SYSTEM DYNAMICS: SYSTEMS THINKING AND MODELING FOR A COMPLEX WO - - PDF document

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 1

SYSTEM DYNAMICS:

SYSTEMS THINKING AND MODELING FOR A COMPLEX WO WORLD

James Paine System Dynamics Group MIT Sloan School of Management

IAP 2 2020 SESSION – JANUARY 13, 2020

01 01 Welcome

me!

• Grab some food!
• Introduction to the SD Group at MIT

02 02 Ov

Overview of Sy Syste tems ms Thinking

• What is ‘System Dynamics’?
• What is ‘Systems Thinking’?
• Tools of the trade and key concepts

03 03 Ha

Hands on! Fishbank Si Simu mulati tion

• Teams of 4 (+/- 1)
• One laptop per team needed

04 04 Debri

rief and wrapup

• Fishbanks debrief
• Tying it into Systems Thinking
• Other SD resources at MIT

Plan Plan for

• r Tod
• day

ay

1

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 2

Abo bout ut Me

2

Ba Background

• B.S. Chemical Engineering (UF)
• M.S. Mechanical Engineering (Ga Tech)
• MBA Operations Management and Marketing (WFU)
• Worked for ≈10 years in GE-Hitachi (nuclear engineering), Inmar (reverse

logistics and continuous improvement), HanesBrands (product marketing)

Jame mes Paine

• MIT Sloan School of Management
• System Dynamics Group, emphasis on Behavioral Operations

Management

Research Inte terests ts

• Product development (and failure)
• Supply chain management and cost mitigation via behavioral modeling (BOM)
• Managerial decision making in non-optimal environments

jpaine@mit.edu jpaine.mit.edu

Sys System tem Dynamics ynamics Gr Grou

• up

p at MIT t MIT

3

https://mitsloan.mit.edu/faculty/academic-groups/system-dynamics/about-us

John Sterman

Jay W. Forrester Professor of Management

Nelson Repenning

School of Management Distinguished Professor of System Dynamics and Organization Studies

Hazhir Rahmandad

Mitsubishi Career Development Professor and Associate Professor of System Dynamics

David Keith

Assistant Professor of System Dynamics

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 3

Sys System tem Dynamics ynamics Gr Grou

• up

p at MIT t MIT

4

https://mitsloan.mit.edu/phd/students/current-phd-students

Mahdi Hashemian

B.S. Electrical Engineering; M.S. Management

James Houghton

S.B. Aeronautics and Astronautics

Tianyi Li

B.S. Geophysics; B.S. Applied Mathematics; M.A. Geosciences

Tse-Yang Lim

B.S. Biology; Master of Environmental Management

Jose Luis Lopez

B.S. University of Costa Rica; INCAE Business School, M.B.A.

Jad Sassine

M.S. Applied Mathematics

James Paine

B.S. Chemical Engineering; M.S. Mechanical Engineering; M.B.A.

Sys System tem Dynamics ynamics Gr Grou

• up

p at MIT t MIT

5

A (very) brief history “Everything I have ever done has converged to become system dynamics.”

• Jay W. Forrester

at the 1989 International meeting of the System Dynamics Society

MI MIT-originated field

Created by Dr. Jay Forrester in the mid-1950’s while at MIT First formalized in 1958 with “Industrial Dynamics - A Major Breakthrough for Decision Makers"

Ori Origins in control theory

• Dr. Forrester had background in EE and pioneer in early digital computers.

Inventor of Random Access Memory while working on MIT’s WHIRLWIND I general purpose digital computer Came to understand that social systems are much harder to control than physical systems, and often source of difficulties faced in projects First major application was stock-flow-feedback structure of GE appliance plant three-year employment cycle, refined ideas of System Dynamics Broadened beyond corporate management throughout 60’s and 70’s, including resource management such as WORLD2 simulation for Club of Rome Evolved beyond methodology to thinking framework with applications in numerous fields

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 4

OVE VERVI VIEW EW OF F SY SYSTE STEM M DYN YNAMIC AMICS S AN AND D SY SYSTE STEMS MS TH THIN INKIN ING

*Portions of the following overview slides are modified from source material by Drs. John Sterman, Hazhir Rahmandad, and Robert Nachtrieb

