Open World - User contributions [en]

General Entry point

2024-01-07T17:24:31Z

Jrising: Created page with "[https://existencia.org/docs/tiki-index.php?page=HomePage Personal Docs]"

[https://existencia.org/docs/tiki-index.php?page=HomePage Personal Docs]

Main Page

2024-01-07T17:15:09Z

Jrising:

'''Welcome to the Open World Project'''

== Notes ==
=== Papers ===

* [[Relevant Past CNH Grants]]
* [[Recent Papers]]
* [[Fisheries Papers]]
* [[Complexity Papers]]

=== Meetings ===

* [[Feb 16 2012]]: Manu, Pierre, James
* ([[Mar 22 2012]]: James and Kimberly)
* [[Apr 17 2012]]: Manu, Pierre, Bruce, Kimberly, James

== Drafts ==

* [[Key Research Questions]]
* [[Vision Statements]]

== Tasks ==

* [[James's Tasks]]
** [[Complexity and the Political Economy of Fisheries]]
** [[Analysis of Leverage]]
** [[System Regression]]
** [[New Language of Systems]]
** [[Amalgamated Modeling]]
** [[Networked Systems Framework]]
** [[Spatial Fisheries]]
** [[Information Flow in Spatial State Machines]]
** Past Related Projects:
*** [[Importance of Space in Economics]]
*** [[Self-Organized Criticality in SD]]
*** [[Defining Complexity]]
*** [[Quartic Growth Functions]]
* [[Progress Since Last Meeting]]
* [[Potential Collaborators]]

== Arachne Entry points ==

* [[General Entry point]]
* [[Climate Change Economics 2024 Entry point]]

== Getting started ==

* Dropbox folder (ask to be added): https://www.dropbox.com/home#:::103415423
* Consult the [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software.

Extra features on this wiki (MediaWiki extensions):
* [http://openwetware.org/wiki/Wikiomics:Biblio Biblio]: references manager
* LaTeX: You can use <nowiki><math></nowiki> for bits of LaTeX or <nowiki><latex></nowiki> for longer blocks
* [http://www.mediawiki.org/wiki/Extension:LiquidThreads LiquidThreads]: discussion threads on the talk pages
* WikiEditor: a nicer interface to edit pages
* [www.mediawiki.org/wiki/Extension:SyntaxHighlight_GeSHi SyntaxHighlight]: allows blocks of source code

Main Page

2024-01-07T17:14:48Z

Jrising:

'''Welcome to the Open World Project'''

== Notes ==
=== Papers ===

* [[Relevant Past CNH Grants]]
* [[Recent Papers]]
* [[Fisheries Papers]]
* [[Complexity Papers]]

=== Meetings ===

* [[Feb 16 2012]]: Manu, Pierre, James
* ([[Mar 22 2012]]: James and Kimberly)
* [[Apr 17 2012]]: Manu, Pierre, Bruce, Kimberly, James

== Drafts ==

* [[Key Research Questions]]
* [[Vision Statements]]

== Tasks ==

* [[James's Tasks]]
** [[Complexity and the Political Economy of Fisheries]]
** [[Analysis of Leverage]]
** [[System Regression]]
** [[New Language of Systems]]
** [[Amalgamated Modeling]]
** [[Networked Systems Framework]]
** [[Spatial Fisheries]]
** [[Information Flow in Spatial State Machines]]
** Past Related Projects:
*** [[Importance of Space in Economics]]
*** [[Self-Organized Criticality in SD]]
*** [[Defining Complexity]]
*** [[Quartic Growth Functions]]
* [[Progress Since Last Meeting]]
* [[Potential Collaborators]]

== Arachne Entry points ==

[[General Entry point]]
[[Climate Change Economics 2024 Entry point]]

== Getting started ==

* Dropbox folder (ask to be added): https://www.dropbox.com/home#:::103415423
* Consult the [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software.

Extra features on this wiki (MediaWiki extensions):
* [http://openwetware.org/wiki/Wikiomics:Biblio Biblio]: references manager
* LaTeX: You can use <nowiki><math></nowiki> for bits of LaTeX or <nowiki><latex></nowiki> for longer blocks
* [http://www.mediawiki.org/wiki/Extension:LiquidThreads LiquidThreads]: discussion threads on the talk pages
* WikiEditor: a nicer interface to edit pages
* [www.mediawiki.org/wiki/Extension:SyntaxHighlight_GeSHi SyntaxHighlight]: allows blocks of source code

Main Page

2023-12-26T02:39:50Z

Jrising:

'''Welcome to the Open World Project'''

== Notes ==
=== Papers ===

* [[Relevant Past CNH Grants]]
* [[Recent Papers]]
* [[Fisheries Papers]]
* [[Complexity Papers]]

=== Meetings ===

* [[Feb 16 2012]]: Manu, Pierre, James
* ([[Mar 22 2012]]: James and Kimberly)
* [[Apr 17 2012]]: Manu, Pierre, Bruce, Kimberly, James

== Drafts ==

* [[Key Research Questions]]
* [[Vision Statements]]

== Tasks ==

* [[James's Tasks]]
** [[Complexity and the Political Economy of Fisheries]]
** [[Analysis of Leverage]]
** [[System Regression]]
** [[New Language of Systems]]
** [[Amalgamated Modeling]]
** [[Networked Systems Framework]]
** [[Spatial Fisheries]]
** [[Information Flow in Spatial State Machines]]
** Past Related Projects:
*** [[Importance of Space in Economics]]
*** [[Self-Organized Criticality in SD]]
*** [[Defining Complexity]]
*** [[Quartic Growth Functions]]
* [[Progress Since Last Meeting]]
* [[Potential Collaborators]]

== Getting started ==

* Dropbox folder (ask to be added): https://www.dropbox.com/home#:::103415423
* Consult the [http://meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software.

Extra features on this wiki (MediaWiki extensions):
* [http://openwetware.org/wiki/Wikiomics:Biblio Biblio]: references manager
* LaTeX: You can use <nowiki><math></nowiki> for bits of LaTeX or <nowiki><latex></nowiki> for longer blocks
* [http://www.mediawiki.org/wiki/Extension:LiquidThreads LiquidThreads]: discussion threads on the talk pages
* WikiEditor: a nicer interface to edit pages
* [www.mediawiki.org/wiki/Extension:SyntaxHighlight_GeSHi SyntaxHighlight]: allows blocks of source code

Modeling framework for Water Demand

2013-08-26T02:00:45Z

Jrising:

File:Econnet.png

2013-08-26T01:58:54Z

Jrising:

Modeling framework for Water Demand

2013-08-26T01:37:43Z

Jrising:

[https://existencia.org/docs/tiki-index.php?page=HomePage Personal Docs]

Jrising:

Modeling framework for Water Demand

2013-08-25T03:15:14Z

Jrising:

Modeling framework for Water Demand

2013-08-25T02:48:01Z

Jrising:

2013-08-24T20:24:11Z

Jrising:

Modeling framework for Water Demand

2013-08-24T20:23:11Z

Jrising:

<slide title="System Models">
* Aggregate System: population, land use, water flows
* Sentiments: Pro-market vs. pro-environment, inequality, conflict
* Spatial water flow
* Spatial population movement
* Water demand economic model
* Infrastructure building
* Diet Model: livestock, wild-caught, nourishment, prices
* Politicized dynamic optimization of decisions
* Linear programming for spatial optimization
</slide>

