Presentation by Igor LINKOV, Senior Science and Technology Manager, US Army Corps of Engineers and Adjunct Professor, Carnegie Mellon University, US at the 12th Spatial Productivity Lab meeting of the OECD Trento Centre in cooperation with Productivity Insights Network held in virtual format on 4 February 2021.

1. IGOR LINKOV
Senior ScienceandTechnologyManager,USArmy Corps of Engineers
Productivity as property of complex systems
#spatialproductivity
4 February 2021 | 15.00-18.15 CET | Webinar
Spatial productivity in the post-COVID-19 world
and Adjunct Professor,CarnegieMellonUniversity,US
Benjamin Trump (University of Michigan)

2. PRODUCTIVITY PARADOX: EURO AREA TOTAL FACTOR PRODUCTIVITY
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3. POST-COVID PRODUCTIVITY INDICATORS FOR THE STATE OF MAINE
52
Consumer Spending:
Back to “normal”
40% of small businesses are closed

4. 3
Is Productivity a System Property?
How System Should be Defined?
How Productivity Should be
Measured?

5. Supply Chains are Global

6. VACCINE PRODUCTION DEPENDENT ON MANY COMPONENTS AND
CONNECTIONS THAT MAY BE FAILING
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Are these likely
points of failure and
if so, how will
vaccine manufacture
persevere to meet
vaccination targets?
Quality
biological
agents
Bioreactors,
microcarriers,
serums, etc.
Quality
chemical
agents

7. HOW TO ENHANCE SUPPLY CHAIN PRODUCTIVITY AND EFFICIENCY AND
BE RESILIENT?
https://www.hda.org/~/media/pdfs/publications/hda-role-of-distributors-in-the-us-health-care-industry.ashx
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8. VISION FOR SYSTEM MODELING
57

9. How to Quantify Systemic Properties?
After Kott & Linkov,
2019

10. Resilience Matrix
Physical
Information
Cognitive
Social
PREPARE ABSORB RECOVER ADAPT
System Domains
Disruptive Event Stages
Scale
Home Neighborhood Town County Region State Country
After Linkov et al (2013)

11. RESILINC – EXAMPLE OF METRIC-BASED APPROACH
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12. Network-based Approaches
System’s critical functionality (K)
Network topology: nodes (𝓝) and links (𝓛)
Network adaptive algorithms (𝓒) defining
how nodes’ (links’) properties and
parameters change with time
A set of possible damages stakeholders
want the network to be resilient against (𝑬)
𝑅 = 𝑓 𝓝,𝓛, 𝓒, 𝑬
61
Ganin et al., 2016

13. Poor Efficiency:
System cannot not accommodate
a large volume of commuters
driving at the same time.
Traffic congestions are
predictable and are typically of
moderate level.
Lack of Resilience:
System cannot recover from
adverse events
(car accidents, natural disasters)
Traffic disruptions are not
predictable and of variable scale.

14. Resilience
Model
Delays
Random
Network
Disruptions Delays
Scenario 1
“Baseline”
Scenario 2
Interested in:
1. Temporal Patterns of Disruptions
2. Compare Multiple Cities

15. Resilience vs Efficiency at 5% disruption
Efficiency compared to mean, hours
Resilience
compared
to
mean,
hours
After Ganin et al., 2017
After Ganin et al., 2017
2017

16. 65
Impact on GDP
Baseline Case (in color)
Explicit Resilience Modeling (Bars)

17. Resilience in Big Cities
After Kurth et al., 2020
GDP
Resilience

18. DIFFERENCE IN EFFICIENCY/PRODUCTIVITY AND RESILIENCE
Poor efficiency:
System cannot accommodate
a large volume of vaccine
manufacture at the same time.
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Lack of resilience:
System cannot recover from
adverse events (lack of safe
biologicals, glass vials, natural
disaster, cyberattacks, etc.)

19. SUMMARY
• Current economic/production/supply chain, etc.…
are complex and adaptive system and system
analysis is necessary
• Productivity/efficiency/resilience can be quantified
using Metrics-based and Network Science tools
• Productivity, Efficiency, Resilience are different,
have different economic impacts and ways to
quantify and manage. There are inherent tradeoffs
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20. REFERENCES