Op Open en Lo Loop

• p Think

hinking ing

7

Identify Problem Gather Data Evaluate Alternative Select Solution Implement

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 5

Op Open en Lo Loop

• p Think

hinking ing

8

Goals Decisions State of the System

We e ar are e emb embedded i in n a a lar large ger r sy system stem

9

Goals Decisions State of the System “Side Effects” “Side Effects” Actions of Others Goals of Other Agents

DELAY DELAY DELAY DELAY DELAY DELAY DELAY DELAY DELAY DELAY

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 6

Sys Systems tems Think hinking ing Fou

• unda

datio tions

What is a System? m?

A system is a set of interdependent parts sharing a common purpose. The performance of the whole is affected by each and every one of its parts.

10

Social and Economi mic Systems ms

Are highly complex systems:

• Dynamic
• Tightly coupled
• Governed by feedback
• Nonlinear
• Limited Information
• Ambiguity and delays in cause and effect

…and are typically more complex than human- made, made, physical physical systems. systems.

Sys Systems tems Think hinking ing an and d Sys System tem Dynamics ynamics

...is ...is not not onl

• nly

y tools tools and and but r but rather ther fr framew amewor

• rk

k to to help ‘close the loops’ and:

Elicit and articulate mental models and impact of social and

• rganizational structure

Expand mental models by explicitly accounting for feedback Test and improve mental models and structure via simulation Develop shared mental models and more effective organizations

11

The simulation’s purpose is not to ‘be right’ but rather to help improve mental models and identify high leverage policy choices

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 7

Sys Systems tems Think hinking ing Fou

• unda

datio tions

Str Structur ucture e Gener Generates tes Beha Behavior vior

Dynamics emerge from the interaction of:

• Physics
• Information availability
• Decision rules

12

Mental Mental Models Models Ma Matter tter (a lot!) (a lot!)

It’s not enough to change the physical structure, information, and incentives.

The he Fundamental Fundamental Attribution Attribution Er Error

• r

Our first instinct is to blame the people in the system. Almost always this is a low-leverage response

Brea eaking king Away ay fr from

• m the

the Fu Fund ndamenta tal l Attributio ttribution Er Error

13

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 8

Str Struc uctu ture Ge Gene nerates s Beh ehavi vior

• r

14

EV EVENT ENTS ST STRUCT UCTURE URE PATT TTER ERNS NS OF OF BE BEHA HAVIOR VIOR

Str Struc uctu ture Ge Gene nerates s Beh ehavi vior

• r

15

EV EVENT ENTS

STR STRUCTURE UCTURE PATTERNS TTERNS OF OF BEHA BEHAVIO VIOR

• “Drunk trader caused a spike in oil prices” (NY

Post, 2012)

• “Oil prices keep falling — this is why” (Washington

Post, 12/21/15)

• “OPEC Rumors Continue To Pull Oil Prices Higher”

(Oil Price, Aug 2016)

• “Trump slams OPEC for high oil prices” (Fortune,

4/20/18)

• “Another Sign of Economic Worry: Tumbling Oil

Prices” (NYT, 6/5/19)

• “Oil prices surge after tanker attack in Gulf of

Oman” (CNN, 6/13/19)

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 9

Str Struc uctu ture Ge Gene nerates s Beh ehavi vior

• r

16

EVENTS EVENTS STR STRUCTURE UCTURE

PATT TTER ERNS NS OF OF BE BEHA HAVIOR VIOR

• Chronic boom and bust cycles
• Real Prices rising on average

$- $20 $40 $60 $80 $100 $120 $140 $160

1860 1880 1900 1920 1940 1960 1980 2000 2020

2018 $/bbl

Str Struc uctu ture Ge Gene nerates s Beh ehavi vior

• r

17

EVENTS EVENTS

ST STRUCT UCTURE URE

PATTERNS TTERNS OF OF BEHA BEHAVIO VIOR

• Physical structure:
• Stocks and flows
• Material delays
• Feedback processes
• Information availability
• Delays, biases, error, gaps
• Access & transparency
• Mental Models
• Actor goals and incentives
• Time horizon, model boundary
• Misperceptions of feedback

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 10

(So (Some) me) B Bar arrier iers s to to Le Lear arnin ning i in n Dynamic ynamic Comp