<slide title="Model Connections">
[[File:Allmods.png|center]]
</slide>

<slide title="Aggregate System 1">
[[File:Agg1.png|center]]
</slide>

<slide title="Aggregate System 1">
[[File:Politics.png|center]]
</slide>

<slide title="Aggregate System 1 Calibration">
* 12.7-37 kg ha-1 per mm = 25: French, R. J., & Schultz, J. E. (1984). Water use efficiency of wheat in a Mediterranean-type environment. I. The relation between yield, water use and climate.
Crop and Pasture Science, 35(6), 743-764.
* 1961: 49% agriculture, 1.66% urban = 2.6% urban in 2002 * 183.7e6 pop / 287.6e6 pop (Major Uses of Land in the United States, 2002)
</slide>

<slide title="Core Elements">
* Amalgamated Modeling
* Multiple Network Maps
* Networked System Dynamics
* Computational Tools
* Open Interface
* Smart Variables

[[File:Elements.png|center]]
</slide>

<slide title="District Population Shifts">
[[File:Districtnetwork2.png|center]]
</slide>

<slide title="Smart Variables: Dimensions">
* <math>3</math> vs. <math>3</math> [tonnes] vs. <math>1350487537</math> [seconds since Jan. 1, 1970]
* Dimensional analysis at the heart of science
* Automatic model checking, unit conversion
</slide>

<slide title="Smart Variables: Maps" fs="1.5em">
"Maps" are dimension-aware functions in space-time, often tied to data streams (e.g., IRI tsvs, geotiffs).
Maps of different resolutions can be manipulated transparently. Example:

<syntaxhighlight lang="cpp">
GeographicMap<double>& degreeDayMelt =
(degreeDayFactor + degreeDaySlope * elevation)
* (snowCover / 100) * (surfaceTemp - ZERO_CELSIUS)
* (surfaceTemp >= ZERO_CELSIUS);
</syntaxhighlight>

* elevation is a static map at <math>1 km</math> resolution
* surfaceTemp is a daily varying map at <math>.25^\circ</math> resolution, read from the file 1 day at a time
* snowCover is a weekly varying map at <math>.33^\circ</math> resolution, reconstructed for past years
</slide>

<slide title="Case Study: Hydrological Modeling">
{img src="/images/9/91/Flowcauses.png" width="500" height="333" /}
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Bhakramap.png]]
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Netmap_ext.png]]
</slide>

<slide title="Smart Variables: Relations">
Variables can represent relationships or differential equations between other variables.

Example (the heat equations):
<syntaxhighlight lang="cpp">
q = -k * Grad(u);
Diff(u) = (-1 / c_p * rho) * Div(q);
</syntaxhighlight>

The equations themselves are saved within <code>q</code> and <code>Diff(u)</code>, so the model can be run.
</slide>

<slide title="Case Study: Networked Economics">
Step 1: Reconstruct Solow Growth (with some random shocks):

* <math>\frac{d L}{d t} = \lambda L(t)</math>
* <math>Y(t) = K(t)^\alpha L(t)^{1-\alpha} \epsilon(t)</math>
* <math>\frac{d K}{d t} = s Y(t) - \delta K(t)</math>

{img src="/images/d/db/Solow.png" width="300" height="160" /}
</slide>

<slide title="Case Study: Networked Economics">
Step 2: Make a "distributed" analog to Solow growth:

* Multiple firms, with individual capital stocks
* Separate growth and decay: <math>g[t] = s Y[t]</math>, <math>d[t] = \delta K[t]</math>
* If <math>g[t] \ge d[t]</math>, growth: <math>K[t+1] = K[t] + g[t]</math>
* If <math>g[t] < d[t]</math>, stagnation: <math>K[t+1] = K[t]</math>
** And probability of collapse, so expected value follows Solow
** <math>K[t+1] = K[t] + g[t] - d[t] = (1 - P(c)) K[t] \implies P(c) = (d[t] - g[t]) / K[t]</math>
* Firms can make connections to each other, which increase "technology" (specialization) factor

[[File:Smallworld.png]]

* But when collapse, connections severed, capital goes to 0
</slide>

<slide title="Case Study: Networked Economics">
[[File:Distrib.png]]

[[File:Economies.png]]
</slide>

<slide title="Amalgamated Modeling" fs="1.6em">
Amalgamated modeling allows models to interact, specialize, and "overlap".

Every model is incomplete; applies to a constrained context.
:'''Let's embrace partial models!'''
Want a "plugin architecture", where models can easily be allowed to interact

Coupling causes feedback, and models are defined at different scales.
:'''Need a new way to couple models!'''

Allow overlapping-- models inform different variables, at different scales.
</slide>

<slide title="Amalgamated Modeling">
[[File:Blobs.png]]
</slide>

<slide title="Bayesian Coupling">
[[File:Amalgelt.png]]
</slide>

<slide title="Bayesian Coupling">
For a variable <math>\theta</math> described by multiple models, each
model provides both a PDF across values at a given time <math>t</math>
when run in isolation, <math>p(\theta, \bar{S}^i)</math>, and a
distribution that includes feedback effects, <math>p(\theta, \tilde{S}^i)</math>. The final distribution is
<math>
p(\theta | \cdot) \propto p(\theta) \prod_i p(\theta |
\bar{S}^i)^\lambda p(\theta, \tilde{S}^i)^{1-\lambda}
</math>

[[File:Amalgcombo.png|center]]
</slide>

<slide title="Amalgamation Challenges">
* How do I test it?
* Efficient probability function calculations
* Smooth or spectrally-informed transitions?
* What does downsampling contribute?
* How to ensure that different scales add up?
* How do we understand a multi-scale model?

[[File:Trialestimate.png|left]]
</slide>

<slide title="A New System Dynamics">
Coupling natural and human systems makes things complex:
: feedback, non-linearity, resilience, and spacial heterogeneity

Combine the temporal sophistication of system dynamics,
: with spatial heterogeneity

[[File:ssdarch-mod.png]]
(Ahmad et al 2004; flood management, water resources modeling, invasive species spread)
</slide>

<slide title="Multiple Networks">
Models use multiple networks simultaneously
* Different paths on which stocks flow
* Disaggregations into structured classes
* Capturing network properties
</slide>

<slide title="Multiple Networks in Ohio">
[[File:Ohionet.png]]
</slide>

<slide title="Disaggregating System Models">
[[File:Popmod-vensim.png|center]]

[[File:Popmod.png|center]]
</slide>

<slide title="Networked System Dynamics">
[[File:Netstocks.png]]
</slide>

<slide title="Networked System Dynamics">
[[File:Ohiomod.png]]
</slide>

<slide title="Self-Similar Networks">
[[File:Selfsimodel.jpeg|center]]
</slide>

<slide title="Networking Challenges">
* Ensure that the separate blocks match the aggregate
* What is a full language of networked system dynamics?
* Can a model only apply to part of a network?
* How to ensure that missing models "fail gracefully"?
</slide>

<slide title="Computational Tools">
* Evaluate model performance (Barlas 1996)
* Identify driving feedback loops
* Identify tipping and leverage points
* Construct simplified models for communication
* System Regression: construct models from data
</slide>

<slide title="Integrating Data" fs="2em">
* Calibration
* Validation
* Filling in missing models
:'''We need a smart (context-aware and incomplete-welcoming) data library!'''
</slide>

<slide title="Networked Equations Language">
Custom '''Modeling Language''' combines a units-aware equation-like syntax with networks and GIS.

