## table

- [gpt_process](https://chat.openai.com/share/ffd4f418-f196-4333-8538-5d60f085d1d3) given [needs.pdf](https://github.com/Data4DM/BayesSD/files/14422108/needs.pdf) and [solutions.pdf](https://github.com/Data4DM/BayesSD/files/14422109/solutions.pdf)

- [table]() comparing digital vs physics

| # | Need (Summary from "needs.pdf") | Solution (Suggested in "solutions.pdf") | Example Tool |
| --- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | **Responsive Class Problem**<br>Summary: Difficulties in segmenting behavioral data into meaningful categories based on causally relevant attributes. | Solution: Adopt a more solution-oriented approach, directly tackling practical problems to refine theoretical models and methodologies. | A machine learning classification system that dynamically updates its criteria based on emerging data patterns. |
| 2 | **Situation Taxonomy Problem**<br>Summary: Challenges in adequately classifying and sampling environments and situations for study. | Solution: Emphasize interdisciplinary collaboration to enhance the robustness and applicability of situational classifications. | A collaborative platform for cross-disciplinary researchers to contribute and refine situational taxonomies using consensus algorithms. |
| 3 | **Unit of Measurement**<br>Summary: Disagreements on the necessity of genuine interval or ratio scales for certain types of statistical inference. | Solution: Leverage advancements in computational methods and big data to explore new metrics and scales. | A digital measurement framework that uses AI to propose new scales based on data analysis and pattern recognition. |
| 4 | **Individual Differences**<br>Summary: The complexity of accounting for how individual dispositions are shaped and organized. | Solution: Utilize large-scale data and machine learning to better understand and predict individual variance. | A predictive analytics engine that integrates diverse datasets to model and forecast individual behavior patterns. |
| 5 | **Polygenic Heredity**<br>Summary: The complexity of traits influenced by multiple genes, making causal inference challenging. | Solution: Integrate genetic data with social science research to elucidate the polygenic nature of behavioral traits. | A bioinformatics platform that correlates genetic markers with behavioral traits, facilitating interdisciplinary studies. |
| 6 | **Divergent Causality**<br>Summary: Systems where minor differences amplify over time, making long-term predictions challenging. | Solution: Develop dynamic models that can adapt to initial conditions and incorporate stochastic elements. | A simulation software that models complex systems with feedback loops and varying initial conditions to predict divergent outcomes. |
| 7 | **Idiographic Problem**<br>Summary: The unique aspects of individual personalities that challenge broad generalizations. | Solution: Encourage qualitative methodologies that capture the nuanced nature of individual experiences. | A narrative analysis tool that uses natural language processing to distill unique personal stories into broader themes. |
| 8 | **Unknown Critical Events**<br>Summary: Critical but unobservable events that significantly influence personality development. | Solution: Enhance longitudinal studies with technologies that can capture real-time data, providing insights into critical events. | A mobile app that collects continuous, real-time psychological and environmental data for longitudinal research. |
| 9 | **Nuisance Variables**<br>Summary: Variables that are systematic but difficult to measure or control, complicating causal analysis. | Solution: Implement complex statistical techniques like path analysis to better account for nuisance variables. | An advanced statistical analysis software that uses AI to identify and control for nuisance variables in complex datasets. |
| 10 | **Feedback Loops**<br>Summary: The impact of an individual's behavior on others, creating complex interdependencies. | Solution: Use network analysis to study social interactions and feedback mechanisms. | A social network analysis platform that identifies and models feedback loops within communities or groups. |
| 11 | **Autocatalytic Process**<br>Summary: Processes where the output is capable of accelerating the process itself, such as in depression. | Solution: Apply non-linear modeling to capture the dynamics of autocatalytic processes. | A computational modeling tool designed for studying non-linear dynamics and autocatalytic processes in psychological and social phenomena. |
| 12 | **Random Walk**<br>Summary: The role of chance in determining life paths, complicating predictive modeling. | Solution: Integrate random walk models into social science research to better account for stochastic life events. | A Monte Carlo simulation tool that applies random walk theories to explore various life path outcomes based on chance events. |
| 13 | **Sheer Number of Variables**<br>Summary: The overwhelming number of variables that can influence outcomes, making analysis difficult. | Solution: Employ high-dimensional data analysis techniques to manage and interpret the vast number of variables. | A big data analytics platform that employs machine learning algorithms to sift through and make sense of datasets with a high number of variables. |
| 14 | **Importance of Cultural Factors**<br>Summary: The significant but hard-to-quantify impact of culture on individual development and behavior. | Solution: Promote cross-cultural studies and comparative research to elucidate cultural impacts. | A comparative analysis software that allows researchers to systematically compare and contrast cultural factors across different societies. |
| 15 | **Context-Dependent Stochastologicals**<br>Summary: Statistical dependencies that vary significantly across different contexts. | Solution: Develop context-aware models that adjust based on the specific characteristics of the data set. | An adaptive modeling system that adjusts its parameters based on the specific context of the data being analyzed. |
| 16 | **Open Concepts**<br>Summary: The challenge of dealing with concepts that cannot be strictly operationally defined. | Solution: Foster a more open, iterative approach to concept development, allowing for refinement as new data becomes available. | A collaborative conceptual mapping tool that allows for the dynamic definition and refinement of concepts based on user input and data integration. |
| 17 | **Intentionality, Purpose, and Meaning**<br>Summary: The difficulty of capturing the subjective aspects of human experience in research. | Solution: Integrate psychological and social research with insights from cognitive science and philosophy. | An interdisciplinary research platform that combines qualitative and quantitative data to explore the dimensions of human intentionality and meaning. |
| 18 | **Rule Governance**<br>Summary: Human behavior is influenced by perceived rules, which are not always empirically observable. | Solution: Utilize mixed-methods research to better understand how rules influence behavior. | A mixed-methods research tool that combines quantitative data analysis with qualitative insights to understand the impact of rules on behavior. |
| 19 | **Uniquely Human Events and Powers**<br>Summary: Aspects of human life that are not shared with other | | A specialized study framework that integrates anthropological, psychological, and sociological perspectives to examine events and capabilities unique to humans. |