• mple

lexity ity

Dynamic namic Comple

• mplexity

xity Limited Limited Inf nfor

• rma

mation tion Conf

• nfounding v
• unding var

aria iables bles and A and Ambiguit mbiguity Bounded R

• unded Ration

tionality ality and M and Misp isper ercept ceptions ions of

• f

Feedbac eedback Flaw lawed M ed Ment ental Mod al Models els Erroneous

• neous I

Inf nfer erences ences about bout D Dynamics namics Judgment udgmental E al Error

• rs and B

and Bias iases es Def efens ensiv ive e Rout

• utines

ines and and Int nter erper personal Impediments

• nal Impediments

to to Lear Learning ning Implement mplementation tion F Failur ailure

18

‘SYSTEM DYNAMICS’ IS REA REALL LLY Y APPLI APPLIED ED ‘SYSTEMS THINKING’

19

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 11

(SO (SOME) ME) TOOLS LS OF F SY SYSTE STEM M DYN YNAMIC AMICS

Too

• ols

ls an and d Meth ethods

Syste tem m Thinking and Mode hinking and Modeling is ling is Ite terativ tive Spiral approach, and multiple tools available Syste tem m Dynamics namics is is NO NOT jus just t compar compartment tmental models al models System Dynamics practitioners use many modeling and simulation toolsets test the implications of hypothesized causal relationships All ll Models Models ar are e Wr Wrong: B

• ng: But

ut some m

• me models
• dels

ar are us e useful! eful!

21

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 12

Too

• ols

ls in the in the Spir Spiral al App pproac

• ach

h to to Mod

• del

l For

• rmula

mulation ion

• Reference Modes
• Causal Loop Diagrams
• Stock and Flows
• Equation Formulation
• Dimensional Analysis
• Simulation
• Sensitive Analysis
• Policy Testing

22 *Sterman 2000

Results of any step can yield insights affecting other steps

Sys Systems tems Think hinking ing Too

• ols

ls: : Cau ausal sal Link Links

23

Production Inventory Shipments Orders Booked Price Salesforce Population Deaths Births

+ + +

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 13

Sys Systems tems Think hinking ing Too

• ols

ls: : Cau ausal sal Link Links

24

Ice Cream Sales Murder Rate + Ice Cream Sales Murder Rate Average Temperature

+ +

Incorrect! Correct

https://www.tylervigen.com/spurious-correlations Just for fun:

Sys Systems tems Think hinking ing Too

• ols

ls: : Lo Loop

• ps

25

Emplyee Skill Customer Satisfaction Complaints Manager Time Spent Resolving Customer Issues Manager Time Spent Coaching Employees

+

• +

+ Reinforcing Loop:

R

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 14 Attractiveness

• f Market

Number of Competitors Product Price Profits

Sys Systems tems Think hinking ing Too

• ols

ls: : Lo Loop

• ps

26

• +

Balancing Loop: + +

B

Sys Systems tems Think hinking ing Too

• ols

ls: : Lo Loop

• ps

27

Balancing Loops also called Goal Seeking Loops

Performance Desired Performance Need for Additional Effort Effort +

• +

+

B

Loop Polarity Right Way: Trace Effect of a Change Around the Loop Quick Way: Count the ‘-’ connections

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 15

Sys Systems tems Think hinking ing Too

• ols

ls: : Stoc Stock k an and d Flo lows

28

Stock and Flow Diagram (Compartmental Model) Integral Representation

Stock Inflow Outflow

Differential Representation 𝑇𝑢𝑝𝑑𝑙 𝑢 = න

𝑢0 𝑢

𝐽𝑜𝑔𝑚𝑝𝑥 𝑡 − 𝑃𝑣𝑢𝑔𝑚𝑝𝑥 𝑡 𝑒𝑡 + 𝑇𝑢𝑝𝑑𝑙 𝑢0 𝑒 𝑒𝑢 𝑇𝑢𝑝𝑑𝑙 = 𝐽𝑜𝑔𝑚𝑝𝑥 𝑢 − 𝑃𝑣𝑢𝑔𝑚𝑝𝑥 𝑢 = 𝑂𝑓𝑢 𝐷ℎ𝑏𝑜𝑕𝑓 𝑗𝑜 𝑇𝑢𝑝𝑑𝑙 𝑢