<syntaxhighlight lang="cpp">
capacity = 1e10 [tons];
rate = 0.0077 [tons/year];
biomass = Stock(1e7 [tons]);
catches = TimeSeries("catches.tsv", [tons/year]);
biomass += rate * biomass *
(1 - biomass / capacity) - catches;

print(biomass[0:100], "\t");
</syntaxhighlight>
</slide>

<slide title="Extensions">
* Memetic propagation of models
* Integration with climate models
* Importing Vensim models
</slide>

<slide title="Model for Climate Behaviors">
Overdetermined status-quo:
* Politicians won't make unpopular changes
* Businesses won't take action alone
* Consumers have great difficulty without support
* Carbon leakage
* Rebound effects
</slide>

<slide title="Model for Climate Behaviors">
* Climate behaviors as aggregate activity
* Looking for leverage points
* Not trying to predict future states
</slide>

<slide title="Model for Climate Behaviors">
[[File:Architecture.png]]

(Self-similar Meadows 2004 regionally, Forrester 1971 for urban)
</slide>

<slide title="How Many Variables?">
* World3/2000: 283
* System Dynamics National Model: 2000+
* Encyclopedia of World Problems and Human Potential: 56,135
** environmental feedback loops: 2,675
</slide>

<slide title="Multimanaged Fisheries Project">
* Collapsing fisheries, despite new management
* Perverse economic incentives
* Multiple scales of uncertainty
* Unintended policy consequences
</slide>

<slide title="Multimanaged Fisheries Project">
[[File:Food_web_600.jpg]]
</slide>

<slide title="Multimanaged Fisheries Project">
* <math>g_t^i = r^i s_t^i \left(1 - \frac{s_t^i}{K_t^i}\right)</math>
* <math>K_t^i = \sum_{j \in q(i)} w^{ij} s_t^j</math>

* Nature: ecosystem and regional models
* Social: Fishing community, policy-makers, NGOs
* Plug-in different "fish" and "policy" modules
* Working with stakeholders
</slide>

Modeling framework for Water Demand

2013-08-24T19:37:59Z

Jrising:

<slide title="System Models">
* Aggregate System: population, land use, water flows
* Sentiments: Pro-market vs. pro-environment, inequality, conflict
* Spatial water flow
* Spatial population movement
* Water demand economic model
* Infrastructure building
* Diet Model: livestock, wild-caught, nourishment, prices
* Politicized dynamic optimization of decisions
* Linear programming for spatial optimization
</slide>

<slide title="System Models">
[[File:Allmods.png|center]]
</slide>

<slide title="Aggregate System 1">
[[File:Agg1.png|center]]
</slide>

<slide title="Aggregate System 1">
[[File:Politics.png|center]]
</slide>

<slide title="Aggregate System 1 Calibration">
* 12.7-37 kg ha-1 per mm = 25: French, R. J., & Schultz, J. E. (1984). Water use efficiency of wheat in a Mediterranean-type environment. I. The relation between yield, water use and climate.
Crop and Pasture Science, 35(6), 743-764.
* 1961: 49% agriculture, 1.66% urban = 2.6% urban in 2002 * 183.7e6 pop / 287.6e6 pop (Major Uses of Land in the United States, 2002)
</slide>

<slide title="Core Elements">
* Amalgamated Modeling
* Multiple Network Maps
* Networked System Dynamics
* Computational Tools
* Open Interface
* Smart Variables

[[File:Elements.png|center]]
</slide>

<slide title="District Population Shifts">
[[File:Districtnetwork2.png|center]]
</slide>

<slide title="Smart Variables: Dimensions">
* <math>3</math> vs. <math>3</math> [tonnes] vs. <math>1350487537</math> [seconds since Jan. 1, 1970]
* Dimensional analysis at the heart of science
* Automatic model checking, unit conversion
</slide>

<slide title="Smart Variables: Maps" fs="1.5em">
"Maps" are dimension-aware functions in space-time, often tied to data streams (e.g., IRI tsvs, geotiffs).
Maps of different resolutions can be manipulated transparently. Example:

<syntaxhighlight lang="cpp">
GeographicMap<double>& degreeDayMelt =
(degreeDayFactor + degreeDaySlope * elevation)
* (snowCover / 100) * (surfaceTemp - ZERO_CELSIUS)
* (surfaceTemp >= ZERO_CELSIUS);
</syntaxhighlight>

* elevation is a static map at <math>1 km</math> resolution
* surfaceTemp is a daily varying map at <math>.25^\circ</math> resolution, read from the file 1 day at a time
* snowCover is a weekly varying map at <math>.33^\circ</math> resolution, reconstructed for past years
</slide>

<slide title="Case Study: Hydrological Modeling">
{img src="/images/9/91/Flowcauses.png" width="500" height="333" /}
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Bhakramap.png]]
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Netmap_ext.png]]
</slide>

<slide title="Smart Variables: Relations">
Variables can represent relationships or differential equations between other variables.

Example (the heat equations):
<syntaxhighlight lang="cpp">
q = -k * Grad(u);
Diff(u) = (-1 / c_p * rho) * Div(q);
</syntaxhighlight>

The equations themselves are saved within <code>q</code> and <code>Diff(u)</code>, so the model can be run.
</slide>

<slide title="Case Study: Networked Economics">
Step 1: Reconstruct Solow Growth (with some random shocks):

* <math>\frac{d L}{d t} = \lambda L(t)</math>
* <math>Y(t) = K(t)^\alpha L(t)^{1-\alpha} \epsilon(t)</math>
* <math>\frac{d K}{d t} = s Y(t) - \delta K(t)</math>

{img src="/images/d/db/Solow.png" width="300" height="160" /}
</slide>

<slide title="Case Study: Networked Economics">
Step 2: Make a "distributed" analog to Solow growth:

* Multiple firms, with individual capital stocks
* Separate growth and decay: <math>g[t] = s Y[t]</math>, <math>d[t] = \delta K[t]</math>
* If <math>g[t] \ge d[t]</math>, growth: <math>K[t+1] = K[t] + g[t]</math>
* If <math>g[t] < d[t]</math>, stagnation: <math>K[t+1] = K[t]</math>
** And probability of collapse, so expected value follows Solow
** <math>K[t+1] = K[t] + g[t] - d[t] = (1 - P(c)) K[t] \implies P(c) = (d[t] - g[t]) / K[t]</math>
* Firms can make connections to each other, which increase "technology" (specialization) factor

[[File:Smallworld.png]]

* But when collapse, connections severed, capital goes to 0
</slide>

<slide title="Case Study: Networked Economics">
[[File:Distrib.png]]

[[File:Economies.png]]
</slide>

<slide title="Amalgamated Modeling" fs="1.6em">
Amalgamated modeling allows models to interact, specialize, and "overlap".

Every model is incomplete; applies to a constrained context.
:'''Let's embrace partial models!'''
Want a "plugin architecture", where models can easily be allowed to interact

Coupling causes feedback, and models are defined at different scales.
:'''Need a new way to couple models!'''