## tree
1. **High Causal Density**
- 1.1 **Nuisance Variance**
- 1.1.1 Sampling Variability
- 1.1.2 Measurement Error
- 1.2 **Individual Differences**
- 1.2.1 Personality Traits
- 1.2.2 Cognitive Styles
- 1.3 **Environmental Factors**
- 1.3.1 Market Dynamics
- 1.3.2 Regulatory Impact
2. **Operationalization Issues**
- 2.1 **Definitional Ambiguity**
- 2.1.1 Entrepreneurship Conceptualization
- 2.1.2 Innovation Metrics
- 2.2 **Measurement Challenges**
- 2.2.1 Outcome Assessment
- 2.2.2 Process Tracking
3. **Theory-Practice Gap**
- 3.1 **Applicability of Research**
- 3.1.1 Practical Implementation
- 3.1.2 Policy Relevance
- 3.2 **Knowledge Transfer**
- 3.2.1 Academic-Industry Collaboration
- 3.2.2 Entrepreneurship Education
4. **Responsive Class Problem**
- 4.1 **Segmentation Difficulties**
- 4.1.1 Behavioral Categorization
- 4.1.2 Causally Relevant Attributes
- 4.2 **Dynamic Market Adaptation**
- 4.2.1 Agility and Flexibility in Entrepreneurial Strategy
- 4.2.2 Response to Market Volatility
5. **Methodological Diversification**
- 5.1 **Quantitative Methods**
- 5.1.1 Statistical Modeling
- 5.1.2 Econometric Analysis
- 5.2 **Qualitative Methods**
- 5.2.1 Case Studies
- 5.2.2 Ethnographic Research

## tree / notes added by Johanna
### Core Problems
- **High Causal Density**
- **Opaquness of the mechanism**
- **Limited cognitive Capacity of Humans**
- **Inability to directly observe**
- **Lack of high validity/reliability instruments**
- **Incentives in the (Social) Sciences**

### Symptoms
- **Flatland Fallacy** caused by opaquness + high causal density + lingua france
- Unjustified simplification - limitations of cognitive capacity - inability to reason in more than just a few dimensions (Jolly, 2018)
- Preemptive conclusions of understanding -cognitive biases lead humans to believe their actual understanding of phenomena exceeds their true understanding (Jolly, 2018)
- Inability to communicate complexity -max. three dimensions available to visualise findings (Jolly, 2018)

*Solution Ideas: machine augmented research*

- **Results being biased by environmental factors**: *Solutions: inluding random effects (Yarkoni, 2022), randomly varying experimental facotrs (Baribault, 2018)*