Sys Systems tems Think hinking ing Too

• ols

ls: : Stoc Stock k an and d Flo lows

29

Hydraulic Metaphor

Stock

Inflows Outflows

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 16

Sys Systems tems Think hinking ing Too

• ols

ls: : Stoc Stock k an and d Flo lows

30

Hydraulic Metaphor

Greenhouse Gasses in the Atmosphere

Greenhouse Gas Emissions Net Removal

Sys Systems tems Think hinking ing Too

• ols

ls: : Stoc Stock k an and d Flo lows

31

Stocks Flows Balance Sheet Wealth CO2 in Atmosphere Integrals Water in a bathtub Accounts Payable Income and Expenditures Cash Flow Statement CO2 Emissions Derivatives Flows through faucet and drain Vehicle Production Product preorders

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HAN ANDS DS-ON ON MAN MANAGEMEN EMENT T FLIG FLIGHT T SIMU SIMULA LATOR

James Paine

System Dynamics Group MIT Sloan School of Management

Fishbanks

*Briefing and debriefing borrowed heavily from Dr. Hazhir Rahmandad and Dr. John Sterman

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 18

Fishbanks

• Intro (almost over)
• Fishbanks!
• Short break
• Results and

Discussion

Winslow Homer, The Herring Net Fishbanks game by originally developed by Prof. Dennis Meadows, 1986. Web version developed by Prof. John Sterman, MIT Sloan School of Management, with help from

• Prof. Andrew King, Tuck School
• f Business, Dennis Meadows,

Keith Eubanks, and Forio.com,

• 2010. Translations available:

Chinese, Spanish, Portuguese.

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 19

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 20

Three oceans, Seven teams each

Teams of three to five (aim for 4)

A1 A2 A3 A4 A5 A6 A7 I1 I2 I3 I4 I5 I6 I7 P1 P2 P3 P4 P5 P6 P7

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 21

Your Goal

Maximize your team’s Net Worth at the end of the game.

Net Worth = Bank Balance + Value of Fleet

The winner is the team with the highest Net Worth at game end

$

Profit

Profit = Income - Expenses

Fish Sales Ship Trade Sells Interest Earning Harbor Operation Costs Ship Purchases New Ship Orders Interest Charges

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 22

Income

Catch x Price ($20 per fish)

Fish Sales Ship Sales Interest Earnings

2%/year if Minimum Bank Balance is greater than zero Price set by auction

Expenses

Harbor: $ 50/year per ship Coastal Fishery: $150/year per ship Deep Sea Fishery: $250/year per ship

Harbor Operation Costs Construction

New Ships: $300 each. Charged at end of current

• year. Delivered the following year

Ship Purchases

Buy a ship at auction. Cost: your winning bid per ship * number bought

Interest Charges

5%/yr if Minimum Bank Balance is less than zero.

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 23

Sequence of Debits and Credits

Bank Balance

Start year with bank balance that accumulated through all past years If you buy ships at auction or from

• ther teams, the

cost is subtracted START AUCTION You are then charged for the

• perating costs of

your fleet Your fish catch is calculated and sales are credited as income DECISION COSTS AND INCOME CALCULATED The minimum balance is calculated and your account adjusted up or down based on the interest charged or earned Finally, your account is charged for any new ships you

• rdered at the

beginning of the year

Fishing Fleet

• Initial Fleet =

3 Ships/team

• Fleet Growth
• Purchase from other teams via

auctions

• Order new ships
• Fleet Reduction
• Sales to other teams via auctions

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 24

Ordering New Ships

Each year you may order the construction of new ships. The maximum order is half of your current fleet (initial fleet + auction purchases). If total fleet is an odd number, your maximum order is rounded up to the next whole number.