Allow overlapping-- models inform different variables, at different scales.
</slide>

<slide title="Amalgamated Modeling">
[[File:Blobs.png]]
</slide>

<slide title="Bayesian Coupling">
[[File:Amalgelt.png]]
</slide>

<slide title="Bayesian Coupling">
For a variable <math>\theta</math> described by multiple models, each
model provides both a PDF across values at a given time <math>t</math>
when run in isolation, <math>p(\theta, \bar{S}^i)</math>, and a
distribution that includes feedback effects, <math>p(\theta, \tilde{S}^i)</math>. The final distribution is
<math>
p(\theta | \cdot) \propto p(\theta) \prod_i p(\theta |
\bar{S}^i)^\lambda p(\theta, \tilde{S}^i)^{1-\lambda}
</math>

[[File:Amalgcombo.png|center]]
</slide>

<slide title="Amalgamation Challenges">
* How do I test it?
* Efficient probability function calculations
* Smooth or spectrally-informed transitions?
* What does downsampling contribute?
* How to ensure that different scales add up?
* How do we understand a multi-scale model?

[[File:Trialestimate.png|left]]
</slide>

<slide title="A New System Dynamics">
Coupling natural and human systems makes things complex:
: feedback, non-linearity, resilience, and spacial heterogeneity

Combine the temporal sophistication of system dynamics,
: with spatial heterogeneity

[[File:ssdarch-mod.png]]
(Ahmad et al 2004; flood management, water resources modeling, invasive species spread)
</slide>

<slide title="Multiple Networks">
Models use multiple networks simultaneously
* Different paths on which stocks flow
* Disaggregations into structured classes
* Capturing network properties
</slide>

<slide title="Multiple Networks in Ohio">
[[File:Ohionet.png]]
</slide>

<slide title="Disaggregating System Models">
[[File:Popmod-vensim.png|center]]

[[File:Popmod.png|center]]
</slide>

<slide title="Networked System Dynamics">
[[File:Netstocks.png]]
</slide>

<slide title="Networked System Dynamics">
[[File:Ohiomod.png]]
</slide>

<slide title="Self-Similar Networks">
[[File:Selfsimodel.jpeg|center]]
</slide>

<slide title="Networking Challenges">
* Ensure that the separate blocks match the aggregate
* What is a full language of networked system dynamics?
* Can a model only apply to part of a network?
* How to ensure that missing models "fail gracefully"?
</slide>

<slide title="Computational Tools">
* Evaluate model performance (Barlas 1996)
* Identify driving feedback loops
* Identify tipping and leverage points
* Construct simplified models for communication
* System Regression: construct models from data
</slide>

<slide title="Integrating Data" fs="2em">
* Calibration
* Validation
* Filling in missing models
:'''We need a smart (context-aware and incomplete-welcoming) data library!'''
</slide>

<slide title="Networked Equations Language">
Custom '''Modeling Language''' combines a units-aware equation-like syntax with networks and GIS.

<syntaxhighlight lang="cpp">
capacity = 1e10 [tons];
rate = 0.0077 [tons/year];
biomass = Stock(1e7 [tons]);
catches = TimeSeries("catches.tsv", [tons/year]);
biomass += rate * biomass *
(1 - biomass / capacity) - catches;

print(biomass[0:100], "\t");
</syntaxhighlight>
</slide>

<slide title="Extensions">
* Memetic propagation of models
* Integration with climate models
* Importing Vensim models
</slide>

<slide title="Model for Climate Behaviors">
Overdetermined status-quo:
* Politicians won't make unpopular changes
* Businesses won't take action alone
* Consumers have great difficulty without support
* Carbon leakage
* Rebound effects
</slide>

<slide title="Model for Climate Behaviors">
* Climate behaviors as aggregate activity
* Looking for leverage points
* Not trying to predict future states
</slide>

<slide title="Model for Climate Behaviors">
[[File:Architecture.png]]

(Self-similar Meadows 2004 regionally, Forrester 1971 for urban)
</slide>

<slide title="How Many Variables?">
* World3/2000: 283
* System Dynamics National Model: 2000+
* Encyclopedia of World Problems and Human Potential: 56,135
** environmental feedback loops: 2,675
</slide>

<slide title="Multimanaged Fisheries Project">
* Collapsing fisheries, despite new management
* Perverse economic incentives
* Multiple scales of uncertainty
* Unintended policy consequences
</slide>

<slide title="Multimanaged Fisheries Project">
[[File:Food_web_600.jpg]]
</slide>

<slide title="Multimanaged Fisheries Project">
* <math>g_t^i = r^i s_t^i \left(1 - \frac{s_t^i}{K_t^i}\right)</math>
* <math>K_t^i = \sum_{j \in q(i)} w^{ij} s_t^j</math>

* Nature: ecosystem and regional models
* Social: Fishing community, policy-makers, NGOs
* Plug-in different "fish" and "policy" modules
* Working with stakeholders
</slide>

2013-08-24T18:59:21Z

Jrising:

Modeling framework for Water Demand

2013-08-24T16:17:59Z

Jrising:

<slide title="System Models">
* Aggregate System 1
</slide>

<slide title="Aggregate System 1">
[[File:Agg1.png|center]]
</slide>

<slide title="Aggregate System 1 Calibration">
* 12.7-37 kg ha-1 per mm = 25: French, R. J., & Schultz, J. E. (1984). Water use efficiency of wheat in a Mediterranean-type environment. I. The relation between yield, water use and climate.
Crop and Pasture Science, 35(6), 743-764.
* 1961: 49% agriculture, 1.66% urban = 2.6% urban in 2002 * 183.7e6 pop / 287.6e6 pop (Major Uses of Land in the United States, 2002)
</slide>

<slide title="Core Elements">
* Amalgamated Modeling
* Multiple Network Maps
* Networked System Dynamics
* Computational Tools
* Open Interface
* Smart Variables

[[File:Elements.png|center]]
</slide>

<slide title="District Population Shifts">
[[File:Districtnetwork2.png|center]]
</slide>

<slide title="Smart Variables: Dimensions">
* <math>3</math> vs. <math>3</math> [tonnes] vs. <math>1350487537</math> [seconds since Jan. 1, 1970]
* Dimensional analysis at the heart of science
* Automatic model checking, unit conversion
</slide>

<slide title="Smart Variables: Maps" fs="1.5em">
"Maps" are dimension-aware functions in space-time, often tied to data streams (e.g., IRI tsvs, geotiffs).
Maps of different resolutions can be manipulated transparently. Example:

<syntaxhighlight lang="cpp">
GeographicMap<double>& degreeDayMelt =
(degreeDayFactor + degreeDaySlope * elevation)
* (snowCover / 100) * (surfaceTemp - ZERO_CELSIUS)
* (surfaceTemp >= ZERO_CELSIUS);
</syntaxhighlight>

* elevation is a static map at <math>1 km</math> resolution
* surfaceTemp is a daily varying map at <math>.25^\circ</math> resolution, read from the file 1 day at a time
* snowCover is a weekly varying map at <math>.33^\circ</math> resolution, reconstructed for past years
</slide>

<slide title="Case Study: Hydrological Modeling">
{img src="/images/9/91/Flowcauses.png" width="500" height="333" /}
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Bhakramap.png]]
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Netmap_ext.png]]
</slide>

<slide title="Smart Variables: Relations">
Variables can represent relationships or differential equations between other variables.

