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DISPLACE - Introduction to Model Architecture & Parameterization

Agent-Based Modelling; DISPLACE; Fisheries
by

Francois Bastardie

on 30 May 2016

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Transcript of DISPLACE - Introduction to Model Architecture & Parameterization

Help?
DISPLACE
DISPLACE
Look at individual vessels
bird's eye
of the bigger picture
Do you need
Introduction to model architecture
& parameterisation
A Dynamic Individual-Vessel based Modelling for Fishing Planning and Spatial Displacement
view
a modelling approach to anticipate interlinked effects on fish and fisheries of alternative management and spatial plans
like a flight simulator...
but still possible to look at spatial
detail...
...then take a
http://prezi.com/7txrmkstwsze/free-fall-template/
The template:
Images used under license from Shutterstock.com and colourbox.com
Mistakes or uncertain parameterization is likely to create crashes...which are still hard to detect (WIP)
Hands in the c++ code?
www.displace-project.org
tutorials
fba@aqua.dtu.dk
...and individual decision-making
...and individual footprint
<>
....for parameterization
10 steps
Model structure
Simulator
core model
Parameterisation
routines
Graphical/GIS
User Interface
Raw input data

High Performance
Computing
post processing routines
Plots &
tables
DISPLACE
input data
report
Logbooks coupled to VMS data (at best)
Spatial distribution of stocks
Stock biological features (N at size, Wt at size, growth transition matrix, etc.)
Individual vessel building incl. specific fishing grounds and harbours
Scenario crafting including TAC mngt, fishing closure, (fishing credits), etc. and decision trees possibly indexed on external time series
Geographical arena crafting (grid of cells/nodes at sea e.g. of 4 by 4 km and harbour nodes)
Fish prices on harbour nodes
Stock-, vessel-, and metier- specific spatial catch rate
Metier building for size-based selectivity accounting for gear type
Other non-explicit vessels depleting on stocks
https://github.com/frabas/DISPLACE_GUI (Not Yet Public)
Code repository
https://github.com/frabas/DISPLACE_R_inputs
https://github.com/frabas/DISPLACE_input
https://github.com/frabas/DISPLACE_R_outputs (Not Yet Public)
...for Windows and Linux
http://displace-project.org/blog/download/
For users, parameterizing a new app:
For users, testing the software with an illustrative dataset:
For (c++) developers, looking at core simulator and GUI:
Folder hierarchy
e.g. lgbooks, VMS, ICES, fishbase,
surveys like IBTS , GIS layers
...e.g. where and when to go fishing, when return to port, to which port?
IsInAreaClosure
smartCatch
KnowledgeOfThisGround
notThatFar
notThatFar
notThatFar
notThatFar
KnowledgeOfThisGround
1.00
1.00
0.50
0.50
0.70
0.70
0.70
0.70
0.00
yes
yes
yes
yes
yes
yes
yes
yes
no
no
no
no
no
no
no
no
...and account for diversity
...emerging outcomes (local knowledge to overall statistics)
outcomes
....type of
A graphical user interface to help parameterize, run several simulations, compare and explore the outcomes, store in sql databases and replay simulations, etc.
...or all outputs in simple text files and use R post-processing routines to produce table & figures......
...alteratively to the ui, a large number of simulations/replicates can be generated using shell scripts in command line mode and making benefit of High Performance Computing facilities
other uses
....type of
Critical path
...for parameterization
...economic and ecological performance
of alternative spatial plans
...from the analysis of fishermen´s behaviour
...to understand the intertwined dynamics
https://www.youtube.com/watch?v=Um255Aud2Nkhttps://www.youtube.com/watch?v=Um255Aud2Nk


...doesn´t stick to the law of the large numbers...
instead it is recognized that the dynamic of the system can be strongly non-linear therefore sometime leading to some bifurcations from divergent individual trajectories magnified by different initial conditions..So the final statistics/risk probabilities should reflect large uncertainty interval when e.g. two different modes are produced!

In our case ABM is to go beyond an *average* vessel approach by saying that all the uncertainties at the individual scales do not compensate to statistically leading to the median outcome....

Law of large numbers does not apply e.g. in the case of the Simpson paradox justifying that ABM + decision trees approach to capture non linearity is a way forward to avoid bias from high level of aggregation models...


Agent-Based Modelling....
from Tamás Székely Jr. and Kevin Burrage 2014
.
..a simulator is needed because likely no analytical solution when user react to the controls
Full transcript