Theoretical and Empirical Perspectives on Entrepreneurship
Mar.19-26
I shared my belief on inspection paradox (whenever the probability of observing a quantity is related to the quantity being observed) can help me model the difference between system-level and (two) agent-level (perceived) uncertainty. I framed it as bilateral information asymmetry, but "different observer type" seems to be more fundamental. Josh recommended a paper (digesting) and also reaching out to Bob Gibbons. Based on my current belief and goal, I made mockup to get Josh's evaluation.
Belief: different types of observer (actor and environment) from inspection paradox is relevant to different ENT dynamics (e.g. Decker et al, guzman-stern)
Goal: understand different ENT dynamics with the help of concrete models of observation and their relation in transportation
Action: prepare mockup to get Josh's evaluation on my goal; start from different observer types which is fundamental cause of inspection paradox. Mockup from process which synthesized traffic flow with Josh's Entrepreneurship and Industry Evolution slide:
Observer/Measurement Type
Entrepreneurial Dynamics (Including Cause of Heterogeneity)
Traffic Flow (Including Cause of Non-Stationary Traffic)
Quantities of Interest (QoI)
Growth
Speed
Global Observer
Macro: May overlook nuances of local markets and trends, leading to generalized growth understanding. Cause: Sector-specific trends and macroeconomic factors can obscure individual dynamics.
Micro: In-depth understanding of specific markets or sectors, identifying unique growth opportunities. Cause: Micro-level factors like firm strategies and local market conditions influence dynamics.
Stationary Observer: Captures detailed, location-specific dynamics. Cause: Localized events and regional policies affect traffic flow in particular areas.
QoI Observed by Global Observer Across Time
Longitudinal Studies: Focuses on evolutionary entrepreneurial activities over time, possibly overlooking cross-sectional differences. Cause: Changes in macro conditions and sector-wide shifts.
Time Mean Speed: Measures dynamics change over time, missing spatial variations. Cause: Infrastructure developments or major policy shifts altering traffic patterns over time.
QoI Observed by Local Observer Across Space
Cross-sectional Studies: Highlights geographical or sectoral growth differences, possibly missing temporal trends. Cause: Geographic and sector-specific heterogeneity, including local economic conditions.
Space Mean Speed: Captures spatial variations in dynamics, missing temporal changes. Cause: Physical characteristics of road network and localized demand fluctuations affect flow.
Cause of Heterogeneity
Variability stems from entrepreneurship types (subsistence vs. transformational), innovation rates, and sector dynamics.
Variations in driver behavior, vehicle types, and travel purposes lead to fluctuating traffic characteristics.
Information Asymmetry
In entrepreneurial ecosystems, startups have more detailed knowledge about their potential, while investors need to screen the best opportunities.
Drivers have more information about their intended routes and timings than the system designed to optimize traffic flow.
Role of Intermediaries
Intermediaries like venture capitalists and incubators help bridge the information gap between investors and startups, facilitating efficient resource allocation.
GPS and traffic management systems act as intermediaries, using real-time data to optimize routes and reduce congestion.
Integration with Cause of Heterogeneity
Information asymmetry and the strategic role of intermediaries contribute to the observed heterogeneity in entrepreneurial success, growth rates, and innovation levels.
Technology-mediated intermediation mitigates inefficiencies caused by asymmetric information, leading to smoother traffic flow and reduced congestion.
This integrated table elucidates the complex interplay between observation perspectives, the dynamic nature of entrepreneurial activities and traffic flows, and the myriad causes contributing to the observed heterogeneity. It demonstrates how different observational lenses (global vs. local; across time vs. space) influence our understanding of growth and speed in these domains while underscoring the multifaceted factors that contribute to the variability in observed phenomena. This approach provides a nuanced perspective that acknowledges the richness and complexity of analyzing dynamic systems, whether they be in the realm of entrepreneurship or traffic flow analysis.
Mar.26 Q
does the table make sense to you?
is it correct across sector and across firm size are different layers of heterogeneity? How would it affect measurements of growth based on the table above?
Theoretical and Empirical Perspectives on Entrepreneurship
Mar.19-26
I shared my belief on inspection paradox (whenever the probability of observing a quantity is related to the quantity being observed) can help me model the difference between system-level and (two) agent-level (perceived) uncertainty. I framed it as bilateral information asymmetry, but "different observer type" seems to be more fundamental. Josh recommended a paper (digesting) and also reaching out to Bob Gibbons. Based on my current belief and goal, I made mockup to get Josh's evaluation.
Belief: different types of observer (actor and environment) from inspection paradox is relevant to different ENT dynamics (e.g. Decker et al, guzman-stern)
Goal: understand different ENT dynamics with the help of concrete models of observation and their relation in transportation
Action: prepare mockup to get Josh's evaluation on my goal; start from different observer types which is fundamental cause of inspection paradox. Mockup from process which synthesized traffic flow with Josh's Entrepreneurship and Industry Evolution slide:
This integrated table elucidates the complex interplay between observation perspectives, the dynamic nature of entrepreneurial activities and traffic flows, and the myriad causes contributing to the observed heterogeneity. It demonstrates how different observational lenses (global vs. local; across time vs. space) influence our understanding of growth and speed in these domains while underscoring the multifaceted factors that contribute to the variability in observed phenomena. This approach provides a nuanced perspective that acknowledges the richness and complexity of analyzing dynamic systems, whether they be in the realm of entrepreneurship or traffic flow analysis.
Mar.26 Q
raw materials in https://github.com/Data4DM/BayesSD/discussions/214