Example (the heat equations):
<syntaxhighlight lang="cpp">
q = -k * Grad(u);
Diff(u) = (-1 / c_p * rho) * Div(q);
</syntaxhighlight>

The equations themselves are saved within <code>q</code> and <code>Diff(u)</code>, so the model can be run.
</slide>

<slide title="Case Study: Networked Economics">
Step 1: Reconstruct Solow Growth (with some random shocks):

* <math>\frac{d L}{d t} = \lambda L(t)</math>
* <math>Y(t) = K(t)^\alpha L(t)^{1-\alpha} \epsilon(t)</math>
* <math>\frac{d K}{d t} = s Y(t) - \delta K(t)</math>

{img src="/images/d/db/Solow.png" width="300" height="160" /}
</slide>

<slide title="Case Study: Networked Economics">
Step 2: Make a "distributed" analog to Solow growth:

* Multiple firms, with individual capital stocks
* Separate growth and decay: <math>g[t] = s Y[t]</math>, <math>d[t] = \delta K[t]</math>
* If <math>g[t] \ge d[t]</math>, growth: <math>K[t+1] = K[t] + g[t]</math>
* If <math>g[t] < d[t]</math>, stagnation: <math>K[t+1] = K[t]</math>
** And probability of collapse, so expected value follows Solow
** <math>K[t+1] = K[t] + g[t] - d[t] = (1 - P(c)) K[t] \implies P(c) = (d[t] - g[t]) / K[t]</math>
* Firms can make connections to each other, which increase "technology" (specialization) factor

[[File:Smallworld.png]]

* But when collapse, connections severed, capital goes to 0
</slide>

<slide title="Case Study: Networked Economics">
[[File:Distrib.png]]

[[File:Economies.png]]
</slide>

<slide title="Amalgamated Modeling" fs="1.6em">
Amalgamated modeling allows models to interact, specialize, and "overlap".

Every model is incomplete; applies to a constrained context.
:'''Let's embrace partial models!'''
Want a "plugin architecture", where models can easily be allowed to interact

Coupling causes feedback, and models are defined at different scales.
:'''Need a new way to couple models!'''

Allow overlapping-- models inform different variables, at different scales.
</slide>

<slide title="Amalgamated Modeling">
[[File:Blobs.png]]
</slide>

<slide title="Bayesian Coupling">
[[File:Amalgelt.png]]
</slide>

<slide title="Bayesian Coupling">
For a variable <math>\theta</math> described by multiple models, each
model provides both a PDF across values at a given time <math>t</math>
when run in isolation, <math>p(\theta, \bar{S}^i)</math>, and a
distribution that includes feedback effects, <math>p(\theta, \tilde{S}^i)</math>. The final distribution is
<math>
p(\theta | \cdot) \propto p(\theta) \prod_i p(\theta |
\bar{S}^i)^\lambda p(\theta, \tilde{S}^i)^{1-\lambda}
</math>

[[File:Amalgcombo.png|center]]
</slide>

<slide title="Amalgamation Challenges">
* How do I test it?
* Efficient probability function calculations
* Smooth or spectrally-informed transitions?
* What does downsampling contribute?
* How to ensure that different scales add up?
* How do we understand a multi-scale model?

[[File:Trialestimate.png|left]]
</slide>

<slide title="A New System Dynamics">
Coupling natural and human systems makes things complex:
: feedback, non-linearity, resilience, and spacial heterogeneity

Combine the temporal sophistication of system dynamics,
: with spatial heterogeneity

[[File:ssdarch-mod.png]]
(Ahmad et al 2004; flood management, water resources modeling, invasive species spread)
</slide>

<slide title="Multiple Networks">
Models use multiple networks simultaneously
* Different paths on which stocks flow
* Disaggregations into structured classes
* Capturing network properties
</slide>

<slide title="Multiple Networks in Ohio">
[[File:Ohionet.png]]
</slide>

<slide title="Disaggregating System Models">
[[File:Popmod-vensim.png|center]]

[[File:Popmod.png|center]]
</slide>

<slide title="Networked System Dynamics">
[[File:Netstocks.png]]
</slide>

<slide title="Networked System Dynamics">
[[File:Ohiomod.png]]
</slide>

<slide title="Self-Similar Networks">
[[File:Selfsimodel.jpeg|center]]
</slide>

<slide title="Networking Challenges">
* Ensure that the separate blocks match the aggregate
* What is a full language of networked system dynamics?
* Can a model only apply to part of a network?
* How to ensure that missing models "fail gracefully"?
</slide>

<slide title="Computational Tools">
* Evaluate model performance (Barlas 1996)
* Identify driving feedback loops
* Identify tipping and leverage points
* Construct simplified models for communication
* System Regression: construct models from data
</slide>

<slide title="Integrating Data" fs="2em">
* Calibration
* Validation
* Filling in missing models
:'''We need a smart (context-aware and incomplete-welcoming) data library!'''
</slide>

<slide title="Networked Equations Language">
Custom '''Modeling Language''' combines a units-aware equation-like syntax with networks and GIS.

<syntaxhighlight lang="cpp">
capacity = 1e10 [tons];
rate = 0.0077 [tons/year];
biomass = Stock(1e7 [tons]);
catches = TimeSeries("catches.tsv", [tons/year]);
biomass += rate * biomass *
(1 - biomass / capacity) - catches;

print(biomass[0:100], "\t");
</syntaxhighlight>
</slide>

<slide title="Extensions">
* Memetic propagation of models
* Integration with climate models
* Importing Vensim models
</slide>

<slide title="Model for Climate Behaviors">
Overdetermined status-quo:
* Politicians won't make unpopular changes
* Businesses won't take action alone
* Consumers have great difficulty without support
* Carbon leakage
* Rebound effects
</slide>

<slide title="Model for Climate Behaviors">
* Climate behaviors as aggregate activity
* Looking for leverage points
* Not trying to predict future states
</slide>

<slide title="Model for Climate Behaviors">
[[File:Architecture.png]]

(Self-similar Meadows 2004 regionally, Forrester 1971 for urban)
</slide>

<slide title="How Many Variables?">
* World3/2000: 283
* System Dynamics National Model: 2000+
* Encyclopedia of World Problems and Human Potential: 56,135
** environmental feedback loops: 2,675
</slide>

<slide title="Multimanaged Fisheries Project">
* Collapsing fisheries, despite new management
* Perverse economic incentives
* Multiple scales of uncertainty
* Unintended policy consequences
</slide>

<slide title="Multimanaged Fisheries Project">
[[File:Food_web_600.jpg]]
</slide>

<slide title="Multimanaged Fisheries Project">
* <math>g_t^i = r^i s_t^i \left(1 - \frac{s_t^i}{K_t^i}\right)</math>
* <math>K_t^i = \sum_{j \in q(i)} w^{ij} s_t^j</math>

* Nature: ecosystem and regional models
* Social: Fishing community, policy-makers, NGOs
* Plug-in different "fish" and "policy" modules
* Working with stakeholders
</slide>

Modeling framework for Water Demand

2013-08-24T15:06:50Z

Jrising:

2012-10-19T03:14:08Z

Jrising:

Friday Forum Presentation

2012-10-19T03:11:33Z

Jrising:

<slide header="none" center="both">
= Open World Project =
Friday Forum

Oct. 19, 2012

 

James Rising, Upmanu Lall,

Bruce Shaw, Pierre Gentine
</slide>

<slide title="Talk Plan">
* Other Projects of Interest
* Motivation and Vision
* Core Elements
* Case Study Projects
* Climate Behaviors
* Fisheries Model
* Next Steps
</slide>

<slide title="Other Projects of Interest">
* [http://www.existencia.org/carbon/ Carbon Transition Working Group]
* [http://sdresearch.wikischolars.columbia.edu/jar2234+Peruvian+Fisheries Peruvian Spatial Fisheries]
* [http://sdresearch.wikischolars.columbia.edu/Ocean+Health Ocean Health Metric]
* [http://sdresearch.wikischolars.columbia.edu/jar2234+Marine+Protected+Areas Empirical Benefits from Marine Protected Areas]
* [http://existencia.org/weaver/ Web-Weaver Data Extractor]
* [http://cantovario.com CantoVario]
* [http://openworld.existencia.org/index.php?title=Friday_Forum_Presentation Slider Extension]
</slide>

<slide title="Introduction" fs="2em">
Many of the human behaviors that drive climate change and
environmental degradation are deeply embedded in our society,
economy, and government, and are mutually reinforcing. Better
modeling of human-natural systems can help in many ways:
* Analyzing feedback loops can help identify '''leverage points''', where small policy changes can have pervasive impacts.
* Allowing models at diverse scales and contexts to interact can help scientists '''integrate knowledge'''.
* Interactive models can facilitate '''communication''' with policymakers and make complex problems intelligible.

