## day1 ## day2 morning session on theory - "content validity" focuses on whether a test adequately covers all aspects of the concept it aims to measure (e.g. A math test that covers all necessary operations (addition, subtraction, multiplication, division) for a specific grade level would demonstrate high content validity), - "context validity" examines whether the assessment is appropriate and relevant within the specific situation or setting where it is being used (e.g. A job interview that includes questions related to the specific responsibilities of the position would have high context validity) angie's question: 1. does cause always temporally comes before effect? 2. given your answer for 1, may I ask whether you think every update from prior to posterior is cause and effect relationship? 3. common mistake in dynamic modeling is applying causal logic of one level of agency to another level ### day3 i'm very confused about how i should react to the word "pre-Bayesian" as i may be wrong but i think hierarchical bayesian modeling explains exactly how goals that seemed to be on the same level or conflicting can be integrated, how to discover the presence of latent parameter given exchangeability. for example biotech that is suprised by their technology working well in seemingly unrelated market: cancer and diabetes. this may lead to search of deeper reason and find mrna technology platform. arnaldo: based on some shared action state? then comes belief. ("disagree to agree" also needs some shared targeted action (task), before belief hetereogeneity comes in) --- ⭐️ follow up with rob, ryan (market vs tech uncertainty), atticus, atticus: Q1. product quality (fitness of use) for non-existing market? Q2. temporal concept for mu? fail fast can make mu same, but Q3. low and high bar experiement ![[Pasted image 20250111191725.png]] ### cognitive bridge: how lay theories shape consumer preferences and choices 1. consumers form lay theories while evaluating <font color = "#C0A0C0">product characteristics</font> and making <font color = "Red">choices</font>. 2. lay theories, defined as consumer constructed mappings between <font color = "#C0A0C0">observable product attributes</font> and <font color = "Green">perceptual expectations</font>, mediate the relationship between <font color = "#C0A0C0">observable product attributes</font> and <font color = "Green">perceptual expectations</font>, mediate the relationship between <font color = "#C0A0C0">product characteristics</font> and consumer <font color = "Red">choice</font>. 3. for instance, prior research has shown that <font color = "#C0A0C0">environmentally friendly products</font> are often perceived as <font color = "Green">less efficacious</font>, influencing consumer preference and purchase likelihood. 4. We propose a conceptual framework where consumers map <font color = "#C0A0C0">concrete product features</font> onto <font color = "Green">perceptual beliefs</font> which in turn shape brand preferences(❓how does state shape utility? you mean affect? argmax U(a,S) vs argmax U_S(a,S)?) 5. this framework reveals how lay theories influence decision-making by partially mediating the impact of <font color = "Green">product characteristics</font> on <font color = "Red">consumer choice</font> 6. the paper contributes to the literature on consumer judgement and <font color = "Red">choice</font> by advancing a structured approach to understanding how <font color = "Green">product perceptions</font> evolve (❓how does it show evolvement) and affect <font color = "Red">decision outcomes</font> merges two views: 1. prior affect the how they update the belief self consistency condition for learning rule; p(theta, y, theta_tilde) - theta and theta_tilde should be indistinguishable and the granularity are test statistics. T1(x) = T2(x) conflicting goals are reconciled as integrated goals -> path dependency is resolved?? = discover deeper meanings =