Catch

Catch influenced by: Number of Ships, Ship Effectiveness, Weather

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 25

Recent History of the Fisheries Ship Effectiveness

2 5

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 26

• Three oceans: Atlantic, Pacific, Indian
• 7 teams in each ocean, 3-5 people per team
• The oceans are separate
• Fish do not move between oceans
• Ships do not move between oceans
• Conditions identical except for your decisions

FishBanks Let’s go fishing…

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 27

Login

• 1 Laptop per team

(put all others away please)

• Go to: http://bit.ly/fishbanks
• STOP – wait for instructions

Alt link: http://forio.com/simulate/mit/fishbanks/simulation/login.html

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 28

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 29

Fishing Areas

Deep Sea Maximum Population 2000 - 4000 Fish Annual Operating Cost $250 per Ship-Year Productivity (Max Ship Effectiveness) 25 (Fish/year)/ship

Coast

Maximum Population 1000 - 2000 Fish Annual Operating Cost $150 per Ship-Year Productivity (Max Ship Effectiveness) 15 (Fish/year)/Ship

Profit Example

FISH SALES = 25 X $20 OPERATING COST DEEP SEA SUBTOTAL FISH SALES = 15 X $20 OPERATING COST COASTAL SUBTOTAL HARBOR COST

1 SHIP TO DEEP SEA 1 SHIP TO COASTAL 1 SHIP TO HARBOR

PROFIT

$250 $150

• $50

$350

$500

• $250

$300

• $150

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 30

Regeneration of Fish

New Fish Per Year Deep Sea Coastal Fish Max

Develop your strategy

• 1. Your goal is to end the game with the

maximum possible assets.

• 2. Discuss within your team what strategies

for boat acquisition and allocation you will follow to attain this.

• 3. Write your strategy down.

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 31

Step 1: Auctions

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 32

Step 2: Buy and Allocate ships

Any Questions?

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 33

Game is on!

Game over!

You can take a 10-min break

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Total Assets (Net worth) by team

The Winner is: Pacific 4 with 35,421

• What was your personal intention at the start?
• What was your team’s strategy at the start?
• What did you actually do?
• What did your team do?
• What could you have done differently?

Reflection

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Fishbanks Debrief

The Iceberg: A Metaphor for Systems Thinking

More Leverage Events Patterns of Behavior Systemic Structure

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 36 Loopholes found In fishing rules

Fishing banned at Georges Bank

Local fishermen fear overcrowding

Lobstermen Snag record 38m pounds

Codfish depleted

• ff Maine

Restrictions could Hurt local fishermen

N.E. lawmakers seek boat buyback ideas

Hearing casts fishery as sinking ship Canada’s Gunboat Diplomacy

Chrétien to protect Atlantic fish stocks

Limits may follow as cod diminishes in Gulf of Maine Feds approve boat buyback program

Hope to thin fishing fleet with $2m in incentives

Event level: the Headlines

Pattern #1: Overshoot and Collapse

Atlantic Swordfish Catch

1 2 3 4 5 1950 1960 1970 1980 1990 2000 Thousand Metric Tons/year

Pacific Bluefin Tuna Catch

4 8 12 16

1950 1960 1970 1980 1990 2000

Thousand Metric Tons/year

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 37

North Sea Herring Catch Consider the Cod

• Northern or Atlantic Cod
• Long-lived, slow to mature
• Once immensely abundant
• Early fishers (e.g., Basque) claimed fish so dense you could walk

from Spain to the New World on their backs.

• John Cabot, exploring Newfoundland in 1497, noted fish so thick

they practically blocked his ship.

• Harvest ≈ 250,000 metric tons/yr through 1950s
• Vital in feeding the Old World, in the development of the

New World, …and of Massachusetts:

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 38

The Sacred Cod Massachusetts State House

Prevailing Mental Model: Unlimited Abundance

“Probably all the great fisheries are inexhaustible; that is to say that nothing we do seriously affects the number of fish.”

• Thomas Henry Huxley, 1883

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 39

Source: US National Marine Fisheries Service Source: https://foss.nmfs.noaa.gov/

10000 20000 30000 40000 50000 60000 1950 1970 1990 2010

(Updated) US Atlantic Cod Commercial Landings

(Metric Tons/Year)

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 40

Estimated Biomass in 1852 Estimated Carrying Capacity (Myers et al. 2001) 1200 800 400 1850 1870 1890 1910 1930 1950 1970 1980 Total Cod Biomass Total Cod Biomass Age 5+

Estimated Cod Stocks, Scotian Shelf (1000 Metric Tons)

Rosenberg et al., Frontiers in Ecology, 2005

Rebranding “Trash” Fish

• Slimehead

* “Orange Roughy”

• Patagonian Toothfish

* “Chilean Sea Bass”

• Whore’s eggs

* “Maine Sea Urchin”

• Mud Crabs

* “Peekytoe Crab”

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Aquaculture expanding to feed the world

Source: http://www.fao.org/state-of- fisheries-aquaculture

Overshoot and Collapse

Why the pervasive pattern of

• vershoot and collapse of fisheries?