The Open Model is a modeling framework aimed at these issues.
</slide>

<slide title="Applicability" fs="2em">
* Systemically intractable due over-determined, reinforcing drives, and spatially heterogeneous.
* Environmental and public health issues: environmental degradation, agricultural practices in poor countries, obesity, substance abuse, groundwater use, fishery management, passenger transport
* Rebound effects and cross-border shifts (e.g., carbon leakage)

 

<center>'''Something for everyone!'''</center>
</slide>

<slide title="Why bigger models?">
* '''Accuracy?''' Debatable
* '''Precision?''' Marginally better
* '''As a platform?''' If it's popular

* ''Interaction of the components''
* ''Finer tipping points''
</slide>

<slide title="Core Elements">
* Amalgamated Modeling
* Multiple Network Maps
* Networked System Dynamics
* Computational Tools
* Open Interface
* Smart Variables

[[File:Elements.png|center]]
</slide>

<slide title="What is Coupling?">
[[File:Coupling.png|center]]

Bioeconomic model from Smith and Wilen, 2003
</slide>

<slide title="What is Coupling?">
Problems:
* Runaway feedback (resonance)
* Non-realistic calibrated parameters
* Making elements commensurable
</slide>

<slide title="Amalgamated Modeling" fs="1.6em">
Amalgamated modeling allows models to interact, specialize, and "overlap".

Every model is incomplete; applies to a constrained context.
:'''Let's embrace partial models!'''
Want a "plugin architecture", where models can easily be allowed to interact

Coupling causes feedback, and models are defined at different scales.
:'''Need a new way to couple models!'''

Allow overlapping-- models inform different variables, at different scales.
</slide>

<slide title="Amalgamated Modeling">
[[File:Blobs.png]]
</slide>

<slide title="Bayesian Coupling">
[[File:Amalgelt.png]]
</slide>

<slide title="Bayesian Coupling">
For a variable <math>\theta</math> described by multiple models, each
model provides both a PDF across values at a given time <math>t</math>
when run in isolation, <math>p(\theta, \bar{S}^i)</math>, and a
distribution that includes feedback effects, <math>p(\theta, \tilde{S}^i)</math>. The final distribution is
<math>
p(\theta | \cdot) \propto p(\theta) \prod_i p(\theta |
\bar{S}^i)^\lambda p(\theta, \tilde{S}^i)^{1-\lambda}
</math>

[[File:Amalgcombo.png|center]]
</slide>

<slide title="Amalgamation Challenges">
* How do I test it?
* Efficient probability function calculations
* Smooth or spectrally-informed transitions?
* What does downsampling contribute?
* How to ensure that different scales add up?
* How do we understand a multi-scale model?

[[File:Trialestimate.png|left]]
</slide>

<slide title="A New System Dynamics">
Coupling natural and human systems makes things complex:
: feedback, non-linearity, resilience, and spacial heterogeneity

Combine the temporal sophistication of system dynamics,
: with spatial heterogeneity

[[File:ssdarch-mod.png]]
(Ahmad et al 2004; flood management, water resources modeling, invasive species spread)
</slide>

<slide title="Multiple Networks">
Models use multiple networks simultaneously
* Different paths on which stocks flow
* Disaggregations into structured classes
* Capturing network properties
</slide>

<slide title="Multiple Networks in Ohio">
[[File:Ohionet.png]]
</slide>

<slide title="Disaggregating System Models">
[[File:Popmod-vensim.png|center]]

[[File:Popmod.png|center]]
</slide>

<slide title="Networked System Dynamics">
[[File:Netstocks.png]]
</slide>

<slide title="Networked System Dynamics">
[[File:Ohiomod.png]]
</slide>

<slide title="Self-Similar Networks">
[[File:Selfsimodel.jpeg|center]]
</slide>

<slide title="Networking Challenges">
* Ensure that the separate blocks match the aggregate
* What is a full language of networked system dynamics?
* Can a model only apply to part of a network?
* How to ensure that missing models "fail gracefully"?
</slide>

<slide title="Computational Tools">
* Evaluate model performance (Barlas 1996)
* Identify driving feedback loops
* Identify tipping and leverage points
* Construct simplified models for communication
* System Regression: construct models from data
</slide>

<slide title="Integrating Data" fs="2em">
* Calibration
* Validation
* Filling in missing models
:'''We need a smart (context-aware and incomplete-welcoming) data library!'''
</slide>

<slide title="Open Interface" fs="2em">
[[File:Climatepred.png|center]]

* For researchers: Testing partial models, Ask questions of the whole, Contributing models
* For policy-makers: Interact with the model, Visualize results, Outline scenarios
* For model: A large model, Many eyes
</slide>

<slide title="Smart Variables: Dimensions">
* <math>3</math> vs. <math>3</math> [tonnes] vs. <math>1350487537</math> [seconds since Jan. 1, 1970]
* Dimensional analysis at the heart of science
* Automatic model checking, unit conversion
</slide>

<slide title="Smart Variables: Maps" fs="1.5em">
"Maps" are dimension-aware functions in space-time, often tied to data streams (e.g., IRI tsvs, geotiffs).
Maps of different resolutions can be manipulated transparently. Example:

<syntaxhighlight lang="cpp">
GeographicMap<double>& degreeDayMelt =
(degreeDayFactor + degreeDaySlope * elevation)
* (snowCover / 100) * (surfaceTemp - ZERO_CELSIUS)
* (surfaceTemp >= ZERO_CELSIUS);
</syntaxhighlight>

* elevation is a static map at <math>1 km</math> resolution
* surfaceTemp is a daily varying map at <math>.25^\circ</math> resolution, read from the file 1 day at a time
* snowCover is a weekly varying map at <math>.33^\circ</math> resolution, reconstructed for past years
</slide>

<slide title="Smart Variables: Relations">
Variables can represent relationships or differential equations between other variables.

Example (the heat equations):
<syntaxhighlight lang="cpp">
q = -k * Grad(u);
Diff(u) = (-1 / c_p * rho) * Div(q);
</syntaxhighlight>

The equations themselves are saved within <code>q</code> and <code>Diff(u)</code>, so the model can be run.
</slide>

<slide title="Networked Equations Language">
Custom '''Modeling Language''' combines a units-aware equation-like syntax with networks and GIS.

<syntaxhighlight lang="cpp">
capacity = 1e10 [tons];
rate = 0.0077 [tons/year];
biomass = Stock(1e7 [tons]);
catches = TimeSeries("catches.tsv", [tons/year]);
biomass += rate * biomass *
(1 - biomass / capacity) - catches;

print(biomass[0:100], "\t");
</syntaxhighlight>
</slide>

<slide title="Toolbox">
Transparently combine Matlab, R, shell scripting, Mathematica and other code.