- **Operationalization Issues** - caused by inability to directly observe
- **Untracktability**
- **pseudoempirical research** (Smedslund, 1991)
- 'central postulates are so obviously true ther there is nothing to be gained by subjecting it to furthe empirical testing' (Yarkoni, 2022) -> *Potential solutions: embrace qualitative research (Yarkoni, 2022), generate more riksy/precise predictions instead of directional ones (Meehl, Yarkoni)*
- **Construct Validity** (Yarkoni, 2022)
- **'Face Validity'**
- 'if a particular operationalization seems like it has sth to do with the construct of interest, then it is an acceptable stand in for that construct' (Yarkoni, 2022)
- **Statistical & Econometrical Issues**
- Stimulus-as-fixed-effect fallacy (Yarkoni,2022) -> *Partial Solution: adding random effects for stimulus BUT often factors not observable in the data*
- Underestimation of standard error in standard random effects model (Yarkoni, 2022) -> *Solution: adding random effects for subjects*
- **lack of mathmatical thinking ability**
- 'many psychologists have less mathematical training than researchers in some other social sciences' (Mutukrishna, 2019) -> *Solution: more maths/stats in psychlogy education*
- **use of directional hypothesis testing**
- 'psychology has suffered from an inability to communicate complexity, instead generating theories which make crude and imprecise predictions that may be difficult to falsify' (Jolly, 2019) -> *Solution: computational models*
- **Science Culture Issues**
- **Cargo Cult Science** - concern with superficial form of scientific activity rather than the quest for uncovering truth (Feynman, 1974)
- **Disregard of generalizability**
- 'when the manifestation of a phenomenon is highly variable across potential measurement contexts, it simply does not matter very much wehther any single realisation is replicable or not' (Yarkoni, 2022) -> *(Partial) Solution: first focus on the experimental design, measurement approach and model specification (Yarkoni, 2022), emphasize the estimation of variance components (Yarkoni, 2022)*
- **Overemphasizing of quantitative outcomes in inherently qualitative work**
- 'inferential statistics so often reported in soft psychology articles primarily serve as a ritual intended to convince one's colleagues an/or one's self that something very scientific and important is taking place' (Yarkoni, 2022) -> *Solution: embrace qualitative research, e.g. ban reporting of p-values*
- 'psychologist can only hope to assess local events accurately to improve short-run control' (Cronbach, 1975)
- **Reward of general \& surprising findings** (Yarkoni,2022) -> *Solutions: papers should use more 'operational language' (Yarkoni, 2022)*
- **Not taking descriptive research seriously** - purely descriptive research is looked down on in many areas, however obtaining a attempting to obtain a reasonable descriptive characterization of some small part of the world is a perfectly valid reason to conduct studies' (Yarkoni, 2022) -> *Solution: Take descriptive research more seriously*
- **WEIRD psychologists**
- 'most pyschologists are WEIRD, their lives and intuitions differ dramatially from those of people in most societies, undercutting our efforts to accumulate knowledge' (Mutukrishna, 2019)

- **Simple Thinking**: ‘generations of psychological scientists are trained to pursue empirical investigations that favour simple factorial designs but also think about psychology in a low-dimensional way’ (Jolly, 2018)

- **Lack of overarching theoretical framework**
- **Unconnectedness of research findings** -> *Solution: develop comprehensive frameworks (Muthukrishna, 2019)*
- 'psychology textbooks are largely a potpurri of disconnected empirical findings' (Muthukrishna,2019)
- 'psychologist can only develop explanatory concepts, concepts that will help people use their heads' (Cronbach, 1975)

- **Impossibility?**

- Scientific progress being too slow
- 'We tend to speak of a scientific conclusion as it was, eternal but in every field empirical relations change'(Cronbach, 1975)

### Core Solutions?
- **Giving up**: what is the probability that the research one does is going to contribute meaningfully to our collective understanding of the mind or to practically improve the human condition? "there is no shame in arriving at a negative answer."(Yarkoni, 2022)
- **Instrumentalism**: Focus on predictive utility (Yarkoni,2022)

| - | Guard System (digital) | Attack System (physical) |
| -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| e.g. of man | Angel [Michael](https://en.wikipedia.org/wiki/Michael_(archangel)) asks "what is it that you fear?", White hacker | Devil [Lucifer](https://en.wikipedia.org/wiki/Lucifer) asks "what is it that you desire?", Black hacker |
| e.g. of machine | Shield, Time, Red fiber muscle (aerobic, fatigue-tolerant) | Spear, Space, White fiber muscle (anaerobic, rapid use) |
| e.g. of material | Water (Long time, Potential, high latent heat, hydrogen bond) | Fire (Short time, Ignition, Energy space), |
| e.g. of information | Cyber-security, Privacy, Fairness, Sustainability | Hacking, Phishing, Discrimination, |
| e.g. of energy | Renewable energy | Non-renewable energy |
| synonym | Prevention, Defense | Promotion, Offense |
| objective function | minimize the cost | maximize the benefit |
| Goal | Sustain, Stable, Negative-loop dominant | Subvert, Non-stable, Positive-loop dominant, Path-dependence |
| interconnectedness | dense connection, high causal density | sparse connection |
| time horizon | long term, noise-free (aggregated to 0) | short term, noise-dependent |
| difficulty | proving non-existence is harder, nobody gets credit for something that didn't happen | |
| math symbol | $\forall$ | $\exists$ |
| uncertainty | fight with the `unknown` (=its complement = nature) | fight with the `known` |
| statistical support | uniformity test like SBC (computationally heavy) | |
| process | ![[Pasted image 20240305130419.png]] | ![[Pasted image 20240305130400.png]] |
| production direction | additive manufacturing (row generation; adding variables; approximation from inside)<br>![[Pasted image 20240305125833.png]] | subtractive manufacturing (column generation; adding constrains; approximation from inside)<br>![[Pasted image 20240305125845.png]]<br> |
| cut generation | ![[Pasted image 20240305130048.png]] | ![[Pasted image 20240305130102.png]] |
| testing | ![[Pasted image 20240305130134.png]] | ![[Pasted image 20240305130124.png]] |
| |  |  |
| supply chain | ![[Pasted image 20240305132347.png]] | |
| | | |