Time

Annual fish catch Where are the leverage points for creating a sustainable fishery? Where are they not?

What could you have done differently?

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 42

• 1. Renewable resources

can be used no faster than they regenerate.

• 2. Pollution and wastes

can be emitted no faster than natural systems can absorb them, recycle them, or render them harmless.

• 3. Nonrenewable resources

can be used no faster than renewable substitutes can be introduced.

Source: Herman Daly (e.g., H. Daly (1990) Ecological Economics 2, 1).

Human Activity Ecosystem Services The “Daly Rules”

Feedback structure Governing Renewable Resources

Fish Stock Net Recruitment Catch + Fish Density Maximum Fish Stock +

• Fractional Net

Recruitment + + Catch per Ship Fleet Size + + +

R1 Population Growth B1 Limits to Growth B2 Fishing Effectiveness

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 43

Fractional Net Recruitment vs. Fish Density

1 Fish Density, S/Smax (Dimensionless) Fractional Net Recruitment (1/year)

Impact of Technology on Ship Effectiveness

1 Fish Density, S/Smax (Dimensionless) Catch per Ship (Fish/Ship/year) Low Technology High Technology

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 44

1 Fish Density, S/Smax (Dimensionless) Net Recruitment (fish/year)

Net Recruitment vs. Fish Density Net Recruitment vs. Fish Density

1 Fish Density, S/Smax (Dimensionless) Net Recruitment (fish/year)

Reinforcing Feedback Dominant (R1) Balancing Feedback Dominant (B1) Unstable Equilibrium Stable Equilibrium MSY

System Dynamics: Systems Thinking and Modeling for a Complex World 1/13/2020 DO NOT DISTRIBUTE: Intended for registrants of the MIT System Dynamics Group IAP 2020 Session only 45

Overshoot and Collapse

Simulation: Fleet (potential catch) grows 2%/year

Overshoot and Collapse: Examples

• Carrying capacity of the Earth:
• climate change,
• ozone layer,
• ground water,
• agricultural soils,
• forests,
• etc.
• Borrowing to maintain lifestyle
• Speculative bubbles

(housing, tech stocks, art, etc.)

• Abusing trust and good will
• Misleading accounting
• Telephone Marketing
• False online reviews
• Drinking

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“The Tragedy of The Commons” Garrett Hardin. Science 1968; 162:1243-8.

• G. Hardin,

1915-2003 Photo: 1986

The Tragedy of the Commons

“No technical solution can rescue us.…” “Each man is locked into a system that compels him to increase his herd without limit—in a world that is limited. Ruin is the destination toward which all men rush, each pursuing his own best interest…” “We may well call it ‘the tragedy of the commons,’ using the word ‘tragedy’ as the philosopher Whitehead used it: ‘The essence of dramatic tragedy is not unhappiness. It resides in the solemnity of the remorseless working of things.’

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“Common Pool Resources”

• Limited Stock and Rate of Renewal
• Easily Appropriable (Low barriers to access)
• Rival (What you use, I can’t use)
• EXAMPLES:
• Pastures
• Fish
• Forests
• Irrigation
• Clean Air & Water
• Climate
• Roads and Highways
• Parking Spaces
• Views
• Server Resources
• Trust among

consumers

Causes of Collapse (1)

Collapse of the carrying capacity can occur when underlying resources are

Nonrenewable or Renewable but Consumable or Degradable

Collapse is worse with

• Common pool resources (Tragedy of the Commons)
• Slow or limited regeneration potential
• Tipping points created by positive feedbacks
• Irreversibilities due to e.g.