[[File:Toolbox.png]]
</slide>

<slide title="Extensions">
* Memetic propagation of models
* Integration with climate models
* Importing Vensim models
</slide>

<slide title="Case Study: Networked Economics">
Step 1: Reconstruct Solow Growth (with some random shocks):

* <math>\frac{d L}{d t} = \lambda L(t)</math>
* <math>Y(t) = K(t)^\alpha L(t)^{1-\alpha} \epsilon(t)</math>
* <math>\frac{d K}{d t} = s Y(t) - \delta K(t)</math>

{img src="/images/d/db/Solow.png" width="200" height="160" /}
</slide>

<slide title="Case Study: Networked Economics">
Step 2: Make a "distributed" analog to Solow growth:

* Multiple firms, with individual capital stocks
* Separate growth and decay: <math>g[t] = s Y[t]</math>, <math>d[t] = \delta K[t]</math>
* If <math>g[t] \ge d[t]</math>, growth: <math>K[t+1] = K[t] + g[t]</math>
* If <math>g[t] < d[t]</math>, stagnation: <math>K[t+1] = K[t]</math>
** And probability of collapse, so expected value follows Solow
** <math>K[t+1] = K[t] + g[t] - d[t] = (1 - P(c)) K[t] \implies P(c) = (d[t] - g[t]) / K[t]</math>
* Firms can make connections to each other, which increase "technology" (specialization) factor

[[File:Smallworld.png]]

* But when collapse, connections severed, capital goes to 0
</slide>

<slide title="Case Study: Networked Economics">
[[File:Distrib.png]]

[[File:Economies.png]]
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Bhakramap.png]]
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Netmap_ext.png]]
</slide>

<slide title="Model for Climate Behaviors">
Overdetermined status-quo:
* Politicians won't make unpopular changes
* Businesses won't take action alone
* Consumers have great difficulty without support
* Carbon leakage
* Rebound effects
</slide>

<slide title="Model for Climate Behaviors">
* Climate behaviors as aggregate activity
* Looking for leverage points
* Not trying to predict future states
</slide>

<slide title="Model for Climate Behaviors">
[[File:Architecture.png]]

(Self-similar Meadows 2004 regionally, Forrester 1971 for urban)
</slide>

<slide title="How Many Variables?">
* World3/2000: 283
* System Dynamics National Model: 2000+
* Encyclopedia of World Problems and Human Potential: 56,135
** environmental feedback loops: 2,675
</slide>

<slide title="Multimanaged Fisheries Project">
* Collapsing fisheries, despite new management
* Perverse economic incentives
* Multiple scales of uncertainty
* Unintended policy consequences
</slide>

<slide title="Multimanaged Fisheries Project">
[[File:Food_web_600.jpg]]
</slide>

<slide title="Multimanaged Fisheries Project">
* <math>g_t^i = r^i s_t^i \left(1 - \frac{s_t^i}{K_t^i}\right)</math>
* <math>K_t^i = \sum_{j \in q(i)} w^{ij} s_t^j</math>

* Nature: ecosystem and regional models
* Social: Fishing community, policy-makers, NGOs
* Plug-in different "fish" and "policy" modules
* Working with stakeholders
</slide>

Friday Forum Presentation

2012-10-19T03:10:21Z

Jrising:

<slide header="none" center="both">
= Open World Project =
Friday Forum

Oct. 19, 2012

 

James Rising, Upmanu Lall,

Bruce Shaw, Pierre Gentine
</slide>

<slide title="Talk Plan">
* Other Projects of Interest
* Motivation and Vision
* Core Elements
* Case Study Projects
* Climate Behaviors
* Fisheries Model
* Next Steps
</slide>

<slide title="Other Projects of Interest">
* [http://www.existencia.org/carbon/ Carbon Transition Working Group]
* [http://sdresearch.wikischolars.columbia.edu/jar2234+Peruvian+Fisheries Peruvian Spatial Fisheries]
* [http://sdresearch.wikischolars.columbia.edu/Ocean+Health Ocean Health Metric]
* [http://sdresearch.wikischolars.columbia.edu/jar2234+Marine+Protected+Areas Empirical Benefits from Marine Protected Areas]
* [http://existencia.org/weaver/ Web-Weaver Data Extractor]
* [http://cantovario.com CantoVario]
* [http://openworld.existencia.org/index.php?title=Friday_Forum_Presentation Slider Extension]
</slide>

<slide title="Introduction" fs="2em">
Many of the human behaviors that drive climate change and
environmental degradation are deeply embedded in our society,
economy, and government, and are mutually reinforcing. Better
modeling of human-natural systems can help in many ways:
* Analyzing feedback loops can help identify '''leverage points''', where small policy changes can have pervasive impacts.
* Allowing models at diverse scales and contexts to interact can help scientists '''integrate knowledge'''.
* Interactive models can facilitate '''communication''' with policymakers and make complex problems intelligible.

The Open Model is a modeling framework aimed at these issues.
</slide>

<slide title="Applicability" fs="2em">
* Systemically intractable due over-determined, reinforcing drives, and spatially heterogeneous.
* Environmental and public health issues: environmental degradation, agricultural practices in poor countries, obesity, substance abuse, groundwater use, fishery management, passenger transport
* Rebound effects and cross-border shifts (e.g., carbon leakage)

 

<center>'''Something for everyone!'''</center>
</slide>

<slide title="Why bigger models?">
* '''Accuracy?''' Debatable
* '''Precision?''' Marginally better
* '''As a platform?''' If it's popular

* ''Interaction of the components''
* ''Finer tipping points''
</slide>

<slide title="Core Elements">
* Amalgamated Modeling
* Multiple Network Maps
* Networked System Dynamics
* Computational Tools
* Open Interface
* Smart Variables

[[File:Elements.png|center]]
</slide>

<slide title="What is Coupling?">
[[File:Coupling.png|center]]

Bioeconomic model from Smith and Wilen, 2003
</slide>

<slide title="What is Coupling?">
Problems:
* Runaway feedback (resonance)
* Non-realistic calibrated parameters
* Making elements commensurable
</slide>

<slide title="Amalgamated Modeling" fs="1.6em">
Amalgamated modeling allows models to interact, specialize, and "overlap".

Every model is incomplete; applies to a constrained context.
:'''Let's embrace partial models!'''
Want a "plugin architecture", where models can easily be allowed to interact

Coupling causes feedback, and models are defined at different scales.
:'''Need a new way to couple models!'''

Allow overlapping-- models inform different variables, at different scales.
</slide>

<slide title="Amalgamated Modeling">
[[File:Blobs.png]]
</slide>

<slide title="Bayesian Coupling">
[[File:Amalgelt.png]]
</slide>

<slide title="Bayesian Coupling">
For a variable <math>\theta</math> described by multiple models, each
model provides both a PDF across values at a given time <math>t</math>
when run in isolation, <math>p(\theta, \bar{S}^i)</math>, and a
distribution that includes feedback effects, <math>p(\theta, \tilde{S}^i)</math>. The final distribution is
<math>
p(\theta | \cdot) \propto p(\theta) \prod_i p(\theta |
\bar{S}^i)^\lambda p(\theta, \tilde{S}^i)^{1-\lambda}
</math>

[[File:Amalgcombo.png|center]]
</slide>

<slide title="Amalgamation Challenges">
* How do I test it?
* Efficient probability function calculations
* Smooth or spectrally-informed transitions?
* What does downsampling contribute?
* How to ensure that different scales add up?
* How do we understand a multi-scale model?

