicable tests and evaluating the effectiveness of interventions through reality checks in epidemiology. Transcript https://otter.ai/u/APNuLVul0oAWu-l70_Vhh-7e7Fk?view=transcript Action Items |
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@tomfid Am reading the new Dotson24_Bayesian Stats in Mangement.pdf recommended from Bayes stats PDW at AOM conference and feel stats, cs (data management) education, especially on "slope endogeneity" are important for modelers who should serve the role of connecting software and policy feedback loops. |
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nate recommend https://pubmed.ncbi.nlm.nih.gov/38397684/
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ReflectionsAmazing collaboration with @jandraor and @tomfid ! Thanks to you I had a meaningful conversation on connecting dynamics tool with stan tool, based on the talk (+ introduced our research to Stan community): Link for the final slide. If Jair had given the talk it would have been much clearer talk for the audience. Change of presenter (due to Jair's sudden schedule), trigger the pivot of the talk's goal (to the very last minute). Looking back, deep inside my mind was the desire to use this presentation opportunity to push forward the technology I was developing (which downgraded the importance of customer (audience)'s comfort by sharing information in clean, simple, conventional format. Synthesizing the feedback from ten people, below might have been the better introduction:
Five learnings: 🗣️honest communication of my situation relevant to the talki think audience might have understood if i said speaker changed, my major is business not infectious disease and i have less knowledge for the domain (😂 people now perceive me as infectious disease modeler) 🥶 cold call, culture, recognizing hierarchy to some extent
making slide simple and emphasizing my contributionspending more time walking through the cool translator (stanify, readsdr) we've developed might have been better sean shared he was expecting to see how I made below - but the problem was we haven't translated this model to stan yet using our Different background, different absorptive capacity, different satisfactionIndustry people who had first hand experience in the communication difficulty & people who were aware of system thinking gave more positive feedback (compared to pure statistician who were expecting to see concrete models, with much less meta-approach). However, for statisticians, my talk was too meta, businessy, abstract. Focusing on selling our translation tool might have been great alternative approach. Stan needs a vision and searching for scalability
how i should flexibility explain definitionI couldn't find the best word to explain when Bob asked what i mean by "dynamics" was. It's different from just time series modeling and now come to think of it (especially after taking bayesian optimization tutorial), it may be the sequential decision making component that might be the dynamics that exists less (not irrelevant with probabilistic inference driving the action) skeptical view of statisticians to business scholars/theoriesThere were some statistician who had deep trust in the lack of usefulness in management scholar. Thanks for all who gave me honest feedback to help me learn: Mitzi Morris, Paul Burkner (should have made xmile - r/python- stan photo much bigger and stick with simple example (given tool is the key contribution)), Charles Margossian (people would have had better understanding if I showed how system dynamics people could benefit from Stan, if the goal was bridging - which makes sense since I'm bridging from vensim to stan, not the other way around), Liza (why and how weren't clear), Steve (industries make the dashboard inhouse, but it'd be useful to make it more accessible and automatable for individual modelers), Judith (background in infectious disease, she found talk to be interesting but mentioned audience might have been surprised by its format, think innovative format talk should be more welcomed), Sean (need clearer setup, was expecting me to explain how i made the interactive simulation tool using stan), Juan (he thought stan needs vision while listening to my talk), James (infectious disease modeler who knew system thinking), Etie (stan organizer who showed interest in being part if I organize stan connect with the topic of using interactive tool to elicit goal / communicate with stakeholders) Actions
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FiddamanMoon23_BayesSD.pdf
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