➢ Trophic cascade ➢ Evolutionary impacts

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Causes of Overshoot (2)

• Long Delays in

Changes in Resource level (Physical/Biological delay) Measuring resource level (Perception delay) Understanding causes (Research delay) Recommending action (Political/social delay) Implementing policies (Political/social delay) Policy impact (Physical/Biological delay)

Note: Delays are partly physical and partly political and social. Those with vested interests in the status quo often misrepresent the situation to delay action (e.g. tobacco, lead in gasoline, toxics in food, climate change).

Privatization can help, but has Limits

• Enforcement
• Requires effective rule of law to enforce property rights.
• Conflict around equity, fairness.
• High discount rate
• Owners may find it in their interest to harvest unsustainably
• Irreducible externalities
• You can pen up your sheep, but your fish? Air? Water? Climate?
• Dynamic complexity
• Difficulty of measuring resource stocks, consumption, regeneration
• Long delays in detecting and responding to overexploited resource
• Long recovery delays
• Nonlinearities, thresholds, ‘side effects’

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Limits to Privatization

• Moxnes (1998): Experiment similar to Fishbanks

but with perfect private property rights: no Tragedy of Commons

• 74% of subjects overbuilt fleets; average fleet 60% above optimal
• Average fish stocks 15% below optimal
• Average subject wealth 46% below optimal
• Subjects: Fishers, Fishing Resource Managers, Researchers

Typical subject behavior: No uncertainty case

Adequacy of Resources +

• B1

Global Carrying Capacity + Resource Prices, Social Concern, Gov't Policy

• Innovation

+ Technology + Technological "Side Effects" + Consumption and Degradation Regeneration and Restoration

• +

+ B2

B4

R3

Regeneration Capacity

• +

+ Voluntary Limits

• B5

B3

R2

DELAY DELAY DELAY DELAY DELAY DELAY DELAY

Beyond Fishbanks

Source: John Sterman, MIT Sloan

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Collective Action

Elinor Ostrom (1933-2012): Winner, 2009 Nobel Memorial Prize in Economic Sciences

Elinor Ostrom’s Optimism

“I would rather address the question of how to enhance the capabilities of those involved to change the constraining rules of the game to lead to outcomes

• ther than remorseless

tragedies…”

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• Individuals know the boundaries and limits
• Of the resource (“Common Pool Resource”)
• Of the community of users (“Appropriators”)
• Rules are locally made and adapted to context
• Decisions are made together
• Active measurement and monitoring
• Effective, graduated sanctions
• Accessible mechanisms for conflict resolution
• Latitude from higher authorities to act locally

Design Principles for “Governing the Commons”

Leadership question: how do we enroll and mobilize people to create these conditions?

OTH THER ER SY SYSTE STEM M DYNAMIC MICS S RE RESO SOURCE CES

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Wan ant t mor more? e?

Classes Classes at t MIT MIT 15.871- Introduction to System Dynamics 15.873 - System Dynamics for Business and Policy

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• +

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Books Books

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All are in the MIT Library!

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Ar Artic ticles les (per (personal sonal favorites):

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All are free when accessing from MIT’s network!

System Dynamics at Sixty: The Path Forward Selling System Dynamics to (other) Social Scientists Making the Numbers? “Short Termism” and the Puzzle of Only Occasional Disaster Nobody ever gets credit for fixing problems that never happened: Creating and sustaining process improvement Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement.

https://sdjournalclub.mit.edu/sites/default/files/documents/Sys%20Dyn%20Reading%20List.xls

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Websites bsites (per (personal sonal favorites):

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Creative Learning Exchange

http://www.clexchange.org/

Tom Fiddaman’s MetaSD

https://metasd.com/model-library/

MIT OCW System Dynamics Self Study

https://ocw.mit.edu/courses/sloan-school-of-management/15-988- system-dynamics-self-study-fall-1998-spring-1999/

MIT System Dynamics Journal Club

http://sdjournalclub.mit.edu/

The System Dynamics Society

https://www.systemdynamics.org/what-is-sd

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(So (Some) me) Sof Softw tware

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(So (Some me mor more) e) Sof Softw tware

107

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Sys System tem Dynamics ynamics in A in Action ction

En En-Roads

• ads Clima

Climate te Polic

• licy

y Simula Simulator tor

https://en-roads.climateinteractive.org/scenario.html?v=2.7.6

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