[[File:Trialestimate.png|left]]
</slide>

<slide title="A New System Dynamics">
Coupling natural and human systems makes things complex:
: feedback, non-linearity, resilience, and spacial heterogeneity

Combine the temporal sophistication of system dynamics,
: with spatial heterogeneity

[[File:ssdarch-mod.png]]
(Ahmad et al 2004; flood management, water resources modeling, invasive species spread)
</slide>

<slide title="Multiple Networks">
Models use multiple networks simultaneously
* Different paths on which stocks flow
* Disaggregations into structured classes
* Capturing network properties
</slide>

<slide title="Multiple Networks in Ohio">
[[File:Ohionet.png]]
</slide>

<slide title="Disaggregating System Models">
[[File:Popmod-vensim.png|center]]

[[File:Popmod.png|center]]
</slide>

<slide title="Networked System Dynamics">
[[File:Netstocks.png]]
</slide>

<slide title="Networked System Dynamics">
[[File:Ohiomod.png]]
</slide>

<slide title="Self-Similar Networks">
[[File:Selfsimodel.jpeg|center]]
</slide>

<slide title="Networking Challenges">
* Ensure that the separate blocks match the aggregate
* What is a full language of networked system dynamics?
* Can a model only apply to part of a network?
* How to ensure that missing models "fail gracefully"?
</slide>

<slide title="Computational Tools">
* Evaluate model performance (Barlas 1996)
* Identify driving feedback loops
* Identify tipping and leverage points
* Construct simplified models for communication
* System Regression: construct models from data
</slide>

<slide title="Integrating Data" fs="2em">
* Calibration
* Validation
* Filling in missing models
:'''We need a smart (context-aware and incomplete-welcoming) data library!'''
</slide>

<slide title="Open Interface" fs="2em">
[[File:Climatepred.png|center]]

* For researchers: Testing partial models, Ask questions of the whole, Contributing models
* For policy-makers: Interact with the model, Visualize results, Outline scenarios
* For model: A large model, Many eyes
</slide>

<slide title="Smart Variables: Dimensions">
* <math>3</math> vs. <math>3</math> [tonnes] vs. <math>1350487537</math> [seconds since Jan. 1, 1970]
* Dimensional analysis at the heart of science
* Automatic model checking, unit conversion
</slide>

<slide title="Smart Variables: Maps" fs="1.5em">
"Maps" are dimension-aware functions in space-time, often tied to data streams (e.g., IRI tsvs, geotiffs).
Maps of different resolutions can be manipulated transparently. Example:

<syntaxhighlight lang="cpp">
GeographicMap<double>& degreeDayMelt =
(degreeDayFactor + degreeDaySlope * elevation)
* (snowCover / 100) * (surfaceTemp - ZERO_CELSIUS)
* (surfaceTemp >= ZERO_CELSIUS);
</syntaxhighlight>

* elevation is a static map at <math>1 km</math> resolution
* surfaceTemp is a daily varying map at <math>.25^\circ</math> resolution, read from the file 1 day at a time
* snowCover is a weekly varying map at <math>.33^\circ</math> resolution, reconstructed for past years
</slide>

<slide title="Smart Variables: Relations">
Variables can represent relationships or differential equations between other variables.

Example (the heat equations):
<syntaxhighlight lang="cpp">
q = -k * Grad(u);
Diff(u) = (-1 / c_p * rho) * Div(q);
</syntaxhighlight>

The equations themselves are saved within <code>q</code> and <code>Diff(u)</code>, so the model can be run.
</slide>

<slide title="Networked Equations Language">
Custom '''Modeling Language''' combines a units-aware equation-like syntax with networks and GIS.

<syntaxhighlight lang="cpp">
capacity = 1e10 [tons];
rate = 0.0077 [tons/year];
biomass = Stock(1e7 [tons]);
catches = TimeSeries("catches.tsv", [tons/year]);
biomass += rate * biomass *
(1 - biomass / capacity) - catches;

print(biomass[0:100], "\t");
</syntaxhighlight>
</slide>

<slide title="Toolbox">
Transparently combine Matlab, R, shell scripting, Mathematica and other code.

[[File:Toolbox.png]]
</slide>

<slide title="Extensions">
* Memetic propagation of models
* Integration with climate models
* Importing Vensim models
</slide>

<slide title="Case Study: Networked Economics">
Step 1: Reconstruct Solow Growth (with some random shocks):

* <math>\frac{d L}{d t} = \lambda L(t)</math>
* <math>Y(t) = K(t)^\alpha L(t)^{1-\alpha} \epsilon(t)</math>
* <math>\frac{d K}{d t} = s Y(t) - \delta K(t)</math>

{img src="/images/d/db/Solow.png" width="200" height="160" /}
</slide>

<slide title="Case Study: Networked Economics">
Step 2: Make a "distributed" analog to Solow growth:

* Multiple firms, with individual capital stocks
* Separate growth and decay: <math>g[t] = s Y[t]</math>, <math>d[t] = \delta K[t]</math>
* If <math>g[t] \ge d[t]</math>, growth: <math>K[t+1] = K[t] + g[t]</math>
* If <math>g[t] < d[t]</math>, stagnation: <math>K[t+1] = K[t]</math>
** And probability of collapse, so expected value follows Solow
** <math>K[t+1] = K[t] + g[t] - d[t] = (1 - P(c)) K[t] \implies P(c) = (d[t] - g[t]) / K[t]</math>
* Firms can make connections to each other, which increase "technology" (specialization) factor

[[File:Smallworld.png]]

* But when collapse, connections severed, capital goes to 0
</slide>

<slide title="Case Study: Networked Economics">
[[File:Distrib.png]]

[[File:Economies.png]]
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Bhakramap.png]]
</slide>

<slide title="Case Study: Hydrological Modeling">
[[File:Netmap_ext.png]]
</slide>

<slide title="Model for Climate Behaviors">
Overdetermined status-quo:
* Politicians won't make unpopular changes
* Businesses won't take action alone
* Consumers have great difficulty without support
* Carbon leakage
* Rebound effects
</slide>

<slide title="Model for Climate Behaviors">
* Climate behaviors as aggregate activity
* Looking for leverage points
* Not trying to predict future states
</slide>

<slide title="Model for Climate Behaviors">
[[File:Architecture.png]]

(Self-similar Meadows 2004 regionally, Forrester 1971 for urban)
</slide>

<slide title="How Many Variables?">
* World3/2000: 283
* System Dynamics National Model: 2000+
* Encyclopedia of World Problems and Human Potential: 56,135
** environmental feedback loops: 2,675
</slide>

<slide title="Multimanaged Fisheries Project">
* Collapsing fisheries, despite new management
* Perverse economic incentives
* Multiple scales of uncertainty
* Unintended policy consequences
</slide>

<slide title="Multimanaged Fisheries Project">
[[File:Food_web_600.jpg]]
</slide>

<slide title="Multimanaged Fisheries Project">
* Plug-in different "fish" and "policy" modules
* <math>g_t^i = r^i s_t^i \left(1 - \frac{s_t^i}{K_t^i}\right)</math>
* <math>K_t^i = \sum_{j \in q(i)} w^{ij} s_t^j</math>
</slide>